用蒙特卡洛树搜索实现自动驾驶长时程变道规划
An Extended Horizon Tactical Decision-Making for Automated Driving Based on Monte Carlo Tree Search
- 结合资源守恒模型与MCTS，实现长视野变道决策
- 在高速、匝道等场景下显著提升规划稳定性
- 适合需要提前预判的复杂交通环境应用
本文提出COR-MCTS（资源守恒-蒙特卡洛树搜索）方法，针对自动驾驶中战术决策的长期规划需求。传统算法通常受限于固定规划时长，经典方法最多6秒，学习型方法仅3秒，难以适应高速路、环岛、出入口等动态场景。为此，将蒙特卡洛树搜索（MCTS）与我们先前提出的基于效用的机动规划框架COR-MP（Conservation of Resources Model for Maneuver Planning）相结合，实现长时间跨度下的实时决策。通过多种驾驶场景的仿真验证，COR-MCTS在扩展时域内有效提升了规划鲁棒性与决策效率。
原文摘要 · Abstract (English)
This paper introduces COR-MCTS (Conservation of Resources - Monte Carlo Tree Search), a novel tactical decision-making approach for automated driving focusing on maneuver planning over extended horizons. Traditional decision-making algorithms are often constrained by fixed planning horizons, typically up to 6 seconds for classical approaches and 3 seconds for learning-based methods limiting their adaptability in particular dynamic driving scenarios. However, planning must be done well in advance in environments such as highways, roundabouts, and exits to ensure safe and efficient maneuvers. To address this challenge, we propose a hybrid method integrating Monte Carlo Tree Search (MCTS) with our prior utility-based framework, COR-MP (Conservation of Resources Model for Maneuver Planning). This combination enables long-term, real-time decision-making, significantly enhancing the ability to plan a sequence of maneuvers over extended horizons. Through simulations across diverse driving scenarios, we demonstrate that COR-MCTS effectively improves planning robustness and decision efficiency over extended horizons.
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