Definition of run-off-road crash clusters—For safety benefit estimation and driver assistance development
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. The authors 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.
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Availability:
- Find a library where document is available. Order URL: http://worldcat.org/issn/00014575
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Supplemental Notes:
- Abstract reprinted with permission of Elsevier.
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Authors:
- Nilsson, Daniel
- Lindman, Magdalena
- Victor, Trent
- Dozza, Marco
- Publication Date: 2018-4
Language
- English
Media Info
- Media Type: Web
- Features: Figures; References; Tables;
- Pagination: pp 97-105
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Serial:
- Accident Analysis & Prevention
- Volume: 113
- Issue Number: 0
- Publisher: Elsevier
- ISSN: 0001-4575
- Serial URL: http://www.sciencedirect.com/science/journal/00014575
Subject/Index Terms
- TRT Terms: Cluster analysis; Crash data; Highway safety; Ran off road crashes
- Uncontrolled Terms: Lane keeping
- Subject Areas: Highways; Safety and Human Factors;
Filing Info
- Accession Number: 01667072
- Record Type: Publication
- Files: TRIS
- Created Date: Apr 25 2018 11:14AM