Schubert Angela, Babisch Stefan, Scanlon John M, Campolettano Eamon T, Roessler Robby, Unger Thomas, McMurry Timothy L
VUFO - Verkehrsunfallforschung an der TU Dresden GmbH, Dresden, Germany.
VUFO - Verkehrsunfallforschung an der TU Dresden GmbH, Dresden, Germany.
Accid Anal Prev. 2023 Sep;190:107139. doi: 10.1016/j.aap.2023.107139. Epub 2023 Jun 13.
Automated Driving System (ADS) fleets are currently being deployed in several dense-urban operational design domains within the United States. In these dense-urban areas, pedestrians have historically comprised a significant portion, and sometimes the majority, of injury and fatal collisions. An expanded understanding of the injury risk in collision events involving pedestrians and human-driven vehicles can inform continued ADS development and safety benefits evaluation. There is no current systematic investigation of United States pedestrian collisions, so this study used reconstruction data from the German In-Depth Accident Study (GIDAS) to develop mechanistic injury risk models for pedestrians involved in collisions with vehicles.
The study queried the GIDAS database for cases from 1999 to 2021 involving passenger vehicle or heavy vehicle collisions with pedestrians.
We describe the injury patterns and frequencies for passenger vehicle-to-pedestrian and heavy vehicle-to-pedestrian collisions, where heavy vehicles included heavy trucks and buses. Injury risk functions were developed at the AIS2+, 3+, 4+ and 5+ levels for pedestrians involved in frontal collisions with passenger vehicles and separately for frontal collisions with heavy vehicles. Model predictors included mechanistic factors of collision speed, pedestrian age, sex, pedestrian height relative to vehicle bumper height, and vehicle acceleration before impact. Children (≤17 y.o.) and elderly (≥65 y.o.) pedestrians were included. We further conducted weighted and imputed analyses to understand the effects of missing data elements and of weighting towards the overall population of German pedestrian crashes.
We identified 3,112 pedestrians involved in collisions with passenger vehicles, where 2,524 of those collisions were frontal vehicle strikes. Furthermore, we determined 154 pedestrians involved in collisions with heavy vehicles, where 87 of those identified collisions were frontal vehicle strikes. Children were found to be at higher risk of injury compared to young adults, and the highest risk of serious injuries (AIS 3+) existed for the oldest pedestrians in the dataset. Collisions with heavy vehicles were more likely to produce serious (AIS 3+) injuries at low speeds than collisions with passenger vehicles. Injury mechanisms differed between collisions with passenger vehicles and with heavy vehicles. The initial engagement caused 36% of pedestrians' most-severe injuries in passenger vehicle collisions, compared with 23% in heavy vehicles collisions. Conversely, the vehicle underside caused 6% of the most-severe injuries in passenger vehicle collisions and 20% in heavy vehicles collisions.
U.S. pedestrian fatalities have risen 59% since their recent recorded low in 2009. It is imperative that we understand and describe injury risk so that we can target effective strategies for injury and fatality reduction. This study builds on previous analyses by including the most modern vehicles, including children and elderly pedestrians, incorporating additional mechanistic predictors, broadening the scope of included crashes, and using multiple imputation and weighting to better estimate these effects relative to the entire population of German pedestrian collisions. This study is the first to investigate the risk of injury to pedestrians in collisions with heavy vehicles based on field data.
目前,自动驾驶系统(ADS)车队正在美国的几个密集城市运营设计领域中部署。在这些密集城市地区,行人历来在受伤和致命碰撞事故中占很大比例,有时甚至占多数。对涉及行人和有人驾驶车辆的碰撞事件中的伤害风险有更深入的了解,可以为ADS的持续发展和安全效益评估提供参考。目前尚未对美国行人碰撞事故进行系统调查,因此本研究使用来自德国深度事故研究(GIDAS)的重建数据,为与车辆碰撞的行人建立机械伤害风险模型。
该研究查询了GIDAS数据库中1999年至2021年涉及乘用车或重型车辆与行人碰撞的案例。
我们描述了乘用车与行人以及重型车辆与行人碰撞的伤害模式和频率,其中重型车辆包括重型卡车和公共汽车。针对与乘用车正面碰撞的行人以及与重型车辆正面碰撞的行人,分别在AIS2+、3+、4+和5+级别建立了伤害风险函数。模型预测因素包括碰撞速度、行人年龄、性别、行人相对于车辆保险杠高度的身高以及碰撞前车辆加速度等机械因素。纳入了儿童(≤17岁)和老年人(≥65岁)行人。我们进一步进行了加权和插补分析,以了解缺失数据元素的影响以及对德国行人碰撞事故总体人群加权的影响。
我们确定了3112名与乘用车碰撞的行人,其中2524起碰撞是车辆正面撞击。此外,我们确定了154名与重型车辆碰撞的行人,其中87起已识别的碰撞是车辆正面撞击。结果发现,儿童比年轻人受伤风险更高,数据集中年龄最大的行人遭受重伤(AIS 3+)的风险最高。与重型车辆碰撞在低速时比与乘用车碰撞更有可能导致重伤(AIS 3+)。与乘用车碰撞和与重型车辆碰撞的伤害机制有所不同。在乘用车碰撞中,初始接触导致36%的行人最严重伤害,而在重型车辆碰撞中为23%。相反,车辆底部在乘用车碰撞中导致6%的最严重伤害,在重型车辆碰撞中为20%。
自2009年最近一次有记录的低点以来,美国行人死亡人数上升了59%。我们必须了解和描述伤害风险,以便能够制定有效的减少伤害和死亡的策略。本研究在以往分析的基础上,纳入了最现代的车辆,包括儿童和老年行人,纳入了更多的机械预测因素,扩大了所包括碰撞事故的范围,并使用多重插补和加权来更好地估计相对于德国行人碰撞事故总体人群的这些影响。本研究首次基于现场数据调查了与重型车辆碰撞的行人的伤害风险。