Stigson Helena, Kullgren Anders, Rosén Erik
Folksam Research, Stockholm, Sweden, Division of Insurance Medicine, Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden Folksam Research, Stockholm, Sweden, Department of Applied Mechanics, Chalmers University of Technology, Göteborg, Sweden. Autoliv Research, Vårgårda, Sweden.
Ann Adv Automot Med. 2012;56:267-76.
Knowledge of how crash severity influences injury risk in car crashes is essential in order to create a safe road transport system. Analyses of real-world crashes increase the ability to obtain such knowledge.The aim of this study was to present injury risk functions based on real-world frontal crashes where crash severity was measured with on-board crash pulse recorders.Results from 489 frontal car crashes (26 models of four car makes) with recorded acceleration-time history were analysed. Injury risk functions for restrained front seat occupants were generated for maximum AIS value of two or greater (MAIS2+) using multiple logistic regression. Analytical as well as empirical injury risk was plotted for several crash severity parameters; change of velocity, mean acceleration and peak acceleration. In addition to crash severity, the influence of occupant age and gender was investigated.A strong dependence between injury risk and crash severity was found. The risk curves reflect that small changes in crash severity may have a considerable influence on the risk of injury. Mean acceleration, followed by change of velocity, was found to be the single variable that best explained the risk of being injured (MAIS2+) in a crash. Furthermore, all three crash severity parameters were found to predict injury better than age and gender. However, age was an important factor. The very best model describing MAIS2+ injury risk included delta V supplemented by an interaction term of peak acceleration and age.
了解碰撞严重程度如何影响汽车碰撞中的受伤风险对于创建安全的道路运输系统至关重要。对现实世界中的碰撞进行分析可增强获取此类知识的能力。本研究的目的是基于现实世界中的正面碰撞呈现受伤风险函数，其中碰撞严重程度通过车载碰撞脉冲记录仪进行测量。对489起记录了加速度 - 时间历史的正面汽车碰撞（四个汽车品牌的26种车型）的结果进行了分析。使用多元逻辑回归为受约束的前排座椅乘客生成了最大简明损伤定级值为2或更高（MAIS2 +）的受伤风险函数。针对几个碰撞严重程度参数绘制了分析性和经验性受伤风险；速度变化、平均加速度和峰值加速度。除了碰撞严重程度外，还研究了乘客年龄和性别的影响。发现受伤风险与碰撞严重程度之间存在很强的相关性。风险曲线反映出碰撞严重程度的微小变化可能对受伤风险产生相当大的影响。发现平均加速度，其次是速度变化，是最能解释碰撞中受伤（MAIS2 +）风险的单一变量。此外，发现所有三个碰撞严重程度参数比年龄和性别更能预测受伤情况。然而，年龄是一个重要因素。描述MAIS2 +受伤风险的最佳模型包括速度变化量，并辅以峰值加速度和年龄的交互项。
