Human-Centered Design for Connected Automation: Predicting Pedestrian Crossing Intentions

More than half of the 1.19 million annual traffic fatalities globally involve vulnerable road users, such as pedestrians, with a significant proportion attributable to human error. Level-5 automated driving systems (ADSs) have the potential to reduce these incidents; However, their effectiveness depends not only on automation performance but also on their ability to communicate intent and coordinate safely with pedestrians in the absence of traditional driver cues. This study aims to model pedestrian decision-making in road-crossing scenarios involving level-5 ADSs by extending the Theory of Planned Behavior (TPB) with safety, trust, compatibility, and understanding. An online survey (n = 212) found that perceived behavioral control, attitude, and social information significantly influence pedestrians’ crossing intentions, with perceived safety and understanding exerting their influence primarily through attitude and perceived behavioral control. The results offer guidance for designing eHMIs and cooperative V2X communication strategies that promote safe pedestrian-ADS interactions and advance human-centered design for autonomous vehicles.