Definition of run-off-road crash clusters - for safety benefit estimation and driver assistance development
Artikel i vetenskaplig tidskrift, 2018
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 crash test scenarios that are representative of the variability found in real-world crashes. Current approaches subdivide crash data into predefined conflict situations. However, this approach does not take into account inherent patterns in the crash data variables, and may miss common mechanisms or factors. We apply hierarchical agglomerative cluster analysis of crash data variables to define similarities in a set of test scenarios that are representative of run-off-road crashes in the German In-Depth Accident Study (GIDAS) database. 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.