Lane-changing behavior significantly affects traffic safety, often leading to severe casualties and substantial property damage. A lane-changing safety warning model can help drivers make safer lane-changing decisions, thereby reducing accidents. This study is based on the US-101 segment of the NGSIM dataset, where sample data of lane-changing passenger vehicles were selected following specific filtering criteria. A set of driving style indicators related to driving safety was chosen. Factor analysis was employed to reduce the dimensionality of the selected indicators, and the k-means clustering method was then used to classify driving styles into two categories: conservative and aggressive. The results show that aggressive drivers exhibit greater speed fluctuations compared to conservative drivers. Therefore, in the development of a lane-changing safety warning model based on Time to Collision (TTC), an additional minimum safe distance was introduced as a secondary warning criterion for aggressive drivers, aiming to reduce false warnings caused by sudden speed changes. Validation results indicated that the model achieved an accuracy of 82.7%, demonstrating its effectiveness. This warning model provides a foundation for further research into the safety of lane-changing behavior on highways and the reduction of lane-changing accidents.


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    Titel :

    A Lane-Changing Safety Warning Model Considering Driving Style Characteristics


    Weitere Titelangaben:

    Lect. Notes Electrical Eng.


    Beteiligte:
    Jia, Limin (Herausgeber:in) / Yao, Dechen (Herausgeber:in) / Ma, Feng (Herausgeber:in) / Zhang, Liguo (Herausgeber:in) / Chen, Yuejian (Herausgeber:in) / Xue, Qingwan (Herausgeber:in) / Zu, Xinquan (Autor:in) / Xu, Liangjie (Autor:in) / Li, Jingyi (Autor:in)

    Kongress:

    International Conference on Artificial Intelligence and Autonomous Transportation ; 2024 ; Beijing, China December 06, 2024 - December 08, 2024



    Erscheinungsdatum :

    28.03.2025


    Format / Umfang :

    9 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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