The urban intersection is a typically dynamic and complex scenario for intelligent vehicles, which exists a variety of driving behaviors and traffic participants. Accurately modelling the driver behavior at the intersection is essential for intelligent transportation systems (ITS). Previous researches mainly focus on using attention mechanism to model the degree of correlation. In this research, a canonical correlation analysis (CCA)-based framework is proposed. The value of canonical correlation is used for feature selection. Gaussian mixture model and Gaussian process regression are applied for driver behavior modelling. Two experiments using simulated and naturalistic driving data are designed for verification. Experimental results are consistent with the driver’ s judgment. Comparative studies show that the proposed framework can obtain a better performance.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Driver Behavior Modelling at the Urban Intersection via Canonical Correlation Analysis


    Contributors:
    Li, Zirui (author) / Lu, Chao (author) / Gong, Cheng (author) / Gong, Jianwei (author) / Li, Jinghang (author) / Wei, Lianzhen (author)


    Publication date :

    2020-11-27


    Size :

    526097 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English




    Urban Intersection Study. Volume III. Exploratory Study of Individual Driver Behavior

    T. E. Anderson / K. R. Laughery / D. E. Maurer | NTIS | 1968


    Driver Turning Behavior Prediction at Intersection -Improving Prediction Performance with Driver Behavior Adaptation

    Naito, T. / Morimoto, H. / Ito, T. et al. | British Library Conference Proceedings | 2007



    Microscopic Analyses of Driver Behavior at Signalized Intersection

    Shigeta, K. / Tsugawa, S. | British Library Conference Proceedings | 1995