United States Department of Transportation
i
Radar-based over-the-air message generator for accelerating connected vehicle deployment.
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2016-10-15
[PDF-1.75 MB]
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Corporate Contributors:United States. Department of Transportation. Research and Innovative Technology Administration ; University of Virginia. Center for Transportation Studies ; Morgan State University ; United States. Department of Transportation. University Transportation Centers (UTC) Program ; Connected Vehicle/Infrastructure University Transportation Center
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Abstract:The market penetration levels needed to realize the full safety, economic, and environmental benefits of connected vehicle (CV) systems will not be met for some time. During the transition, it would be beneficial if data on non-CVs could be measured and included within the real-time CV data stream. Conceptually, a connected vehicle with advanced sensors, such as radar, could measure the dynamics of adjacent vehicles and, in addition to broadcasting its own Basic Safety Message (BSM), broadcast a pseudo BSM representing the non-connected vehicles. This project investigated the use of radar sensors to compute the position, speed, and heading of a non-connected vehicle (non-CV) for packaging into a pseudo BSM. An algorithm was developed to estimate the speed, position, and heading of a nearby non-CV via speed, Global Positioning System (GPS) coordinates, and radar data from the CV. Field tests were conducted with two vehicles on the Virginia Smart Road and on public roads in the New River Valley of Virginia. The field tests were designed to cover a variety of vehicle formations, traffic densities, velocities, and roadway environments. The final results showed that 67.9% of the position estimates were within 3 m of the measured position along the x-axis (longitudinal) and within 1.5 m of the measured position along the y-axis (lateral). Heading and speed estimates were generally excellent. Although the estimated position accuracy was lower than desired, the data that were collected and analyzed were sufficient to suggest ways to improve the system, such as fusing the radar data with camera-based vision data or using a more accurate GPS.
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