Evaluation of Vehicle-Based Crash Severity Metrics

Objective: Vehicle change in velocity (delta-v) is a widely used crash severity metric used to estimate occupant injury risk. Despite its widespread use, delta-v has several limitations. Of most concern, delta-v is a vehicle-based metric which does not consider the crash pulse or the performance of occupant restraints, e.g. seatbelts and airbags. Such criticisms have prompted the search for alternative impact severity metrics based upon vehicle kinematics. The purpose of this study was to assess the ability of the occupant impact velocity (OIV), acceleration severity index (ASI), vehicle pulse index (VPI), and maximum delta-v (delta-v) to predict serious injury in real world crashes.Methods: The study was based on the analysis of event data recorders (EDRs) downloaded from the National Automotive Sampling System / Crashworthiness Data System (NASS-CDS) 2000–2013 cases. All vehicles in the sample were GM passenger cars and light trucks involved in a frontal collision. Rollover crashes were excluded. Vehicles were restricted to single-event crashes that caused an airbag deployment. All EDR data were checked for a successful, completed recording of the event and that the crash pulse was complete. The maximum abbreviated injury scale (MAIS) was used to describe occupant injury outcome. Drivers were categorized into either non-seriously injured group (MAIS2−) or seriously injured group (MAIS3+), based on the severity of any injuries to the thorax, abdomen, and spine. ASI and OIV were calculated according to the Manual for Assessing Safety Hardware. VPI was calculated according to ISO/TR 12353-3, with vehicle-specific parameters determined from U.S. New Car Assessment Program crash tests. Using binary logistic regression, the cumulative probability of injury risk was determined for each metric and assessed for statistical significance, goodness-of-fit, and prediction accuracy.Results: The dataset included 102,744 vehicles. A Wald chi-square test showed each vehicle-based crash severity metric estimate to be a significant predictor in the model (p Conclusions: The broad findings of this study suggest it is feasible to improve injury prediction if we consider adding restraint performance to classic measures, e.g. delta-v. Applications, such as advanced automatic crash notification, should consider the use of different metrics for belted versus unbelted occupants.一、研究目标：车辆速度变化量（delta-v）是当前用于评估乘员损伤风险的主流碰撞严重度指标。尽管该指标应用广泛，但delta-v存在诸多局限。最受诟病的是，delta-v属于基于车辆的指标，未考虑碰撞脉冲（crash pulse）以及乘员约束系统（如安全带与安全气囊）的工作性能。此类缺陷促使学界探索基于车辆运动学的替代碰撞严重度指标。本研究旨在评估乘员碰撞速度（occupant impact velocity, OIV）、加速度严重度指数（acceleration severity index, ASI）、车辆脉冲指数（vehicle pulse index, VPI）以及最大delta-v（delta-v）在真实世界碰撞场景中预测严重损伤的能力。 二、研究方法：本研究基于对2000至2013年美国国家汽车采样系统/碰撞耐撞性数据系统（National Automotive Sampling System / Crashworthiness Data System, NASS-CDS）中下载的事件数据记录仪（event data recorder, EDR）数据的分析。样本中的所有车辆均为通用汽车（General Motors, GM）生产的乘用车与轻型卡车，且均涉及正面碰撞；翻覆碰撞案例被排除在外。研究仅纳入触发安全气囊展开的单次碰撞事故车辆。所有EDR数据均经过校验，确保事故记录完整且碰撞脉冲数据无缺失。本研究采用最高简略损伤量表（maximum abbreviated injury scale, MAIS）对乘员损伤结果进行分级。研究人员根据乘员胸部、腹部与脊柱的损伤严重程度，将驾驶员分为非严重损伤组（MAIS2−）与严重损伤组（MAIS3+）。ASI与OIV的计算依据《安全硬件评估手册》（Manual for Assessing Safety Hardware）完成。VPI的计算依据ISO/TR 12353-3标准，车辆专属参数通过美国新车评价规程（U.S. New Car Assessment Program）碰撞测试数据获取。本研究采用二元逻辑回归分析，计算各指标对应的损伤风险累积概率，并对其统计显著性、拟合优度与预测精度进行评估。 三、研究结果：本数据集共纳入102744台车辆。Wald卡方检验显示，所有基于车辆的碰撞严重度指标均为模型中的显著预测因子（p 四、研究结论：本研究的整体结果表明，若在经典碰撞严重度指标（如delta-v）中纳入约束系统性能参数，可有效提升损伤预测效果。诸如高级自动碰撞通知系统等应用场景，应考虑为佩戴安全带与未佩戴安全带的乘员采用差异化的指标体系。