This report summarizes work on Honeywell's Driver Adaptive Warning System project funded by a TRB-IDEA grant. The goal was to explore the application of learning algorithms to developing individualized models of a human driver's 'style'--and then using these models to tailor warnings to be more appropriate and useful to the individual. We call this approach a Driver-Adaptive Warning System (DAWS). Such tailored warnings hold promise for reducing the number of false alarms in collision warning and thus enhancing the acceptance and use of such systems by drivers.


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    Driver-Adaptive Warning System

    R. Goldman / C. Miller / S. Harp et al. | NTIS | 1995


    Engineered Visibility Warning Signals: An IDEA Project

    Cohn, T. E. | British Library Conference Proceedings | 1995



    Forward Collision Warning Requirements Project: Task 4 Final Report

    R. C. Curry / J. A. Greenberg / R. J. Kiefer | NTIS | 2005