Abstract:

A monocular pose-estimation-based method for evaluating pedestrian collision injuries in front of a vehicle was proposed to enable quantitative injury assessment of pedestrian emergency postures in traffic accidents. Posture parameters were extracted from single-view accident images using a pose estimation algorithm, and a skinned multi-person linear (SMPL) model was mapped to a multi-body model through joint matching to achieve rapid reconstruction of emergency postures. Subsequently, collision simulations of femur injuries were conducted using MADYMO under four typical postures, including standing, leaning forward, squatting, and evasive running. The results demonstrate that the proposed method achieves simulation accuracy comparable to that of manual modeling, with the mean differences in AIS2+ and AIS3+ injury-risk probabilities across the four postures as low as -3.5% and -1.22%, respectively, indicating that the proposed method improves the automation and reproducibility of posture modeling, and can provide quantitative references for vehicle structural optimization and pedestrian protection design.

Key words: emergency pose, pose estimation, skinned multi-person linear (SMPL) model, multi-body model, injury assessment