Opportunistic Collaborative Estimation for Vehicular Systems

As the automotive industry shifts towards enabling self-driving vehicles, real-time situational awareness is becoming a crucial requirement. This paper introduces a novel information-sharing mechanism to opportunistically improve the vehicles’ local environment estimates via infrastructure-assisted collaborative sensing, while still allowing them to operate autonomously when no assistance is available. As vehicles might have different sensing capabilities, combining and sharing information from a judiciously selected subset is often sufficient to considerably improve all the vehicles’ estimation errors. We develop an opportunistic framework for vehicular collaborative sensing determining (1) which nodes require assistance, (2) which ones are best suited to provide it, and (3) the corresponding information-sharing rates, so as to minimize the communication overheads while meeting the vehicles’ target estimation error. We leverage the supermodularity of the problem to devise an efficient vehicle information sharing algorithm with suboptimality guarantees to solve this problem and make it suitable to deploy in dynamic environments where network conditions might fluctuate rapidly. We support our analysis with simulations showing evidence that vehicles can considerably benefit from the proposed opportunistic collaborative sensing framework compared to operating autonomously. Finally, we explore the value of information-sharing in vehicular collaborative sensing networks by evaluating the associated safe driving velocity gains.