Thanks to the important improvements in perception technologies as well as computing efficiency the development of Driver Assistance Systems is now a real growing market. Nevertheless these new functions that interact with the drivers are not without acceptance problems from the users. The DrivEasy concept that is presented is proposing adaptive solutions taking care about the current and contextual situations to improve the efficiency of such systems. In a first step, information provided by existing driver assistance functions like lane departure warning and obstacle detection were used to demonstrate the adaption of the warnings to both the driver and the situation. DrivEasy concept was implemented into an experimental vehicle and tested in real use conditions.The driver drowsiness algorithms are based on the real time analysis of the driver's eyelid movements and closure duration. The driver's inattention module is based on the estimation of the head orientation classifying the head position in two classes On Road vs. Off Road. Lane departure warning algorithms are processing the image provided by a camera looking ahead the vehicle.A multi beam lidar is observing the road ahead the vehicle and measuring the distance to on-road moving obstacles.The GPS system provides an accurate positioning of the vehicle. A data base including the legal speed limits is loaded in the system or learnt for specific areas. Then a match between real time position and database is performed to give overspeeding information.


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    Titel :

    Adapted human machine interaction concept for driver assistance systems DrivEasy


    Beteiligte:
    Boverie, S. (Autor:in) / Cour, M. (Autor:in) / Gall, J.Y. Le (Autor:in)


    Erscheinungsdatum :

    2011


    Format / Umfang :

    6 Seiten, 13 Bilder, 1 Tabelle, 5 Quellen




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Datenträger


    Sprache :

    Englisch






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