How pedestrians react to imminent vehicle threats: a virtual reality study in safety-critical scenarios

As vulnerable road users, pedestrians face a high collision risk in safety-critical traffic scenarios. When faced with approaching vehicles, pedestrians need to balance their desire to cross the road with the demand for safety, resulting in either normal walking or avoidance behaviors. Such uncertain decision affects the occurrence of collisions. Therefore, it is important to understand pedestrian decisions so that highly automated vehicles (HAVs) can better develop safe interaction strategies. To study these decisions under controlled conditions, we designed an immersive virtual reality (VR) experiment where participants encountered traffic scenarios with different spatiotemporal pressures. The experiment observed three distinct decision modes under increasing spatiotemporal pressures: a no-risk mode in which pedestrians predominantly cross normally, a game mode showing a mix of crossing and avoidance, and a life-saving mode dominated by avoidance responses. Based on these decision modes, we proposed a regression model to predict pedestrian decisions. The model achieved an average precision of 0.87 and 0.82 for predicting whether and how pedestrian avoid. Finally, real pedestrian-vehicle conflict data were used to validate the effectiveness of the experimental results. This investigation observes pedestrians' decision modes in various urgent scenarios and presents a decision model based on pedestrians' decision modes, reflecting their interaction logic with hazardous vehicles in traffic scenarios. We expect that the observed decision modes can help develop the safety algorithms of HAVs to achieve safe interactions with pedestrians on roads.