Collision dynamic characteristic and injury prediction for drivers of electric self-balancing scooter considering different driving postures

The widespread adoption of electric self-balancing scooters (ESS) has led to frequent traffic accidents. Existing research primarily focuses on collision injury analysis under single driving postures, yet suffers from inaccuracies in reconstructing real-world driving postures and insufficient exploration of the intrinsic relationship between human posture and injury. This limits the ability to support targeted safety protection design and risk assessment. In this study, we conducted volunteer experiments to extract the physical parameters of four different driving postures: driving posture at constant speed (Dpacs), driving posture at shifting speeds (Dpass), driving posture for competition (Dpfc), and driving posture for emergency evasive maneuvers (Dpfeem), and then built and validated a vehicle-ESS-pedestrian collision model based on a typical traffic accident scenario. The collected physical parameters were input into three dummies (5th, 50th, and 95th pedestrian dummies), and a total of 360 simulations (including 30 volunteer experiments, three dummies, and four driving postures)were conducted. Finally, a head injury prediction model based on the decision tree model (DTM) was established. The results show that the Dpfc and Dpfeem driving postures significantly reduce the severity of head injuries for other normal driving postures in the studied crash scenarios. Based on the DTM, hip flexion angle had a significant effect on reducing head injury severity in ESS drivers. This study provides a possible real-time risk assessment framework for future vehicles in autonomous driving scenarios. In addition, the research process, combined with volunteer experiments, provides new ideas for vehicle form design considering VRU safety.