VDOT’s Connected Vehicle Program

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 • Braking status • Tire pressure • Steering wheel angle • Headlights on/off • Turn signals on/off • Rain sensors • 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 • 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 • 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

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

27. 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 Vehicle B Vehicle C Vehicle A Inserted Vehicle (Estimate) Speed = 29 mph Acceleration = 0 Speed = 45 mph Acceleration = 0 ft/s2 Speed = 30 mph Acceleration = -4 ft/s2 Expected Accel = 7 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 Densities 32

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 Algorithm 35

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 Goodallnoah.goodall@vdot.virginia.gov