Software Verification and Validation of Safe Autonomous Cars: A Systematic Literature Review

In this study, the relevant research literature in recent years has been systematically reviewed and classified in order to investigate the state-of-the-art in the software V&V of autonomous cars. By appropriate criteria, a subset of primary studies has been selected for more in-depth analysis. The first part of the review addresses certification issues against reference standards, challenges in assessing machine learning, as well as general V&V methodologies. The second part investigates more specific approaches, including simulation environments and mutation testing, corner cases and adversarial examples, fault injection, software safety cages, techniques for cyber-physical systems, and formal methods. Relevant approaches and related tools have been discussed and compared in order to highlight open issues and opportunities. N. Rajabli, F. Flammini, R. Nardone and V. Vittorini, "Software Verification and Validation of Safe Autonomous Cars: A Systematic Literature Review," in IEEE Access, vol. 9, pp. 4797-4819, 2021, doi: 10.1109/ACCESS.2020.3048047