1. 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

2. Smartphones Very sophisticated computer Sensors
GPS
3-axis accelerometer
Camera
Magnetometer
Carried with you all day

3. Modern Vehicles – Very Sophisticated
How many lines of code in a:
F-22 Raptor:
Average new Ford:
1.7 million
10 million

4. Computerized Measurement
Speed
Heading
Acceleration (lateral, longitudinal, vertical)
Position (from GPS)
Other diagnostics
Wipers on/off
Headlights on/off
Braking status
Turn signals on/off
Tire pressure
Rain sensors
Steering wheel angle
Stability control

5. Vehicle-to-Vehicle Communication: Not Sophisticated
Hi-tech vehicles
Low-tech communication with other vehicles
Brake lights
Turn signals
Horn
Flash headlights

6. 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

7. Field Detection

8. 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

9. Infrastructure-to-Vehicle
Difficult to communicate with the driver both in real-time and across a wide area

10. Connected Vehicles

11. Wireless Vehicle Communication

12. 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

13. Potential of Connected Vehicles
Three ways to connect:
Vehicle-to-vehicle:
Electronic brake lights
Crash avoidance
Vehicle-to-infrastructure:
Incident detection
Weather/ice detection
Infrastructure-to-vehicle
Broadcast traffic signal timing
Dynamic re-routing

14. Similarity to Other Safety Systems
Similar to radar- and laser-based safety systems, but much cheaper
Adaptive Cruise Control
Google Self-Driving Car

15. Connected Vehicles Today
Real-time speed data from cell phones

16. 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

17. 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

18. 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

19. Connected Vehicle Testbed
Virginia Tech Smart Road
7 RSUs
Northern VA
48 installed RSUs
2 portable RSUs

20. Roadside Units

21. Roadside Units
I-495
I-66
US-29
Gallows Rd
US-50

22. On Board Equipment

23. 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

24. Connected Vehicle Research Projects
19 projects have been funded that focus on freeway and arterial applications:
Adaptive Stop/Yield
Adaptive Lighting
Intersection Management Using
Speed Adaptation
Eco-Speed Control
Awareness System for Roadway Workers
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
CV Freeway Speed Harmonization Systems
Reducing School Bus Conflicts through CVI
NextGen Transit Signal Priority with CVI
Smartphone DMS Application
Willingness to Pay and User Acceptance
Increasing Benefits at Low Penetration Rates

25. 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)

26. Connected Vehicle Applications
Lots of connected vehicle mobility applications in development
Most of these applications need at least 25% of vehicles to be “connected” to see benefits
Higher percentages = more benefit
Application
% Connected Vehicles Needed for Benefits
Traffic signal control
20-30%
Incident detection
20%
Queue length estimation
30%
Performance measurement
10-50%

27. Assumed Location of Unequipped Vehicle
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.
Assumed Location of Unequipped Vehicle
Equipped Vehicles

28. Methodology How to estimate vehicle locations
Depends on unexpected behavior of equipped vehicles – indicates an unequipped vehicle ahead
What is “unexpected”?
Car-following model

29. Algorithm Vehicles assumed to follow Wiedemann car-following model
Widely accepted, basis for VISSIM
A deviation from expected acceleration indicates an unequipped vehicle ahead
Headway = 97 feet
Wiedemann is widely accepted
Vehicle B
Vehicle A
Vehicle C
Speed = 30 mph
Acceleration = -4 ft/s2
Expected Accel = 7 ft/s2
Inserted Vehicle
(Estimate)
Speed = 29 mph
Acceleration = 0
Speed = 45 mph
Acceleration = 0 ft/s2
Unexpected Behavior

30. 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

32. Heat Map of Vehicle Densities32

33. Densities Along I-80 at 25% Market Penetration
Actual Densities (Sampled and Observed Vehicles)
Distance (1/4 mile total)
Estimated Densities (Sampled and Estimated Vehicles)
Distance (1/4 mile total)
Time (s)

34. Ramp Metering Application

35. Applied to Ramp Metering GAP Algorithm35

36. 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

37. For more information: Noah Goodall noah.goodall@vdot.virginia.gov