用树搜索与逆强化学习结合，让自动驾驶更安全像人
TreeIRL: Safe Urban Driving with Tree Search and Inverse Reinforcement Learning
- 用树搜索找安全候选路径，再用深度逆强化学习选最像人类的
- 在拉斯维加斯500多英里真实道路测试中表现最优
- 适合研究自动驾驶规划、融合传统方法与学习模型的场景
我们提出TreeIRL，一种结合蒙特卡洛树搜索（MCTS）与逆强化学习（IRL）的新型自动驾驶规划器，在大规模仿真和真实道路驾驶中均达到领先性能。核心思路是利用MCTS生成一组安全候选轨迹，并通过深度IRL评分函数从中选出最符合人类驾驶行为的路径。我们在拉斯维加斯都会区进行了超过500英里的真实道路测试，涵盖密集城市交通、自适应巡航、突然切入和红绿灯等复杂场景。TreeIRL在安全性、行驶进展、舒适性与人类相似度之间取得最佳平衡。据我们所知，这是首个在公共道路上实现基于MCTS的规划系统，凸显了跨多种指标和真实环境评估规划器的重要性。该框架高度可扩展，未来可通过强化学习与模仿学习进一步优化，为融合经典方法与学习范式解决自动驾驶规划瓶颈提供新思路。
原文摘要 · Abstract (English)
We present TreeIRL, a novel planner for autonomous driving that combines Monte Carlo tree search (MCTS) and inverse reinforcement learning (IRL) to achieve state-of-the-art performance in simulation and in real-world driving. The core idea is to use MCTS to find a promising set of safe candidate trajectories and a deep IRL scoring function to select the most human-like among them. We evaluate TreeIRL against both classical and state-of-the-art planners in large-scale simulations and on 500+ miles of real-world autonomous driving in the Las Vegas metropolitan area. Test scenarios include dense urban traffic, adaptive cruise control, cut-ins, and traffic lights. TreeIRL achieves the best overall performance, striking a balance between safety, progress, comfort, and human-likeness. To our knowledge, our work is the first demonstration of MCTS-based planning on public roads and underscores the importance of evaluating planners across a diverse set of metrics and in real-world environments. TreeIRL is highly extensible and could be further improved with reinforcement learning and imitation learning, providing a framework for exploring different combinations of classical and learning-based approaches to solve the planning bottleneck in autonomous driving.
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