McMurry Timothy L, Cormier Joseph M, Daniel Tom, Scanlon John M, Crandall Jeff R
University of Virginia and T.L. McMurry, LLC, Charlottesville, Virginia.
Biocore, LLC, Charlottesville, Virginia.
Traffic Inj Prev. 2021;22(sup1):S122-S127. doi: 10.1080/15389588.2021.1955108. Epub 2021 Aug 17.
Automated driving systems (ADS) are actively being deployed within the driving fleet. ADS are designed to safely navigate roadways, which entails an expectation of encountering varying degrees of potential conflict with other road users. The ADS design and evaluation process benefits from estimating injury severity probabilities for collisions that may occur. Current regression models in the literature are typically bespoke analyses involving targeted principal directions of force (PDOFs) and occupant positions. It is preferable to rely on injury severity models derived from a single source to provide a continuous function of risk for all planar collisions, while also accounting for specific vehicle and occupant characteristics. The novel feature of the proposed models is continuous, parametric injury risk surfaces that encompass the full spectrum of available United States field data. We used years 2001-2015 of the National Automotive Sampling System, Crashworthiness Data System (NASS-CDS) and years 2017-2019 of the Crash Investigation Sampling System (CISS) to estimate injury risk at the maximum abbreviated injury scale (MAIS) 3 and higher (3+) and 5 and higher (5+) levels for all adult occupants traveling in 2002 or newer passenger vehicles which were less than 10 years old at the time of the crash. The models account for occupant, vehicle, and crash characteristics. Interactions with vulnerable road users (e.g., pedestrian, bicyclist) were not considered. We present statistical models suitable to predict injury in all non-rollover crashes at the maximum MAIS3+ and 5+ levels, and show that these models can be comparable to similar single scenario (e.g., frontal) crash models. We discuss challenges with imputing missing field data, and discuss handling of covariates that may not be known at the time of the crash. Collision severity assessment is a vital component of the ADS design process. We developed a novel injury risk function that can assess occupant injury risks across the spectrum of foreseeable planar collisions. These models can provide insight on potential outcomes of counterfactual simulations, injury risk and crashworthiness considerations for human driven vehicles, and provide an evaluation tool that can be applied in ADS safety impact evaluation.
自动驾驶系统（ADS）正在积极部署到驾驶车队中。ADS旨在安全地在道路上行驶，这意味着预计会与其他道路使用者发生不同程度的潜在冲突。ADS的设计和评估过程受益于对可能发生的碰撞的伤害严重程度概率进行估计。文献中当前的回归模型通常是涉及目标力主方向（PDOF）和乘员位置的定制分析。最好依赖于从单一来源得出的伤害严重程度模型，以提供所有平面碰撞风险的连续函数，同时还要考虑特定的车辆和乘员特征。所提出模型的新颖之处在于连续的参数化伤害风险曲面，它涵盖了美国所有可用现场数据的全谱。我们使用了2001 - 2015年的国家汽车抽样系统、耐撞性数据系统（NASS - CDS）以及2017 - 2019年的碰撞调查抽样系统（CISS），来估计在2002年或更新的乘用车中行驶的所有成年乘员在碰撞时年龄小于10岁的情况下，在最大简略伤害量表（MAIS）3及以上（3 +）和5及以上（5 +）级别时的伤害风险。这些模型考虑了乘员、车辆和碰撞特征。未考虑与易受伤害道路使用者（如行人、骑自行车者）的相互作用。我们提出了适用于预测所有非翻滚碰撞中最大MAIS3 +和MAIS5 +级别伤害的统计模型，并表明这些模型可与类似的单一场景（如正面）碰撞模型相媲美。我们讨论了插补缺失现场数据的挑战，以及碰撞时可能未知的协变量的处理。碰撞严重程度评估是ADS设计过程的重要组成部分。我们开发了一种新颖的伤害风险函数，可评估可预见平面碰撞范围内的乘员伤害风险。这些模型可以提供关于反事实模拟潜在结果、人类驾驶车辆的伤害风险和耐撞性考虑的见解，并提供一种可应用于ADS安全影响评估的评估工具。
