Towards Event-Based MCTS for Autonomous Cars

Uncertainty in the behaviours of vehicles surrounding a self- driving car introduces substantial computational complexity in autonomous driving. In this study 1 , a data-driven approach was used to extract probabilistic models of the behaviours of other cars and exploit them to support a driving system based on Monte Carlo Tree Search (MCTS). The model selection component of the architecture infers which models better explain the current behaviours of the other vehicles using maximum likelihood estimation for Bayesian model comparison. The inferred behaviours are then used for MCTS- based control to prevent rollouts on models that are not relevant in the current context. While the use of multiple models allows improved efficiency and higher flexibility, it also introduces identification-related issues, which were solved here using Bayesian machine learning. The results obtained from the performed simulations are presented comparing the proposed MCTS architecture when employing multiple models with a naive model of the other vehicles.