Cooperation-Aware Decision Making for Autonomous Vehicles in Merge Scenarios

Highway merging is a challenging task for an autonomous vehicle, because the vehicle must interact with other vehicles to identify a gap to merge into before the ending of current lane while maintaining safety (i.e., avoiding collisions). In this paper, we treat the problem of autonomous vehicle planning and control for forced merge scenarios by proposing a novel decision-making algorithm based on a partially observable leader-follower game that models the interaction among merging and highway driving vehicles. In the proposed algorithm, the autonomous ego vehicle applies a receding-horizon optimization-based control strategy that adapts to online estimated driving intents of the other vehicles to simultaneously achieve safety (i.e., avoiding collisions) and liveness (i.e., completing the merging task). The algorithm is validated by multiple simulation-based case studies.