Esenturk, Emre, Wallace, Albert, Khastgir, Siddartha and Jennings, Paul. A. (2021) Analyzing real-world accidents for test scenario generation for automated vehicles. In: 32nd IEEE Intelligent Vehicles Symposium, Virtual conference, 11-17 Jul 2021. Published in: 2021 IEEE Intelligent Vehicles Symposium (IV) ISBN 9781728153940. doi:10.1109/IV48863.2021.9576007

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Abstract

Identification of test scenarios for Automated Driving Systems (ADSs) remains a key challenge for the Verification & Validation of ADSs. Various approaches including data based approaches and knowledge based approaches have been proposed for scenario generation. Identifying the conditions that lead to high severity traffic accidents can help us not only identify test scenarios for ADSs, but also implement measures to save lives and infrastructure resources. Taking a data based approach, in this paper, we introduce a novel accident data analysis method for generating test scenarios where we analyze UK’s Stats19 accident data to identify trends in high severity accidents for test scenario generation. This paper first focuses on the severity of the accidents with the goal of relating it to static and time-dependent internal and external factors in a comprehensive way taking into account Operational Design Domain (ODD) properties, e.g. road, environmental conditions, and vehicle properties and driver characteristics. For this purpose, the paper utilizes a data grouping strategy (coarse-graining) and builds a logistic regression approach, derived from conventional regression models, in which emerging features become more pronounced, while uninteresting features and noise weaken. The approach makes the relationship between the factors and outcome variable more visible and hence well suited for the severity analysis. The method shows superior performance as compared to ordinary logistic models measured by goodness of fit and accounting for model variance (R2=0.05 for the ordinary model, R2=0.85 for the current model). The model is then used to solve the inverse problem of constructing high-risk pre-crash conditions as test scenarios for simulation based testing of ADSs.

Item Type:	Conference Item (Paper)
Subjects:	H Social Sciences > HE Transportation and Communications T Technology > TJ Mechanical engineering and machinery T Technology > TL Motor vehicles. Aeronautics. Astronautics
Divisions:	Faculty of Science, Engineering and Medicine > Engineering > WMG (Formerly the Warwick Manufacturing Group)
Library of Congress Subject Headings (LCSH):	Automated vehicles -- Safety measures, Accidents -- Prevention, Traffic accidents -- Prevention
Journal or Publication Title:	2021 IEEE Intelligent Vehicles Symposium (IV)
Publisher:	IEEE
ISBN:	9781728153940
Official Date:	7 November 2021
Dates:	Date Event 7 November 2021 Published 23 April 2021 Accepted
DOI:	10.1109/IV48863.2021.9576007
Status:	Peer Reviewed
Publication Status:	Published
Reuse Statement (publisher, data, author rights):	© 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Access rights to Published version:	Restricted or Subscription Access
Date of first compliant deposit:	18 October 2021
Date of first compliant Open Access:	18 October 2021
RIOXX Funder/Project Grant:	Project/Grant ID RIOXX Funder Name Funder ID 104529 Innovate UK http://dx.doi.org/10.13039/501100006041 104529 Centre for Connected & Autonomous Vehicles (Great Britain) http://viaf.org/viaf/33156495423617562147 MR/S035176/1 UK Research and Innovation http://dx.doi.org/10.13039/100014013
Conference Paper Type:	Paper
Title of Event:	32nd IEEE Intelligent Vehicles Symposium
Type of Event:	Workshop
Location of Event:	Virtual conference
Date(s) of Event:	11-17 Jul 2021
Related URLs:	Organisation

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