Self-driving cars are on the horizon, making it necessary to consider safety assurance and homologation of these autonomously operating vehicles. In this study, we systematically review literature that proposes new methods for these areas. The available methods were categorized into a novel taxonomy, dividing them into the strategies of combinatorial testing, robustness testing and search-based testing. We analyzed the literature in regard to modeling capabilities, targeted automation subsystem, targeted driving task level and the metrics used for criticality evaluation and coverage of the scenario space. We found that there are significant differences and shortcoming in the modeling capabilities of the existing research and that methods of each strategy usually target a specific driving task level. Additionally the criticality assessment of scenario-based validation methods was examined, revealing the need for more comprehensive metrics to assess complex scenarios. The developed taxonomy furthers the understanding in different scenario-based testing approaches for automated vehicles and serves as a guide for future research.
Bibliographical noteThis is an Accepted Manuscript of an article published by Taylor & Francis in Journal of Intelligent Transportation Systems. on 20/03/2020, available online: http://www.tandfonline.com/10.1080/15472450.2020.1738231
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- highly automated driving
- intelligent transportation systems
- scenario based testing
- test case generation