Inter-vehicular communication (IVC) can be used to enhance the sensing region of vehicles for improved safety on the roads. For many applications based on IVC, the relative locations and communication identities (e.g., IP addresses) of other collaborating vehicles are important for accurate identification. This is particularly challenging to achieve in the presence of legacy vehicles which may not have any sensing or IVC capabilities. We present a system called RoadMap, that matches IP addresses with respective vehicles observed through a camera. It assumes a smartphone or a dashboard camera deployed in vehicles, to identify the vehicles in field of view (FoV), and IVC capability. It runs in the adopted vehicles and accurately matches information obtained through multiple sensing modalities (e.g., visual and electronic). RoadMap matches the motion-trajectories of vehicles observed from the dash-board camera with the motion-trajectories transmitted by other vehicles. To the best of our knowledge, RoadMap is the first work to explore motion-trajectories of vehicles observed from a camera to create a map of vehicles by smartly fusing electronic and visual information. It has low hardware requirement and is designed to work in low adoption rate scenarios. Through real-world experiments and simulations, RoadMap matches IP-Addresses with camera observed vehicles with a median matching precision of 80%, which is 20% improvement compared to existing schemes.
citation: ‘Tummala, Gopi Krishna, Dong Li, and Prasun Sinha. “RoadMap: mapping vehicles to IP addresses using motion signatures.” In Proceedings of the First ACM International Workshop on Smart, Autonomous, and Connected Vehicular Systems and Services, pp. 30-37. ACM, 2016.