DAVI Progress & Ambitions
Dutch Automated Vehicle Initiative www.davi.connekt.nl
R. Happee r.happee@tudelft.nl
Dutch Automated Vehicle Initiative – 2014
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Dutch Automated Vehicle Initiative
To investigate, improve and demonstrate
automated driving on public roads
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Assess & improve technology
Study human behaviour
Prove safety
Pursue legalisation
Create public awareness
Dutch Automated Vehicle Initiative – 2014
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DAVI in time
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Inception
Official Launch Innovatie-estafette
Experiments with non-expert drivers
Demo with non-expert drivers
Dutch Automated Vehicle Initiative – 2014
March 2013
Nov 2013
2015
2016
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key projects & partners
DAVI
open initiative
TU Delft, RDW, TNO, Connekt, SWOV, UT, Fontys,
Hanze, HAN, RUG, Toyota Motor Europe, NXP, Imtech,
VisLab (IT), SKF, Technolution, Almende, V-tron, Trinité
Mapscape, DLR, TRL, ITS-Leeds
Human Factors of Automated Driving
(HFAuto)
Marie Curie FP7
TU Delft, TU München, Univ of Southampton, Univ of
Twente, Chalmers Univ of Technology, IFSTTAR (France),
VTI (Sweden), Volvo Truck, Volvo Car, BMW, Jaguar,
Toyota Motor Europe, Continental, TNO, SWOV
Truck Merging Support
HTSM
TU Delft, TU/e, DAF, SKF, TNO, NXP
From Individual Automated Vehicles to TU Delft, Toyota Motor Europe, TNO, NXP, Imtech Traffic
Cooperative Traffic Management
& Infra, RDW, Connekt, SWOV, Technolution, Almende,
(IAVTRM) STW-OTP
V-tron, Trinité Automation, VisLab
Foodvalley
Dutch Automated Vehicle Initiative – 2014
Province of Gelderland
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Hands free
• Cruise Control with
automated steering
• Highway in 2020
• Capable driver
Autonomous
• No steer & pedals
• User only has to select
the destination
Mixed with normal traffic
Dutch Automated Vehicle Initiative – 2014
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Vehicles
• 3 Priuses TNO / DITCM
• CACC
• Automated steering
• Prius Fontys
• Automated steering
• 2 Priuses TU Delft
• Towards 360 deg sensing
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Ambitions
• Vehicle automation
• Robust environment perception
• Safe control strategies
• Link automation to traffic management
• Human factors
• Transitions of control
• Acceptance, trust, use & misuse
• Interaction with other road users
• Benefit prediction
• Legalisation
• Public awareness & acceptance
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Environment Perception
• Cameras, Radar, Ultrasound, Lidar
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Cars, trucks, pedestrians, cyclists
Road markings
Road Signs
Traffic lights
• Challenges
• Reliable detection
in various weather conditions
• Intent recognition & motion prediction
• Integration environment sensing
with communication and detailed maps
Dutch Automated Vehicle Initiative – 2014
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Custom of the shelf sensors
ARS 309-2 HS radars
(Continental)
HDR vision systems
(Melexis)
High accuracy Digital maps & eHorizon systems
(Mapscape – Maccom - Electrobit)
Dutch Automated Vehicle Initiative – 2014
Main Processing Unit
PXIe-1062Q RTOS (NI)
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Graceful degradation of CACC
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ACC: no V2V communication
CACC: full V2V communication
Graceful degradation in case of packet loss: dCACC
CACC
ACC
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dCACC
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Automated lane changing & merging
• Shown in movies carmakers
• Proof of safety not found
• DAVI simulation studies
• Optimize gap & timing1
• Optimize collective benefits1
• Trajectory control with
non-linear vehicle model2
• Next
• Sensors fusion & inaccuracy
• Role of the driver?
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Wang M, Hoogendoorn SP, Daamen W, van Arem B, Happee R. Novel predictive approach for unified lanechanging and car-following control. Submitted ISTT21
Gottardis P, Manazza SS. Automated Controlled Vehicle Based on Non-Linear Model Predictive Control Connected
to a Safety Path Planner with Online Collision Risk Estimation MSc thesis TUDelft / Polimi 3 Oct 2014
Dutch Automated Vehicle Initiative – 2014
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Human Factors
• Literature survey1
• Extensive simulator studies
Highly Automated Driving
• Workload affects HAD
much larger than ACC
• Only expert drivers tested with
HAD on public roads (3 studies)
• Challenges
• Monitor & Manage driver awareness
• Develop safe transition procedures &
interfaces
• Investigate human interaction
with HAD on public roads
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Secondary Task Performance (%)
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de Winter JCF, Happee R, Martens MA, Stanton NA. (2014).
Effects of ACC and highly automated driving on workload and
situation awareness: A review of the empirical evidence.
TRPF: Traffic Psychology and Behaviour.
Dutch Automated Vehicle Initiative – 2014
Control
ACC
HAD
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Opinions on automated driving 1
• Diverse / extreme responses
• 22% unwilling to pay for fully automated driving
• 5% willing to pay more than $ 30,000
• 69% estimated fully automated driving to reach 50%
market share before 2050.
• concerned about
• software hacking/misuse
• legal issues and safety
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Kyriakidis M, Happee R, de Winter JCF. Public opinion on automated driving: Results of an
international questionnaire among 5,000 respondents. Submitted.
Dutch Automated Vehicle Initiative – 2014
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Automated Transport Foodvalley
(Gelderland)
• “De eerste zelfsturende auto’s van Nederland moeten
gaan rijden in Ede en Wageningen” (Gelderlander 1 Oct 14).
• Planstudie, suitable routes, sensors & control
• Innovation
• From closed track to public roads
• Safe interaction with pedestrians, cyclists, cars
• On demand public transport
• Safety by
• Low speed
• Conservative control strategies
• Control room monitoring
Dutch Automated Vehicle Initiative – 2014
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Milestones testing automation
on public roads
• Technical safety assessment
• Sensing reliability
• Safe operating conditions
• Automation linked to traffic management
• Behaviour non-expert drivers investigated
• Safe transitions of control
• Quantify how drivers will use automation
• Prove safety taking into account the human interaction
• Benefits predicted with traffic flow models
• Legalisation
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Questions
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DAVI Partnership Charter
• DAVI aims to
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implement automated vehicles,
obtain permission for testing on public roads,
link vehicle automation to traffic management,
assess and improve automation technology,
study human behaviour with automation,
prove safety,
optimise transport efficiency,
create public awareness,
and pursue legalisation of automated driving.
• DAVI pursues these objectives through complimentary
research projects and demonstration activities with
varying partnerships and national and international
funding sources.
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Vacancies
• Vacancy 1 (PhD Candidate) Human interaction with automation
• Human machine interface, driver state monitor
• Investigate the human interaction with automation
• Vacancy 2 (PhD Candidate) Benefits & risks of automated driving
• Develop computer models capturing mixed traffic
• Predict & optimise benefits
• Vacancy 3 (Post Doc) Automation & safety assessment
• Automation algorithms, merging, lane changing and overtaking
• Safety assessment methods
• Vacancy 4 (Engineer) Environment sensing
• Systems architecture
• Sensors & fusion providing robust 360 degree environment perception
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