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VDOTs Connected Vehicle Program Noah Goodall, Ph.D., P.E. Research Scientist Virginia Center for Transportation Innovation and Research ASHE Old Dominion.

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Presentation on theme: "VDOTs Connected Vehicle Program Noah Goodall, Ph.D., P.E. Research Scientist Virginia Center for Transportation Innovation and Research ASHE Old Dominion."— Presentation transcript:

1 VDOTs 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,

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

3 Modern Vehicles – Very Sophisticated How many lines of code in a: –F-22 Raptor: –Average new Ford: 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 4

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

6 Vehicle-to-Infrastructure Communication: Not Much Better We want to know where vehicles are, what theyre doing Many sensors already in the field to do this 6

7 Field Detection 7

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 8

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

10 Connected Vehicles 10

11 Wireless Vehicle Communication 11

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 12

13 Potential of Connected Vehicles Three ways to connect: 1)Vehicle-to-vehicle: Electronic brake lights Crash avoidance 2)Vehicle-to-infrastructure: Incident detection Weather/ice detection 3)Infrastructure-to-vehicle Broadcast traffic signal timing Dynamic re-routing 13

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

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

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 16

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 17

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 18

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

20 Roadside Units 20

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

22 On Board Equipment 22

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 23

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 24 –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 Projected rollout of on-board equipment in US Fleet (Volpe, 2008) 16 years between kickoff and 80% 25

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 control20-30% Incident detection20% Queue length estimation30% Performance measurement10-50% 26

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. Equipped Vehicles Assumed Location of Unequipped Vehicle 27

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 28

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 Vehicle B Speed = 45 mph Acceleration = 0 ft/s 2 Speed = 30 mph Acceleration = -4 ft/s 2 Expected Accel = 7 ft/s 2 Vehicle A Unexpected Behavior Headway = 97 feet Inserted Vehicle (Estimate) Speed = 29 mph Acceleration = 0 Vehicle C 29

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 30

31 31

32 Heat Map of Vehicle Densities 32

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

34 Ramp Metering Application 34

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 36

37 For more information: Noah Goodall 37

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