Applying Heuristics to Generate Test Cases for Automated Driving Safety Evaluation

Comprehensive safety evaluation methodologies for automated driving systems that account for the large complexity real traffic are currently being developed. This work adopts a scenario-based safety evaluation approach and aims at investigating an advanced methodology to generate test cases by apply...

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Autores principales:	Leonard Stepien, Silvia Thal, Roman Henze, Hiroki Nakamura, Jacobo Antona-Makoshi, Nobuyuki Uchida, Pongsathorn Raksincharoensak
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	autonomous vehicles intelligent vehicles vehicle safety advanced driver assistance systems scenario generation safety evaluation Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
Acceso en línea:	https://doaj.org/article/e9eacb0b9bce42b39e59ba87dfbaf48c
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spelling	oai:doaj.org-article:e9eacb0b9bce42b39e59ba87dfbaf48c2021-11-11T15:13:57ZApplying Heuristics to Generate Test Cases for Automated Driving Safety Evaluation10.3390/app1121101662076-3417https://doaj.org/article/e9eacb0b9bce42b39e59ba87dfbaf48c2021-10-01T00:00:00Zhttps://www.mdpi.com/2076-3417/11/21/10166https://doaj.org/toc/2076-3417Comprehensive safety evaluation methodologies for automated driving systems that account for the large complexity real traffic are currently being developed. This work adopts a scenario-based safety evaluation approach and aims at investigating an advanced methodology to generate test cases by applying heuristics to naturalistic driving data. The targeted requirements of the generated test cases are severity, exposure, and realism. The methodology starts with the extraction of scenarios from the data and their split in two subsets—containing the relatively more critical scenarios and, respectively, the normal driving scenarios. Each subset is analysed separately, in regard to the parameter value distributions and occurrence of dependencies. Subsequently, a heuristic search-based approach is applied to generate test cases. The resulting test cases clearly discriminate between safety critical and normal driving scenarios, with the latter covering a wider spectrum than the former. The verification of the generated test cases proves that the proposed methodology properly accounts for both severity and exposure in the test case generation process. Overall, the current study contributes to fill a gap concerning the specific applicable methodologies capable of accounting for both severity and exposure and calls for further research to prove its applicability in more complex environments and scenarios.Leonard StepienSilvia ThalRoman HenzeHiroki NakamuraJacobo Antona-MakoshiNobuyuki UchidaPongsathorn RaksincharoensakMDPI AGarticleautonomous vehiclesintelligent vehiclesvehicle safetyadvanced driver assistance systemsscenario generationsafety evaluationTechnologyTEngineering (General). Civil engineering (General)TA1-2040Biology (General)QH301-705.5PhysicsQC1-999ChemistryQD1-999ENApplied Sciences, Vol 11, Iss 10166, p 10166 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	autonomous vehicles intelligent vehicles vehicle safety advanced driver assistance systems scenario generation safety evaluation Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
spellingShingle	autonomous vehicles intelligent vehicles vehicle safety advanced driver assistance systems scenario generation safety evaluation Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999 Leonard Stepien Silvia Thal Roman Henze Hiroki Nakamura Jacobo Antona-Makoshi Nobuyuki Uchida Pongsathorn Raksincharoensak Applying Heuristics to Generate Test Cases for Automated Driving Safety Evaluation
description	Comprehensive safety evaluation methodologies for automated driving systems that account for the large complexity real traffic are currently being developed. This work adopts a scenario-based safety evaluation approach and aims at investigating an advanced methodology to generate test cases by applying heuristics to naturalistic driving data. The targeted requirements of the generated test cases are severity, exposure, and realism. The methodology starts with the extraction of scenarios from the data and their split in two subsets—containing the relatively more critical scenarios and, respectively, the normal driving scenarios. Each subset is analysed separately, in regard to the parameter value distributions and occurrence of dependencies. Subsequently, a heuristic search-based approach is applied to generate test cases. The resulting test cases clearly discriminate between safety critical and normal driving scenarios, with the latter covering a wider spectrum than the former. The verification of the generated test cases proves that the proposed methodology properly accounts for both severity and exposure in the test case generation process. Overall, the current study contributes to fill a gap concerning the specific applicable methodologies capable of accounting for both severity and exposure and calls for further research to prove its applicability in more complex environments and scenarios.
format	article
author	Leonard Stepien Silvia Thal Roman Henze Hiroki Nakamura Jacobo Antona-Makoshi Nobuyuki Uchida Pongsathorn Raksincharoensak
author_facet	Leonard Stepien Silvia Thal Roman Henze Hiroki Nakamura Jacobo Antona-Makoshi Nobuyuki Uchida Pongsathorn Raksincharoensak
author_sort	Leonard Stepien
title	Applying Heuristics to Generate Test Cases for Automated Driving Safety Evaluation
title_short	Applying Heuristics to Generate Test Cases for Automated Driving Safety Evaluation
title_full	Applying Heuristics to Generate Test Cases for Automated Driving Safety Evaluation
title_fullStr	Applying Heuristics to Generate Test Cases for Automated Driving Safety Evaluation
title_full_unstemmed	Applying Heuristics to Generate Test Cases for Automated Driving Safety Evaluation
title_sort	applying heuristics to generate test cases for automated driving safety evaluation
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/e9eacb0b9bce42b39e59ba87dfbaf48c
work_keys_str_mv	AT leonardstepien applyingheuristicstogeneratetestcasesforautomateddrivingsafetyevaluation AT silviathal applyingheuristicstogeneratetestcasesforautomateddrivingsafetyevaluation AT romanhenze applyingheuristicstogeneratetestcasesforautomateddrivingsafetyevaluation AT hirokinakamura applyingheuristicstogeneratetestcasesforautomateddrivingsafetyevaluation AT jacoboantonamakoshi applyingheuristicstogeneratetestcasesforautomateddrivingsafetyevaluation AT nobuyukiuchida applyingheuristicstogeneratetestcasesforautomateddrivingsafetyevaluation AT pongsathornraksincharoensak applyingheuristicstogeneratetestcasesforautomateddrivingsafetyevaluation
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Applying Heuristics to Generate Test Cases for Automated Driving Safety Evaluation

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