Center for Applied Biomechanics, Department of Mechanical and Aerospace Engineering, University of Virginia, Charlottesville, VA, USA.
Biocore LLC, Charlottesville, VA, USA.
Comput Methods Biomech Biomed Engin. 2021 Mar;24(4):384-399. doi: 10.1080/10255842.2020.1830380. Epub 2020 Oct 14.
The use of standardized anthropomorphic test devices and test conditions prevent current vehicle development and safety assessments from capturing the breadth of variability inherent in real-world occupant responses. This study introduces a methodology that overcomes these limitations by enabling the assessment of occupant response while accounting for sources of human- and non-human-related variability. Although the methodology is generic in nature, this study explores the methodology in its application to human response in far-side motor vehicle crashes as an example. A total of 405 human body model simulations were conducted in a mid-sized sedan vehicle environment to iteratively train two neural networks to predict occupant head excursion and thoracic injury as a function of occupant anthropometry, impact direction and restraint configuration. The neural networks were utilized in Monte Carlo simulations to calculate the probability of head-to-intruding-door impacts and thoracic AIS 3+ as a function of the restraint configuration. This analysis indicated that the vehicle used in this study would lead to a range of 667 to 2,448 head-to-intruding-door impacts and a range of 3,041 to 3,857 cases of thoracic AIS 3+ in the real world, depending on the seatbelt load limiter. These real-world results were later successfully validated using United States field data. This far-side assessment illustrates how the methodology incorporates the human and non-human variability, generates response surfaces that characterize the effects of the variability, and ultimately permits vehicle design considerations and injury predictions appropriate for real-world field conditions.
使用标准化的拟人测试设备和测试条件，防止当前的车辆开发和安全评估捕捉到真实世界中乘员反应固有的广泛变化。本研究介绍了一种方法，通过在考虑人与非人相关变化源的情况下评估乘员的反应，克服了这些限制。尽管该方法具有通用性，但本研究探讨了将其应用于远侧汽车碰撞中人体反应的方法，作为一个示例。在中型轿车环境中进行了总共 405 个人体模型模拟，以迭代训练两个神经网络，以预测乘员头部位移和胸部损伤作为乘员人体测量、碰撞方向和约束配置的函数。神经网络用于蒙特卡罗模拟，以计算头部与侵入式车门碰撞的概率和胸部 AIS 3+作为约束配置的函数。该分析表明，在现实世界中，根据安全带限力器，本研究中使用的车辆将导致 667 至 2448 次头部与侵入式车门碰撞，以及 3041 至 3857 例胸部 AIS 3+的范围。这些现实世界的结果后来使用美国现场数据成功验证。这种远侧评估说明了该方法如何纳入人与非人变化，生成描述变化影响的响应曲面，并最终允许为现实世界的现场条件进行车辆设计考虑和损伤预测。
