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Autonomous vehicles (AVs) need to interact with other traffic participantswho can be either cooperative or aggressive, attentive or inattentive. Suchdifferent characteristics can lead to quite different interactive behaviors.Hence, to achieve safe and efficient autonomous driving, AVs need to be awareof such uncertainties when they plan their own behaviors. In this paper, weformulate such a behavior planning problem as a partially observable MarkovDecision Process (POMDP) where the cooperativeness of other trafficparticipants is treated as an unobservable state. Under differentcooperativeness levels, we learn the human behavior models from real trafficdata via the principle of maximum likelihood. Based on that, the POMDP problemis solved by Monte-Carlo Tree Search. We verify the proposed algorithm in bothsimulations and real traffic data on a lane change scenario, and the resultsshow that the proposed algorithm can successfully finish the lane changeswithout collisions.

Interaction-Aware Behavior Planning for Autonomous Vehicles Validated with Real Traffic Data