Inherent uncertainty in pedestrian collision reconstruction: How evidence variability affects head kinematics and injury prediction
研究行人-汽车碰撞重建中输入参数的不确定性对头部运动学指标的影响，发现头部-车辆撞击阶段的运动学变异性较小，且平均运动学指标能正确预测所有行人的硬膜下血肿和颅骨穹窿骨折。
• Mean contact force from the head-vehicle interaction predicted skull vault fracture presence at different locations. • Mean peak rotational acceleration from the head-vehicle interaction predicted subdural haemorrhage presence. • A greater range of risk curves are needed to assess risk of different types of intracranial haemorrhage. • Across the study simulations, the ground impact produced greater variation in head kinematics than the vehicle impact. • Varying ground landing kinematics could be grouped by mechanisms that produced different head contact forces. • Inherent uncertainty in collision data should be accounted for in reconstructions and injury risk curve creation and use. Reconstructing individual cases from real-world collision data is used as a tool to better understand injury biomechanics and determine injury thresholds. However, real-world data tends to have inherent uncertainty within parameters, such as ranges of impact speed, pre-impact pedestrian stance or pedestrian anthropometric characteristics. The implications of this input parameter uncertainty on the conclusions made from case reconstruction about injury biomechanics and risk is not well investigated, with a ‘best-fit’ approach more frequently adopted, leaving uncertainty unexplored. This study explores the implications of uncertain parameters in real-world data on the biomechanical kinematic metrics related to head injury risk in reconstructed real-world pedestrian-car collisions. We selected six pedestrian-car cases involving seven pedestrians from the highly detailed GB Road Accident In-Depth Studies (RAIDS) database. The collisions were reconstructed from the images, damage measurements and dynamics available in RAIDS. For each case, we varied input parameters within uncertain ranges and report the range of head kinematic metrics from each case. This includes variations of reconstructed collision scenarios that fits within the constraints of the available evidence. We used a combination of multibody and finite element modelling in Madymo to test whether the effect of input data uncertainty is the same on the initial head-vehicle and latter head-ground impact phase. Finally, we assessed whether the predicted range of head kinematics correctly predicted the injuries sustained by the pedestrian. Varying the inputs resulted in a range of output head kinematic parameters. Real-world evidence such as CCTV footage enabled predicted simulated values to be further constrained, by ruling out unrealistic scenarios which do not fit the available evidence. We found that input data uncertainty had different implications for the initial head-vehicle and latter head-ground impact phase. There was a narrower distribution of kinematics associated with the head-vehicle impact (initial 400 ms of the collision) than in the latter head-ground impact. The mean head-vehicle kinematics were able to correctly predict the presence or absence of both subdural haematoma (using peak rotational acceleration) and skull vault fracture (using peak contact force) in all pedestrians presented. This study helps increase our understanding of the effects of uncertain parameters on head kinematics in pedestrian-car collision reconstructions. Extending this work to a broad range of pedestrian-vehicle collision reconstructions spanning broad population demographics will improve our understanding of injury mechanisms and risk, leading to more robust design of injury prevention measures.