Tsoi Ada H, Gabler Hampton C
a Virginia Tech , Biomedical Engineering and Mechanics Department , Blacksburg , VA.
Traffic Inj Prev. 2015;16 Suppl 2:S132-9. doi: 10.1080/15389588.2015.1067693.
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.
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.
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 < 0.05). For the belted drivers, both OIV and VPI were significantly better predictors of serious injury than delta-v (p < 0.05). For the unbelted drivers, there was no statistically significant difference between delta-v, OIV, VPI, and ASI.
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.
车辆速度变化量（Δv）是一种广泛使用的碰撞严重程度指标，用于估计车内人员受伤风险。尽管其被广泛使用，但Δv存在若干局限性。最令人担忧的是，Δv是基于车辆的指标，未考虑碰撞脉冲或乘员约束系统（如安全带和安全气囊）的性能。此类批评促使人们寻求基于车辆运动学的替代碰撞严重程度指标。本研究的目的是评估乘员碰撞速度（OIV）、加速度严重程度指数（ASI）、车辆脉冲指数（VPI）和最大速度变化量（Δv）预测现实世界碰撞中严重伤害的能力。
该研究基于对从国家汽车抽样系统/碰撞耐撞性数据系统（NASS-CDS）2000 - 2013年案例中下载的事件数据记录器（EDR）的分析。样本中的所有车辆均为通用汽车乘用车和轻型卡车，涉及正面碰撞。翻滚碰撞被排除。车辆仅限于导致安全气囊展开的单事件碰撞。检查所有EDR数据，确保事件记录成功且完整，并且碰撞脉冲完整。使用最大简略损伤评分（MAIS）来描述车内人员的损伤结果。根据胸部、腹部和脊柱的任何损伤严重程度，将驾驶员分为非重伤组（MAIS2-）或重伤组（MAIS3+）。ASI和OIV根据安全硬件评估手册进行计算。VPI根据ISO/TR 12353-3进行计算，车辆特定参数从美国新车评估计划碰撞测试中确定。使用二元逻辑回归，确定每个指标的累积受伤风险概率，并评估其统计显著性、拟合优度和预测准确性。
数据集包括102,744辆车。Wald卡方检验表明，每个基于车辆的碰撞严重程度指标估计值在模型中都是显著预测因子（p < 0.05）。对于系安全带的驾驶员，OIV和VPI对严重伤害的预测均显著优于Δv（p < 0.05）。对于未系安全带的驾驶员，Δv、OIV、VPI和ASI之间无统计学显著差异。
本研究的广泛结果表明，如果我们考虑在经典指标（如Δv）中加入约束性能，改进伤害预测是可行的。诸如高级自动碰撞通知等应用应考虑针对系安全带和未系安全带的乘员使用不同指标。
