In this paper, we demonstrate a driver intent inference system that is based on lane positional information, vehicle parameters, and driver head motion. We present robust computer vision methods for identifying and tracking freeway lanes and driver head motion. These algorithms are then applied and evaluated on real-world data that are collected in a modular intelligent vehicle test bed. Analysis of the data for lane change intent is performed using a sparse Bayesian learning methodology. Finally, the system as a whole is evaluated using a novel metric and real-world data of vehicle parameters, lane position, and driver head motion.


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    Titel :

    Lane change intent analysis using robust operators and sparse Bayesian learning


    Beteiligte:
    McCall, J.C. (Autor:in) / Wipf, D.P. (Autor:in) / Trivedi, M.M. (Autor:in) / Rao, B.D. (Autor:in)


    Erscheinungsdatum :

    2007


    Format / Umfang :

    10 Seiten, 19 Quellen




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Print


    Sprache :

    Englisch





    Lane Change Intent Analysis Using Robust Operators and Sparse Bayesian Learning

    McCall, J.C. / Trivedi, M.M. / Wipf, D. et al. | IEEE | 2005


    Lane Change Intent Analysis Using Robust Operators and Sparse Bayesian Learning

    McCall, J.C. / Wipf, D.P. / Trivedi, M.M. et al. | IEEE | 2007


    Predicting driver lane change intent using HCRF

    Wen, Yu / Zhang, Xuetao / Wang, Fei et al. | IEEE | 2015


    Comparison of Machine Learning Algorithms for Predicting Lane Changing Intent

    Choi, Dongho / Lee, Sangsun | Springer Verlag | 2021