TreeIRL: Safe Urban Driving with Tree Search and Inverse Reinforcement Learning
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2025
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| author | Tomov, Momchil S. Lee, Sang Uk Hendrago, Hansford Huh, Jinwook Han, Teawon Howington, Forbes da Silva, Rafael Bernasconi, Gianmarco Heim, Marc Findler, Samuel Ji, Xiaonan Boule, Alexander Napoli, Michael Chen, Kuo Miller, Jesse Floor, Boaz Hu, Yunqing |
| author_facet | Tomov, Momchil S. Lee, Sang Uk Hendrago, Hansford Huh, Jinwook Han, Teawon Howington, Forbes da Silva, Rafael Bernasconi, Gianmarco Heim, Marc Findler, Samuel Ji, Xiaonan Boule, Alexander Napoli, Michael Chen, Kuo Miller, Jesse Floor, Boaz Hu, Yunqing |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_13579 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | TreeIRL: Safe Urban Driving with Tree Search and Inverse Reinforcement Learning Tomov, Momchil S. Lee, Sang Uk Hendrago, Hansford Huh, Jinwook Han, Teawon Howington, Forbes da Silva, Rafael Bernasconi, Gianmarco Heim, Marc Findler, Samuel Ji, Xiaonan Boule, Alexander Napoli, Michael Chen, Kuo Miller, Jesse Floor, Boaz Hu, Yunqing Robotics Artificial Intelligence Machine Learning 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. |
| title | TreeIRL: Safe Urban Driving with Tree Search and Inverse Reinforcement Learning |
| topic | Robotics Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2509.13579 |