VDOT’s Connected Vehicle Program
Noah Goodall, Ph.D., P.E.
Research Scientist
Virginia Center for Transportation Innovation and Research
ASHE Old Dominion Section Meeting
June 13, 2013
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Smartphones
• Very sophisticated
computer
• Sensors
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GPS
3-axis accelerometer
Camera
Magnetometer
• Carried with you all
day
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Modern Vehicles – Very Sophisticated
• How many lines of code in a:
– F-22 Raptor: 1.7 million
– Average new Ford: 10 million
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Computerized Measurement
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Speed
Heading
Acceleration (lateral, longitudinal, vertical)
Position (from GPS)
Other diagnostics
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Wipers on/off
Braking status
Tire pressure
Steering wheel angle
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Headlights on/off
Turn signals on/off
Rain sensors
Stability control
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Vehicle-to-Vehicle Communication:
Not Sophisticated
• Hi-tech vehicles
• Low-tech communication with other
vehicles
– Brake lights
– Turn signals
– Horn
– Flash headlights
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Vehicle-to-Infrastructure
Communication: Not Much Better
• We want to know where vehicles are, what
they’re doing
• Many sensors already in the field to do this
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Field Detection
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Field Sensor Shortcomings
• Limited data quality
• Point detection, not continuous coverage
• Difficult/expensive to repair = frequent
downtime
• Limited types of data
– Aggregated speed, density, and volumes at a
single point
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Infrastructure-to-Vehicle
• Difficult to communicate with the driver
both in real-time and across a wide area
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Connected Vehicles
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Wireless Vehicle Communication
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How it Works
• Transmit data from the vehicle
– Captured from GPS, accelerometers,
magnetometers, or in-vehicle sensors
• Transmit to other vehicles or roadside
equipment
– Cellular, Bluetooth, WiMAX, Wi-Fi, DSRC
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Potential of Connected Vehicles
• Three ways to connect:
1) Vehicle-to-vehicle:
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Electronic brake lights
Crash avoidance
2) Vehicle-to-infrastructure:
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Incident detection
Weather/ice detection
3) Infrastructure-to-vehicle
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Broadcast traffic signal timing
Dynamic re-routing
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Similarity to Other Safety Systems
• Similar to radar- and laser-based safety
systems, but much cheaper
Adaptive Cruise Control
Google Self-Driving Car
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Connected Vehicles Today
• Real-time speed data from cell phones
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Research
• VDOT is the lead state in the Cooperative
Transportation Systems Pooled Fund Study
– Traffic signal control
– Broadcasting traffic signal timing to approaching
vehicles
– Potential of aftermarket add-on devices
– Standardization
– Pavement maintenance
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Connected Vehicle Test Bed
• University Transportation Centers grant for
two small-scale field deployments of these
technologies
• Will use combination of cellular and
Dedicated Short-Range Communications
(DSRC)
– Low latency, high bandwidth
– Allows for most powerful safety and mobility
applications
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Connected Vehicle Test Bed
• Partners:
– VDOT
– Virginia Tech
– University of Virginia
– Morgan State
– Nissan and Volvo (advisory roles)
• Available to other universities to test
projects
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Connected Vehicle Testbed
• Virginia Tech
Smart Road
– 7 RSUs
• Northern VA
– 48 installed RSUs
– 2 portable RSUs
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Roadside Units
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Roadside Units
I-495
I-66
US-29
Gallows Rd
US-50
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On Board Equipment
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On Board Equipment
• 200 Aftermarket Safety
Devices are being developed
• 10 instrumented cars
• 4 sedans (GM brand)
• 2 SUVs (GM brand)
• 4 motorcycles
• 2 instrumented heavy
vehicles
• Semi-truck
• Motorcoach
• System offers Road Scout
(Lane Detection), MASK (Head
Tracker) and epoch detection
• Data is captured over the
vehicle network (CAN)
• Parametric Data
• Accel X,Y,Z
• Gyro X,Y,Z
• GPS Speed and Position
• Network speed
• Turn signal
• Brake
• Accelerator position
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Connected Vehicle Research Projects
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19 projects have been funded that focus on freeway and arterial applications:
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– CV Freeway Speed Harmonization Systems
Adaptive Stop/Yield
– Reducing School Bus Conflicts through CVI
Adaptive Lighting
– NextGen Transit Signal Priority with CVI
Intersection Management Using
– Smartphone DMS Application
Speed Adaptation
– Willingness to Pay and User Acceptance
Eco-Speed Control
Awareness System for Roadway Workers – Increasing Benefits at Low Penetration Rates
Emergency V2V Communication
Freeway Merge Management
Infrastructure Safety Assessment
Safety and Congestion Issues
Related to Public Transportation
Connected Motorcycle Crash Warning
Connected Motorcycle System Performance
Smartphone App Reducing Motorcycle and Bicycle Crashes
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Background
• Rollout of connected vehicles will not be instantaneous
16 years between kickoff
and 80%
Projected rollout of on-board equipment in US Fleet (Volpe, 2008)
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Connected Vehicle Applications
• Lots of connected vehicle mobility applications in
development
Application
% Connected Vehicles
Needed for Benefits
Traffic signal control
20-30%
Incident detection
20%
Queue length estimation
30%
Performance measurement
10-50%
• Most of these applications need at least 25% of vehicles
to be “connected” to see benefits
• Higher percentages = more benefit
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What it Means
• Problem – Mobile sensors and connected
vehicle data are not constant or ubiquitous.
There are gaps.
• Solution – “Location Estimation”
– Behavior of equipped vehicles may suggest location
of unequipped vehicles.
Equipped Vehicles
Assumed Location of
Unequipped Vehicle
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Methodology
• How to estimate vehicle locations
– Depends on unexpected behavior of equipped
vehicles – indicates an unequipped vehicle
ahead
– What is “unexpected”?
– Car-following model
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Algorithm
• Vehicles assumed to follow Wiedemann carfollowing model
– Widely accepted, basis for VISSIM
• A deviation from expected acceleration indicates
an unequipped vehicle ahead
Headway = 97 feet
Vehicle A
Speed = 30 mph
Acceleration = -4 ft/s2
Expected Accel = 7 ft/s2
Vehicle C
Inserted Vehicle
(Estimate)
Unexpected
Speed = 29Behavior
mph
Acceleration = 0
Vehicle B
Speed = 45 mph
Acceleration = 0
ft/s2
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Testing
• Using NGSIM datasets as
ground truth
– 30-minutes of individual
vehicle movements
– ¼ mile segment of I-80 in
Emeryville, CA
• Designate some vehicles
as “unequipped” and
remove from data set
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Heat Map
of Vehicle
Densities
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Densities Along I-80 at 25% Market Penetration
Actual Densities (Sampled and Observed Vehicles)
Distance (1/4 mile total)
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6
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10
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0
200
400
600
800
1000
1200
1400
1600
1800
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1600
1800
Estimated Densities (Sampled and Estimated Vehicles)
Distance (1/4 mile total)
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2
4
6
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10
12
0
200
400
600
800
1000
Time (s)
1200
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Ramp Metering Application
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Applied to Ramp Metering GAP
Algorithm
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Summary
• Connected vehicles is important,
innovative, and evolving
• VCTIR/VDOT is committed to being at the
forefront
• Ensure that connected vehicles will meet
the needs of Virginia
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For more information:
Noah Goodall
noah.goodall@vdot.virginia.gov
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