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Please use this identifier to cite or link to this item: https://dspace.lboro.ac.uk/2134/25902

Title: Pre-crash scenarios at road junctions: a clustering method for car crash data
Authors: Nitsche, Philippe
Thomas, Pete
Stuetz, Rainer
Welsh, Ruth
Keywords: Automated cars
Road safety
Car crashes
Pre-crash scenarios
Issue Date: 2017
Publisher: Elsevier
Citation: NITSCHE, P. ... et al, 2017. Pre-crash scenarios at road junctions: a clustering method for car crash data. Accident Analysis and Prevention, 107, pp.137–151
Abstract: Given the recent advancements in autonomous driving functions, one of the main challenges is safe and efficient operation in complex traffic situations such as road junctions. There is a need for comprehensive testing, either in virtual simulation environments or on real-world test tracks. This paper presents a novel data analysis method including the preparation, analysis and visualization of car crash data, to identify the critical pre-crash scenarios at T- and four-legged junctions as a basis for testing the safety of automated driving systems. The presented method employs k-medoids to cluster historical junction crash data into distinct partitions and then applies the association rules algorithm to each cluster to specify the driving scenarios in more detail. The dataset used consists of 1056 junction crashes in the UK, which were exported from the in-depth “On-the-Spot” database. The study resulted in thirteen crash clusters for T-junctions, and six crash clusters for crossroads. Association rules revealed common crash characteristics, which were the basis for the scenario descriptions. The results support existing findings on road junction accidents and provide benchmark situations for safety performance tests in order to reduce the possible number parameter combinations.
Description: This paper is embargoed until 23rd August 2018.
Version: Accepted for publication
DOI: 10.1016/j.aap.2017.07.011
URI: https://dspace.lboro.ac.uk/2134/25902
Publisher Link: https://doi.org/10.1016/j.aap.2017.07.011
ISSN: 1879-2057
Appears in Collections:Closed Access (Design School)

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