A Study of Human-like Lane-Changing Strategies Considering Driving Style Characteristics

To address the ‘mechanical’ return to original lane and similar non-humanized lane-changing issues that may occur in existing intelligent driving systems after completing overtaking maneuvers, this study proposes a humanized lane-changing decision method that incorporates driving style characteristics. First, based on the NGSIM dataset, we employ cluster analysis to systematically dissect human drivers’ lane-changing behavior patterns, laying the theoretical foundation for constructing a human-like decision framework. Second, a game model is established to precisely represent diverse driving styles by adjusting the weights of safety, efficiency, and comfort objectives. A reference line dynamic switching mechanism is then proposed to optimize lane-change paths by integrating vehicle speed and safety distance. Joint simulation results demonstrate superiority over dynamic programming (DP) methods in multiple aspects: under conservative driving mode, dual safety thresholds for following distance and speed significantly enhance safety and reliability. In general driving mode, driving stability and smoothness improved by 2.64% and 75.28%, respectively; in aggressive driving mode, lane-change speed increased by 7.06%. These improvements demonstrate that the human-like lane-changing strategy can autonomously achieve the optimal dynamic balance between safety, comfort, and efficiency tailored to different driving styles, providing an effective pathway for constructing high-performance autonomous driving decision systems.