Modeling vehicle-cyclists' interactions to support automated driving and advanced driving assistance systems

Artikel i vetenskaplig tidskrift, 2026

Cycling has gained increasing popularity across Europe, yet the frequency and severity of cyclist-vehicle conflicts at unsignalized intersections remain key road-safety concerns. This study investigates the interaction between drivers and cyclists in such settings, focusing on the role of intersection visibility (IV), difference in time to arrival (DTA) of the car and bicycle, and drivers' gaze behavior in shaping yielding decisions, braking patterns, and speed profiles. Using a driving simulator equipped with eye-tracking technology, participants completed multiple drives through the digital twin of a real-world intersection. The IV was systematically varied by repositioning a parked truck, while the DTA was controlled by triggering the virtual cyclist's approach at different temporal offsets relative to the car's arrival.
Mixed-effects Bayesian regression models revealed that both IV and DTA significantly influenced the drivers' likelihood of yielding: higher visibility and a shorter time difference between vehicle and cyclist arrivals consistently increased yielding rates. Gaze behavior also emerged as a critical factor; earlier fixation on the crossing cyclist strongly correlated with the likelihood of deciding to yield. In contrast, no single predictor significantly explained the distance at which drivers initiated braking. Speed-profile analyses further underscored the finding that drivers' deceleration strategies are shaped by visibility constraints and perceived temporal pressure from oncoming cyclists.
These findings highlight the importance of visibility, temporal cues, and visual attention metrics in intersection designs and advanced driver assistance systems. Safety technologies and automated features can more accurately anticipate driver-cyclist interactions when gaze behavior is integrated into their predictive models. Future work should confirm these insights through on-road studies, as well as exploring additional intersection layouts and environmental conditions to obtain more data that can lead to enhance both infrastructure design and automated vehicle algorithms.