Safety-to-trust dynamics: can automated vehicles navigate yellow-light dilemma zones from the driver's perspective?

At signalized intersections, the yellow-light Dilemma Zone (DZ) is the roadway segment within which, at the onset of yellow, drivers cannot clearly determine whether stopping before the stop line or proceeding through the intersection is the safer maneuver, thereby elevating the risk of rear-end and right-angle crashes. Existing research primarily relies on signal-based or Connected Vehicle strategies (e.g., broadcasting signal phase/countdown information to drivers) to mitigate DZ-related safety risks, but these approaches often compromise intersection efficiency or impose additional cognitive demand on drivers. Recent advances in automated driving suggest a potential alternative, yet a clear gap remains: existing research has not conclusively quantified whether automated driving improves safety in DZ scenarios, particularly across Levels 3 and 4 automation, nor has it clarified whether such safety improvements transform into greater drivers' trust during the transitional period in which human supervision and possible intervention are still required. To address this gap, this study develops a safety-to-trust framework to examine whether higher levels of automation improve objective safety in yellow-light DZ scenarios and whether these safety improvements constitute the mechanism through which automation shapes drivers' trust. A driving simulator study was conducted by recruiting 52 participants to drive through a signalized intersection at the onset of yellow under Levels 0, 3, and 4, respectively. Driving behavior and its related safety performance were collected and analyzed. A linear mixed-effects model and a logistic regression model were applied to evaluate the effects of automation level on minimum time-to-collision (TTC) and traffic conflict, respectively, thereby assessing the safety benefits of different automation levels. Structural equation modeling (SEM) was further used to examine whether safety performance mediated the relationship between automation level and drivers' trust. Results suggest that, under DZ conditions, higher automation levels significantly improve safety. SEM further reveals that increased automation improves safety, which in turn elevates drivers' trust in automated driving, highlighting safety as the key mediating linkage between automation level and trust. Together, these findings quantify the safety benefits of automated driving systems in yellow-light DZ and clarify how those benefits shape trust, thereby providing an integrated basis for informing Automated Vehicle (AV) deployment and human-machine interface strategies at urban signalized intersections.