AAP_manuscript_Nitsche.pdf (4.06 MB)
Pre-crash scenarios at road junctions: a clustering method for car crash data
journal contribution
posted on 2017-07-27, 09:52 authored by Philippe Nitsche, Pete Thomas, Rainer Stuetz, Ruth WelshRuth WelshGiven 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.
History
School
- Design and Creative Arts
Department
- Design
Published in
Accident Analysis and PreventionVolume
107Pages
137-151Citation
NITSCHE, P. ... et al, 2017. Pre-crash scenarios at road junctions: a clustering method for car crash data. Accident Analysis and Prevention, 107, pp.137–151Publisher
ElsevierVersion
- AM (Accepted Manuscript)
Publisher statement
This work is made available according to the conditions of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) licence. Full details of this licence are available at: https://creativecommons.org/licenses/by-nc-nd/4.0/Acceptance date
2017-07-10Publication date
2017-08-23Copyright date
2017Notes
This paper was accepted for publication in the journal Accident Analysis and Prevention and the definitive published version is available at https://doi.org/10.1016/j.aap.2017.07.011ISSN
0001-4575Publisher version
Language
- en