Effectiveness of connected-vehicle-based curve speed warning systems with considerations of heads-up display language type’s effects on driver behavior

Abstract

Treatments regarding Curve Warning Systems (CWS) were proposed to assist drivers with safer performances driving through horizontal curves. These treatments include curve warning signs and Connected Vehicle (CV) technology. However, there lacks an investigation of CWS-impacted speed compliance from the mechanism perspective, quantitatively linking the real-time speed compliance to human factors, curve geometric, visibility, pavement conditions, and particularly, the language types of in-vehicle visual display messages. Hence, this research aims to identify factors contributing to the effectiveness of CV-based CWS by focusing on these systems’ influences in real-time speed. Three CWS are chosen, namely Level 0 (curve sign-only), Level 1 (one-time curve warning before entering the curve), and Level 2 (guidance-oriented, adaptive to vehicle speed, location, and pavement friction). Three CWS are implemented in a driving simulator. Thirty participants were recruited to drive through a series of curves under CWS with different geometric, illumination, and pavement conditions. Real-time speed compliance was collected in evaluating the CWS’ impacts on speed control. Results show that gender, curve radius, illumination, and pavement conditions significantly impact the effectiveness of CWS. All findings reveal that guidance-oriented and adaptive CWS (Level 2) significantly guides drivers with better speed compliance performances.