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Pré-Publication, Document De Travail Année : 2023

MBAPPE: MCTS-Built-Around Prediction for Planning Explicitly

Résumé

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.
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Dates et versions

hal-04222148 , version 1 (29-09-2023)

Identifiants

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Raphael Chekroun, Thomas Gilles, Marin Toromanoff, Hornauer Sascha, Fabien Moutarde. MBAPPE: MCTS-Built-Around Prediction for Planning Explicitly. 2023. ⟨hal-04222148⟩
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