Multi-Timescale Hierarchical Reinforcement Learning for Unified Behavior and Control of Autonomous Driving

• URL: http://arxiv.org/abs/2506.23771v1
• Date: Mon, 30 Jun 2025 12:17:42 GMT
• Title: Multi-Timescale Hierarchical Reinforcement Learning for Unified Behavior and Control of Autonomous Driving
• Authors: Guizhe Jin, Zhuoren Li, Bo Leng, Ran Yu, Lu Xiong,
• Abstract summary: We propose a multi-timescale hierarchical reinforcement learning approach for autonomous driving.<n>High- and low-level RL policies are unified-trained to produce long-timescale motion guidance and short-timescale control commands.<n>Our approach significantly improves AD performance, effectively increasing driving efficiency, action consistency and safety.
• Score: 4.750705843012836
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Reinforcement Learning (RL) is increasingly used in autonomous driving (AD) and shows clear advantages. However, most RL-based AD methods overlook policy structure design. An RL policy that only outputs short-timescale vehicle control commands results in fluctuating driving behavior due to fluctuations in network outputs, while one that only outputs long-timescale driving goals cannot achieve unified optimality of driving behavior and control. Therefore, we propose a multi-timescale hierarchical reinforcement learning approach. Our approach adopts a hierarchical policy structure, where high- and low-level RL policies are unified-trained to produce long-timescale motion guidance and short-timescale control commands, respectively. Therein, motion guidance is explicitly represented by hybrid actions to capture multimodal driving behaviors on structured road and support incremental low-level extend-state updates. Additionally, a hierarchical safety mechanism is designed to ensure multi-timescale safety. Evaluation in simulator-based and HighD dataset-based highway multi-lane scenarios demonstrates that our approach significantly improves AD performance, effectively increasing driving efficiency, action consistency and safety.

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