Adequate Testing Unmanned Autonomous Vehicle Systems - Infrastructures, Approaches, Issues, Challenges, and Needs
Proceedings - 16th IEEE International Conference on Service-Oriented System Engineering, SOSE 2022
Development of autonomous vehicles is seen as a solution to many of today's societal and everyday issues such as traffic congestion, road accidents, and air pollution. Unmanned autonomous vehicles are complex systems and testing such systems raises many challenges. This paper, written in a position paper to review main aspects of testing of unmanned autonomous vehicles, from test requirements and needs to test modes, approaches and methods, platforms, and infrastructures. Artificial intelligence especially machine learning, is part of such systems. We show throughout the paper the existing interplay between testing and artificial intelligence as testing could take benefit from artificial intelligence and machine learning, but also artificial intelligence approaches need to be validated. In addition, challenges, issues, and needs are discussed, and future research directions are highlighted.
artificial intelligence, machine learning, quality assurance, test, tutorial, Unmanned autonomous vehicles
Jerry Gao, Wen Cen Wu, and Oum El Kheir Aktouf. "Adequate Testing Unmanned Autonomous Vehicle Systems - Infrastructures, Approaches, Issues, Challenges, and Needs" Proceedings - 16th IEEE International Conference on Service-Oriented System Engineering, SOSE 2022 (2022): 154-164. https://doi.org/10.1109/SOSE55356.2022.00025