KB-Tree: Learnable and Continuous Monte-Carlo Tree Search for Autonomous Driving Planning

Abstract

In this paper, we present a novel learnable and continuous Monte-Carlo Tree Search method, named as KB-Tree, for motion planning in autonomous driving. The proposed method utilizes an asymptotical PUCB based on Kernel Regression (KR-AUCB) as a novel UCB variant, to improve the exploitation and exploration performance. In addition, we further optimize the sampling in continuous space by adapting Bayesian Optimization (BO) in the selection process of MCTS. Moreover, we use a customized Graph Neural Network (GNN) as our feature extractor to improve the learning performance. To the best of our knowledge, we are the first to apply the continuous MCTS method in autonomous driving. To validate our method, we conduct extensive experiments under several weakly and strongly interactive scenarios. The results show that our proposed method performs well in all tasks, and outperforms the learning-based continuous MCTS method and the state-of-the-art Reinforcement Learning (RL) baseline.