In the field of autonomous driving, lane-changing decision is an important content of driving decision mechanism. To solve the problem that autonomous vehicles in the driving process, due to environmental factors, are difficult to accurately make lane-changing decisions, this paper proposes a XGBoost prediction model using Bayesian optimization, and based on NGSIM project data, analysis of the vehicles lane-changing decisions under influence of the environment. The experimental results showed that the established SMAC-XGBoost model has better performance compared with other models. In the experiment of lane-changing decisions recognition and prediction, the recognition accuracy of SMAC-XGBoost model can reach more than 95%, which has a good prediction effect.


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    Titel :

    A Lane-Changing Decision Model of Structured Roads Based on Optimized XGBoost Algorithm


    Weitere Titelangaben:

    Lect. Notes Electrical Eng.


    Beteiligte:
    Jia, Yingmin (Herausgeber:in) / Zhang, Weicun (Herausgeber:in) / Fu, Yongling (Herausgeber:in) / Yu, Zhiyuan (Herausgeber:in) / Zheng, Song (Herausgeber:in) / Zhao, Zhen (Autor:in) / Ren, Xuemei (Autor:in) / Wang, Haoyuan (Autor:in)


    Erscheinungsdatum :

    08.10.2021


    Format / Umfang :

    10 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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