Scalable Monte Carlo Tree Search for CAV s Action Planning in Colliding Scenarios
Dhruvkumar Patel, Rym Zalila-Wenkstern · 2021
Connected and autonomous vehiles (CAVs) require an effective cooperative action planning strategy in an emergency situation. Monte Carlo Tree Search (MCTS) is a promising planning technique for such problems with large state spaces. However, traditional MCTS-based techniques do not scale well with the number of vehicles. In this paper, we present a novel MCTS-based cooperative action planning algorithm for CAV s driving in a coalition formation. Our proposed algorithm improves the reliability and the scalability of M CTS. Explicit communication is used to ensure that mitigation action plans chosen by the CAVs are conflict-free when possible. We perform the evaluation of the proposed algorithm in a large scale multi-agent based traffic simulation system. Our simulated experiments show that our approach improves upon current state-of-the-art centralized and decentralized algorithms.