A Conflicts-free, Speed-lossless KAN-based Reinforcement Learning Decision System for Interactive Driving in Roundabouts

• URL: http://arxiv.org/abs/2408.08242v2
• Date: Fri, 12 Sep 2025 16:03:33 GMT
• Title: A Conflicts-free, Speed-lossless KAN-based Reinforcement Learning Decision System for Interactive Driving in Roundabouts
• Authors: Zhihao Lin, Zhen Tian, Jianglin Lan, Qi Zhang, Ziyang Ye, Hanyang Zhuang, Xianxian Zhao,
• Abstract summary: This paper presents a learning-based algorithm that promotes safe and efficient driving across varying roundabout traffic conditions.<n>A deep Q-learning network is used to learn optimal strategies, while a Kolmogorov-Arnold Network (KAN) improves the AVs' environmental understanding.<n> Experimental results demonstrate that the proposed system consistently outperforms state-of-the-art methods.
• Score: 16.714573474722282
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Safety and efficiency are crucial for autonomous driving in roundabouts, especially mixed traffic with both autonomous vehicles (AVs) and human-driven vehicles. This paper presents a learning-based algorithm that promotes safe and efficient driving across varying roundabout traffic conditions. A deep Q-learning network is used to learn optimal strategies in complex multi-vehicle roundabout scenarios, while a Kolmogorov-Arnold Network (KAN) improves the AVs' environmental understanding. To further enhance safety, an action inspector filters unsafe actions, and a route planner optimizes driving efficiency. Moreover, model predictive control ensures stability and precision in execution. Experimental results demonstrate that the proposed system consistently outperforms state-of-the-art methods, achieving fewer collisions, reduced travel time, and stable training with smooth reward convergence.

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