Most of ADAS (advanced driver assistance systems) have some drawbacks because they do not use all but only some parts of the information on traffic environment- vehicle-driver (TVD). Recently, researches on making more efficient and effective assistant system by fusing all the information from TVD are being executed to overcome this limitation. As a part of this research, this paper focuses on decision-level fusion to estimate the driver's vigilance from the vision information of traffic environment and driver state. The driver state is defined as the tracked gazing direction and face feature points of the driver which is obtained by using the Adaboost face detector and active appearance model (AAM). The state of traffic environment is defined as lane-off or collision from the information of the vehicle's forward area, i.e., lanes, vehicles, and ego-motion. Warnings for lane-off, collision, and driver inattention are generated by fusing these in and out vehicle vision information.


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    Titel :

    Vision based driver interactive safety driving agent system


    Weitere Titelangaben:

    Visionbasiertes Fahrersicherheitsassistenzsystem


    Beteiligte:
    Choi, Hyun-Chul (Autor:in) / Kim, Sam-Yong (Autor:in) / Oh, Se-Young (Autor:in) / Won, Woong-Jae (Autor:in)


    Erscheinungsdatum :

    2007


    Format / Umfang :

    7 Seiten, 12 Bilder, 13 Quellen



    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Print


    Sprache :

    Englisch




    In and out vision-based driver-interactive assistance system

    Choi, H. C. / Kim, S. Y. / Oh, S. Y. | Online Contents | 2010


    In and out vision-based driver-interactive assistance system

    Choi, H. C. / Kim, S. Y. / Oh, S. Y. | Springer Verlag | 2010


    In and out vision-based driver-interactive assistance system

    Choi, H. C. / Kim, S. Y. / Oh, S. Y. | British Library Online Contents | 2010


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    Li, Rui / Brand, Howard / Gopinath, Aditya et al. | British Library Conference Proceedings | 2020


    Driver Drowsiness Behavior Detection and Analysis Using Vision-Based Multimodal Features for Driving Safety

    Li, Rui / Brand, Howard / Gopinath, Aditya et al. | British Library Conference Proceedings | 2020