Driver-related factors (e.g., driver inattention) are a cause of majority of traffic accidents. To reduce the number of accidents and improve traffic safety a variety of driver assistance systems have been proposed. Today, many of these systems do not adapt recommendations and warning to the particular driver (having his-/her own driving style, reaction time etc.). However, in many cases utilization of personal characteristics and preferences may improve the quality of the driver assistance., besides if a driver's expectations about the functionality provided by the assistance system are not met, it may decrease the trust to the system and lead to turning it off, therefore ignoring its potential utility and influence on increasing the safety. In this paper we review scientific publications in the area of driver assistance systems and a) identify most widely used directions of personalization and adaptation in driver assistance systems, b) identify and describe the most widely used models and methods leveraged for personalization and adaptation, c) identify existing research gaps. The paper may serve as mapping study as well as a reference and a toolset of how to deal with driver variability in driver assistance systems.


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    Titel :

    Adaptation and Personalization in Driver Assistance Systems


    Beteiligte:


    Erscheinungsdatum :

    01.04.2019


    Format / Umfang :

    286326 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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