Examining Speech-Based Auditory Alerts for Intersection Collision Warning Systems using a Driving Simulator

Abstract

Collision warning systems alert drivers of potential safety hazards. Forward collision warning (FCW) systems have been widely implemented and studied. However, intersection collision warning systems (ICWS), such as intersection movement assist (IMA), are more complex. Additional studies are needed to identify the best alert for directing the driver toward the hazard. A driving simulator study with 48 participants was conducted to examine three speech-based auditory alerts (general, directional, and command) in a simulated red light running (RLR) collision scenario. The command alert that informed the drivers to brake was the most effective in reducing the number of collisions. The post-drive questionnaire showed that drivers also rated the brake alert to be best in terms of interpretation (based on the Kruskal Wallis test). This study provides insight into the performance of different types of speech-based alerts for an intersection collision warning system and can provide guidance for future studies.