Highway lane-changing behavior: a data-driven analysis of driver intentions

doi:10.11835/j.issn.1000-582X.2023.111

Home > Archive>Volume 47, Issue 11, 2024 >37-50. DOI:10.11835/j.issn.1000-582X.2023.111

Highway lane-changing behavior: a data-driven analysis of driver intentions
DOI:
                        10.11835/j.issn.1000-582X.2023.111
                    
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Author:
                        YANG Chonghui1YANG Chonghui
State Key Laboratory of Mechanical Transmissions, Chongqing University, Chongqing400044, P. R. China
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ZHENG Ling1ZHENG Ling
State Key Laboratory of Mechanical Transmissions, Chongqing University, Chongqing400044, P. R. China
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ZUO Yifang1ZUO Yifang
State Key Laboratory of Mechanical Transmissions, Chongqing University, Chongqing400044, P. R. China
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WANG Kan2WANG Kan
China Merchants Testing Vehicle Technology Research Institute Co., Ltd., Chongqing400067, P. R. China
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ZENG Jie2ZENG Jie
China Merchants Testing Vehicle Technology Research Institute Co., Ltd., Chongqing400067, P. R. China
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DING Xuecong2DING Xuecong
China Merchants Testing Vehicle Technology Research Institute Co., Ltd., Chongqing400067, P. R. China
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Affiliation:1.State Key Laboratory of Mechanical Transmissions, Chongqing University, Chongqing400044, P. R. China;2.China Merchants Testing Vehicle Technology Research Institute Co., Ltd., Chongqing400067, P. R. China
Clc Number:U448.213
Fund Project:Supported by National Natural Science Foundation of China (51875061).

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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.

Key words:driver characteristics;lane-changing behavior analysis;NGSIM (Next Generation Simulation);driving safety

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杨崇辉,郑玲,左益芳,王勘,曾杰,丁雪聪.基于高速公路场景的换道驾驶行为研究[J].重庆大学学报,2024,47(11):37~50

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Received:February 13,2023
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Online: December 04,2024
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