CHALMERS-University of Technology, Department of Mechanics and Maritime Sciences, Division of Vehicle Safety, Sweden; Volvo Car Safety Centre-Volvo Car Corporation, Sweden.
Volvo Car Safety Centre-Volvo Car Corporation, Sweden.
Accid Anal Prev. 2018 Apr;113:97-105. doi: 10.1016/j.aap.2018.01.011. Epub 2018 Mar 7.
Single-vehicle run-off-road crashes are a major traffic safety concern, as they are associated with a high proportion of fatal outcomes. In addressing run-off-road crashes, the development and evaluation of advanced driver assistance systems requires test scenarios that are representative of the variability found in real-world crashes. We apply hierarchical agglomerative cluster analysis to define similarities in a set of crash data variables, these clusters can then be used as the basis in test scenario development. Out of 13 clusters, nine test scenarios are derived, corresponding to crashes characterised by: drivers drifting off the road in daytime and night-time, high speed departures, high-angle departures on narrow roads, highways, snowy roads, loss-of-control on wet roadways, sharp curves, and high speeds on roads with severe road surface conditions. In addition, each cluster was analysed with respect to crash variables related to the crash cause and reason for the unintended lane departure. The study shows that cluster analysis of representative data provides a statistically based method to identify relevant properties for run-off-road test scenarios. This was done to support development of vehicle-based run-off-road countermeasures and driver behaviour models used in virtual testing. Future studies should use driver behaviour from naturalistic driving data to further define how test-scenarios and behavioural causation mechanisms should be included.
单车道路外碰撞是一个主要的道路交通安全问题，因为它们与高比例的致命后果相关。在解决道路外碰撞问题时，先进驾驶员辅助系统的开发和评估需要具有代表性的测试场景，这些场景能够反映实际碰撞中的变化。我们应用层次凝聚聚类分析来定义一组碰撞数据变量之间的相似性，然后可以将这些聚类用作测试场景开发的基础。在 13 个聚类中，衍生出了 9 个测试场景，对应于以下特征的碰撞：驾驶员在白天和夜间驶离道路、高速驶离、在狭窄道路、高速公路、雪地、湿滑路面上失去控制、急转弯和严重路面状况道路上高速行驶。此外，还针对与碰撞原因和意外偏离车道原因相关的碰撞变量对每个聚类进行了分析。研究表明，代表性数据的聚类分析提供了一种基于统计学的方法来识别与道路外测试场景相关的属性。这是为了支持基于车辆的道路外对策和虚拟测试中使用的驾驶员行为模型的开发。未来的研究应该使用自然驾驶数据中的驾驶员行为来进一步定义如何包含测试场景和行为因果机制。
