Authors:
Kaushik Madala
1
;
Hyunsook Do
1
and
Carlos Avalos-Gonzalez
2
Affiliations:
1
Department of Computer Science and Engineering, University of North Texas, Denton, U.S.A.
;
2
kVA by UL, Portland, U.S.A.
Keyword(s):
Scenario-based Analysis, Safety Verification, Operation Design Domain, Autonomous Vehicle Safety.
Abstract:
For an autonomous vehicle, to assure safety, we need to perform a thorough analysis considering the vehicle’s intended operational design domain (ODD). This requires analysts and engineers to consider various operating environments (OEs) that can occur in the ODD, and the various scenarios that are possible within each OE. However, the automotive safety standards ISO 26262 and ISO 21448 do not offer in-depth guidance on what and how many scenarios need to be analyzed to ensure safety of a vehicle. Moreover, many existing simulation tools and verification approaches consider limited OEs and generate test cases exhaustively for each scenario created by engineers within an OE. Such an analysis requires a significant amount of time and effort, but it still cannot ensure that various dependencies among ODD elements are covered. To address these limitations, we propose a dependency-based combinatorial approach (DBCA), which uses in-parameter- order-general (IPOG), a combinatorial testing a
lgorithm to generate OEs and test cases for each scenario. To evaluate DBCA, we applied it to the ODD elements extracted from ISO 21448, and to a highway cut-in scenario. Our results show that DBCA reduced time and effort for analysis, and reduced the the number of OEs and test cases for the scenario without missing dependencies.
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