With automated vehicles (AV) development, interactions between these vehicles and pedestrians, especially in road crossing situations, are increasingly investigated. Most studies focused on the design and effectiveness of external human-machine interfaces, intended to compensate for the lack of implicit communication (eye contact, gestures) between pedestrians and vehicles. However, factors shaping pedestrian decisions like time pressure and the timing of pedestrian signal activation, influencing road crossing in front of conventional vehicles remain unexplored in the context of AVs, despite their critical importance. In this context the present study aimed to investigate the effects of time pressure and the timing of pedestrian signal activation when pedestrians cross in front of AVs.
Forty participants, equally divided between young adults (20-35 years) and older adults (70-85 years), completed immersive virtual reality street-crossing tasks in the presence of two AVs. A fully crossed factorial design manipulated: (i) time pressure with cognitive time pressure (high versus low urgency), and/or informational time pressure (short versus long waiting time); and (ii) signal-timing (standard versus early activation). Behavioral outcomes include gap acceptance, initiation delay, and red-light violations. Eye-tracking analyzes gaze allocation, such as fixation duration on AVs, signals, or road zones, and scan paths. Subjective ratings evaluate perceived safety, confidence, and urgency.
We expect time pressure to shorten initiation delays, increase small-gap acceptance, and raise red-light violations, particularly among young adults. Early signal activation is anticipated to foster premature crossings and misplaced trust in AV yielding. Findings will clarify how time pressure and signal timing shape pedestrian decision-making, attention, and trust in AV interactions. Beyond applied implications for traffic signal design, the study advances psychological understanding of risk regulation and cognitive adaptation in AVs interaction context.