Gangopadhyay, Briti, Khastgir, Siddartha, Dey, Sumanta, Dasgupta, Pallab, Montana, Giovanni and Jennings, Paul. A. (2019) Identification of test cases for Automated Driving Systems using Bayesian optimization. In: 2019 IEEE Intelligent Transportation Systems Conference (ITSC), Auckland, New Zealand, 27-30 Oct 2019. Published in: 2019 IEEE Intelligent Transportation Systems Conference (ITSC) pp. 1961-1967. ISBN 9781538670255. doi:10.1109/ITSC.2019.8917103

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Abstract

With advancements in technology, the automotive industry is experiencing a paradigm shift from assisted driving to highly automated driving. However, autonomous driving systems are highly safety critical in nature and need to be thoroughly tested for a diverse set of conditions before being commercially deployed. Due to the huge complexities involved with Advanced Driver Assistance Systems (ADAS) and Automated Driving Systems (ADS), traditional software testing methods have well-known limitations. They also fail to cover the infinite number of adverse conditions that can occur due to a slight change in the interactions between the environment and the system. Hence, it is important to identify test conditions that push the vehicle under test to breach its safe boundaries. Hazard Based Testing (HBT) methods, inspired by Systems-Theoretic Process Analysis (STPA), identify such parameterized test conditions that can lead to system failure. However, these techniques fall short of discovering the exact parameter values that lead to the failure condition. The presented paper proposes a test case identification technique using Bayesian Optimization. For a given test scenario, the proposed method learns parameter values by observing the system's output. The identified values create test cases that drive the system to violate its safe boundaries. STPA inspired outputs (parameters and pass/fail criteria) are used as inputs to the Bayesian Optimization model. The proposed method was applied to an SAE Level-4 Low Speed Automated Driving (LSAD) system which was modelled in a driving simulator.

Item Type:	Conference Item (Paper)
Subjects:	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, Motor vehicles -- Automatic control, Heuristic algorithms, Automated vehicles -- Testing, Computer programs -- Testing
Journal or Publication Title:	2019 IEEE Intelligent Transportation Systems Conference (ITSC)
Publisher:	IEEE
ISBN:	9781538670255
Book Title:	2019 IEEE Intelligent Transportation Systems Conference (ITSC)
Official Date:	28 November 2019
Dates:	Date Event 28 November 2019 Published 24 June 2019 Accepted
Page Range:	pp. 1961-1967
DOI:	10.1109/ITSC.2019.8917103
Status:	Peer Reviewed
Publication Status:	Published
Reuse Statement (publisher, data, author rights):	© 2020 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:	31 July 2020
Date of first compliant Open Access:	31 July 2020
Conference Paper Type:	Paper
Title of Event:	2019 IEEE Intelligent Transportation Systems Conference (ITSC)
Type of Event:	Conference
Location of Event:	Auckland, New Zealand
Date(s) of Event:	27-30 Oct 2019
Related URLs:	Organisation

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