Highway lane-changing behavior: a data-driven analysis of driver intentions
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Abstract:
Understanding human driving behaviors has significant implications for promoting decision-making in intelligent vehicles and improving driving safety. This study focuses on highway lane-changing behavior, using the NGSIM (Next Generation Simulation) Dataset to extract key parameters and analyze the correlation between these parameters and driving behaviors. A GMM-HMM-based model for lane-changing intention recognition was developed, achieving an accuracy of 95.6% in predicting lane changes 1.0 s before they occur, and an accuracy of over 80% in recognizing lane-changing intentions. This model can be applied to intelligent vehicle design to effectively reduce lane-changing risks and improve driving safety.
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Supported by National Natural Science Foundation of China (51875061).
