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

Autonomous, or self-driving, cars are emerging as the solution to several problems primarily caused by humans on roads, such as accidents and traffic congestion. However, those benefits come with great challenges in the verification and validation (V&V) for safety assessment. In fact, due to the...

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Bibliographic Details
Published in:IEEE access Vol. 9; pp. 4797 - 4819
Main Authors: Rajabli, Nijat, Flammini, Francesco, Nardone, Roberto, Vittorini, Valeria
Format: Journal Article
Language:English
Published: Piscataway IEEE 01.01.2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Accidents
Advanced driver assistance systems
Artificial intelligence
automotive engineering
Autonomous automobiles
Autonomous cars
autonomous vehicles
Autonomy
Computer and Information Sciences Computer Science
Cyber-physical systems
Data- och informationsvetenskap
Formal method
formal verification
intelligent vehicles
Literature reviews
Machine learning
Mutation
Program verification (computers)
Roads
Safety
Software
system testing
system validation
Systematics
Traffic congestion
vehicle safety
Vehicles
ISSN:2169-3536, 2169-3536
Online Access:Get full text
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Summary:Autonomous, or self-driving, cars are emerging as the solution to several problems primarily caused by humans on roads, such as accidents and traffic congestion. However, those benefits come with great challenges in the verification and validation (V&V) for safety assessment. In fact, due to the possibly unpredictable nature of Artificial Intelligence (AI), its use in autonomous cars creates concerns that need to be addressed using appropriate V&V processes that can address trustworthy AI and safe autonomy. 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.
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ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2020.3048047