Tactical Cooperative Planning for Autonomous Vehicles using MCTS

David Lenz, Tobias Kessler und Alois Knoll

IEEE Intelligent Vehicles Symposium,



Abstract—Human drivers use nonverbal communication and anticipation of other drivers' actions to master conflicts oc- curring in everyday driving situations. Without a high pen- etration of vehicle-to-vehicle communication an autonomous vehicle has to have the possibility to understand intentions of others and share own intentions with the surrounding traffic participants. This paper proposes a cooperative combinatorial motion planning algorithm without the need for inter vehicle communication based on Monte Carlo Tree Search (MCTS).We motivate why MCTS is particularly suited for the autonomous driving domain. Furthermore, adoptions to the MCTS algo- rithm are presented as for example simultaneous decisions, the usage of the Intelligent Driver Model as microscopic traffic simulation, and a cooperative cost function. We further show simulation results of merging scenarios in highway-like situations to underline the cooperative nature of the approach.