This paper introduces an adaptive lane-changing behavior learning agent designed for autonomous vehicles. The agent employs machine learning techniques to adaptively learn and optimize lane-changing decisions based on real-time traffic conditions and environmental cues. By leveraging reinforcement learning algorithms and sensor data, the agent continuously refines its decision-making process to navigate safely and efficiently through complex traffic scenarios. The paper discusses the implementation details of the learning agent and explores its practical applications in autonomous driving systems. Through simulation studies and real-world experiments, the effectiveness and robustness of the adaptive lane-changing behavior learning agent are evaluated, demonstrating its potential to enhance traffic flow, improve safety, and optimize driving efficiency in diverse road environments.

Adaptive lane-changing behavior, learning agent, autonomous vehicles

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