We show how an in-vehicle algorithm that alerts drivers to pedestrians can be defined using an empirical approach that quantifies the relative level with which drivers are likely to accept alerts to pedestrians. The approach was used in two studies to investigate a range of contextual factors known to influence driver ratings of alerts for pedestrians issued by a driver-assistance system. Regression analysis shows that four factors consisting of combinations of pedestrian location and motion relative to the road ahead of the vehicle explain over 80% of the variability in drivers' ratings of alerts. This finding suggests that four contextual factors largely define the perceptual cues that drivers use to rate alerts to pedestrians. The work demonstrates the utility of subjective driver responses to FOT events as a tool to inform the development of pedestrian alerting criteria.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Modeling Ratings of In-vehicle Alerts to Pedestrian Encounters in Naturalistic Situations


    Contributors:


    Publication date :

    2015-09-01


    Size :

    187255 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Pedestrian alerts for mobile devices

    SINGH PARMJEET / MUMMIDI LAKSHMI NARAYANA | European Patent Office | 2018

    Free access

    Pedestrian alerts for mobile devices

    SINGH PARMJEET / MUMMIDI LAKSHMI NARAYANA | European Patent Office | 2018

    Free access

    Cluster Naturalistic Driving Encounters Using Deep Unsupervised Learning

    Li, Sisi / Wang, Wenshuo / Mo, Zhaobin et al. | IEEE | 2018


    Interdependence in Vehicle-Pedestrian Encounters and its Implications for Vehicle Automation

    Domeyer, Joshua E. / Lee, John D. / Toyoda, Heishiro et al. | IEEE | 2022


    Augmented reality multi-view video scheduling under vehicle-pedestrian situations

    Wu, Jiulong / Zhao, Quanxin / Yang, Ning et al. | IEEE | 2015