A Driver-Adaptive Warning System (DAWS) applies learning algorithms to develop individualized models of a human driver's 'style' and uses these models to tailor warnings to be more appropriate and useful for the individual. Alarms will be state-based, provided when the driver's current state indicates a potentially dangerous situation, or event-based, provided when a current event poses imminent danger to the driver. Of the methods tried, the best performance was obtained using a multi-layered perceptron neural network approach with three layers of nodes operating over five inputs. Such networks were 25% better at predicting the future lane following behaviors for the individual than for other drivers in the sample. As there are significant individual differences in driving style, a DAWS holds promise for increasing driver acceptance and use, and thus driver safety.


    Access

    Access via TIB

    Check availability in my library


    Export, share and cite



    Title :

    Driver-Adaptive Warning System


    Contributors:
    R. Goldman (author) / C. Miller (author) / S. Harp (author) / T. Plocher (author)

    Publication date :

    1995


    Size :

    22 pages


    Type of media :

    Report


    Type of material :

    No indication


    Language :

    English





    Driver adaptive warning systems : thesis proposal

    Batavia, Parag H. | TIBKAT | 1998


    DRIVER WARNING SYSTEM

    XU AOLIN / LI CHENRAN / SACHDEVA ENNA et al. | European Patent Office | 2025

    Free access

    DRIVER WARNING SYSTEM

    KATSUI SHUICHI / IKEDA TAKAYUKI | European Patent Office | 2020

    Free access

    DRIVER WARNING SYSTEM

    DANIEL HAIM | European Patent Office | 2020

    Free access