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

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.