Suarez-Del Fueyo Rocio, Junge Mirko, Lopez-Valdes Francisco, Gabler Hampton Clay, Woerner Lukas, Hiermaier Stefan
Body Engineering Safety Management, Dr. Ing. h.c. F. Porsche AG, Weissach, Germany.
Volkswagen Group Innovation, Volkswagen AG, Wolfsburg, Germany.
Traffic Inj Prev. 2020 Oct 12;21(sup1):S78-S83. doi: 10.1080/15389588.2020.1862805. Epub 2021 Mar 10.
Crashworthiness assessments in the United States (U.S.) and the European Union (EU) include a large number of safety regulations and consumer testing programs. However, safety standards and testing procedures differ between the two regions. Not much research has been done in relation to this topic, because it has always been assumed that the accident environments in the U.S. and EU are not comparable. The objective of this study is to compare how vehicle occupants are severely injured in motor vehicle collisions in the U.S. and the EU by applying unsupervised learning to accident data.
A new methodology to identify clusters of seriously injured occupants in NASS-CDS was proposed by the authors in previous research. The current study goes one step further and uses the clusters to compare the injury patterns at the Maximum Abbreviated Injury Scale (MAIS) 3+ level of passenger vehicle occupants in the U.S. and German accident environments. The clustering model developed with NASS-CDS data is applied in this study to German In-Depth Accident Study (GIDAS) data. A machine learning algorithm automatically assigned each GIDAS case to its most similar NASS-CDS cluster controlling for nine different parameters. Those included the injury severity at the body region level, biomechanical characteristics of the occupants, and technical severity of the crash.
Differences and analogies between GIDAS and NASS-CDS data within clusters of seriously injured occupants are highlighted. One of the clusters groups the collisions with the greatest mass incompatibility in NASS-CDS and GIDAS data. The injury patterns in the clusters that include elderly people match significantly between the U.S. and German data sets. The lack of younger population and elevated body mass index (BMI) values in the GIDAS sample make the injury patterns within these population groups less comparable than in the other clusters.
Remarkably similar injury patterns at the MAIS 3+ level have been found in U.S. and German accident data sets after controlling for nine different parameters. This research provides evidence to indicate that how belted vehicle occupants are severely injured in the U.S. and in the EU is not necessarily different.
美国和欧盟的车辆碰撞安全性评估包含大量安全法规和消费者测试项目。然而,这两个地区的安全标准和测试程序有所不同。关于这一主题的研究不多,因为一直以来人们认为美国和欧盟的事故环境不可比。本研究的目的是通过对事故数据应用无监督学习,比较美国和欧盟机动车碰撞中车内人员受重伤的情况。
作者在之前的研究中提出了一种新方法,用于识别国家汽车抽样系统 - 碰撞数据系统(NASS - CDS)中受重伤乘员的集群。当前研究更进一步,利用这些集群比较美国和德国事故环境中乘用车乘员在最高简略损伤量表(MAIS)3级及以上水平的损伤模式。用NASS - CDS数据开发的聚类模型应用于本研究中的德国深度事故研究(GIDAS)数据。一种机器学习算法在控制九个不同参数的情况下,自动将每个GIDAS案例分配到与其最相似的NASS - CDS集群。这些参数包括身体区域层面的损伤严重程度、乘员的生物力学特征以及碰撞的技术严重程度。
突出显示了GIDAS和NASS - CDS数据在受重伤乘员集群中的差异和相似之处。其中一个集群将NASS - CDS和GIDAS数据中质量不相容性最大的碰撞归为一组。美国和德国数据集中包含老年人的集群中的损伤模式显著匹配。GIDAS样本中年轻人群体的缺乏以及体重指数(BMI)值的升高,使得这些人群组内的损伤模式与其他集群相比可比性较低。
在控制九个不同参数后,在美国和德国的事故数据集中发现了MAIS 3级及以上水平显著相似的损伤模式。本研究提供了证据表明,在美国和欧盟,系安全带的车内人员受重伤的方式不一定不同。