Blame-Free Motion Planning in Hybrid Traffic
Sanggu Park, Edward Andert, Aviral Shrivastava · IEEE Transactions on Intelligent Vehicles · 2023
Despite the potential of autonomous vehicles (AV) to improve traffic efficiency and safety, many studies have shown that traffic accidents in a hybrid traffic environment where both AVs and human-driven vehicles (HVs) are present are inevitable because of the unpredictability of HVs. Given that eliminating accidents is impossible, an achievable goal is to design AVs in a way so that they will not be blamed for any accident in which they are involved in. In this paper, we proposeBlaFT Rules– orBlame-Free hybridTraffic motion planningRules. An AV followingBlaFT Rulesis designed to be cooperative with HVs as well as other AVs, and will not be blamed for accidents in a structured road environment. We provide proofs that no accident will happen if all AVs are using aBlaFT Rulesconforming motion planner, and that an AV usingBlaFT Ruleswill be blame-free even if it is involved in a collision in hybrid traffic. We implemented a motion planning algorithm that conforms toBlaFT RulescalledBlaFT. We instantiated scores ofBlaFTcontrolled AVs and HVs in an urban roadscape loop in the SUMO simulator and show that over time that as the percentage ofBlaFTvehicles increases, the traffic becomes safer even with HVs involved. AddingBlaFTvehicles increases the efficiency of traffic as a whole by up to 34% over HVs alone.