# MBAPPE: MCTS-Built-Around Prediction for Planning Explicitly

arXiv: https://arxiv.org/abs/2309.08452 · HTML: https://mlsanity.com/paper/2309.08452/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Sep 2023.

## Built on by (most cited first, 11 of 11)

- [LLM-Assist: Enhancing Closed-Loop Planning with Language-Based Reasoning](https://mlsanity.com/paper/2401.00125/) (2023)
- [CaRL: Learning Scalable Planning Policies with Simple Rewards](https://mlsanity.com/paper/2504.17838/) (2025)
- [Learning Multiple Probabilistic Decisions from Latent World Model in Autonomous Driving](https://mlsanity.com/paper/2409.15730/) (2024)
- [When Planners Meet Reality: How Learned, Reactive Traffic Agents Shift nuPlan Benchmarks](https://mlsanity.com/paper/2510.14677/) (2025)
- [Learning Online Belief Prediction for Efficient POMDP Planning in Autonomous Driving](https://mlsanity.com/paper/2401.15315/) (2024)
- [AGENTS-LLM: Augmentative GENeration of Challenging Traffic Scenarios with an Agentic LLM Framework](https://mlsanity.com/paper/2507.13729/) (2025)
- [Driving is a Game: Combining Planning and Prediction with Bayesian Iterative Best Response](https://mlsanity.com/paper/2512.03936/) (2025)
- [C-TRAIL: A Commonsense World Framework for Trajectory Planning in Autonomous Driving](https://mlsanity.com/paper/2603.29908/) (2026)
- [DiffuSearch: How Hybrid Trajectory Planning Benefits from Aligned Objectives in Diffusion and Action Space](https://mlsanity.com/paper/2609.02252/) (2026)
- [TreeIRL: Safe Urban Driving with Tree Search and Inverse Reinforcement Learning](https://mlsanity.com/paper/2509.13579/) (2025)
- [INTERACT: Interactive Planning for Autonomous Driving via Anchor-Conditioned Prediction and Trust-Region Refinement](https://mlsanity.com/paper/2609.31137/) (2026)

## Abstract (arXiv)

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.