A Conflicts-free, Speed-lossless KAN-based Reinforcement Learning Decision System for Interactive Driving in Roundabouts
2025-09-15
Summary
The article introduces a novel reinforcement learning-based decision system designed to enhance autonomous vehicle (AV) navigation through roundabouts, particularly in mixed traffic with human-driven vehicles (HDVs). Utilizing a deep Q-learning network enhanced by a Kolmogorov-Arnold Network (KAN), the system improves environmental understanding, integrates an action inspector for safety, employs a route planner for efficiency, and uses model predictive control for stability. Experimental results indicate superior performance over existing methods, with fewer collisions, reduced travel time, and stable training.
Why This Matters
This research is significant as it addresses critical challenges in autonomous driving, particularly in complex environments like roundabouts where AVs must interact with human drivers. With the anticipated increase in AVs on the road, ensuring safety and efficiency in traffic systems is crucial. The proposed system's ability to reduce collisions and enhance driving efficiency can contribute significantly to the development of safer and more reliable autonomous driving solutions.
How You Can Use This Info
Professionals in the transportation and automotive industries can leverage these findings to enhance the safety and efficiency of autonomous driving systems in urban settings. Traffic planners and engineers could integrate such advanced decision-making technologies into smart city infrastructures. Moreover, companies developing AV technologies can adopt these methodologies to improve their systems' robustness and performance in complex traffic scenarios.