Autonomous lane-changing decision making and planning represent a fundamental aspect of advanced driving technologies, playing a pivotal role in improving operational safety, enhancing passenger comfort, and optimizing traffic flow. Current research predominantly emphasizes environmental perception and path planning, yet systematically modeling human behavioral patterns during lane changes remains underexplored, leading to inadequate anthropomorphic decision-making capabilities. Moreover, the conventional fragmented approach to implementing decision-making, trajectory planning, and interaction signaling modules results in insufficient coordination and feedback mechanisms, ultimately compromising dynamic adaptability in real-world driving scenarios. To solve these problems, this study systematically investigates driver behavior patterns through naturalistic driving data analysis, establishes a taxonomy of lane-changing scenarios, and develops a human-like decision architecture incorporating cognitive mechanisms. The model consists of a multi-layered decision framework encompassing lane-changing motivation recognition, lane selection, feasibility evaluation, and risk assessment. Furthermore, an information feedback mechanism is established between the decision-making and trajectory planning modules, enabling dynamically coupled and closed-loop control. Simulation experiments conducted on the Prescan/Simulink platform confirm that the proposed method significantly enhances the naturalness and safety of lane-changing behavior in complex traffic environments. This study provides both theoretical support and technical guidance for the development of intelligent lane-changing systems that emulate human cognitive characteristics.
Wu et al. (Thu,) studied this question.
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