MBAPPE: MCTS-Built-Around Prediction for Planning Explicitly

Raphaël Chekroun, Thomas Gilles, Marin Toromanoff, Sascha Hornauer, Fabien Moutarde

Abstract: We present MBAPPE, a novel approach to motion planning for autonomous driving combining tree search with a partially-learned model of the environment. Leveraging the inherent explainable exploration and optimization capabilities of the Monte-Carlo Tree Search (MCTS), our method addresses complex decision-making in a dynamic environment. We propose a framework that combines MCTS with supervised learning, enabling the autonomous vehicle to effectively navigate through diverse scenarios. Experimental results demonstrate the effectiveness and adaptability of our approach, showcasing improved real-time decision-making and collision avoidance. This paper contributes to the field by providing a robust solution for motion planning in autonomous driving systems, enhancing their explainability and reliability. Code is available under https://github.com/raphychek/mbappe-nuplan.