Research on drivers’ hazard perception and visual characteristics before vehicle-to-powered two-wheeler collisions

• A model was built to quantify drivers’ hazard perception using TTC and deceleration. • Drivers’ hazard perception varied significantly across four PTW collision scenarios. • Visual behavior was strongly linked to hazard perception and response effectiveness. • Fatigue and risk level influenced attention allocation and visual scanning strategies. • Findings support scenario-specific DMS and adaptive driver intervention strategies. Understanding drivers’ hazard perception levels and visual behavior in conflict scenarios is crucial for improving traffic safety and advancing intelligent driving systems, especially given the growing complexity of traffic conditions and the rapid evolution of intelligent driving technologies. This study examines typical near-collision scenarios involving vehicles and powered two-wheelers, focusing on the effects of collision scenarios, driving states, and risk conditions on drivers’ hazard perception and visual characteristics. Using quantile regression and generalized linear mixed models, the study quantitatively assesses how these factors influence hazard perception and visual behavior, uncovering the visual response mechanisms underlying hazard perception. The results reveal that different vehicle-to-powered two-wheeler collision scenarios significantly affect drivers’ hazard perception and visual behavior. Drivers exhibited higher hazard perception levels and collision avoidance success rates in “Crossing from Right” and “Cut-in from Right” scenarios, whereas lower hazard perception abilities were observed in “Crossing from Left” and “Oncoming” scenarios. Fatigue was shown to severely impair drivers’ alertness and visual search abilities, resulting in diminished hazard perception levels. Under high-risk conditions, while drivers exhibited reduced collision avoidance success rates, their heightened attention and vigilance toward powered two-wheeler enhanced hazard perception. Besides, the study also highlights a strong correlation between visual characteristics and drivers’ hazard perception. These findings are significant for understanding the mechanisms underlying drivers’ hazard perception in intersection scenarios and may provide a scientific basis for future developments in human–machine collaborative monitoring and intelligent traffic safety strategies.