MBAPPE: MCTS-Built-Around Prediction for Planning Explicitly

2023/09/15 by Raphael Chekroun, Thomas Gilles, Chekroun, Raphael +7 · 6 citations
Computer Science · Engineering · #Autonomous Vehicle Technology and Safety #FOS: Computer and information sciences #Multimodal Machine Learning Applications #Robotic Path Planning Algorithms #Robotics (cs.RO)

paper · pdf · doi:10.48550/arxiv.2309.08452

openalex publication_date 2023/09/15 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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.

Cited by

• Scenario-aware Uncertainty Quantification for Trajectory Prediction with Statistical Guarantees
• Driving is a Game: Combining Planning and Prediction with Bayesian Iterative Best Response
• When Planners Meet Reality: How Learned, Reactive Traffic Agents Shift nuPlan Benchmarks
• TreeMind: Automatically Reproducing Android Bug Reports via LLM-empowered Monte Carlo Tree Search
• TreeIRL: Safe Urban Driving with Tree Search and Inverse Reinforcement Learning
• AGENTS-LLM: Augmentative GENeration of Challenging Traffic Scenarios with an Agentic LLM Framework

Related

• Monte-Carlo Tree Search for Behavior Planning in Autonomous Driving
• Planning by Simulation: Motion Planning with Learning-based Parallel Scenario Prediction for Autonomous Driving
• An Extended Horizon Tactical Decision-Making for Automated Driving Based on Monte Carlo Tree Search
• Interpretable Goal-based Prediction and Planning for Autonomous Driving
• Towards High Efficient Long-horizon Planning with Expert-guided Motion-encoding Tree Search