Development and Demonstration of Merge-Assist System Using Connected Vehicle Technology

Principal Investigator:

M. Imran Hayee, Professor, Dir. of Grad Studies, UMD-Electrical Engineering

Project Summary:

The U.S. Department of Transportation (USDOT) continues to emphasize the need for having DSRC-based vehicle-to-vehicle (V2V) and/or vehicle-to-infrastructure (V2I) communication to enhance driver safety and traffic mobility. One potential area for improvement is around merging junctions of two roadways e.g., at a freeway entrance ramp. In order to develop automated merging or a merge-assist application, we need to reach the technological milestone of acquiring accurate relative trajectories of vehicles on the main freeway. Previously in our RSI project, we successfully acquired relative trajectories of vehicles travelling on multiple lanes toward a merging junction with an accuracy of +/- 0.5m using DSRC-based V2V communication and standard GPS receivers. Although the achieved accuracy in relative trajectory was sufficient to differentiate vehicles traveling on adjacent lanes of a multiple-lane freeway, the trajectories were not acquired in real time. Continuing the same project, the researchers are now aiming to acquire relative trajectories of vehicles in real time. The DSRC-equipped vehicles traveling on the freeway and on the merging ramp will periodically communicate important traffic parameters to each other such as their location, direction of travel, and speed. Using that information, the relative trajectories of all DSRC-equipped vehicles will be acquired and then processed in real time to filter out irrelevant vehicles e.g., vehicles on the crossing roads or on the main freeway in the opposite direction. Furthermore, using the acquired real-time trajectories, speed, and direction of travel of the relevant vehicles, a merge time cushion will be estimated which could potentially be used as an important parameter to develop a merge-assist application.

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