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Autonomous Vehicles and Freight Transportation Analysis
Technical Report · August 2019
DOI: 10.13140/RG.2.2.28484.78726
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Autonomous Vehicles
and
Freight Transportation Analysis
By
Mohsen Shahandashti, PhD, PE
Binaya Pudasaini, PhD
and
Sean Logan McCauley
Department of Civil Engineering
The University of Texas at Arlington
Final Project Report
Prepared For:
The North Central Texas Council of Governments
P.O. Box 5888
Arlington, TX 76005
August 2019
DISCLAIMER
This report is prepared in cooperation with the North Central Texas Council of
Governments. The contents of this report reflect the views of the authors who are responsible for
the opinions, findings, and conclusions presented herein. The contents do not necessarily reflect
the views of the North Central Texas Council of Governments.
1
ACKNOWLEDGMENT
The authors wish to acknowledge the sponsorship of the North Central Texas Council of
Governments through its University Partnership Program. In particular, the directions, insights,
and support offered by Mr. Jeff Hathcock, Mr. Mike Johnson, and Mr. Clint Hall at every stage of
the project are greatly appreciated. Additionally, we would also like to acknowledge the helpful
insights and comments of every member of NCTCOG’s technical review panel who offered
provided helpful insights for finalizing the survey questionnaires and this report.
2
TABLE OF CONTENTS
DISCLAIMER ................................................................................................................................ 1
Acknowledgment ............................................................................................................................ 2
LIST OF FIGURES ........................................................................................................................ 5
LIST OF TABLES .......................................................................................................................... 7
NOMENCLATURE ....................................................................................................................... 8
EXECUTIVE SUMMARY ............................................................................................................ 9
CHAPTER 1. INTRODUCTION AND SCOPE .......................................................................... 13
CHAPTER 2. CURRENT STATUS OF AUTONOMOUS TRUCKING TECHNOLOGIES .... 15
2.1 Levels of Driving Automation ............................................................................................. 15
2.2 Current State of Availability and Development of Automated Trucking Technologies across
Different SAE Levels ................................................................................................................. 16
Level 0: No Automation ............................................................................................. 17
Level 1: Driver Assistance .......................................................................................... 21
Level 2: Partial Automation ........................................................................................ 23
Level 3: Conditional Automation ............................................................................... 25
Level 4: High Automation: ......................................................................................... 29
Level 5: Full Automation ............................................................................................ 35
CHAPTER 3. IMPACT ANALYSIS ........................................................................................... 37
3.1 Impact on DFW’s Transportation Infrastructure ................................................................. 37
Road Signs and Markings ........................................................................................... 37
Warehousing and Fulfillment Centers ........................................................................ 40
Parking ........................................................................................................................ 42
Managed Lanes/Dedicated Lanes ............................................................................... 44
Inspection and Maintenance Infrastructure ................................................................. 47
Transfer Hub ............................................................................................................... 49
Roadside Equipment ................................................................................................... 51
3.2 Impact on Critical Trucking Issues ...................................................................................... 54
Compliance, Safety, Accountability (CSA) ................................................................ 55
Hours-of-Service Regulations ..................................................................................... 57
Driver Shortage and Driver Retention: ....................................................................... 60
3
Driver Health.............................................................................................................. 62
Driver Distraction ....................................................................................................... 63
Economy ..................................................................................................................... 64
Parking ........................................................................................................................ 66
CHAPTER 4. SURVEY DESIGN AND DISTRIBUTION ......................................................... 67
4.1 Survey Design ...................................................................................................................... 67
4.2 Distribution of Surveys ........................................................................................................ 67
CHAPTER 5. SURVEY RESULTS ............................................................................................. 69
5.1 Background Information on Truck Drivers ......................................................................... 69
5.2 Perceived Advantages of Autonomous Trucking Technologies .......................................... 71
5.3 Perceived Obstacles to Autonomous Trucking Technologies ............................................. 77
5.4 New Skills Requirement ...................................................................................................... 84
5.5 Future of Autonomous Trucking Technologies ................................................................... 85
5.6 Liability Issue....................................................................................................................... 88
5.7 Dedicated/Managed lanes .................................................................................................... 90
5.8 Lane Markings and Highways Signs ................................................................................... 91
5.9 Warehousing Issues ............................................................................................................. 92
5.10 Trust Enhancement for Autonomous Trucks ..................................................................... 93
CHAPTER 6. CONCLUSIONS AND RECOMMENDATIONS ................................................ 95
REFERENCES ............................................................................................................................. 98
APPENDIX A: SURVEY QUESTIONNAIRES ............................................................................ I
APPENDIX B: Example of SURVEY INVITATION MESSAGE ............................................... II
4
LIST OF FIGURES
Figure 2-1. SAE's visual chart defining six levels of driving automation (SAE (2018)) ............. 16
Figure 2-2. Technologies Used in Automated Trucking Technologies (GAO (2019)) ................ 17
Figure 2-3. Features required by different levels of truck automation (Roland Berger (2016)) .. 17
Figure 2-4. Blind spot monitoring system in Volvo trucks (Volvo Trucks (2019)) ..................... 18
Figure 2-5. An illustration of right turn assist detecting cyclist and passenger (Continental (2018))
....................................................................................................................................................... 19
Figure 2-6. Driver monitoring system alerting the driver to take a break (Volvo Trucks (2019)) 20
Figure 2-7. Speed limit being displayed in Freightliner Cascadia Class-8 semi-truck (Hall (2019))
....................................................................................................................................................... 20
Figure 2-8. Illustration of adaptive cruise control in Volvo trucks (Volvo Trucks (2019)) ......... 21
Figure 2-9 Illustration of (a) Forward Collision Warning System alerting the driver (b) Emergency
braking system applying brakes to prevent imminent collision (Volvo Trucks (2019)) .............. 22
Figure 2-10. Illustration of lane keep assist developed by Volvo trucks (Volvo Trucks (2018)) 22
Figure 2-11. Workings of Driver Assisted Truck Platooning (FDOT (2018)) ............................. 23
Figure 2-12. Adaptive Cruise Control (ACC) to 0 MPH” being used by Daimler’s Cascadia truck
in a traffic jam (Daimler (2018a)) ................................................................................................. 24
Figure 2-13. The interface of adjust settings of PPC in Mercedes-Benz’s Actros/Arocs Trucks
(Mercedes-Benz Trucks UK Ltd (2018)) ...................................................................................... 25
Figure 2-14. (a) V2V based Intersection Movement Assist (IMA) being tested (b) Less urgent alert
by IMA (c) More urgent alert by IMA (Stephens et al. (2014)) ................................................... 27
Figure 2-15. An illustration of synchronized emergency braking in Daimler’s Highway Pilot
Connect (Daimler 2016) ............................................................................................................... 28
Figure 2-16. Self-driving class 8 trucks being tested by Alphabet’s Waymo (Waymo 2019) ..... 30
Figure 2-17. Cascadia trucks by Daimler AG with highway pilot enabled in CES 2019 (O’Kane
(2019))........................................................................................................................................... 31
Figure 2-18. TuSimple’s semi-autonomous truck at CES 2019 (O’Kane (2019)) ....................... 32
Figure 2-19. Semi-autonomous trucks being developed by Embark (Embark (2018)) ................ 33
Figure 2-20. Truck fitted with Starsky Robotics driver less trucking technology drives down the
Florida expressway (Starsky Robotics (2019)) ............................................................................ 34
Figure 2-21. An autonomous electric tractor called Vera being developed by Volvo Trucks (Volvo
(2018))........................................................................................................................................... 35
Figure 2-22. Illustration of Level 5 autonomous trucking (GAO (2019)) .................................... 36
Figure 2-23. Slow and fast adoption scenario for autonomous trucking (Mudge et al. (2018)) ... 36
Figure 3-1. Impact of Autonomous Trucking on DFW Transportation Infrastructure ................. 37
Figure 3-2. Transfer Hub Model (Roland Berger (2018)) ............................................................ 50
Figure 3-3. Impact of Autonomous Trucking on Critical Trucking Issues of DFW Region ........ 55
Figure 3-4. Behavior Analysis and Safety Improvement Categories – BASICs (FMCSA (2016))
....................................................................................................................................................... 55
Figure 4-1. Survey subjects of interest ......................................................................................... 67
5
Figure 4-2. Survey distribution and analysis process ................................................................... 68
Figure 5-1. Automated trucking technologies used by responding truck drivers ......................... 70
Figure 5-2. Distribution of drivers based on their familiarity with the term “autonomous trucking”
....................................................................................................................................................... 70
Figure 5-3. Perception of truck drivers regarding the advantages of autonomous trucking
technologies .................................................................................................................................. 72
Figure 5-4. Perception of trucking company managers/owners regarding the advantages of
autonomous trucking technologies ............................................................................................... 73
Figure 5-5. Perception of autonomous trucking technology developers regarding the advantages of
autonomous trucking technologies ............................................................................................... 74
Figure 5-6. Perception of transportation planners regarding the advantages of autonomous trucking
technologies .................................................................................................................................. 75
Figure 5-7. Advantage perception score averaged over all the respondent’s groups ................... 76
Figure 5-8. Advantage perception score averaged over all the perceived advantages ................. 77
Figure 5-9. Perception of truck drivers regarding the obstacles to autonomous trucking
technologies .................................................................................................................................. 79
Figure 5-10. Perception of trucking company managers and owners regarding the obstacles to
autonomous trucking technologies ............................................................................................... 80
Figure 5-11. Perception of autonomous trucking technology developers regarding the obstacles to
autonomous trucking technologies ............................................................................................... 81
Figure 5-12. Perception of autonomous trucking technology developers regarding the obstacles to
autonomous trucking technologies ............................................................................................... 82
Figure 5-13. Obstacle perception score averaged over all the respondent’s groups ..................... 83
Figure 5-14. Obstacle perception score averaged over all the perceived advantages ................... 84
Figure 5-15. Willingness of the responding drivers to learning new skills .................................. 85
Figure 5-16. Prediction of responding drivers regarding the operation of autonomous trucks in the
highways of DFW ......................................................................................................................... 86
Figure 5-17. Prediction of responding trucking company managers/owners regarding the operation
of autonomous trucks in the highways of DFW ........................................................................... 87
Figure 5-18. Prediction of responding drivers regarding the percentage of autonomous trucks
within next ten years ..................................................................................................................... 88
Figure 5-19. Liable party when an autonomous truck is at fault during a collision, when the truck
is at fault, based on responding truck drivers ............................................................................... 89
Figure 5-20. Factors impacting highway safety and efficiency based on the responding truck
drivers ........................................................................................................................................... 90
Figure 5-21. Issues related to lane markings in DFW highways based on responding truck drivers
....................................................................................................................................................... 91
Figure 5-22. Issues related to traffic signs in DFW highways based on the responding truck drivers
....................................................................................................................................................... 92
6
Figure 5-23. Reasons reported by the responding truck drivers for long wait times in the
warehouses .................................................................................................................................... 93
Figure 5-24. Effective methods to make autonomous driving trustworthy based on the responding
truck drivers .................................................................................................................................. 94
LIST OF TABLES
Table 3-1. V2I application needing DSRC roadside unit installation (Hendrickson et al. (2014))
....................................................................................................................................................... 52
Table 5-1. Background information regarding the responding truck drivers ............................... 69
7
NOMENCLATURE
AI: Artificial Intelligence
AT: Autonomous Trucking
AV: Automated Vehicles
BASIC: Behavior Analysis and Safety Improvement Categories
CSA: Compliance, Safety, and Accountability
CV: Connected Vehicles
DFW: Dallas-Fort Worth
DSRC: Digital Short-Range Communication
FCAM: Forward Collision Avoidance and Mitigation
FMCSA: Federal Motor Carrier Safety Administration
HD: High Definition
IT: Information Technology
LiDAR: Light Detection and Ranging
NCTCOG: North Central Council of Governments
OEM: Original Equipment Manufacturer
SAE: Society of Automotive Engineers
V2I: Vehicle-to-Infrastructure
V2V: Vehicle-to-Vehicle
V2X: Vehicle-to-Infrastructure or Vehicle-to-Vehicle
8
EXECUTIVE SUMMARY
Despite broad implications of autonomous trucks on the trucking industry, it is not clear
how the advent of emerging autonomous trucking technologies would impact the Dallas-Fort
Worth’s (DFW) trucking industry or the infrastructure needed to support the industry. This lack of
understanding is due to the inherent uncertainties about the pace of the adoption of autonomous
trucks across various levels of automation (from driver assistance to full automation) and
complexities of the DFW region as a major metropolitan area. Therefore, this study was carried
for a direct and thorough investigation of the impacts of the adoption of autonomous trucking
technologies on the future of the DFW’s trucking industry and the infrastructure needs that arise
due to such adoption.
The research findings were obtained through a comprehensive literature review, structured
survey, and statistical analysis. A thorough review of automated trucking technologies was carried
out at first to assess the state-of-the-art of automated trucking technologies. Then, a further
literature review was performed to identify potential impacts of different levels of automated
trucking technologies on (1) the DFW’s transportation infrastructure and (2) the critical issues of
the trucking industry. The findings of the literature review were then used to design survey
questionnaires for truck drivers, trucking company managers/owners, autonomous trucking
technology developers, and transportation planners. The surveys were distributed, and the
responses were collected using a paper-based method, phone interviews, and online survey
platforms. Altogether, 21 responses from truck drivers, five responses from trucking company
managers/owners, two responses from autonomous trucking technology developers, and three
responses from transportation planners were obtained.
The data collected from the literature review and the survey responses were critically
analyzed. Based on the analysis, transportation infrastructure areas such as road signs & markings,
parking, managed/dedicated lanes, inspection & maintenance infrastructure, transfer hubs, and
roadside equipment were identified to be majorly impacted by the use of autonomous trucking
technologies. Similarly, trucking issues such as compliance, safety & accountability, Hours-ofService regulations, driver shortage & retention, driver health, driver distraction, economy, and
parking were identified to be majorly impacted by the use of autonomous trucking. Finally, based
on the literature review, survey, and subsequent analysis, eleven recommendations were made,
which are as follows:
Based on the literature review and the survey conducted as a part of this study, the
following recommendations can be made to reduce the risks and maximize the opportunities
associated with the use of autonomous trucking technologies.
1)
Highway Signs and Markings: The survey responses indicate that the lane markings and
highway signs in the DFW highways need proper maintenance and consistency. Since all levels
of affordable autonomous trucking systems require adequately maintained lane markings and
9
highway signs, a well-defined plan is needed to maintain proper lane markings and highway signs.
Hence, innovative marking technologies such as retroreflective road markings for nighttime
visibility, dirt-resistant marking for high marking contrast, and reflective radar road marking for
detection by radar-based sensors during fog and snow should be considered.
Inconsistency in markings and signs is one of the frequent issues reported in the survey. Hence,
federal regulations governing signs and markings should be developed and implemented so that
consistent signs and markings are used in interstate highways as opposed to the current system
where different variations of same markings can be found depending on the state regulations.
2)
Warehousing Enhancement: An inefficient warehouse can severely offset the productivity
gains by autonomous trucks. However, advanced sensors and rich data environment of
autonomous trucking can be capitalized to automate and optimize the current warehousing
processes. Real-time tracking of trucks is possible, which will enable shippers and receivers to
proactively prepare the shipments. This proactive preparation will help to avoid delay in shipping
and loading. Additionally, autonomous trucking enables the digitization of paperwork. This
digitization leads to reduced waiting times as lengthy paperwork is one of the major causes of
long waiting times.
3)
Parking Infrastructure: Parking is one of the most stressful aspects of truck driving. The
availability of parking can dictate the entire routing process of the trucking assignments. Thus,
real autonomy is not possible until and unless the parking is also automated. Therefore, to achieve
real autonomy, real-time parking monitoring, and prediction systems combined with a convenient
parking information dissemination system are needed. Hence, investment in these systems is
required for timely adoption of autonomous trucking technologies.
4)
Managed/Dedicated Lanes: Survey responses from truck drivers have indicated that the
interaction between trucks and other traffic can negatively impact highway safety. These impacts
can be even more critical for the first autonomous trucking systems. For these systems, to be
safely deployed, investment in managed lanes or dedicated autonomous lanes at least should be
considered during the formulation of long-term transportation plans that aims to support the
development of autonomous trucking technologies.
5)
Inspection and Maintenance Infrastructure: Autonomous trucking technologies are
powered by numerous advanced sensors and on-board equipment. Repair and maintenance of
these sensors and equipment need specialized training. Thus, a training and certification program,
regulated at the federal level, should be designed in collaboration with autonomous trucking
technology developers to ensure the job security of current repair technicians. These trainings
will also help to ensure adequate availability of repair service to the autonomous trucking systems.
10
6)
Transfer Hubs: Many of the autonomous trucking technologies being developed today are
only capable of exit-to-exit highway driving. Hence, the initial deployment of these systems
would follow the transfer hub model where a large space is needed at the end of autonomous
driving sections for switching between human-driven tractors and autonomous tractors. Since
transfer hubs need to be in proximity to major freight corridors, many ideal locations for these
hubs will coincide with current location of facilities such as rest stops and truck parking areas.
Hence, these rest stops and parking areas will need to be repurposed with minimum inconvenience
to the non-autonomous vehicle users of these facilities.
7)
Roadside Equipment: Even though many of the autonomous trucking technology being
developed today rely solely on the on-board sensors and computers, the reliability of these
systems can be significantly using V2X connectivity. Such connectivity requires significant
investment in DSRC communication infrastructure, smart traffic signs, roadside High Definition
(HD) cameras, and cloud-based traffic management centers.
8)
Safety Measurement System: Newly proposed Information Theory-Based Safety
Measurement System can be well integrated with the information-rich infrastructure of
autonomous trucks. This integration will potentially create a more powerful and more accurate
Safety Measurement System.
9)
Collaboration between stakeholders: Routine stakeholder’s meeting focused on the impacts
of Automated Trucking technologies on the major issue of the trucking industry, and the freight
infrastructure should be carried out to enhance information exchange between different
stakeholders (GAO 2019). Continuing to convene stakeholders could also help agencies to
identify any information or data gaps that may need to be addressed to understand the potential
workforce effects of automated trucking (GAO 2019).
10) Regulations: Legislation remains one of the main obstacles to autonomous trucking
development and testing. Legislations governing Hours-of-Service, minimum follow distance,
the necessity of driver in the driving seat, and liability should be established to provide a legal
framework for the operation of autonomous trucks. These regulations should be formulated at the
federal level to avoid patchwork of regulations that varies state-by-state.
11)
Long Haul Trucking Jobs: Considering the current pace of development of autonomous
trucking technologies, the first autonomous trucks will likely be implemented in the long-haul
portion of trucking. Initially, such deployment will help to nullify the current driver shortage in
long haul assignments. However, such deployment, in the long run, will lead to reduced need for
long haul truck drivers. Hence, a proper system should be designed to help the long-haul drivers
for transitioning to either autonomous trucking jobs or jobs in other industries. Most of the
11
trucking company managers/owners participating in this study communicated their desire to use
the drivers for administrative and paperwork during autonomous driving phases. Hence, training
programs can be created to help the drivers prepare themselves for such tasks.
12
CHAPTER 1. INTRODUCTION AND SCOPE
Autonomous and connected vehicles (collectively, “automated vehicles” or “AVs”) have
promising potential to dramatically impact almost all aspects of today’s most critical trucking
issues, such as Hours-of-Service regulations, compliance, safety, driver retention, truck parking,
driver health and wellness, congestion, and driver distraction. Despite the broad implications of
automated trucks on the trucking industry, it is not clear how the advent of emerging automated
trucking technologies would impact the Dallas-Fort Worth’s (DFW) trucking industry or the
infrastructure needed to support the industry. This lack of understanding is due to the inherent
uncertainties about the pace of the adoption of automated vehicles across various levels of
automation (from driver assistance to full automation) and complexities of the DFW region as a
major metropolitan area. For example, although the reduced reliance on truck drivers to move
freight enables cost savings, lower emissions, capacity expansion, and safer roads, it could threaten
the careers and lives of thousands of professional truck drivers. The unanswered questions about
the consequences of automation on freight transportation in the DFW region go beyond social
impacts. For instance, we do not know whether existing facilities in the DFW region could be
repurposed for automated vehicles. Also, we do not know the extent of specialized/separate
facilities (e.g., new intermodal exchange nodes, dedicated highway lanes) that should be built for
automated freight transportation. Answering these questions requires a deep understanding of
existing freight transportation facilities in the DFW region and predicting the potential impacts of
various levels of automation on such facilities. This study proposes a direct and thorough
investigation of the impacts of automation on the future of the DFW’s truck industry and the
infrastructure needed to support the industry.
This study proposes the following tasks to fully assess how automated vehicles will impact
the DFW’s trucking industry’s most pressing issues and provide recommendations for a successful
transition: (1) review the status of automated truck technologies, (2) outline the impacts of
automated trucks on the critical issues in the trucking industry across various levels of truck
automation, (3) design a survey to identify the risks and opportunities of adopting automated trucks
in various automation levels in the DFW region, (4) conduct the survey of truck drivers, trucking
company managers/owners, autonomous trucking technology developers, & transportation
planners, and (5) follow up with the survey respondents to provide recommendations to manage
successful transition considering socioeconomic barriers, such as the impact of vehicle automation
on truck driver employment. This study also determines how NCTCOG could factor automated
vehicles into freight planning decisions regarding infrastructure and public opinion.
The report is organized as follows: Chapter 2 presents a comprehensive review of
automated trucking technologies across all six levels of SAE’s automation levels. Particular
emphasis would be given to the review of autonomous trucking technologies. Chapter 3
summarizes the results of detailed impact analysis performed to outline the impacts of autonomous
trucks on the trucking industry and freight transportation infrastructure. Chapter 4 presents the
13
methodology of survey questionnaire design and distribution. Chapter 5 presents the principal
findings based on the survey responses collected from the truck drivers, trucking company
managers/owners, autonomous trucking technology developers, and transportation planners.
Chapter 6 outlines the conclusions and recommendations for freight planning based on the
literature review and survey response analysis. The outlined recommendations are expected to help
NCTCOG formulate better transportation plans for the DFW region by capitalizing on the
opportunities presented by autonomous trucking while minimizing the risks associated with it.
14
CHAPTER 2. CURRENT STATUS OF AUTONOMOUS TRUCKING TECHNOLOGIES
Even though the first evidence of autonomous vehicle experiments can be found as early
as 1926 (The Milwauke Sentinel 1926), the first major university and industry participation in
autonomous vehicle development can be traced back to autonomous vehicle competition organized
by Defense Advanced Research Project Agency (DARPA) in 2004 (Davies 2017). Technology
has come a long way since that competition in which none of the vehicles even managed to reach
the finish line. Due to the advancement in computing, machine learning, machine vision, and
sensor development, companies such as Google promise commercial deployment of self-driving
vehicles by 2020 (Muoio 2015). The trucking industry is seeing a surge in the development of
automated technologies, and companies such as Waymo, Uber, and tuSimple are already
conducting tests for autonomous trucks in the US highways (Cheng 2019). This rapid development
of automated trucking technologies necessitates a systematic and up-to-date review of automated
trucking technologies available to the DFW’s trucking industry currently or shortly.
2.1 Levels of Driving Automation
Before presenting the details of the autonomous trucking technologies, it is appropriate
first to discuss the different levels of automation defined by the Society of Automotive Engineers
(SAE) since this is the most popular system used for classifying the driving automation systems.
Figure 2-1 illustrates the definition of six levels of driving automation as proposed by SAE (SAE
2018). In Figure 2-1, the technologies classified as Level 0-2 are called “driver support feature.”
These levels include some combination of warning features, acceleration assist, brake assist, and
steering assist. However, even for the most advanced features in these levels, the driver must
always be engaged in driving and should compensate for the deficits of these features in braking,
accelerating, and steering. Automation Level 3 to Level 5 are termed as “autonomous systems.”
At Level 3, also called conditional automation, a driver is required to monitor and take control of
the vehicle when prompted. However, in between the prompts, the vehicle can perform all the
driving operations. At Level 4, also called high automation, the vehicle can operate autonomously
under a much more extensive range of conditions as compared to Level 3. At Level 5, called full
automation, vehicle can drive autonomously in all the conditions and the driver is no longer
necessary for driving.
15
Figure 2-1. SAE's visual chart defining six levels of driving automation (SAE (2018))
2.2 Current State of Availability and Development of Automated Trucking Technologies
across Different SAE Levels
Various automated trucking systems are currently under development. The rate of
development of these systems is dependent on the development of various technologies shown in
Figure 2-2. In this section, we classify and discuss various automated trucking technologies that
are commercially available or are under development. The SAE’s driving automation classification
and nomenclature discussed earlier have been used to structure this discussion. Figure 2-3 is an
illustration of different features required for different levels (i.e., stages) of trucking technologies
as proposed by Roland Berger (2016). These levels and features are discussed in detail in the
following sections.
16
Figure 2-2. Technologies Used in Automated Trucking Technologies (GAO (2019))
Figure 2-3. Features required by different levels of truck automation (Roland Berger
(2016))
Level 0: No Automation
Conventional large commercial trucks currently belong to this category. The automation
belonging to this category are passive automation that can only provide warnings to alert the driver.
This level of automation cannot, however, assist in steering, braking, or acceleration to change the
motion of the truck. Some of the standard automated features belonging to this category are briefly
described below:
17
i)
Blind Spot detection:
Sensors such as radars combined with machine vision are used to detect the presence of a
vehicle in the driver’s blind spot (Brierley 2018). The presence is notified to the driver with a
visual or auditory warning. This feature is currently available in many commercial trucks
(Brierley 2018; Camden et al. 2017; Daimler 2019; Scania Group 2013; Volvo Trucks 2019).
Figure 2-4 shows an illustration of a blind spot monitoring system detecting a car in the blind
spot of a truck (Volvo Trucks 2019).
Figure 2-4. Blind spot monitoring system in Volvo trucks (Volvo Trucks (2019))
ii)
Turn Assist:
Turn assist feature aids the driver when making turns towards the passenger side.
Passenger side turns are usually risky as it is often difficult for the truck driver to see a vehicle
or a bicyclist in the truck’s blind spot when the truck is making the turn. Some commercially
available turn assist systems (Continental 2018; Mercedez-Benz 2019a) can detect an
impending collision of the truck with a pedestrian or cyclist and can alert the driver to take
appropriate preventive actions.
18
Figure 2-5. An illustration of right turn assist detecting cyclist and passenger
(Continental (2018))
iii)
Collision Warning Systems:
This system can identify obstacles (such as vehicles or pedestrians) in the truck’s path and
warns the driver if the current trajectory of the truck and the obstacle results in a collision. These
features are often useful for avoiding forward/rear-end collisions. These warning systems are
usually combined with preventive features in modern trucks (Daimler 2019; Volvo Trucks 2019).
iv)
Driver Monitoring Systems:
These systems track driver behaviors and driving maneuvers to identify any abnormal
driving or tiredness in a driver (Volvo Trucks 2019). They can also report events such as
compliance violations, speed limit violations, aggressive driving, and accidents to fleet
management (ORBCOMM 2019). Figure 2-6 shows a driver monitoring system developed by
Volvo Trucks (2019) alerting the driver to take a break.
19
Figure 2-6. Driver monitoring system alerting the driver to take a break (Volvo Trucks (2019))
v)
Traffic Sign Recognition:
Traffic sign recognition systems use on-board cameras combined with machine visionbased models to recognize different traffic signs and subsequently communicate that information
through on-board display and alerts (Figure 2-7). Traffic sign recognition systems are currently
available as a standard function in some trucks (Hall 2019). These can also be added from thirdparty developers (APTIV Staff 2018).
Speed limit sign
recognition
Figure 2-7. Speed limit being displayed in Freightliner Cascadia Class-8 semi-truck (Hall
(2019))
20
Level 1: Driver Assistance
Trucks having automation of Level 1 can change the motion of the vehicle through either
steering assist or brake assist or acceleration assist in addition to the warning provided. The most
common Level 1 automation found in trucks today are as follows:
i)
Adaptive Cruise Control:
Adaptive cruise control, with the use of radar and camera-based machine vision, adjusts
the speed of the vehicle to maintain a specified distance between the truck and the vehicle ahead.
This standard feature is currently available in many commercially available trucks (Camden et al.
2017; Hall 2019; Volvo Trucks 2019). The limitation of this system is that it is only activated when
the truck is moving above a certain speed and is not active during “stop and go” traffic. Figure 2-8
shows an illustration of adaptive cruise control in Volvo’s latest truck (Volvo Trucks 2019).
Figure 2-8. Illustration of adaptive cruise control in Volvo trucks (Volvo Trucks (2019))
ii)
Emergency Braking System:
The emergency braking system is an extension of adaptive cruise control and forwardcollision warning system. It is also called Forward Collision Avoidance and Mitigation (FCAM)
in literature. This system alerts the driver when it senses an imminent forward collision between
the truck and a vehicle ahead. If the driver does not respond promptly, then the truck itself applies
brakes to reduce the impact of a collision or to avoid the collision entirely. This feature is also
currently available in many trucks available today (Camden et al. 2017; Hall 2019; Volvo Trucks
2019). Emergency Braking System in one of the Volvo’s latest truck is illustrated in Figure 2-9.
21
(a)
(b)
Figure 2-9 Illustration of (a) Forward Collision Warning System alerting the driver (b)
Emergency braking system applying brakes to prevent imminent collision (Volvo Trucks (2019))
iii)
Lane Keep Assist:
This feature first alerts the driver if the truck drifts out of the lane markings and then
performs necessary steering correction to bring the truck inside the lane. Camera-based machine
vision combined with steering assist enables this feature. This feature is also found in many of the
latest truck models (Camden et al. 2017; Hall 2019; Volvo Trucks 2019). An illustration of the
Lane Keep Assist developed by Volvo Trucks (Volvo Trucks 2018) is shown in Figure 2-10.
Figure 2-10. Illustration of lane keep assist developed by Volvo trucks (Volvo Trucks (2018))
iv)
Driver Assisted Truck Platoon:
The Driver Assisted Truck Platooning is the technology that leverages the “connected
braking,” FCAM, and disc brakes (Crane et al. 2018). Connected braking uses Digital Short-Range
Communications (DSRC) to synchronize the acceleration and braking between the leading truck
and the following truck. Such synchronized acceleration and braking would thus relieve some of
22
the driving stresses from the drivers operating the following vehicles. However, in this level of
automation, a driver still must undertake the majority of the driving functions, and the truck merely
assists the driver in platooning operation by giving various warnings and some intervention to
avoid rear-end collision. Figure 2-11 illustrates the working of a typical driver-assisted truck
platooning.
Figure 2-11. Workings of Driver Assisted Truck Platooning (FDOT (2018))
Level 2: Partial Automation
Trucks having automation of Level 2 can change the motion of the vehicle through
combinations of steering assist, brake assist, and acceleration assist. As such, these trucks with
Level 2 automation are capable of both lateral and longitudinal control. Additionally, these systems
can provide all the warnings provided by Level 1 and Level 0 automation. Some of this automation
is just being introduced in the market, and some are still under development. A few of the trucking
technologies belonging to this level of automation are briefly discussed below:
i)
Traffic Jam/Construction Site Assistant:
This feature is an extension of Level 1 adaptive cruise control, which lets the truck
automatically accelerate or decelerate to maintain a safe following distance (Daimler 2019). Unlike
the adaptive cruise control, which is a Level 1 technology, this feature is available for all ranges
of speed. Hence, this feature is especially helpful when a truck is moving through a congested
“stop and go” traffic such as in a traffic jam or near a construction site. Figure 2-12 shows an
illustration of Adaptive Cruise Control in Daimler’s truck (Daimler 2018a).
23
Figure 2-12. Adaptive Cruise Control (ACC) to 0 MPH” being used by Daimler’s
Cascadia truck in a traffic jam (Daimler (2018a))
ii)
Highway Assist:
The Highway Assist supports the driver by performing some of the acceleration, braking,
and steering operations in the monotonous driving conditions on the highways. The steering assist
is limited to standard highway conditions and is an extension of the Lane Keep Assist technology
and adaptive cruise control. However, a driver is always required to supervise the vehicle operation
and should always be ready to intervene as the vehicle may not sense when such intervention is
required. The highway assist developed by ZF Friedrichshafen (Clevenger 2016) is an example of
such highway assist systems.
iii)
Predictive Powertrain Control:
Predictive Powertrain Control (PPC) is an automated feature that combines 3D digital road
maps with the navigation system to implement predictive gearshift and predictive cruise control
(Terwen et al. 2004). It can lead to fuel savings of up to 5% (based on Mercedez-Benz (2019)).
The PPC system developed by Mercedes-Benz (Mercedez-Benz 2019b) can detect the course of
the road up ahead and optimize gear shift points, gear selection, and cruise-control speed setting.
24
Figure 2-13. The interface of adjust settings of PPC in Mercedes-Benz’s Actros/Arocs Trucks
(Mercedes-Benz Trucks UK Ltd (2018))
(Note: “Tempomat” in the German language means “Cruise Control” while “Obere
Geschwindigkeitstoleraz” in the German language means “Upper-Speed Tolerance”)
iv)
Lane Change Assist:
Many original equipment manufacturers and developers advertise their lane change support
system as “Lane Change Assist.” However, “Lane Change Assist” systems offered even in the
latest trucks are limited radar-based blind-spot monitoring systems. These features fall under Level
0 automation classification. For these features to be classified as level 2, they should allow a truck
to monitor a blind spot, analyze the safety of the lane change, and then perform appropriate
longitudinal and lateral maneuvers to switch lanes with minimal driver input. However, such
technology is still being developed and is not available in commercially available trucks yet.
v)
Intelligent Parking Assist System:
Intelligent Parking Assist System refers to a system that allows a truck to identify a suitable
parking spot and then steer itself into that spot with minimal intervention of the driver. Such a
system is standard in many cars commercially available today (Loveday 2018). However, such
systems are yet to be found in commercially available trucks.
Level 3: Conditional Automation
Trucks having automation of Level 3, i.e., conditional automation can fully take control of the
vehicle in a highly favorable condition in addition to being able to perform steering assist, brake
assist, and acceleration assist. As such, the drivers in the trucks with conditional automation may
be “hands-off,” “feet-off,” and “eyes-off” when the vehicle is driving autonomously (Roland
25
Berger 2016). However, since the capacity of autonomous driving is quite limited, the driver must
be ready to take control of the vehicle when alerted. Most of the Level 3 automated trucking
technologies are currently under development. A few of them are briefly discussed below.
i)
Vehicle-to-Vehicle Communication:
Based on NHTSA (2019), Vehicle-to-vehicle (V2V) communication is a technology that
allows a vehicle to broadcast and receive omnidirectional messages rapidly (up to 10 times per
second) for creating a “360-degree” awareness of other vehicles in proximity. Vehicles with V2V
communication capability are called “connected vehicles.” V2V communication enhances the
performance and safety of the currently available crash detection and avoidance system. V2V
communication system is composed of on-board devices that use Digital Short-Range
Communication (DSRC) to exchange information, including vehicle’s speed, heading, and braking
status. This information is then used by the on-board path prediction system to calculate the
probability of a collision. Such a system thus allows the detection of real-time hazards even when
such hazards are hidden by some obstacles or weather. Various warning and collision mitigation
systems are being developed based on vehicle-to-vehicle communication. Intersection Movement
Assist, Left Turn Assist, Emergency Electronic Brake Light, Blind Spot Warning, and Lane
Change Warning are the most notable technologies being developed using V2V communication.
These technologies are still under development, and the details for these technologies can be found
in NHTSA (2014) and Stephens et al. (2014). Figure 2-14 (a) shows a V2V enabled truck (white
truck) stopped at a stop sign where a red car, blocked from the view of the white truck by the
yellow truck, is moving towards the intersection. Figure 2-14 (b) shows an alert displayed for the
oncoming car in the V2V enabled truck while Figure 2-14 (c) shows a more urgent alert displayed
to alert the driver for an imminent collision.
(a)
26
(b)
(c)
Figure 2-14. (a) V2V based Intersection Movement Assist (IMA) being tested (b) Less
urgent alert by IMA (c) More urgent alert by IMA (Stephens et al. (2014))
ii)
Platooning:
Truck platooning with Level 3 automation refers to synchronized driving of two or more
trucks where the lead truck directs the longitudinal as well as lateral movement of the following
truck (Mundy et al. 2018). Level 3 platooning system typically depends on a suite of sensorbased warning and support systems, i.e., Level 0 to Level 2 automated features. However, one
of the most crucial elements of truck platooning is the V2V communication between leading
vehicles and following vehicles through wireless technology (such as DSRC). This
communication is almost instantaneous (i.e., ten times in 1 second) which makes it possible to
reduce the follow distance of trucks from a typical 170 feet (Nodine et al. 2016) to a muchreduced distance. For instance, Pelton technology is designing their platooning system for a
following distance in the range of 30 feet to 80 feet for Pelton technology while Daimler is
designing their platooning systems for a following distance of 50 feet (FDOT 2018). Such
reduced follow distance lessens the drag in following trucks and results in fuel savings up to 7
percent as per Peloton Technology (Hawes 2019). Also, reduced follow distance would
increase highway capacity. Furthermore, at Level 3 platooning, the follower trucks are installed
with an automated steering system that can control lateral movement of the truck to follow the
leading truck. However, at Level 3, the driver in the following vehicle should be prepared to
take control of the truck at any moment the truck sounds an alert. The semi-autonomous
platooning system being tested by Scania group (Scania Group 2018) and Highway Pilot
27
Connect being tested by Daimler are examples of systems that demonstrate Level 3 platooning
technology.
Figure 2-15. An illustration of synchronized emergency braking in Daimler’s Highway
Pilot Connect (Daimler 2016)
Despite the innovative technologies and much enthusiasm, truck platooning has not yet
advanced beyond the testing and development stage (Clevenger 2019a). Also, some of the major
developers of the technology such as Scania and Daimler are currently questioning the business
potential of platooning based on the argument that the real-world fuel savings obtained from
platooning might not justify a business model (Clevenger 2019b).
iii)
Highway Pilot with “Alert” Driver:
Level 3 highway pilot in trucks refers to the technology that allows the truck drivers to be
“hands-off”, “feet off,” and “eyes off” when the truck is driving through normal conditions in the
highways. A truck with Level 3 highway pilot is able to perform all the acceleration, braking, and
steering maneuvers autonomously, and it only needs driver input when the system senses some
abnormal traffic situation, absence of lane markings, and low visibility due to adverse weather. It
differs from Level 2 highway pilot in the sense that Level 2 system cannot sense the need for driver
intervention and thus driver cannot be “eyes off” in those systems, unlike Level 3 highway pilot.
Nonetheless, for Level 3 highway pilot too, a driver needs to be alert enough to take back control
from the vehicle and act promptly when alerted by the truck. Thus, the driver is not fully relieved
28
while using Level 3 highway pilot system. Hence, most of autonomous trucking developers such
as Daimler, tuSimple, Waymo, Uber, and Embark are targeting for higher level of autonomy.
Level 4: High Automation:
Trucks with Level 4 automation can drive autonomously provided that specific traffic and
ambient conditions exist such as 3D maps of highways are available, the highway is certified for
autonomous driving, and there is good visibility. During autonomous operation, trucks with Level
4 automation do not need any human intervention, and the driver can be engaged in other activities.
Thus, a truck with Level 4 automation is a promising concept for the trucking industry since the
industry can highly benefit from reduced driver stress and from the ability to use free driver time
for other productive activities. Hence, most of the current autonomous trucking technology
developers are targeting for Level 4 autonomous trucking. Some of the notable systems that are
under development and promise a Level 4 capability soon are as follows:
▪
Waymo: Waymo, a company under Alphabet Inc., is one of the leading developers working
on autonomous trucks. Waymo’s autonomous trucking technology is based on its in-house
developed system currently being used in its self-driving cars. Waymo’s self-driving
technology uses a machine vision system composed of an array of radar, lidar, and cameras
(Ohnsman 2019). After the initial testing of its class-8 tractor-trailer during 2017 in California
and Arizona, Waymo’s testing has progressed to making freight deliveries for its data center
in Atlanta (Hawkins 2018) and to advanced highway testing in Phoenix, Arizona (Ohnsman
2019). Figure 2-16 shows Waymo’s autonomous trucks.
29
Figure 2-16. Self-driving class 8 trucks being tested by Alphabet’s Waymo (Waymo
2019)
▪
Daimler: Daimler’s “Highway Pilot” system powered Freightliner Inspiration truck (Daimler
2018b), Daimler’s Highway Pilot Connect (Daimler 2016) and Daimler’s Autonomous
Technology Enabled Cascadia Truck (O’Kane 2019) in CES 2019 show the company’s
advancement in the autonomous trucking technology development. Daimler’s autonomous
trucking system is equipped with radar and camera-based sensors. Additionally, Daimler
Highway Pilot connect also has V2V connectivity. Figure 2-17 shows the Cascadia Truck by
Daimler fitted with semi-autonomous technology being driven autonomously during CES 2019
(O’Kane 2019).
30
Figure 2-17. Cascadia trucks by Daimler AG with highway pilot enabled in CES 2019
(O’Kane (2019))
▪
TuSimple: TuSimple uses vision-based sensors that have a range of up to 1000m (Korosec
2019a). TuSimple is currently developing level 4 automated trucks that have completed test
runs across different US highways and is also collaborating with 12 companies to perform
commercial delivery (O’Kane 2019). Recently, the company announced a collaboration with
USPS to complete five round trips between Dallas and Phoenix carrying US mails (Boudway
2019). Figure 2-18 shows a semi-autonomous truck at CES 2019.
31
Figure 2-18. TuSimple’s semi-autonomous truck at CES 2019 (O’Kane (2019))
▪
Embark: Semi-autonomous trucks being developed by Embark also use a vision-based
autonomous trucking model (Cliff 2017). Embark’s trucks are retrofitted 18-wheelers that can
already drive themselves while on interstate highways (Cliff 2017). They are currently
performing freight runs where local trucking companies carry a trailer from a warehouse to a
yard near a highway ramp (Cliff 2017; Embark 2018). In the yard, one of the Embark’s
retrofitted trucks takes over and drives the trailer throughout the highway to another yard near
the destination warehouse where a local truck makes the last mile delivery (Cliff 2017; Embark
2018).
32
Figure 2-19. Semi-autonomous trucks being developed by Embark (Embark (2018))
▪
Starsky Robotics: Starsky robotics is another company developing level 4 autonomous trucks.
The technology they have under development is designed for a driverless system where a
remotely situated operator takes control of the truck when it is driving through critical areas
(Marshall 2018). Starsky robotics very recently completed a test run of their technology in
Florida’s public highway without a driver in the truck (Transportation Topics 2019).
33
Figure 2-20. Truck fitted with Starsky Robotics driverless trucking technology drives
down the Florida expressway (Starsky Robotics (2019))
▪
Volvo: Volvo is developing Vera, an autonomous tractor, that is designed to operate in
restricted areas such as ports or warehouse districts to carry big loads along fixed routes (Volvo
2018). As can be seen in Figure 2-21, Vera is designed to be driverless and to be controlled
and monitored remotely. Unlike other major developers who are targeting long haul highway
driving, Volvo is developing Vera to be used in a closed urban environment such as ports and
warehouse districts where trucks are needed to make short repetitive runs every day. Very
recently, Volvo announced its collaboration with logistics company DFDS to use Vera for
transporting goods between a logistic hub and a port in Sweden (Sawers 2019).
34
Figure 2-21. An autonomous electric tractor called Vera being developed by Volvo
Trucks (Volvo (2018))
▪
Uber: Otto, a company now owned by Uber, made history by making the world’s first
autonomous truck delivery of 50,000 cans of Budweiser from Fort Collins to Colorado Springs
in Colorado (Uber Advanced Technology Division 2016). The autonomous driving stretch was
120 miles over I-25. Similarly, Uber also retrofitted a Volvo truck with its self-driving
technology to transport freight across Arizona (Holley 2018). However, currently, Uber has
announced that it is closing its autonomous truck development to focus on the development of
self-driving cars (Holley 2019).
Level 5: Full Automation
Trucks with Level 5 automation or full automation will be able to drive under any
condition, including service roads and complex urban environment (Figure 2-22). All the
limitations associated with trucks with Level 4 automation would be nonexistent for Level 5.
Trucks with Level 5 automation have the potential to thoroughly disrupt the trucking industry.
They will offer the fastest return on investment. Since the driver would not be necessary for driving
purposes in these trucks, considerable savings in terms of labor costs is possible for freight
transportation (Roland Berger 2018). However, other types of freight transport-related jobs would
be created. For instance, a remote truck monitor who monitors multiple autonomous trucks from
a remote-control center might be needed. Similarly, a repair technician specializing in machine
vision sensors would also be in demand. Although the ultimate goal of all the autonomous trucking
technology developers is Level 5 autonomy, no technology being tested today is close to achieving
35
fully autonomous trucking. Significant advancements in terms of sensor technology and machine
learning models are necessary before trucks with Level 5 automation can be a reality. Also, the
arrival of trucking technology with Level 5 automation largely depends on the legislative
environment (Roland Berger 2018).
Figure 2-22. Illustration of Level 5 autonomous trucking (GAO (2019))
Mudge et al. (2018) estimate that adoption and fleet penetration of different levels of
autonomous trucking. Based on those estimates, Level 4 autonomous trucking will still take
another 9 to 12 years to be commercially available while level 5 technology will take another 21
to 26 years.
Figure 2-23. Slow and fast adoption scenario for autonomous trucking (Mudge et al.
(2018))
36
CHAPTER 3. IMPACT ANALYSIS
A recent study by League of Cities found that only 6 percent of cities have or are
developing rules to make themselves ready for autonomous driving (National League of Cities
2015). This statistic highlights the unpreparedness of US cities for the imminent automation in
transportation technologies. Timely assessment of the impacts of autonomous trucking is essential
to formulate plans that can maximize the gains from the autonomous trucking while minimizing
its risks. To that end, the authors synthesized the literature to analyze the impacts of autonomous
trucking. The results of this impact analysis are summarized in the following sections of this
chapter.
3.1 Impact on DFW’s Transportation Infrastructure
Autonomous trucks would be significantly different from conventional trucks. Thus, it is
expected that the existing DFW transportation infrastructure which is customized for the operation
of conventional trucks may not be adequate for the timely adoption of autonomous trucking
technologies. Hence, some key infrastructure areas will impact or in turn, will be impacted by
autonomous
trucking
technologies.
These
areas
are
illustrated
in
Figure 3-1 and discussed in the following sections.
37
Figure 3-1. Impact of Autonomous Trucking on DFW Transportation Infrastructure
Road Signs and Markings
Visible and consistent highway signs and markings are fundamental requirements for safe
driving. This is true for a human driver as well as autonomous driving (3M 2018; U.S. Department
of Transportation (USDOT) 2018). However, the signs and markings in the current US highways
and roads are in critical need of repair and rehabilitation (Flockett 2017; Sage 2016). There are
many reported cases of highway markings that are faded, misleading, and non-existent (Flockett
2017; Sage 2016). Similar to highway markings, visibility of traffic signs can also be compromised
due to the faded retroreflecting surface, faded color, occlusion, and vandalism (Toth 2012).
Additionally, highways signs and markings which are adequately visible during normal
weather could become barely visible during rain and snow. However, the U.S. Transportation
Department recently estimated that the taxpayers need to spend $90 billion a year to maintain roads
in their current condition. Such an enormous funding requirement for maintenance alone leaves
very limited funds available for upgrading the lane markings and traffic signs as the maintenance
takes the higher priority (Moylan 2018).
3.1.1.1 Impacts of Autonomous Trucking
Automated trucking technologies such as lane departure warning and highway pilot largely
depend on machine vision technology, which is an integral part of typical autonomous trucking.
For these technologies to correctly identify the lanes, the lane markings should be visible.
Similarly, highway signs should also be visible for these systems to autonomously identify and
process information such as speed limit, approaching exit, and approaching merge. Hence, the
current condition of highway signs and markings on the US highways is a significant impediment
for autonomous trucking.
38
Furthermore, lane markings and signs that are visible during normal weather become
illegible during rain and snow. Kockelman et al. (2016) report that under certain conditions (e.g.,
rain, snow), automated vehicles’ ability to correctly recognize lane markings and signs become
severely degraded. This degradation severely impairs the reliability of these technologies and
limits the applicability of all levels of autonomous trucking technologies. Nonetheless,
autonomous trucking technology developers are working on technologies that can drive
themselves in the absence of proper lane markings and signs (Cvijetic 2019). However, these
technologies need substantial investment in sensors and software, which make resulting vehicles
very expensive and thus unaffordable to most of the public (Sage 2016). Hence, autonomous
trucking technologies create a critical need for a well maintained and consistent lane markings and
signs in DFW’s highways.
Level 3: Technologies such as “lane departure warning system” and “lane keep assist”
require adequately visible lane markings and highway signs in all weather conditions. Hence,
innovative marking technologies such as retroreflective road markings for nighttime visibility,
dirt-resistant marking for high marking contrast, and reflective radar road marking for detection
by radar-based sensors during fog and snow should be considered (Ambrosius 2018). Federal
regulations should be developed and implemented so that consistent signs and markings are used
in interstate highways as opposed to the current system where different variations of the same
markings can be found depending on the state regulations.
Additionally, since Level 3 automation will have V2I capabilities, V2I communication
enabled signs can be installed to take advantage of those capabilities. For instance, traffic signs
that communicate phase change data with the trucks could be installed at intersections to allow
cars to regulate their speed and actions near intersections. A simpler yet very effective vehicle-toinfrastructure technology could be signs with embedded barcodes which the people are unable to
see, but the truck can sense (Moylan 2018). These barcodes, after being detected by autonomous
truck’s sensors can be interpreted instantly. This technology will thus compensate for the cases
where actual signs may be damaged or deteriorated or poorly illuminated or improperly installed.
Level 4: With proper regulations, Level 4 trucking automation allows a driver to be away
from the steering wheel during the driving over a non-critical section of the highways. Hence, this
demands even more reliable highway markings that can be identified by the sensors of the
autonomous vehicles. Therefore, federal compliance standards should be created and implemented
to ensure that a standard machine vision technology in an autonomous truck can detect the lane
markings and highway signs in the highway sections. States should be incentivized by giving
access to additional federal funding for maintaining level 4 automation compliant highway signs
and markings.
Level 5: By the time trucks with Level 5 automation are commercially available,
technology would potentially be advanced enough to gather all the traffic control data via a cloudbased traffic management system combined with highly accurate roadway maps (Kong et al.
39
2017). Such cloud-based traffic management control system would make physical traffic signs and
signals obsolete for autonomous vehicles themselves. However, traffic signs could persist and
compensate for the cases where cloud-based systems and on-board equipment in the trucks are
malfunctioning.
3.1.1.2 Opportunities
▪ The use of autonomous trucks warrants investments in V2I communication enabled signs
and markings from federal and state governments. Such investment which is in alignment
with USDOT’s smart infrastructure initiative.
▪ Highways signs that are visible during all possible conditions of weather and illuminance
will significantly enhance the safety of the highways and help in reducing accidents.
▪ V2I communication enabled signs, installed to facilitate automated trucks, will open new
ways for traffic planners to monitor and regulate traffic flows.
▪ A reliable standard of highway signs and markers will make it unnecessary for the
automation developers to create and install more expensive sensors and software in their
vehicles that have to compensate for the substandard lane markings and signs. This would
help drive down the cost of these automation enabled vehicles including freight trucks and
hence make these vehicles more affordable to the public.
3.1.1.3 Challenges
▪ States must coordinate with federal authorities to create a consistent design and
maintenance standards for road signs and markings. These standards should then be applied
across the US to design consistent highway signs and markings and maintain them
adequately. Creating such standards would need significant resources and collaboration
between both state and federal authorities.
▪ Significant investment is needed for the initial implementation and subsequent
maintenance of these smart highway signs and markings.
Warehousing and Fulfillment Centers
Warehouse and fulfillment centers are critical points in a freight network. These are
locations from where carrier trucks either pick up their shipment or drop it. Current warehouses
and fulfillment centers, with few exceptions, are largely unautomated and still use analog systems
and paper-based billing and invoicing. As a result, on average, truckers have to wait about twoand-a-half hours to pick up loads (Premack 2019; WSBT22 2019). These unusually long waiting
times result in substantial productivity losses in the trucking industry. Dunn et al. (2014) report
that U.S. carriers could gain $3.08 billion annually (and society as a whole could gain $6.59 billion
annually) by eliminating loading and unloading inefficiency. The U.S. Department of
Transportation estimates wait times over 2 hours, i.e., detention time, cost truckers a total of $1.1
to $1.3 billion in earnings each year (FMCSA 2018a). These unusually sizeable waiting times for
pickups and drop-offs can be attributed to several factors including facility limitations, arriving for
a scheduled pickup and finding the product not ready for shipment, poor service provided by
40
facility staff, and facility scheduling practices (GAO 2011). With automation and innovative
logistics solutions rapidly optimizing other parts of the supply chain, warehouse operation should
also adopt automation and modern solutions to match the rising demand for more efficient and less
time-intensive warehousing solutions.
3.1.2.1 Impacts of Autonomous Trucking
The use of automated trucking will increase the efficiency of freight trucks. As a result, the
inflow and outflow of trucks will increase to and from warehouse and fulfillment centers. A
warehouse that is already bogged down by the current demand will be overwhelmed by this new
influx of trucks. This automation will lead to even higher wait times and more productivity losses.
Eventually, the warehouses which cannot adapt will lose business. This is already being observed
in the current freight industry. For instance, some carriers have launched “shipper of choice”
programs to prioritize shippers who minimize wait times for drivers (McFadden 2019).
Similarly, a survey of 150 trucking companies in 2018 found that 80 percent of carrier
respondents refused to take loads from facilities for reasons that included inflexible appointment
hours and lengthy detention times (Korosec 2019b). This statistic indicates that eventually carriers
will stop using shippers who have unusually long waiting times, and this will lead to these shippers
being driven out of business. Therefore, one of the significant impacts of autonomous trucking
would be an increased pressure on warehousing infrastructure to optimize, automate, and reduce
waiting times.
Automated trucking also presents a significant opportunity for warehouse managers who
want to innovate and optimize their facilities for reducing wait times and enhancing productivity.
Studies show that optimization of warehousing operations depend on how effectively problems
related to unprepared shipments, error-prone paper works, understaffing, labor fatigue, and load
matching is tackled (GAO 2011; McDonald 2019; Pyzyk 2019). Abundant data streams
broadcasted from automated trucks feeding into sophisticated machine learning-based algorithms
combined with the use of robotics can significantly reduce these problems (Kroll 2019; Pyzyk
2019). Machine learning-based models can increase speed and productivity in transportation
operations by reducing errors, increasing consistency, enhancing driver satisfaction, and
identifying troubling patterns (Pyzyk 2019). Furthermore, natural language processing driven
software can be embedded in a truck’s on-board operating system to transform traditional paperbased billing and invoicing to digital platforms. This transformation will significantly reduce the
delays and inefficiencies associated with traditional billing and invoicing practice (Pyzyk 2019).
The impact of different levels of automated trucks on warehousing infrastructure are
discussed below:
Level 3: Trucks with Level 3 automation will have enhanced communication capabilities
in addition to several sophisticated sensors such as cameras, RADAR, and LIDAR. These
additional sensory data combined with telematics enables the warehouses to know the exact
41
location of the trucks and the time it takes for the truck to come to the warehouses. This capability
will enable warehouses to act proactively for preparing the shipment. This proactive approach will
reduce instances when a carrier arrives to find the shipment still not ready for pick up.
Furthermore, Level 3 automation will enable platooning. However, if a truck has to wait
for more than a few minutes for a platooning truck to catch up, or if the rear truck has to drive
above 65 mph to catch up to its lead truck, cost savings are wasted (Hampstead 2019). Therefore,
for platooning to be profitable, inter-carrier platooning should be made possible where trucks from
any carrier can platoon behind trucks from any other carrier. Warehousing should accommodate
Digital Freight Matching to enable inter-carrier platooning. Digital Freight Matching links shipper
demand (the need to transport a product) with carrier supply (truck capacity) via digital (web- or
mobile-based) platforms, usually in the form of apps. In the past five years, several Digital Freight
Matching (”DFM”) companies have emerged (Armstrong & Associates Inc. 2016). Right now,
Uber Freight, Convoy, uShip, Navisphere, and Carrier 360 have emerged as key players in the
Digital Freight Matching business (Hampstead 2019). Hence, warehouse managers should be
proactive about the services these companies are offering and should look into incorporating them
into their warehousing model.
Level 4: More sophisticated sensors will generate richer data streams which will make
predictive algorithms powering warehouse management systems more accurate. The use of such
algorithms will lead to more optimized warehousing operations and consequently reduced waiting
times. Drivers can be trained to carry out logistics operations so that drivers can perform logistic
duties when the truck is operating in autonomous mode. This capability will significantly enhance
the productivity of drivers.
Level 5: No driver will be present in trucks for this level of automation. Hence, the
warehousing should completely be automated so that the entire pick-up and drop-off operations
can be performed without any human intervention.
3.1.2.2 Opportunities
▪ The use of on-board sensors and Global Positioning System will enable precise
determination of detention times. The precise determination of detention times will help in
avoiding debate among shippers and carriers regarding detention times.
▪ Real-time tracking of trucks is possible, which will enable shippers and receivers to
proactively prepare the shipments. This proactive shipment preparation will help to avoid
delays in shipping and loading.
▪ Connected and digitized warehousing operations can easily be integrated with the Digital
Freight Brokerage system. This integration enables the warehouses to take advantage of
technologies such as inter-fleet platooning.
▪ Real-time data streams combined with optimization and prediction models will lead to the
creation of highly efficient scheduling and resource allocation algorithms. This
advancement will lead to significant productivity gains in warehousing operations.
42
▪
Autonomous trucking’s powerful on-board computer enables the digitization of
paperwork. Such digitization leads to reduced wait times as lengthy paperwork is one of
the major causes of long waiting times.
3.1.2.3 Challenges
▪ According to a recent survey of 549 warehouse managers, directors, and vice presidents by
the Warehousing Education and Research Council (WERC), the majority of warehouse
executives are not planning on implementing robotics, blockchain, or other emerging
technologies (Premack 2018). As such, despite the critical need for warehouse automation,
warehouse managers will need incentives such as tax cuts to implement automation into
their facilities.
Parking
The Hours-of-Service (HOS) regulations require the commercial drivers to take mandatory
long and short breaks (Boris and Brewster 2016). For taking such breaks, the drivers must find an
adequate legal parking space. However, many studies have shown that drivers are often forced to
park in unauthorized or unsafe locations such as freeway interchange ramps, freeway shoulders,
and highway roadsides due to truck parking shortages and truck drivers’ lack of awareness of
available parking nearby (Boris and Brewster 2016; Morris et al. 2017; NCTCOG 2018; USDOT
2015). A survey by American Transportation Research Institute showed that drivers on average
spent 56 minutes of available drive time per day parking early rather than risking not being able to
find parking down the road (Boris and Brewster 2016). This approach reduces their wages by up
to 10 percent annually. Furthermore, today’s logistics management approach, which is based on
the optimized use of inventory space, depends on the delivery of goods to a specific location right
before it is needed. These hard constraints for delivery leave very little time for the drivers to look
for parking space and make the job of finding parking even more stressful ( NCTCOG 2018a).
DFW region suffers from inadequate long term and short term parking spaces along its
major freight corridors (NCTCOG 2018b). Many studies have acknowledged the seriousness of
the parking problem for trucks and have suggested many strategies to mitigate it. The Jason’s Law
Report (USDOT 2015) and numerous other studies by state DOTs support the need for expanding
truck parking capacity along major interstate corridors to prevent fatigue-related crashes and give
truck drivers the means to comply with federal Hours-of-Service regulations and local parking
laws (Boris and Brewster 2016). However, adding the needed capacity is expensive and rarely
politically acceptable, so alternative methods of managing parking resources must be explored
(Boris and Brewster 2016). The use of automated truck technologies could be one of those
alternatives.
3.1.3.1 Impacts of Autonomous Trucking
Provided that necessary legislations are passed, there is potential for the following impacts
of autonomous trucking technologies on parking:
43
Level 3: In Level 3 automation, hands-free driving is possible. This capability enables the
driver to use a smartphone or laptop to access the parking management system/portal, perform
dynamic route planning, determine where parking spaces are available, and determine the ideal
parking location (Short and Murray 2016).
Level 4: When the truck is driving autonomously, that period can be considered as a rest
period for the human driver. Therefore, level 4 automation in trucks makes it unnecessary to find
a parking spot for rest (Short and Murray 2016). Breaks of 30 minutes would likely disappear for
authorized AT users, and 10-hour breaks will likely become less frequent, resulting in additional
truck parking capacity for those who do need to park their vehicles (Short and Murray 2016). This
could help to confine the ever-increasing parking demand.
Level 5: When trucks can drive without drivers, all Hours-of-Service regulations would
become irrelevant. It is expected that this level of automation would eliminate parking demand
due to Hours-of-Service regulations. Parking would only be required for pre-delivery staging, fuel
stops, and maintenance (Short and Murray 2016).
3.1.3.2 Opportunities
▪ The demand for parking space due to Hours-of-Service mandated short-term and long-term
rests would be eliminated or reduced and therefore will lead to lower investment requirements
for expanding parking facilities.
▪ The driving time lost due to parking early (on average 56 min as per Boris and Brewster (2016))
and time spent in searching for parking will be minimized. Consequently, wages lost and
productivity lost due to parking can be minimized.
▪ Stress for finding suitable parking spot can be reduced due to the combined use of automated
trucking technologies, dynamic routing algorithms, and Intelligent Parking Management
System (Morris et al. 2017).
3.1.3.3 Challenges
▪ For autonomous trucking systems to find an ideal parking location, infrastructure to monitor
and communicate available parking space should be created. Systems similar to camera-based
parking monitoring and information dissemination system developed by Morris et al. (2017)
can be used initially. This technology can be further enhanced using infrared sensors to
improve night-time monitoring. Furthermore, a machine learning-based image classification
algorithm can be used to more accurately identify vacant parking spots. Since a driver would
want to know the availability of parking beforehand (Morris et al. 2017), an accurate
forecasting model should be developed that can estimate the available parking spaces at a
location a few hours into the future to enable efficient autonomous route planning.
▪ Local ordinances can have a severe impact on truck parking, especially in areas with heavy
freight activity (NCTCOG 2018a). Reducing short‐term parking around freight‐oriented
developments can negatively impact the autonomous system’s ability to identify suitable
44
parking locations. Hence, cities should maintain and give access to the updated georeferenced
database of city ordinances which impose additional constraints on truck parking.
Managed Lanes/Dedicated Lanes
The density of trucks in the US highways is expected to rapidly increase over the next three
decades (Bucklew 2011). This trend will lead to reduced efficiency, degraded reliability, and
increased safety risk in US highways due to increased congestion. The conventional approach to
avoid such congestion would be to increase the number of lanes on the highways by using the
mixed-traffic flow model where highway traffic is composed of different vehicles. Such an
approach leads to suboptimal highway safety because traffic composed of trucks and smaller
vehicles will always have irregular flow due to the non-homogenous distribution of acceleration
and braking characteristics. Owing to this, many policymakers have considered highway
expansion combined with delineation of dedicated truck lanes as an alternative to the conventional
approach of highway expansion.
Despite a sound rationale behind the use of dedicated truck lanes in highways, no
significant highway project has been completed in the US till now that implements dedicated truck
lanes. This lack of completed dedicated truck lane projects is mainly due to the lack of federal
funding and initiative to support huge infrastructure expenditure required to implement the concept
of dedicated truck lanes (Bucklew 2011). Furthermore, chances of increased congestion in other
lanes, possible prohibitive costs of right-of-way acquisition in urban areas, and environmental
restrictions limit the implementation of dedicated lanes at interstate levels. As such, the
implementation of dedicated lanes is both promising and challenging, especially for a rapidly
evolving metroplex such as DFW.
Meanwhile, managed lane strategies are being applied to freeways in the US to improve
the freeway performance measured in terms of mobility, travel time, travel speed, traffic flow, fuel
consumption, safety, and congestion reduction (Hussain et al. 2016). Managed lane is defined by
the Federal Highway Administration (FHWA) as “freeway-within-a-freeway where a set of lanes
within the freeway cross-section is separated from the general-purpose lanes.” Both of these
traffic-flow management strategies, that aim to optimize the performance of highways through
traffic segregation should be revisited when the impacts of autonomous trucking are discussed.
3.1.4.1 Impacts of Autonomous Trucking
Discussion for dedicated truck lanes and managed lanes is revived by the discussion of
adopting autonomous trucking in areas such as DFW. The expected but yet unrealized advantages
of dedicated truck lanes, when combined with potential advantages of autonomous trucking, can
massively shift the benefit-cost equation in favor of the implementation of the combined use of
dedicated lanes and autonomous trucking (Bucklew 2011). Moreover, the use of intelligent
infrastructure combined with dedicated truck lanes has long been believed to reduce the investment
requirement for physical infrastructure needed to operate dedicated truck lanes (Bucklew 2011).
On the other hand, current autonomous technologies such as platooning do require dedicated truck
45
lanes to exhibit many of its advantages such as reduced fuel consumption and reduced congestion
(Litman 2018; Riemer and Prozzi 2016). Furthermore, tight vehicle spacing on roads during truck
platooning could cause problems for other motorists trying to exit or enter highways, possibly
resulting in the need for new or modified infrastructure with dedicated platoon lanes and thicker
pavements to handle high truck volumes. (Fagnant and Kockelman 2015)
However, fully built-out dedicated autonomous truck lanes, with dual autonomous truck
lanes in both directions, maybe too costly to build, at least for the next few decades during which
these technologies would have a very low penetration rate (Talebpour et al. 2017). In the meantime,
managed lanes that prioritize autonomous trucks during peak freight periods can be implemented
(Williams et al. 2016). The rich and real-time information that is available to autonomous trucks
that could potentially be utilized by traffic managers makes the use of managed lanes combined
with autonomous trucking highly promising. Managed lanes allowing only autonomous trucks
during peak freight hours will thus help improve congestion issues along high freight volume
corridors such as DFW. This strategy is also supported by the study of Talebpour et al. (2017)
which concluded that use of dedicated lanes is only beneficial when market penetration rates of
autonomous vehicles are above 50% for the two-lane highway and 30% for the four-lane highway
for the cases considered in this paper.
With this background, the following impacts of autonomous trucking can be expected for
the DFW area:
Level 3: Truck platooning requires the use of dedicated lanes for exhibiting many of its
potential benefits (Litman 2018; Riemer and Prozzi 2016). Combined potential benefits of truck
platooning and dedicated truck lanes could encourage policymakers to sanction investment for
dedicated autonomous truck lanes as is already seen in the article by Selcraig (2018) where city
managers are making efforts to sanction funds for dedicated lanes for autonomous vehicles in I35. Furthermore, the availability of real-time rich data from trucks and the existence of V2I
communication infrastructure leads to a possibility of highly optimized managed lanes, which can
lead to a much safer and a much less congested highway.
Level 4: More aggressive automation will likely result in trucks that can safely drive with
all forms of highway traffic. Nonetheless, investment in dedicated lanes/managed lanes for
autonomous vehicles is still a good investment, at least for the time when these autonomous
trucking technologies are at their early stages. Dedicated lanes which only allow autonomous
vehicles minimize the interaction between autonomous trucks and non-autonomous vehicle and
thereby reduce chances of potential accidents. On the other hand, managed lanes can be used to
allow varying interaction of normal traffic with an autonomous truck and thus can be used to test
the performance of autonomous highway pilot in a real yet controlled environment.
46
Level 5: Assuming a high penetration of autonomous trucks and a highly evolved
autonomous systems, dedicated lanes and managed lanes would be obsolete as the autonomous
system would be able to drive under any driving conditions.
3.1.4.2 Opportunities
▪ The cost of operating dedicated lanes will be reduced (Bucklew 2011).
▪ Dedicated lanes/ managed lanes will offer a much-needed platform for testing highway
performance of autonomous trucks which will help these autonomous technologies to
mature and be more reliable.
▪ It is expected that using platooning and highway pilot combined with managed/dedicated
lanes would reduce accidents involving trucks and other smaller vehicles (Bucklew 2011;
Cherry and Adelakun 2012).
▪ If a managed or dedicated lane if designed just for the operation of autonomous vehicles,
more efficient use of highway space can be expected in such lanes leading to an enhanced
highway capacity. This will also help minimize congestion and reduce travel time.
▪ Autonomous trucks in dedicated/managed lanes can drive at much higher speeds due to
faster reaction time. The dedicated/managed lanes could help enhance the productivity of
the freight industry.
▪ Segregation of large trucks from normal traffic would enable smooth traffic flow.
3.1.4.3 Challenges
▪ Dedicated highway lanes for autonomous vehicle platooning may reduce the capacity for
lanes allocated to human-operated traffic, making travelers in human-operated vehicles
worse off (Litman 2018).
▪ The initial costs to build dedicated/managed lanes are significantly higher than the cost to
build mixed-flow facilities (Bucklew 2011).
▪ Use of right lanes by platoons will disrupt smooth traffic flow in highways by impeding
exiting and merging vehicle. This can be avoided by letting platooning trucks to use the
left two lanes rather than the right two lanes. This strategy has been shown to be favored
by truck drivers, according to a survey conducted by Cherry and Adelakun (2012).
▪ Any of these new configurations will meet operational challenges, especially moving
entering and exiting trucks to the right-side ramps. In situations where many of these
movements exist, dedicated left-side ramps can be developed (Cherry and Adelakun 2012).
Inspection and Maintenance Infrastructure
The Department of Transportation (DOT) requires that all commercial motor vehicles
(CMV) with a gross vehicle weight rating of more than 10,001 pounds undergo an inspection every
year. This inspection typically includes checking of truck’s seatbelts, brakes, brake lamps,
coupling devices, exhaust system, emergency exits, electrical cables, engine systems, battery
compartments, frame, fuel system, headlamps, lamps on projecting loads, safe loading, securement
of cargo, steering mechanism, stop lamps, suspension, tail lamps, tires, trailer bodies, turn signals,
47
wheels, rims, hubcaps, and windshield wipers (Schubert 2018). It takes a trained inspector about
30 minutes to perform such inspection (InsustrySafe 2013). Additionally, commercial drivers are
required by federal regulations to perform self-inspection of their trucks before the start of their
shift and once within every 24 hours on the road (TruckingTruth 2018). The parts of the truck
checked in this self-inspection is similar to the ones checked in the annual inspection. From the
list of parts checked in these inspections, it is evident that the current inspection regulations are
tailored for traditional commercial vehicles driven by internal-combustible engines. However, the
scope of these inspection regulations and consequently the training of the inspectors must be
widened when autonomous and possibly electric trucks start operating in the highways around
DFW. Additionally, repair technicians currently catering to the maintenance needs of the freight
industry should be made aware of these technologies and should be trained to make them prepared
to inspect and repair the autonomous trucks of the future.
3.1.5.1 Impacts of Autonomous Trucking
In addition to conventional electro-mechanical parts, autonomous trucks will have an array
of sophisticated parts such as infrared sensors arrays, LIDAR arrays, HD cameras, On-Board
Processing Unit. Additionally, such vehicles will have an array of on-board monitoring devices
(Cannon 2018). These devices will come with inspection and maintenance needs, unlike anything
even the most tenured shop foreman has seen, especially when it comes to the needs of
sophisticated radar and camera systems used for autonomous driving (Cannon 2018). As such, the
following impacts can be expected due to the different level of autonomy in freight trucks in the
DFW region:
Level 3: With the passage of proper regulations, trucks with Level 3 automation can
participate in platooning and communicate with another vehicle and infrastructure. For platooning
to be implemented, the legal distance between two trucks must be reduced. This reduced distance
will be less than the stopping distance allowed by human reaction time. Therefore, sensors and
processing units of platooning trucks should function properly to ensure safe platooning
operations. Hence, for this level of automation, the scope of annual inspection and self-inspection
for trucks must be revised to ensure proper operation of sensors and processing units used for
platooning operations. Additionally, companies demonstrating proper operation of other
supplemental automated features such as highway pilot and V2X communication can be
incentivized, and their way of demonstration can be studied to draft future regulations.
Level 4: Automated trucks with level 4 automation, can drive autonomously without the
driver being alert over non-critical segments of the highways. The safety of such autonomous
driving can be ensured only if a wide variety of sensors in the trucks along with the processing
units of the truck are operating properly. Hence, sensors such as infrared arrays, LIDAR arrays,
and cameras along with image processing and path planning software of the vehicles should be
included in the mandatory inspections. Federal regulations should coordinate with autonomous
trucking technology developers to create an interface that can be used to extract information
regarding the current state of all the critical sensors and software. This interface should be
48
tamperproof and straightforward to offer a reliable and efficient way to monitor and regulate
advanced autonomous technologies found in Level 4 automated trucks.
Level 5: Automated trucks with Level 5 automation will be driverless. This level of
automation warrants even more stringent vehicle inspection regulations. In addition to regulations
imposed for Level 4 automation, inspection should ensure the proper functioning of hardware and
software that will enable proper operation of trucks in critical scenarios such as traffic jams, pullover, accidents, traffic diversions, urban streets, and parking.
3.1.5.2 Challenges
▪ Trucks with a higher level of automation (Level 3-5) can store information gathered by
their sensors. This information can potentially be shared with inspectors and transportation
planners to communicate the state of the various autonomous features. However, issues of
intellectual property ownership and privacy might complicate this information sharing.
▪ The complexity of technology will increase with the increase in the level of automation.
This increase should be commensurate with the increase in training provided to the
inspectors tasked with evaluating the state of trucks’ critical hardware and software.
▪ Repair technicians currently specialized in repairing traditional trucks should be trained to
inspect and repair hardware and software installed in automated trucks. Moreover,
automated truck developers should adopt a standard format for creating a repair manual for
facilitating an easier learning process for current technicians. Additionally, a standard
hardware interface (e.g., a single port marked by a standard color and icon) should be used
by all automated truck developers to simplify and optimize the process of fault code
identification and subsequent repair.
3.1.5.3 Opportunities
▪ Higher levels of automation will enable smoother operation of trucks as Artificial
Intelligence (AI)-based systems are capable of greater precision and finer control compared
to an average human driver. This feature will reduce wear and tear of parts and will lead to
a reduced maintenance frequency. (Cannon 2018)
▪ Cloud-based inspection of a vehicle is possible due to the presence of sophisticated sensors
and communication capability of the highly automated (Level 3-5) trucks. This can
potentially optimize the truck inspection process to make it faster and less labor-intensive.
▪ Data recorded by automated truck’s sensors can be combined with machine learning-based
models to predict imminent vehicle failures and formulate proactive risk-averse
maintenance schedules. This prediction would prevent costly downtime for trucks and
enhance long term performance.
Transfer Hub
Highway driving is comparatively less complicated than driving on service roads and
inner-city roads. Therefore, until Level 5 autonomous technology is ready, human drivers are bestsuited for delicate tasks such as maneuvering crowded city streets and labyrinth-like environs of
49
ports, freight yards, and warehouse complexes while autonomous trucks are best-suited for longhaul work because they eliminate issues humans face such as driver fatigue, daily drive-time
limitations and the need to stop for meals, rest or sickness (O’Dell 2018). Many autonomous truck
developers such as Uber, Embark, and Daimler are currently testing their highway pilot
technologies for such usage (Cliff 2017; Newcomer and Webb 2016; O’Dell 2018; O’Kane 2019).
Eventually, a business model called “transfer hub model” will likely emerge out of such usage
which combines conventional trucking for the first and last mile with automated driverless trucking
for the middle mile on the interstate. This model is illustrated in Figure 3-2.
Figure 3-2. Transfer Hub Model (Roland Berger (2018))
The transfer hub model consists of two yards at either end of a dedicated autonomous
highway section (Clevenger 2018). These hubs will require dedicated areas where trailers can be
switched from one truck to the other (Roland Berger 2018). Trucks operated by human drivers
deliver freight to a transfer hub. At the hub, the loaded trailers are hooked to self-driving tractors
and hauled across the state to the opposing hub, where a conventional truck-and-driver team picks
them up for delivery to their destination. These transfer hubs can also be used for platooning as
the assembly of platoon requires a large place, and if it is assembled while driving on an
expressway, the existence of drivers on each truck will be an issue of when and where a driver gets
on or off a truck (Tsugawa 2014).
50
3.1.6.1 Impacts of Autonomous Trucking
Level 3: With the availability of level 3 automation for trucks, transfer hubs can be used to
engage trucks belonging to trucking companies with small fleets in platooning. These hubs can be
used as an assembly area for trucks with common route. As such, these hubs would be used as a
start point and an endpoint of platooning operations. Therefore, enough transfer hubs would be
required to enable a large number of platoon formation in major freight corridors. However,
significant savings from the transfer hub model would be mainly due to the elimination of driver
in the long-haul segment. Therefore, Level 3 automation in the truck alone would not create a
compelling business case for investment in transfer hubs.
Level 4: With more trucking companies employing exit to exit autonomous driving, trucks
would require a dedicated space that facilitates the transition from autonomous driving to human
driving. Changing between human-driven tractors and autonomous tractors at the edge of the
highway, as is currently being done by Embark (Embark 2018), would not be safe and feasible
once many trucking companies start to use trucks with Level 4 automation. Hence, Level 4
automation would require a significant investment to create a well-established network of
autonomous highway segments and transfer hubs in the DFW region.
Level 5: No transfer hub would be required as trucks would be able to drive autonomously
in both highway and non-highway section of the freight route.
3.1.6.2 Opportunities
▪ With short drayage hauls on either end, a fleet would reduce its long-haul transportation
costs between 20% and 40% (Newcom Media Inc. 2018; Roland Berger 2018).
▪ The current shortage of long-haul truck drivers would be reduced as drivers would not be
required for the long-haul portion. This would also enable a better work-life balance for
truck drivers (O’Dell 2018).
▪ 50% of freight in the US could be moved using a transfer hub model while shrinking the
overall U.S. truck fleet by as much as 13% (Newcom Media Inc. 2018). This would lead
to less emission and cost savings for the industry.
3.1.6.3 Challenges
▪ Significant investments must be made to create a well-developed network of transfer hubs
and autonomous highway segments. The autonomous highway segments might have to
operate initially as dedicated highways for autonomous vehicles, at least for the initial
implementation stages. Furthermore, these autonomous highway segments would require
proper maintenance and monitoring to ensure adequate visibility of lane markings and
highway signs.
▪ Since transfer hubs need to be in proximity to major freight corridors, many ideal locations
for these hubs would coincide with the current location of facilities such as rest stops and
truck parking areas. Hence, these rest stops and parking areas will need to be repurposed
with minimum inconvenience to the non-autonomous vehicle users of these facilities.
51
▪
Regulation and legislation should be passed to enable autonomous driving because current
regulations do not allow autonomous operations of trucks. (Daimler 2018b)
Roadside Equipment
In a connected vehicle environment, which is the next generational objective as per the
Intelligent Transportation System initiative of USDOT, Roadside Equipment can communicate
with On-Board Unit in a vehicle using wireless communication. Roadside Equipment can be a
traffic light or a traffic sign or a traveler information system managed by the local transportation
management center. Such communication between Roadside Equipment and On-Board Unit, i.e.,
Vehicle-to-Infrastructure (V2I) communication, can be used to provide a wide range of
information and safety services to the drivers (Boske and Harrison 2014). The information
disseminated this way can be regarding parking availability, traffic congestion, weather conditions,
accidents, routing options, platooning services, and signal phasing (Boske and Harrison 2014;
Hendrickson et al. 2014). Failing to provide these services, however, would be missing a unique
opportunity to transform the current road infrastructure into the smart infrastructure of the
information age (Boske and Harrison 2014).
Compatible wireless communication infrastructure is essential to enable a full-fledged V2I
communication (Boske and Harrison 2014; Kianfar and Edara 2013; Kong et al. 2017; Short and
Murray 2016). For this purpose, in 1999, the Federal Communications Commission (FCC)
allocated a frequency spectrum known as Dedicated Short-Range Communications (DSRC) in the
5.9 GHz band for communication between vehicles. In 2003, the FCC issued corresponding
licensing and service rules. The “Moving Ahead for Progress in the 21st Century Act” of 2012
called for an assessment and evaluation of V2I communication, including DSRC (Boske and
Harrison 2014). These regulations indicate that any future development of connected vehicle
technologies and any V2I communication will highly depend on a well-developed DSRC network.
Hence, to support the development of connected vehicle environment and subsequent V2I
communication, cities must ensure that their traffic signs, signal boards, parking management
systems can communicate with compatible vehicles using DSRC network. Table 3-1 shows the
automated technologies that rely on DSRC network.
Table 3-1. V2I application needing DSRC roadside unit installation (Hendrickson et al.
(2014))
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3.1.7.1 Impacts of Autonomous Trucking
Since many technologies used in autonomous trucks use V2I communication, autonomous
trucking will necessitate and motivate the investment in V2I compatible Roadside Equipment
(Short and Murray 2016). Furthermore, most autonomous truck technologies being developed
today rely on LiDAR. According to (Kong et al. 2017), LiDAR is limited in the following ways:
▪
▪
▪
▪
Lidar is constrained by the line of sight and cannot see through a large obstacle such as a
truck ahead.
LiDAR performs poorly in bad weather.
LiDAR may incorrectly recognize some harmless objects (e.g., plastic bags) as obstacles.
LiDAR cannot discern human signs.
Furthermore, based on an interview conducted by GAO (2019), LiDAR may not be useful
at higher speeds due to its limited range and its inability to process information about the
surrounding environment as quickly as needed at these speeds. Therefore, when LiDAR-based
autonomous system in an autonomous truck cannot perform reliably, Roadside Equipment can
compensate by relaying information such as location and heading of the vehicles that are near the
communicating vehicle, location of communicating vehicle with respect to highway lanes and
routing information. Such information will help to create a more reliable autonomous highway
pilot system. However, this level of V2I will need a highly advanced Roadside Equipment
equipped with HD cameras, a cloud-based traffic management system, and a high-speed wireless
communication to perform properly (Kong et al. 2017).
Hence, with appropriate legislation, following impact on RSE can be expected due to the
implementation of autonomous trucking:
53
Level 3: V2I technologies such as Intersection Movement Assist, Bridge Height Inform,
and Curve Speed Warning will motivate investment for a well-developed RSE that can
communicate using DSRC.
Level 4-5: A reliable and safe highway pilot requires advanced roadside equipment that
has HD cameras, high-speed wireless communication capability, and connection to cloud-based
transportation management centers (Kong et al. 2017). Roadside Equipment of this level can
complement On-Board Unit of the autonomous trucks and help these trucks navigate highways
during adverse weather conditions or when markings are not visible. Furthermore, RSE can
provide important routing information such as accidents, available parking locations, and
alternative routes to avoid congestion, which will be essential for a full-fledged autonomous truck.
3.1.7.2 Opportunities
▪ Advanced Roadside Equipment can enhance the safety of the autonomous trucks by acting
as a backup system during poor visibility conditions and thus enhance the reliability of
autonomous highway pilot.
▪ RSE and autonomous trucking combined with cloud-based traffic management system
would provide access to real-time traffic information. Such information would render
costly traffic surveys obsolete.
▪ Realtime analysis of traffic flow can be used to vary electronic speed limit signs to
maximize traffic throughput (Boske and Harrison 2014).
▪ Cameras and sensors on motorways can help detect accidents and accordingly relay routing
and traffic information to the central ITS server as well as to the drivers (Boske and
Harrison 2014).
▪ The system will have the ability to charge customized tolling fees varying based on vehicle
identity. (Boske and Harrison 2014)
▪ Dynamic message signs can be used to display real-time information collected by sensors
and warn motorists of collisions and road-weather conditions (Boske and Harrison 2014).
▪ Establishing a regional traffic management system to analyze the data feed from RSEs and
autonomous trucks can facilitate the dissemination of real-time traffic data along major
transport routes, which would improve DFW’s traffic management capabilities.
3.1.7.3 Challenges
▪ A substantial investment must be made in RSE and cloud computing infrastructure to
facilitate large scale implementation of autonomous trucking technologies
▪ As with any new technology, the deployment of Autonomous Trucking Technologies
would take time. DFW region may, therefore, experience higher short-term costs (in terms
of technological and infrastructure upgrades) than short-term benefits to safety and
congestion (Boske and Harrison 2014).
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3.2 Impact on Critical Trucking Issues
The impact of autonomous trucking on the key issues of the trucking industry is detailed
in this subsection. The key areas discussed are shown in Figure 3-3 and detailed in the following
section of this report.
Figure 3-3. Impact of Autonomous Trucking on Critical Trucking Issues of DFW Region
Compliance, Safety, Accountability (CSA)
It is the safety compliance and enforcement program of the Federal Motor Carrier Safety
Administration (FMCSA) that holds motor carriers and drivers accountable for their role in safety
(FMCSA 2016). Compliance, Safety, and Accountability helps FMCSA to identify and prioritize
safety issues associated with different motor carriers so it can intervene when needed through
warning letters and investigations. Compliance, Safety, and Accountability also helps to monitor
the safety performance and compliance of individual drivers. Compliance, Safety, and
Accountability data about each carrier are visible through an online platform called FMCSA’s
Safety Measurement System. Compliance, Safety, and Accountability scores consider many
factors, including the number of safety violations, the severity of violations, the severity of crashes,
and the number of vehicles operated by the carrier. These data are collected through roadside
inspections, crash reports from last two years, and investigation reports. The data collected for
each carrier is then reported under seven Behavior Analysis and Safety Improvement Categories
(BASICs). These categories are illustrated in Figure 3-4.
55
Figure 3-4. Behavior Analysis and Safety Improvement Categories – BASICs (FMCSA
(2016))
The Compliance, Safety, and Accountability program and BASICs score have been
reported as the 6th most critical issue in the trucking industry by American Trucking Research
Institute’s 2018 report (McReynolds et al. 2018). Of the seven BASICs shown in Figure 3-4, there
is significant stakeholder concern with the “Crash Indicator” BASIC (McReynolds et al. 2018;
Short and Murray 2016). The current Safety Measurement System reports crash indicator score by
combining “preventable” and “not preventable” crashes uniformly. This approach of calculating
crash indicator score is potentially harmful to the carriers and drivers who are not at-fault in a
crash. The next pressing issue is the improvement of Compliance, Safety, and Accountability
program and Safety Measurement System itself as warranted by the National Academy of
Science’s review (FMCSA 2018b). As reported in FMCSA (2018), the current Safety
Measurement System, despite its merits lacks a principled scientific approach. Therefore, FMCSA
is currently working to replace the current Safety Measurement System’s model with a data drivenInformation Response Theory-Based model-as suggested by National Academy of Engineer’s
recommendation. In this regard, automated trucking with its potential safety enhancement and rich
data streams can be highly relevant.
3.2.1.1 Impacts of Automated Trucking Technologies
The impacts of automated trucking on the Compliance, Safety, and Accountability program
is discussed below under different levels of automation:
Level 3: Level 3 trucking automation will help to minimize unsafe driving and reduce
crashes. With the added sensors and warning system in the trucks, a driver can be more aware of
the vehicle’s surroundings, and such enhanced situational awareness will lead to enhanced safety.
This leads to an improvement of “Unsafe Driving” and “Crash Indicator” BASICS.
Vehicle-to-vehicle and vehicle-to-infrastructure communication adds one more layer of
security to truck driving, which further helps to avoid risky driving maneuvers and crashes. Thus,
vehicle-to-vehicle and vehicle-to-infrastructure connectivity will further help to improve BASIC
score related to “Unsafe Driving” and “Crash Indicator.” Cameras installed in these vehicles would
also help to accurately identify “not preventable” crashes and driver at-fault during these crashes.
Level 4: At Level 4, autonomous trucks can be programmed to avoid unsafe driving and
potential crashes (Short and Murray 2016; Williams et al. 2016). Thus, Level 4 trucking
automation will significantly help improve BASIC score related to “Unsafe Driving” and “Crash
Indicator.” Also, as discussed in the previous section, with proper legislation, Hours-of-Service
compliance can significantly improve with Level 4 automation. Since Level 4 automation comes
with highly connected trucks and a wide array of sensors, algorithms can be trained on the data
streams from these sensors to identify cases such as driver suffering from stroke or driver driving
intoxicated. These features combined with autonomous driving can be used to safely stop the truck
56
at side of the highway. Then, if the driver is in critical health condition such as stroke, the truck
can call emergency services. If the driver is suspected of driving while intoxicated, driver might
need to use the on-board breathalyzer and pass the test before he can drive the vehicle. These
features can effectively minimize issues related to driver fitness and controlled substances. Also,
at this level of automation, most of the vehicle features would be monitored by the on-board
computer. Such monitoring enables the truck itself to proactively suggest maintenance for any
impending hardware failure. Therefore, BASIC score associated with maintenance would also be
improved. Furthermore, data from LIDAR, RADAR, and camera arrays will more accurately help
to identify the reasons for any crashes. These sensors also will help to better visualize the crash
and thus help in identifying the at-fault party and will help to avoid inclusion of “not preventable”
crashes in crash indicator score calculation.
Level 5: All the improvements observed in level 4 would be observed. However, BASICs
related to driver fitness and controlled substances would no longer be needed due to the absence
of a driver in the vehicle. Furthermore, most of the accidents due to driver error would be avoided.
3.2.1.2 Opportunities
▪ Unsafe driving and crashes due to such driving can be significantly reduced. Loss of life,
injuries, and economic loss due to such events can be avoided.
▪ The newly proposed Information Response Theory based Safety Measurement System can
be made more accurate by using data streams from automated trucks.
▪ “Not preventable” crash identification becomes simplified.
3.2.1.3 Challenges
▪ Vehicles operating Level 4 and Level 5 might need a separate Safety Measurement System
since it might not be fair to compare automated trucks to non-automated trucks (Short and
Murray 2016).
▪ Any accidents caused due to equipment malfunction should be properly diagnosed.
However, this diagnosis becomes complicated as the algorithms driving current automated
technologies are, in many instances, quite opaque.
▪ Maintenance-related BASIC score evaluation would be much more complicated as it would
have to include the added sensors and on-board software driving the sophisticated
automated features in these automated trucks.
Hours-of-Service Regulations
Based on FMCSA (2017), truck drivers are subjected to specific Hours-of-Service
regulations. The current regulations mandated by FMCSA (2017) are as follows:
▪
11-Hour Driving Limit: A driver may drive a maximum of 11 hours after ten consecutive hours
off duty.
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▪
14-Hour Limit: A driver may not drive beyond the 14th consecutive hour after coming on duty,
following ten consecutive hours off duty. The off-duty time does not extend the 14 hours.
▪
Rest Breaks: A driver may drive only if 8 hours or less have passed since the end of driver’s
last off-duty or sleeper-berth period of at least 30 minutes. This provision does not apply to
drivers using either of the short-haul exceptions.
▪
60/70-Hour Limit: A driver may not drive after 60/70 hours on duty in 7/8 consecutive days.
A driver may restart a 7/8 consecutive day period after taking 34 or more consecutive hours
off duty.
▪
Sleeper Berth Provision: Drivers using the sleeper berth provision must take at least eight
consecutive hours in the sleeper berth, plus a separate two consecutive hours either in the
sleeper berth, off duty, or any combination of the two.
These regulations are implemented to ensure that truck drivers rest adequately, and
accidents due to driver fatigue can be avoided. However, the Hours-of-Service regulations are one
of the most critical issues of the trucking industry (McReynolds et al. 2018). Drivers often
complain that these rules are too rigid. In fact, due to this very reason, USDOT is proposing a
change in these Hours-of-Service regulations (Jaillet 2019). The use of automated trucks could
further encourage the revising these regulations and making them more flexible to truck drivers.
3.2.2.1 Impacts of Automated Trucking Technologies
Highly automated trucks significantly change the paradigm under which the original
Hours-of-Service regulations were drafted and therefore offers the administration a chance to
revisit those regulations to make them more flexible to truck drivers. The use of automated
technologies reduces the stresses of driving on truck drivers. The magnitude of stress-relief is
dependent on the level of automation available in the trucks. The impacts of different levels of
automation in Hours-of-Service regulations are discussed below.
Level 3: Any automation belonging to this category reduces some of the stresses associated
with truck driving. However, since automation belonging to these levels always need undivided
attention of drivers for driving and do not allow a driver to be engaged in other activities,
automation at these levels are less likely to alleviate current issues of Hours-of-Service regulations.
Level 4: Trucks with Level 4 automation will be capable of executing unsupervised driving
under certain conditions. These unsupervised autonomous driving periods can last from a duration
of a few minutes to a few hours. Depending on these durations of autonomous driving, many of
Hours-of-Service regulations can be considered met if the autonomous truck is viewed as a “teamdriver” to the human driver (Short and Murray 2016). For instance, when the truck has driven
autonomously for more than 30 minutes, these minutes could be logged off as a “30-minute resting
period” since the driver was free from driving stress for these minutes. Such allowance can
eliminate the need to find a safe parking space for the truck to take the mandatory “30-minute
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resting period”. Thus, revised Hours-of-Service regulations can significantly increase the
productivity of drivers. It will also reduce anxiety associated with finding the parking space, which
according to some drivers, is the most stressful aspect of the job (Boris and Brewster 2016).
If the truck has driven autonomously for more than 10 hours and the driver has slept in the
berth that whole time, the time could be logged off as the off-duty hours and this time spent in the
berth can be used to renew his next 14 on-duty hours. Similarly, current weekly working hours
limits of 60/70 hours can be extended or removed entirely when Level 4 automated trucks are
operated autonomously for a significant amount of time during the week. All these scenarios
present a cogent argument of how automated trucking technologies, especially autonomous
highway pilot, could significantly change the application of Hours-of-Service regulations and
make them more amenable to drivers.
Level 5: At this level of automation, trucks are expected to drive by themselves. Hence,
Hours-of-Service regulations would be irrelevant.
3.2.2.2 Opportunities
The impact of automated trucking technologies on Hours-of-Service regulations present
several opportunities:
▪
▪
▪
With the proper Hours-of-Service regulations, the “30-minute resting period” and even the
“8-hour-resting-period” can be made optional. Such provisions will lead to a significant
rise in productivity.
The ever-increasing demand for parking can be curbed by flexible Hours-of-Service
regulations made possible by automated trucking.
Increased opportunity and flexibility to take breaks will make the monotonous long-haul
trucking periods more amenable to truck drivers. Thus, flexible Hours-of-Service
regulations can help mitigate the issues related to driver shortage and retention.
3.2.2.3 Challenges
There are several challenges associated with making the Hours-of-Service regulations
flexible due to the use of automated trucking technologies. They are discussed below:
▪
Currently, the key challenge is legislative. Many of the beneficial impacts of automated
trucking on Hours-of-Service regulations discussed in the preceding section cannot be
realized with the current safety regulations governing commercial trucking. For example,
currently, a driver is not allowed to leave the driving seat and should always be ready to
take control of the vehicle irrespective of the ability of the vehicle to drive autonomously.
Therefore, based on current regulations, the driver cannot be truly free from driving
responsibility irrespective of the ability of the trucking automation. Hence, these
regulations should be revised first to allow the drivers to be relieved off the driving
responsibility during autonomous phases.
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▪
▪
Research is needed to study the reduction in stress and fatigue during autonomous driving
phases because such studies can then form the foundation for the proposals to reduce the
current Hours-of-Service regulations when autonomous trucks are used by the drivers.
Truck drivers should be able to trust the automated technology enough to feel safe and be
able to rest in the vehicle driving autonomously.
Driver Shortage and Driver Retention:
The driver shortage is one of major problems being faced by the freight industry today.
One of the studies by the American Trucking Association (Costello and Suarez 2015) predicts a
driver shortage of 175,000 by 2024 for the trucking industry. Driver retirement and industry
growth are two major factors contributing to the driver shortage. Driver retirement accounts for
45% of the future need of the drivers, while industry growth accounts for 33% of the future need
of the driver (Costello and Suarez 2015). These statistics suggest that the trucking industry does
not have enough inflow of young drivers while the current driver pool is getting older. The
“graying” of the current driver pool is further evinced by the fact that the median age of over-theroad truck drivers is 49 while the median age of US workers is 42 (Costello and Suarez 2015).
One of the main reasons behind the reduced inflow of truck drivers is the lack of qualified
drivers. A study by ATA states that 88 % of the applicant for truck driver jobs are not qualified
(ATA 2012). Trucking industry professionals often argue that the current safety scoring programs
such as BASIC scores and the Pre-employment Screening Program act as barriers for the hiring of
new drivers (Short and Mcreynolds 2014). Other reasons for the reduced supply of truck drivers
could be low pay (V. Burks and Monaco 2019) and poor quality of life of drivers, especially in the
long haul segment (Kar 2019). Drivers spend many hours a day sitting inside a truck cabin,
developing poor eating habits, and having sub-optimal opportunities for physical exercise (Kar
2019). Considering all these factors, trucking automation and autonomous trucking can hugely
impact the issue of the driver shortage.
3.2.3.1 Impacts of Autonomous Trucking
The driver shortage is one of the primary reasons behind the considerable interest in
trucking automation and autonomous trucking technology research (Dalagan 2017). Based on the
current developments in trucking automation, the following impacts can be expected form the
different levels of trucking automation
Level 3: Automations such as blind-spot warning, lane departure warning, adaptive cruise
control, and emergency brake assist will help relieve some of the stresses associated with driving.
These will also make driving safer. Hence, these technologies are likely to make trucking more
attractive and less stressful job, which in turn will help the trucking industry attract and retain
drivers.
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Level 3 automation also introduces the most basic form of autonomy through technologies
such as V2V enables platooning and driver alert-highway pilot. In these systems, the driver can be
truly free from driving stresses during the monotonous stretches of the highway. Hence, such
systems are likely to make the long-haul trucking more attractive to drivers. Enhanced
attractiveness of long-haul trucking will be highly beneficial to the trucking industry since the
long-haul portion of trucking faces more acute problems of driver shortage and driver retention as
compared to other portion of the industry.
Level 4: Level 4 automation includes semi-autonomous trucks that can truly relieve the
drivers from driving stresses during large portions of the highway. During these times, drivers can
potentially sleep in the back of the truck or perform some other activities such as customer support
or logistics management, which can add to their earnings. Hence, such technologies, with suitable
changes to Hours-of-Service regulations, can hugely boost the productivity of the trucking industry
through the loosening of constraints imposed by Hours-of-Service regulations and increasing the
productivity of drivers at no expense of safety. At this automation level, remotely operated semiautonomous trucks may also be available which will allow drivers to work from a fixed office
environment with a fixed number of hours, even for long-haul shipments.
Level 5: At this level of automation, trucks would no longer need drivers for driving.
However, a driver would still be needed in the trucks to perform many other duties besides such
as route planning, impromptu vehicle inspection, refueling, and paperwork.
3.2.3.2 Opportunities:
▪ The use of automated features will help to attract the tech-loving younger generation into the
truck driving occupation. Such attraction will increase the chances of the younger generation
applying for trucking jobs, which will help reduce the current driver shortage in the industry.
▪ Autonomous trucking will ameliorate much of the stresses associated with driving, especially
during the monotonous highway driving. At high automation levels, drivers may also be
allowed to take brief breaks, do some short exercise, have some refreshments and thus
enhance their lifestyle as opposed to a sedentary lifestyle typical in current truck driving jobs.
The possibility of such lifestyle enhancements will hugely help with the current driver
retention problem in the long-haul portion of trucking.
▪ Teleoperated semi-autonomous trucking, which makes it possible to complete long-haul
shipment while working from an office environment for fixed hours, will be a considerable
attraction for drivers who want to spend more time with their family.
▪ The use of automated technologies makes driving less intensive on driver’s judgment and
faculties. Thus, the combined use of the automation and drivers might provide ways to reduce
the constraints of current standards governing new driver hiring. Such a reduction in
constraints can potentially enhance the inflow of new drivers into the industry.
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3.2.3.3 Challenges:
▪ The appropriate regulatory framework should exist before autonomous technology can
indeed reduce driver’s stresses during driving.
Driver Health
Only one-quarter of long-haul truck drivers reported engaging in the recommended amount
of physical activity (Sieber et al. 2014). Due to sitting for long periods without a chance to stretch,
truck drivers are susceptible to a wide range of health risks (Abby 2011, TRB 2012, Short and
Murray 2016). Sitting for a prolonged period has been known to cause several health problems
including anxiety, exhaustion, depression, headaches, heart problems, limb problems, posture
problems, and back problems (Doctors of Osteopathic Medicine 2019). Similarly, obesity is twice
as more prevalent in long haul truck drivers as compared to other US workers (CDC 2018). Costs
for overweight and obese truckers are up 44% more than those for truckers with more normal
weight (Transportation Research Board 2012). These facts suggest that interventions to enhance
health conditions of truck drivers would be beneficial to long haul truck drivers. However, studies
suggest that current working conditions prevalent in long haul trucking act as a barrier in
implementing some of the effective interventions. In this regard, the use of autonomous trucking
technologies for long haul trucking can be highly effective in removing some of those barriers.
3.2.4.1 Impacts of Autonomous Trucking
Provided that suitable regulations exist to properly operate various levels of autonomous
trucking, these technologies can have the following impacts on driver health:
Level 3: Truck platooning with follower trucks can relieve a lot of driving stresses from
the following vehicle. Besides, at level 3 automation, a driver may also be relieved during the use
of limited highway pilot. Additionally, advanced driver monitoring features can allow for the
identification and reporting of driver health issues such as fatigue and stroke. These monitoring
features can allow for suitable intervention and emergency responses.
Level 4: This level of autonomy can have a significant impact on driver health. Here, during
autonomous operations, a driver can move around the vehicle and even engage in short highintensity workouts that can be highly beneficial for cardiovascular health (Doctors of Osteopathic
Medicine 2019). Furthermore, drivers can even sleep during the autonomous driving phases. This
ability to sleep while the truck is driving autonomously allows a driver to avoid health effects
caused by lack of enough sleep. Additionally, the driver can also use autonomous driving phases
to make some healthy meals for themselves inside the truck which will further help in improving
their health as a lack of healthy food has been cited as one of the primary causes of poor health in
truck drivers (Short and Murray 2016).
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Level 5: At this level, the driver would be optional. Also, even if the drivers are needed in
the vehicle, they would not be forced to sedentary duties. Hence, the driver’s health can be much
more improved at this level of automation.
3.2.4.2 Opportunities:
▪ Autonomous vehicles allow drivers to get up from their seat and engage in beneficial
workouts.
▪ Driver monitoring features potentially allow the identification of health-related issues such
as fatigue and stroke, which allow proactive intervention.
▪ Autonomous driving phases can be used by the driver to sleep, and thus, health issues caused
by sleep deprivation is significantly reduced.
▪ Autonomous driving phases can be used to prepare healthy snacks by the drivers will help
to address some of the issues related to the lack of healthy food.
▪ Various alerts and automated features that are integral to autonomous trucks can also
significantly reduce driver fatigue and stresses.
3.2.4.3 Challenges:
▪ Regulations should permit the operation of these autonomous vehicles in a truly autonomous
manner for many of the above-mentioned opportunities and effects to be realized.
▪ Drivers should be motivated enough to capitalize on the opportunities offered by
autonomous trucks to engage in healthy activities.
Driver Distraction
Driver distraction is defined as the diversion of the driver’s focus from the activities critical
for driving to the competing activities where such diversion leads to a deficit or an absence of
attention to activities critical for safe driving (Regan et al. 2008). Based on a report by the National
Highway Transportation Safety Administration (NHTSA 2017), 9.2% of fatal crashes were
distraction-affected crashes. As a result, driver distraction was ranked 7th most critical issues in the
trucking industry (McReynolds et al. 2018). In this regard, it is important to analyze the impacts
of autonomous trucking technologies on distracted driving.
3.2.5.1 Impacts of autonomous trucking
Autonomous trucking technologies are typically powered by a suite of lower level (Level
0-Level 2) automation such as active cruise control, emergency braking, lane keep assist, and blind
spot monitoring. These features do have a tremendous potential to improve situational awareness
of drivers. However, such improvement to situational awareness is only possible if the driver is
focused on driving and is not focused on other non-driving activities. Many studies suggest that
most of the drivers have a propensity to use the attentional resources freed up by the autonomous
driving in carrying out secondary activities to combat monotony induced by autonomous driving
(Cunnigham and Regan 2015). These engagements then make drivers less effective while dealing
with events that require emergency manual intervention. Hence, autonomous trucking
technologies (below Level 5) even though show a lot of potentials to reduce driver distraction may
63
induce distraction by themselves if utilized improperly. The potential impacts of each level of
autonomous trucking technologies are briefly discussed below:
Level 3: Since hands-free driving is possible during suitable stretches of highway at level
3 automation, drivers may use their phones for that period. Furthermore, a wide array of sensors
combined with V2X technology can compensate for the deficits caused by driver distraction.
However, a driver must be prepared to take over the vehicle as soon as prompted by the truck
which can be mentally challenging.
Level 4: A reliable Level 4 autonomous trucking technology inherently eliminates
distracted driving when autonomous highway pilot is being used (Short and Murray 2016). Here,
driver distraction is highly removed as the truck can drive by itself for the monotonous highway
segments. Furthermore, these trucks would be able to perform some safety maneuvers such as
parking on the side of the highway even if the driver is unable to take over within the allocated
time.
Level 5: Here, a driver is no longer needed for driving purposes. Hence, the issue of driver
distraction is completely eliminated.
3.2.5.2 Opportunities
▪ It is possible to avoid significant losses of life and property caused by driver distractionrelated truck accidents.
▪ Well-designed alerts and interface can also enhance the situational awareness of a distracted
driver during critical events such as the presence of a pedestrian in front of the truck.
3.2.5.3 Challenges
▪ Proper training for drivers is required to address the distraction induced by a low level of
autonomy, such as level 3 highway pilot.
▪ Adequate training needs to be provided for timely take-over of the truck after a long period
of autonomous driving when prompted by the truck.
▪ Driver monitoring systems might be needed at level 3 automation to make sure that the
driver is attentive enough during autonomous driving.
▪ The alerts and warnings due to automation should be minimalistic so that these alerts
themselves do not induce distraction.
Economy
Demands for the freight delivery originates from the consumer demands and construction
activities, which in turn depend on the current US economy. Hence, the US economy has a direct
impact on the trucking industry. In a healthy economy, investors are more likely to take more risks
in their investment, leading to a rise in projects that generate more demand for trucking services.
On the other hand, optimization and cost reduction in the trucking industry can also aid the US
economy as the delivery costs of goods will decrease giving industries and businesses more room
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to maximize their profits and to make further investments. Autonomous trucking adds a new
dynamic to this symbiotic relationship between trucking industry and US economy.
3.2.6.1 Impacts of Autonomous trucking
In the US alone, large trucks are involved in about 350,000 crashes a year, resulting in
nearly 4,000 fatalities (Ford 2017). A study by FMCSA shows that the average unit cost of an
accident involving medium to heavy trucks with fatalities can be as large as $3,604,518 (FMCSA
2007). Most of these accidents can be attributed to human error. Thus, any automation that can
compensate for human error can result in considerable savings in terms of casualties, property
damage, and exposure to liability. Such savings can have an enormous economic impact on the
trucking industry and the US economy. Such savings combined with the productivity gains that
are possible through the potential use of drivers for other tasks while the truck is driving
autonomously and the potential to cover more miles by revising the current Hours-of-Service
regulations can impact the US economy significantly. However, the intensity of these impacts will
vary over different levels of autonomy which are briefly discussed below.
Level 3: Level 3 autonomous trucks will have Vehicle-to-Vehicle (V2V) and Vehicle-toInfrastructure (V2I) connectivity in addition to many other features (Level 0 – Level 2) that can
help avoid collisions. Therefore, Level 3 trucking automation can help avoid many accidents
caused due to human error. Fuel savings as large as 7 percent may be realized due to platooning
(Hawes 2019), which further helps to reduce freight delivery costs. Furthermore, if regulations
allowed platooning vehicles to maintain a much-reduced follow distance, highway capacity will
increase and will help in solving current problems of congestion.
Level 4: At this level, collision prevention systems will be even more advanced. Thus,
Level 4 autonomous systems are likely to be involved in even fewer crashes as compared to level
3 systems leading to a reduction in losses due to truck crashes. Furthermore, truck drivers can be
employed in other tasks during autonomous stretches on the highway, which will further increase
the productivity gains in the trucking industry. However, since the drivers used this way will need
further skills, the wages of the drivers will likely increase leading to an increase in the operating
costs for the industry. Autonomous trucks, with suitable Hours-of-Service regulations can
potentially drive for significantly more hours in a day than the conventional trucks. This increase
in Hours-of-service could lead to substantial productivity gains and faster delivery times.
Level 5: Advantages like the ones in Level 4 can be expected. However, even with the
most optimistic projection, such technology will only be implemented decades from now. Hence,
the economic climate at that time will highly dictate the economic impacts of Level 5 autonomous
vehicles, and any economic projections made right now can only be speculative at best.
3.2.6.2 Opportunities:
▪ The suite of automation that comes with autonomous trucking technology will help prevent
large number of crashes involving trucks. Such prevention can thus lead to significant
65
▪
▪
savings in terms of human life, property damage, and liability exposure. Such savings will
then lead to driving down of trucking costs and insurance premiums which will help boost
US economy.
Reduced follow distance and flexibility offered by autonomous driving can help improve
congestion on highways.
Drivers can be used to perform other productive tasks leading to an increase in driver
productivity.
3.2.6.3 Challenges
▪ Upfront equipment costs should be borne by the trucking companies, which will increase the
costs of truck owners at the beginning.
▪ Since the responsibility of drivers will increase, their wages will also increase, thus driving
the trucking costs upward.
▪ Proper regulations should be in place for the economic benefits of the autonomous trucks to
be realized.
Parking
The impact of autonomous trucking technology on parking is already discussed in Section
3.1.3 of this report.
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CHAPTER 4. SURVEY DESIGN AND DISTRIBUTION
After the review of the autonomous trucking technologies and their potential impacts on
the major trucking issues and the freight infrastructure, many questions were raised regarding the
possible risks and opportunities associated with the adoption of various levels of trucking
automation in the DFW region. The authors designed surveys to answer these questions.
4.1 Survey Design
The authors designed four sets of survey questions considering the subject of interests and
the criteria shown in Figure 4-1.
Figure 4-1. Survey subjects of interest
4.2 Distribution of Surveys
Four stakeholders were targeted for the surveys: truck drivers, trucking company
owners/managers, autonomous trucking technology developers, and transportation planners
(Figure 4-2). The survey drafts were submitted to the NCTCOG technical panel for their review
and comments. The survey questionnaires were revised and finalized based on comments and
suggestions received from the panel. The final draft of the four sets of surveys can be found in
Appendix A of this report.
For the drivers, the survey team surveyed the truck drivers in the truck stops using a paperbased survey method. The drivers were selected at random and were requested to participate in the
survey. The survey invitation message (included in appendix B) of this report was read aloud.
After the driver’s verbal approval, the survey was continued, and the driver’s responses were
recorded. For the trucking company managers/owners, several trucking companies were contacted
over the phone and were requested to participate in the survey. After their verbal agreement, their
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responses were collected over the phone or by going to their office by setting an appointment.
Also, responses from some trucking company managers/owners were collected using online
surveys. For the autonomous trucking technology developers and transportation planners, online
versions of the surveys were designed. The online survey was conducted using Qualtrics (Qualtrics
2019). The links to these surveys were then distributed over email. The responses of the survey
were collected for further analysis.
Figure 4-2. Survey distribution and analysis process
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CHAPTER 5. SURVEY RESULTS
In total, 21 responses were collected from the truck drivers, five responses were collected
from the trucking company managers/owners, two responses were collected from autonomous
trucking technology developers, and three responses were collected from the transportation
planners. The major findings from these surveys are discussed in the subsequent sections of this
chapter.
5.1 Background Information on Truck Drivers
Background information characterizing the sample of drivers who responded to the survey
is shown in Table 5-1. Table 5-1 shows that the average trucking industry experience of the
responding drivers was approximately eight years with a standard deviation of approximately ten
years. Also, most of the responding drivers were part of a large fleet.
Table 5-1. Background information regarding the responding truck drivers
Responding Drivers’ Background Information
Percentage living in DFW area
41.67%
Trucking industry experience
Average: 8.47 years; SD*:10.14 years
Part of a small fleet**
9
Part of a large fleet**
12
*SD: Standard deviation;
** 20 or fewer trucks were considered a small fleet while more than 20 trucks were considered a large fleet based on
Mayhew and Quinlan (2006).
Drivers were asked about the automated trucking technologies (Level 0- Level 2) that were
being used in the trucks that they drove or are driving. Their responses are illustrated in Figure
5-1. Figure 5-1 shows that adaptive cruise control is the most commonly found features in the
trucks driven by the responding drivers. Besides that, lane keep assist, collision warning system,
and lane departure warning system were other relatively more frequent automated features being
used by the drivers.
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Figure 5-1. Automated trucking technologies used by responding truck drivers
Drivers were also asked if they had heard about autonomous trucking technologies (Level
3 – Level 5) before. The responses to this question are illustrated in Figure 5-2. The responses
indicate that most of the truck drivers knew about autonomous trucking. Few that did not were
given a brief description of autonomous trucking technology before proceeding further with the
survey.
Figure 5-2. Distribution of drivers based on their familiarity with the term “autonomous
trucking”
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5.2 Perceived Advantages of Autonomous Trucking Technologies
Respondents were asked about their perception regarding the commonly claimed
advantages of autonomous trucks. First, they were asked to choose the advantages they think
would be realized due to the use of autonomous trucking technologies. Then, they were asked to
rate the degree of realization using a Likert scale (highly realized, somewhat realized, minimally
realized, not applicable, and the situation will become worse). Each of these options on the scale
was assigned arbitrary weights. Then, the advantage perception score (APS) was calculated for
each of the advantages selected by at least one respondent using a weighted average formulation
shown in Eq. 1.
𝐴𝑃𝑆𝐺𝑖 = ∑𝑟𝜖𝐺 ∑𝑖 ∑𝑗 𝑦𝑑𝑗𝑖 𝑎𝑤𝑗
Eq. 1
where 𝐴𝑃𝑆𝐺𝑖 is the advantage perception score for advantage 𝑖 based on the responses of
all individuals belong to the group 𝐺 (i.e., drivers, trucking company managers/owners,
autonomous trucking technology developers, and transportation planners), 𝑦𝑑𝑗𝑖 is a binary variable
which takes a value of 1 if the driver 𝑑 selects degree 𝑗 for a selected advantage 𝑖, and 𝑎𝑤𝑗 is the
weight assigned to the degree 𝑗: 3 for “Highly realized,” 2 for “Somewhat realized,” 1 for “Not so
much,” 0 for “Not applicable,” and (-1) for “Situation will become worse.”
Advantage perception scores calculated based on Eq. 1 and the survey responses of truck
drivers are illustrated in Figure 5-3. Figure 5-3 shows that among other advantages, the responding
truck drivers indicated that reduced crashes, improved productivity, and possibility to be employed
during autonomous driving phases are the major advantages of autonomous trucking technologies.
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Figure 5-3. Perception of truck drivers regarding the advantages of autonomous trucking
technologies
The same question was asked to trucking company managers/owners, autonomous trucking
technology developers, and transportation planners as well. Their advantage perception scores,
calculated similarly to the advantage perception score of the drivers, are plotted in Figure 5-4,
Figure 5-5, and Figure 5-6, respectively.
72
Figure 5-4. Perception of trucking company managers/owners regarding the advantages
of autonomous trucking technologies
73
Figure 5-5. Perception of autonomous trucking technology developers regarding the
advantages of autonomous trucking technologies
74
Figure 5-6. Perception of transportation planners regarding the advantages of
autonomous trucking technologies
To further analyze the responses of all the respondents regarding the perceived advantages
of autonomous trucking, advantage perception score was first averaged over all the survey groups
for each advantage and was plotted in Figure 5-7. This figure helps to identify the advantages that
would be most realized based on the responses of all the groups. Figure 5-7 shows that improved
compliance, safety, & accountability, improved productivity, and reduced crashes were identified
as the most promising advantages of autonomous trucks based on all the responses.
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Figure 5-7. Advantage perception score averaged over all the respondent’s groups
Similarly, the advantage perception scores of all the respondents were averaged and plotted
in Figure 5-8. It is evident that autonomous trucking technology developers are more optimistic
regarding the advantages of autonomous trucking technologies. In contrast, truck drivers and
transportation planners have a relatively less optimistic response regarding the potential
advantages of autonomous trucking technologies.
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Figure 5-8. Advantage perception score averaged over all the perceived advantages
5.3 Perceived Obstacles to Autonomous Trucking Technologies
The respondents were asked to select the applicable obstacles to autonomous trucking and
to rate the severity of obstacles using a Likert scale (major obstacle, moderate obstacle, minor
obstacle, and not applicable). Each of these options on the scale was then assigned arbitrary
weights. Then, an obstacle ranking score called obstacle perception score (OPS) was calculated
for each of the obstacles selected by at least one survey respondent using a weighted average
formulation shown in Eq. 2.
𝑂𝑃𝑆𝑟𝑖 = ∑𝑟𝜖𝐺 ∑𝑖 ∑𝑗 𝑦𝑑𝑗𝑖 𝑜𝑤𝑗
Eq. 2
where 𝑂𝑃𝑆𝐺𝑖 is the obstacle perception score for obstacle 𝑖 based on the responses of all the
individuals 𝑟 belonging to the group 𝐺 (i.e., drivers, trucking company managers/owners,
Automated Trucking Technology developers, and transportation planners), 𝑦𝑑𝑗𝑖 is a binary
variable which takes a value of 1 if the respondent 𝑟 selects degree 𝑗 for a selected obstacle 𝑖, and
𝑜𝑤𝑗 is the weight assigned to the degree 𝑗 where 𝑜𝑤𝑗 is 3 for “Major Obstacle,” 2 for “Moderate
Obstacle,” 1 for “Minor Obstacle” and 0 for “Not applicable.”
Obstacle perception scores calculated based on Eq. 2 and the survey responses of truck
drivers, trucking company managers/owners, autonomous trucking technology developers, and
77
transportation
planners
were
plotted
and
Figure 5-9, Figure 5-10, Figure 5-11, and Figure 5-12, respectively.
78
shown
in
Figure 5-9. Perception of truck drivers regarding the obstacles to autonomous trucking
technologies
79
Figure 5-10. Perception of trucking company managers and owners regarding the obstacles to
autonomous trucking technologies
80
Figure 5-11. Perception of autonomous trucking technology developers regarding the obstacles
to autonomous trucking technologies
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Figure 5-12. Perception of autonomous trucking technology developers regarding the obstacles
to autonomous trucking technologies
To further analyze the responses regarding the perceived obstacles to autonomous trucking,
obstacle perception scores were first averaged over all the survey groups to calculate the average
obstacle response score for each advantage. Those values are plotted in Figure 5-13. This figure
helps to identify the obstacles that would be most prohibitive to autonomous trucking technologies
based on the responses of all the responding groups. Figure 5-13 shows that the most critical
obstacles to autonomous trucks are: limitations of AI systems to operate in bad weather &
complicated traffic situations, lack of proper legislation, lack of public trust, and quality of road
and signs.
82
Figure 5-13. Obstacle perception score averaged over all the respondent’s groups
Similarly, the obstacle perception scores were averaged over all the listed obstacles to
calculate the average obstacle score perceived by each responding group, which is plotted in Figure
5-14. It is evident from this figure that the responding autonomous trucking technology developers
believe that there are relatively few and less significant obstacles to autonomous trucking. In
contrast, truck drivers and trucking company managers think that there are more and moderately
severe obstacles to autonomous trucking. These responses, therefore, highlight the optimism to
autonomous trucking from autonomous trucking technology developers and the relatively more
skeptic outlook of truck drivers and trucking company managers/owners towards the autonomous
trucking technologies. These different perceptions highlight the importance of establishing
communication links between these major players to assure successful transition to autonomous
trucking.
.
83
Figure 5-14. Obstacle perception score averaged over all the perceived advantages
5.4 New Skills Requirement
The survey responses helped to better understand the potential need for the new skills due
to the use of autonomous trucks and compare it with the willingness of the drivers to learn the new
skills. For instance, 3 out of 5 surveyed trucking company managers responded that they would
want their drivers to carry out administrative duties and paperwork during autonomous phases of
driving. These responses indicate that, with the use of autonomous trucks, many trucking
companies would require drivers to carry out other duties besides their normal duties. Such
requirements would impose a need on the drivers to learn other skills, besides driving, to carry out
those duties. Now, to assess the willingness of drivers to learn new skills, the drivers were asked
about their willingness to learn new skills such as new software skills, Information Technology
(IT) skills, logistics, and administrative skills. Their responses are plotted in Figure 5-15. Figure
5-15 shows that approximately 40% of the responding drivers were unwilling to learn new skills.
Such a high percentage of unwilling drivers indicates the need for an intervention such as
workshops, interactions, and incentives to bridge the gap between the willingness of the drivers to
learn new skills and the future demand of the trucking industry imposed by the adoption of
autonomous trucking technologies. This intervention is more critical for long-haul trucking where
the autonomous trucks are most likely to be used in the near future (GAO 2019).
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Figure 5-15. Willingness of the responding drivers to learning new skills
5.5 Future of Autonomous Trucking Technologies
Many questions in this survey were also aimed at assessing the outlook of different survey
groups regarding the timeline of autonomous trucking technology development and its adoption
in the future trucking industry. In this regard, the drivers and trucking company owners/managers
were asked to provide their opinions regarding the future of semi-autonomous and autonomous
trucks in the highways of DFW. The results of their responses are summarized in Figure 5-16 and
Figure 5-17.
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Figure 5-16. Prediction of responding drivers regarding the operation of autonomous
trucks in the highways of DFW
Figure 5-16 shows that more than half of the responding drivers think that the semiautonomous trucks would be operating in the DFW highway within the next five years while onethird of the responding drivers think that the semi-autonomous trucks will operate in the DFW
highways within the next two years. These are consistent with the estimates of adoption of
autonomous truck presented in Mudge et al. (2018).
Figure 5-16 shows that one-third of the responding drivers think that fully autonomous
trucks will only be operational in DFW highways within the next ten years while half of them
believe that the fully autonomous truck will be able to operate on the highways of DFW within
next 20 years. These responses show that the majority of the drivers perceive the arrival of fully
autonomous trucks a little later than as presented in Mudge et al. (2018).
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Figure 5-17. Prediction of responding trucking company managers/owners regarding the
operation of autonomous trucks in the highways of DFW
Figure 5-17 shows that the majority of responding trucking company managers/owners
think that the semi-autonomous trucks with driver will be operating in the DFW highways within
the next five years. In contrast, they think that the fully autonomous trucks will operate in the DFW
highways in a relatively long time from now. All the responding trucking company
managers/owners believe that the successful use of the fully autonomous truck in DFW highways
will take another 20 years.
Autonomous trucking technology developers were also asked the same question.
Regarding the semi-autonomous trucks, one respondent said that the technology would be ready
for DFW highways within the next two years while other respondent’s company was already
testing the semi-autonomous trucks with safety drivers in DFW highways. Regarding the fully
autonomous trucks, both respondents indicated that it would still take five more years for fully
autonomous trucks to be able to operate on the DFW highways without a driver.
In response to the same question, 3 out of 3 transportation planners indicated that it would
take the next ten years for semi-autonomous trucking to be able to run in DFW highways.
Regarding fully autonomous trucks, two planners indicated that it would be possible in next 20
years while one planner indicated that it would take 30 years.
Comparing the responses of all the groups of survey respondents, autonomous trucking
technology developers are found more optimistic regarding the timeline of the successful
autonomous truck deployment in DFW highways. These responses are consistent with the
responses to questions related to obstacles of autonomous trucking. The autonomous trucking
technology developers responded that any current obstacles to autonomous trucking are minor
87
enough to be overcome in the next few years. In contrast, responding transportation planners have
a relatively more measured view regarding the timeline of autonomous trucking development.
Such disparity between the responses of transportation planners and developers could be reduced
by having developers and the transportation planners communicate regarding the timeline of the
autonomous trucking development. Such communications will help to align the long-term
transportation plans of a city such as DFW with the development of autonomous trucking
technologies.
Apart from the readiness of autonomous trucking technology, the adoption of autonomous
trucking technology remains another key uncertainty for transportation planning. To better
understand the adoption, drivers were also asked for their response regarding the future adoption
of autonomous trucks. Their responses are plotted in Figure 5-18. Figure 5-18 shows that the
majority of responding truck drivers responded that the percentage of trucks that would be
autonomous in the next ten years would be less than 10%. A few respondents were more
optimistic, while one respondent thought that the legislative framework would play a significant
role in the future penetration of autonomous trucking technologies.
Figure 5-18. Prediction of responding drivers regarding the percentage of autonomous
trucks within next ten years
5.6 Liability Issue
The current legal framework of liability assignment is not adequate for autonomous trucks,
especially when these trucks start operating without any driver supervision. Autonomous trucks
add a new dynamic to crash analysis and liability determination since the autonomous truck is a
vehicle as well as a driver. Lack of regulations that define the liability during the crash of
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autonomous vehicles is often cited as one of the significant obstacles to the autonomous trucking
(Premack 2018). In this regard, questions regarding the liable party during a collision involving an
autonomous truck, where the autonomous truck was at-fault was asked from truck drivers, trucking
technology managers, autonomous trucking technology developers, and transportation planners.
The responses of the truck drivers are plotted in Figure 5-19.
Figure 5-19. Liable party when an autonomous truck is at fault during a collision, when
the truck is at fault, based on responding truck drivers
Figure 5-19 shows that most of the responding drivers think that the autonomous trucking
technology developers should bear the liability when autonomous trucks are involved in a collision
and the truck is at fault. Two out of five responding trucking company managers and a
transportation planner also have a similar opinion. However, two out of five responding trucking
company managers and one out of three transportation planners think that a “no-fault system”
should be adopted where the liability is transferred entirely to an insurance company.
In contrast, both the surveyed autonomous trucking technology developers think that the
liability assignment should be based on the detailed collision analysis of each case and should be
assigned based on the proximate cause of each collision. A few other respondents (one out of 21
drivers, one out of five trucking company managers/owners, and one out of three transportation
planners) agree with the idea of liability assignment based on the identification of the proximate
cause. From these results, it is evident that there exist differing opinions among the trucking
industry stakeholders regarding the liability assignment for an autonomous truck. A more rigorous
discussion of these disparate ideas and a comprehensive analysis of the merits and demerits of
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each idea is essential before a sound regulatory framework can be created to adequately address
the complexities of the liability issues surrounding the autonomous trucking technologies.
5.7 Dedicated/Managed lanes
Based on the discussions related to dedicated lanes in section 3.1.4 of this report, the
dedicated lanes or managed lanes that minimize the interaction between the autonomous trucks
and conventional traffic would be helpful for the initial deployment and adoption of autonomous
trucking technologies. To further investigate this issue in the context of the DFW region, truck
drivers were asked about their opinion regarding the factors impacting highway safety and
efficiency. Their responses are plotted in Figure 5-20.
Figure 5-20. Factors impacting highway safety and efficiency based on the responding
truck drivers
Figure 5-20 shows that aggressive driving, congestion, vehicle entering the ramps, lane
changing cars, and slow trucks are among the major factors negatively impacting highway safety
and efficiency. All these factors are somehow associated with the interaction of small vehicles and
trucks. Such interactions are highly likely to create difficulties during the initial deployment of
autonomous vehicles. Hence, during the stages at which the autonomous vehicle technology is still
maturing, it is helpful to ideally remove or at least minimize the interactions between trucks and
other vehicles. Dedicated lanes and managed lanes could help to minimize such interactions.
Hence, investment in managed lanes or dedicated autonomous lanes should be considered during
the formulation of long-term transportation plans that aims to support the development of
autonomous trucking technologies.
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5.8 Lane Markings and Highways Signs
A well-maintained and adequately detectable lane markings and highway signs are required
for most of the machine vision technologies being used in the autonomous trucks that are currently
under development. Hence, it is highly critical to assess the current state of lane markings and
highway signs of the DFW region. Such an assessment would help the formulation of proactive
rehabilitation and maintenance of lane markings and highway signs to support the imminent
operation of autonomous trucking technologies. In this regard, truck drivers were asked several
questions regarding the issues related to the lane markings and signs in the DFW highways. Their
responses are illustrated in Figure 5-21 and Figure 5-22.
Figure 5-21. Issues related to lane markings in DFW highways based on responding truck drivers
Figure 5-21 shows that the DFW highways suffer from issues related to the visibility of
lane markings during the night, poor weather, and even in daytime and normal weather. The
response shows that the non-existent lane markings are also a significant issue. Furthermore, a few
responding truck drivers also noted the inconvenience due to the lane marking removal during
construction, leading to much confusion. These issues can be prohibitive during the deployment
of autonomous trucks. Hence, any plan aimed at facilitating the operation of autonomous trucks in
the DFW highways should include a monitoring and maintenance of proper lane markings.
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Figure 5-22. Issues related to traffic signs in DFW highways based on the responding truck
drivers
Figure 5-22 illustrated the response of the truck drivers regarding the issues associated with
traffic signs in DFW highways. Figure 5-22 shows that inconsistent signs and poor visibility of
signs during bad weather and at night remain are some of the significant issues faced by the truck
drivers while driving on the highways in the DFW region. These issues will also hamper the
deployment of autonomous trucks and therefore, should be considered during the formulation of
transportation plans aimed at facilitating the development of autonomous trucking.
5.9 Warehousing Issues
As discussed in Section 3.1.2 of this report, delays and inefficiencies associated with
warehouses are some of the major concerns of the current trucking industry. Given such issues,
the inadequacy of current warehouses will grow as a weak link in the future supply chain that will
use autonomous trucks. Any productivity gains achieved through autonomous trucking may be
offset by the inefficiency and lack of automation in warehouses. Therefore, truck drivers were
asked questions to assess the delays they were currently facing in DFW region to identify the need
for warehousing automation and optimization. First, the drivers were asked how often they had to
wait in a warehouse for more than 2 hours to receive or drop a shipment. Fifteen out of 21 drivers
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said that they had to wait for more than two hours for at least half of their assignments. This
response suggests that there is a severe problem of warehousing inefficiencies being faced by truck
drivers in the DFW region. Next the drivers were asked for the reason of unusual wait time in the
warehouses. The responses of the drivers to this question are plotted in Figure 5-23.
Figure 5-23. Reasons reported by the responding truck drivers for long wait times in the
warehouses
As seen in Figure 5-23, insufficient warehouse staff, paperwork, and unprepared shipment
were the primary reasons for the long wait times in the warehouses. Thus, it is critical to encourage
the warehouse managers to address these issues if the advantages presented by autonomous
trucking technologies are to be maximized. As discussed in Section 3.1.7, autonomous trucking
can provide unprecedented opportunities for warehouse managers to adopt automated and
optimized systems for their warehouses.
5.10 Trust Enhancement for Autonomous Trucks
Many of the advantages of autonomous trucking technologies are associated with the
ability of the drivers to engage in other productive tasks while the truck is driving autonomously.
However, for this to happen, the drivers should trust the autonomous driving system and should
be able to focus on other tasks when the truck is driving autonomously. The development of such
trust in truck drivers is a critical step for the early adoption of autonomous trucking technologies.
Recognizing this criticality of trust, drivers were asked regarding the most effective method to
make the autonomous driving system trustworthy to them. Their responses are plotted in Figure
5-24. Figure 5-24 shows the importance of hands-on testing/training, federal regulations, and
product demonstrations in enhancing the trustworthiness of autonomous trucking technologies.
Furthermore, Figure 5-24 shows that almost 25% of the responding drivers indicated that nothing
93
could convince them to trust the autonomous trucking technology adequately enough to focus on
other tasks. This result indicates a need to convince more skeptical drivers to trust the technologies
so that adoption of autonomous trucking will not be hampered due to trust issues.
Figure 5-24. Effective methods to make autonomous driving trustworthy based on the
responding truck drivers
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CHAPTER 6. CONCLUSIONS AND RECOMMENDATIONS
A comprehensive literature review was carried out to assess the state of autonomous
trucking technologies. The availability of different automated trucking technologies was
discussed. A literature review was also performed to identify the critical issues that could be
impacted by the use of autonomous trucking technologies. Similarly, literature review was also
performed to identify the potential impacts of autonomous trucking technologies on the freight
infrastructure of the DFW region. The findings of the literature review were then used to design
survey questionnaires for truck drivers, trucking company managers/owners, autonomous trucking
technology developers, and transportation planners.
Based on the literature review and the survey conducted as a part of this study, the
following recommendations can be made to reduce the risks and maximize the opportunities
associated with the use of autonomous trucking technologies.
1)
Highway Signs and Markings: The survey responses indicate that the lane markings and
highway signs in the DFW highways need proper maintenance and consistency. Since all levels of
affordable autonomous trucking systems require adequately maintained lane markings and
highway signs, a well-defined plan is needed to maintain proper lane markings and highway signs.
Hence, innovative marking technologies such as retroreflective road markings for nighttime
visibility, dirt-resistant marking for high marking contrast, and reflective radar road marking for
detection by radar-based sensors during fog and snow should be considered.
Inconsistency in markings and signs is one of the frequent issues reported in the survey. Hence,
federal regulations governing signs and markings should be developed and implemented so that
consistent signs and markings are used in interstate highways as opposed to the current system
where different variations of same markings can be found depending on the state regulations.
2)
Warehousing Enhancement: An inefficient warehouse can severely offset the productivity
gains by autonomous trucks. However, advanced sensors and rich data environment of
autonomous trucking can be capitalized to automate and optimize the current warehousing
processes. Real-time tracking of trucks is possible, which will enable shippers and receivers to
proactively prepare the shipments. This proactive preparation will help to avoid delay in shipping
and loading. Additionally, autonomous trucking enables the digitization of paperwork. This
digitization leads to reduced waiting times as lengthy paperwork is one of the major causes of long
waiting times.
3)
Parking Infrastructure: Parking is one of the most stressful aspects of truck driving. The
availability of parking can dictate the entire routing process of the trucking assignments. Thus, real
autonomy is not possible until and unless the parking is also automated. Therefore, to achieve real
autonomy, real-time parking monitoring, and prediction systems combined with a convenient
95
parking information dissemination system are needed. Hence, investment in these systems is
required for timely adoption of autonomous trucking technologies.
4)
Managed/Dedicated Lanes: Survey responses from truck drivers have indicated that the
interaction between trucks and other traffic can negatively impact highway safety. These impacts
can be even more critical for the first autonomous trucking systems. For these systems, to be safely
deployed, investment in managed lanes or dedicated autonomous lanes at least should be
considered during the formulation of long-term transportation plans that aims to support the
development of autonomous trucking technologies.
5)
Inspection and Maintenance Infrastructure: Autonomous trucking technologies are
powered by numerous advanced sensors and on-board equipment. Repair and maintenance of these
sensors and equipment need specialized training. Thus, a training and certification program,
regulated at the federal level, should be designed in collaboration with autonomous trucking
technology developers to ensure the job security of current repair technicians. These trainings will
also help to ensure adequate availability of repair service to the autonomous trucking systems.
6)
Transfer Hubs: Many of the autonomous trucking technologies being developed today are
only capable of exit-to-exit highway driving. Hence, the initial deployment of these systems would
follow the transfer hub model where a large space is needed at the end of autonomous driving
sections for switching between human-driven tractors and autonomous tractors. Since transfer
hubs need to be in the proximity of major freight corridors, many ideal locations for these hubs
will coincide with the current location of facilities such as rest stops and truck parking areas.
Hence, these rest stops and parking areas will need to be repurposed with minimum inconvenience
to the non-autonomous vehicle users of these facilities.
7)
Roadside Equipment: Even though many of the autonomous trucking technology being
developed today rely solely on the on-board sensors and computers, the reliability of these systems
can be significantly enhanced using V2X connectivity. Such connectivity requires significant
investment in DSRC communication infrastructure, smart traffic signs, roadside HD cameras, and
cloud-based traffic management centers.
8)
Safety Measurement System: Newly proposed Information Theory-Based Safety
Measurement System can be well integrated with the information-rich infrastructure of
autonomous trucks. This integration will potentially create a more powerful and more accurate
Safety Measurement System.
9)
Collaboration between Stakeholders: Routine stakeholder’s meeting focused on the
impacts of autonomous trucking technologies on the major issue of the trucking industry, and the
96
freight infrastructure should be carried out to enhance information exchange between different
stakeholders. Continuing to convene stakeholders could also help agencies to identify any
information or data gaps that may need to be addressed to understand the potential workforce
effects of automated trucking.
10)
Regulations: Legislation remains one of the major obstacles to autonomous trucking
development and testing. Legislations governing Hours-of-Service, minimum follow distance, the
necessity of driver in the driving seat, and liability should be established to provide a legal
framework for the operation of autonomous trucks. These regulations should be formulated at the
federal level to avoid patchwork of regulations that varies state-by-state.
11)
Long Haul Trucking Jobs: Considering the current pace of development of autonomous
trucking technologies, the first autonomous trucks will likely be implemented in the long-haul
portion of trucking. Initially, such deployment will help to nullify the current driver shortage in
long haul assignments. However, such deployment , in the long run, will lead to a reduced need
for long haul truck drivers. Hence, proper systems should be designed to help the long-haul drivers
for transitioning to either autonomous trucking jobs or new jobs in a different industry. Majority
of the trucking company managers/owners participating in this study communicated their desire
to use the truck drivers for administrative and paperwork during autonomous driving phases.
Hence, training programs can be created to help the truck drivers prepare themselves for such
tasks.
97
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APPENDIX A: SURVEY QUESTIONNAIRES
I
APPENDIX B: EXAMPLE OF SURVEY INVITATION MESSAGE
“The University of Texas at Arlington invites you to participate in a short survey regarding
the impacts of autonomous trucking on the trucking issues and infrastructure of DFW region. This
survey is an integral part of the North Central Texas Council of Governments (NCTCOG) project
titled: “Autonomous Vehicles and Freight Transportation Analysis.” This survey aims to assess
the impacts of automation on the future of the DFW’s trucking industry and the infrastructure
needed to support the industry. We expect this survey to take approximately 5* minutes. NCTCOG
and our research team highly appreciate your contribution to this unique effort. If the results of
this study are published or presented, your name will not be used. Please continue if you
voluntarily agree to participate in this research.
If you have any questions about this research study, please contact Dr. Mohsen
Shahandashti, P.E. at mohsen@uta.edu or directly at 817-271-0440.”
*The time was variable for different survey respondent groups.
II
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