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.


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    Titel :

    Driver-Adaptive Warning System


    Beteiligte:
    R. Goldman (Autor:in) / C. Miller (Autor:in) / S. Harp (Autor:in) / T. Plocher (Autor:in)

    Erscheinungsdatum :

    1995


    Format / Umfang :

    22 pages


    Medientyp :

    Report


    Format :

    Keine Angabe


    Sprache :

    Englisch





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