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.

翻译：中文摘要：我们提出MBAPPE，一种结合树搜索与部分学习环境模型的自动驾驶运动规划新方法。该方法利用蒙特卡洛树搜索（MCTS）固有的可解释探索与优化能力，处理动态环境中的复杂决策问题。我们提出一个融合MCTS与监督学习的框架，使自动驾驶车辆能够有效应对多样化场景。实验结果表明了该方法的有效性与适应性，展示了其在实时决策与碰撞避免方面的改进。本文通过为自动驾驶系统提供鲁棒的运动规划解决方案，增强了其可解释性与可靠性，为该领域做出了贡献。