Abstract
The active behaviors of pedestrians, such as avoidance motions, affect the resultant injury risk in vehicle–pedestrian collisions. However, the biomechanical features of these behaviors remain unquantified, leading to a gap in the development of biofidelic research tools and tailored protection for pedestrians in real-world traffic scenarios. In this study, we prompted subjects (“pedestrians”) to exhibit natural avoidance behaviors in well-controlled near-real traffic conflict scenarios using a previously developed virtual reality (VR)-based experimental platform. We quantified the pedestrian–vehicle interaction processes in the pre-crash phase and extracted the pedestrian postures immediately before collision with the vehicle; these were termed the “pre-crash postures.” We recorded the kinetic and kinematic features of the pedestrian avoidance responses—including the relative locations of the vehicle and pedestrian, pedestrian movement velocity and acceleration, pedestrian posture parameters (joint positions and angles), and pedestrian muscle activation levels—using a motion capture system and physiological signal system. The velocities in the avoidance behaviors were significantly different from those in a normal gait (p < 0.01). Based on the extracted natural reaction features of the pedestrians, this study provides data to support the analysis of pedestrian injury risk, development of biofidelic human body models (HBM), and design of advanced on-vehicle active safety systems.
Highlights
Road traffic injuries remain a major global health issue, resulting in the loss of 310,000 pedestrian lives each year and representing 23% of all road traffic deaths (WHO, 2018)
The actions were classified into four categories based on the perception of the “bullet vehicle” and the relative motion vector: (1) backward avoidance (BA), (2) forward avoidance (FA), (3) oblique stepping (OS; startled response), and (4) walking/no avoidance reaction (NAR; not noticing the approaching vehicle)
This study identified pedestrian active avoidance behaviors and interaction processes with a vehicle in near-real traffic conflict scenarios using immersive virtual reality (VR) technology
Summary
Road traffic injuries remain a major global health issue, resulting in the loss of 310,000 pedestrian lives each year and representing 23% of all road traffic deaths (WHO, 2018). Pedestrian pre-impact posture significantly influences the risks and severity of injury outcomes (Li et al, 2015; Zou et al, 2020). Understanding the active avoidance behavior of pedestrians in dangerous traffic scenarios is necessary to develop advanced pedestrian safety systems that can reduce the risk of injury. The traditional survey method used to investigate accidents involves collecting detailed information (such as the vehicle trajectory and injury distribution) after a collision and reconstructing the scenario. It is not realistic to collect accurate information on pedestrian avoidance behavior using this method (Fredriksson et al, 2010). By watching and analyzing real-world accident video records, researchers have recently observed the emergency postures and kinematics of pedestrians in dangerous impact scenarios (Zou et al, 2020; Wu et al, 2021).
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