MBAPPE: MCTS-Built-Around Prediction for Planning Explicitly

TL;DR

MBAPPE introduces a novel motion planning approach for autonomous driving that combines Monte-Carlo Search Tree (MCTS) with supervised learning, improving decision-making, explainability, and collision avoidance in dynamic environments.

Contribution

The paper presents a new framework integrating MCTS with learned environment models, enhancing autonomous vehicle planning with explainability and adaptability.

Findings

Improved real-time decision-making in autonomous driving scenarios

Enhanced collision avoidance capabilities

Demonstrated robustness across diverse environments

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 Search Tree (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.