Abstract：
OBJECTIVE: 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.
METHODS: The study queried the GIDAS database for cases from 1999 to 2021 involving passenger vehicle or heavy vehicle collisions with pedestrians.
METHODS: 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.
RESULTS: 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.
CONCLUSIONS: 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.
METHODS: The study queried the GIDAS database for cases from 1999 to 2021 involving passenger vehicle or heavy vehicle collisions with pedestrians.
METHODS: 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.
RESULTS: 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.
CONCLUSIONS: 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.
摘要：
目标：自动驾驶系统（ADS）车队目前正在美国的几个密集城市运营设计领域进行部署。在这些密集的城市地区，行人在历史上占了很大一部分，有时大多数人，伤害和致命的碰撞。对涉及行人和人工驾驶车辆的碰撞事件中的伤害风险的深入了解可以为持续的ADS开发和安全效益评估提供信息。目前没有对美国行人碰撞进行系统的调查，因此，这项研究使用德国深度事故研究（GIDAS）的重建数据来开发与车辆碰撞的行人的机械伤害风险模型。
方法：研究在GIDAS数据库中查询了1999年至2021年涉及乘用车或重型车辆与行人碰撞的案例。
方法：我们描述了乘用车对行人和重型车辆对行人碰撞的伤害模式和频率，重型车辆包括重型卡车和公共汽车。损伤风险函数是在AIS2+开发的，3+,涉及与乘用车正面碰撞的行人的4级和5级，以及与重型车辆正面碰撞的行人的4级和5级。模型预测因子包括碰撞速度的机械因素，行人年龄,性别,行人高度相对于车辆保险杠高度,和碰撞前的车辆加速。包括儿童（≤17岁）和老年人（≥65岁）行人。我们进一步进行了加权和估算分析，以了解缺失数据元素以及权重对德国行人撞车总数的影响。
结果：我们确定了3,112名与乘用车发生碰撞的行人，其中2,524次碰撞是正面车辆撞击。此外，我们确定了154名与重型车辆相撞的行人，其中87起确认的碰撞是正面车辆撞击。与年轻人相比，儿童受到伤害的风险更高，严重伤害的风险最高（AIS3+）存在于数据集中最老的行人。与重型车辆的碰撞比与乘用车的碰撞更可能在低速下产生严重（AIS3）伤害。与乘用车和重型车辆的碰撞之间的伤害机制不同。最初的参与导致36％的行人在乘用车碰撞中受到最严重的伤害，与重型车辆碰撞的23%相比。相反，车辆底部在乘用车碰撞中造成了6%的最严重伤害，在重型车辆碰撞中造成了20%的伤害。
结论：自2009年以来，美国行人死亡人数已上升了59％。我们必须了解和描述伤害风险，以便我们能够针对减少伤害和死亡的有效策略。这项研究建立在以前的分析基础上，包括最现代的车辆，包括儿童和老年行人，结合额外的机械预测因子，扩大包括在内的撞车事故的范围，并使用多重估算和加权来更好地估计相对于德国行人碰撞的整个人口的这些影响。这项研究是首次根据现场数据调查与重型车辆碰撞对行人造成伤害的风险。
方法：研究在GIDAS数据库中查询了1999年至2021年涉及乘用车或重型车辆与行人碰撞的案例。
方法：我们描述了乘用车对行人和重型车辆对行人碰撞的伤害模式和频率，重型车辆包括重型卡车和公共汽车。损伤风险函数是在AIS2+开发的，3+,涉及与乘用车正面碰撞的行人的4级和5级，以及与重型车辆正面碰撞的行人的4级和5级。模型预测因子包括碰撞速度的机械因素，行人年龄,性别,行人高度相对于车辆保险杠高度,和碰撞前的车辆加速。包括儿童（≤17岁）和老年人（≥65岁）行人。我们进一步进行了加权和估算分析，以了解缺失数据元素以及权重对德国行人撞车总数的影响。
结果：我们确定了3,112名与乘用车发生碰撞的行人，其中2,524次碰撞是正面车辆撞击。此外，我们确定了154名与重型车辆相撞的行人，其中87起确认的碰撞是正面车辆撞击。与年轻人相比，儿童受到伤害的风险更高，严重伤害的风险最高（AIS3+）存在于数据集中最老的行人。与重型车辆的碰撞比与乘用车的碰撞更可能在低速下产生严重（AIS3）伤害。与乘用车和重型车辆的碰撞之间的伤害机制不同。最初的参与导致36％的行人在乘用车碰撞中受到最严重的伤害，与重型车辆碰撞的23%相比。相反，车辆底部在乘用车碰撞中造成了6%的最严重伤害，在重型车辆碰撞中造成了20%的伤害。
结论：自2009年以来，美国行人死亡人数已上升了59％。我们必须了解和描述伤害风险，以便我们能够针对减少伤害和死亡的有效策略。这项研究建立在以前的分析基础上，包括最现代的车辆，包括儿童和老年行人，结合额外的机械预测因子，扩大包括在内的撞车事故的范围，并使用多重估算和加权来更好地估计相对于德国行人碰撞的整个人口的这些影响。这项研究是首次根据现场数据调查与重型车辆碰撞对行人造成伤害的风险。
