Inter-vehicular communication (IVC) can be explored for enhancing collaborative vehicular applications related to traffic statistics, safety by accident prediction and prevention, and energy efficient route planning. For enhancing these applications, a live map of vehicles associated with their communication identities (e.g., IP/MAC addresses) is needed. This is particularly challenging to achieve in the presence of legacy vehicles which might not have any sensing or IVC capabilities. Additionally, vehicles might have diverse sensing capabilities and can have conflicting estimates of parameters of surrounding vehicles. We present RoadView, a system that builds the live map of surrounding vehicles by intelligently fusing the local maps created by individual vehicles. RoadView runs on top of existing local vehicular matching systems (LM) such as Foresight  or RoadMap . RoadView is the first work that provides a live map of vehicles by leveraging collaboration across vehicles. Our simulations show that for different adoption rates and traffic densities, RoadView can robustly fuse information from a collection of local maps and enhance vehicles to sense 1.8x (average) number of immediate neighboring vehicles compared to state of art LM algorithms.
citation: ‘Tummala, Gopi Krishna, Dong Li, and Prasun Sinha. “Roadview: Live View of On-Road Vehicular Information.” In 2017 14th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON), pp. 1-9. IEEE, 2017.