Testing autonomous vehicles and AI: perspectives and challenges from
  cybersecurity, transparency, robustness and fairness
David Fernández Llorca Ronan Hamon Henrik Junklewitz Kathrin Grosse Lars Kunze Patrick Seiniger Robert Swaim Nick Reed Alexandre Alahi Emilia Gómez Ignacio Sánchez Akos Kriston
This study explores the complexities of integrating Artificial Intelligence
(AI) into Autonomous Vehicles (AVs), examining the challenges introduced by AI
components and the impact on testing procedures, focusing on some of the
essential requirements for trustworthy AI. Topics addressed include the role of
AI at various operational layers of AVs, the implications of the EU's AI Act on
AVs, and the need for new testing methodologies for Advanced Driver Assistance
Systems (ADAS) and Automated Driving Systems (ADS). The study also provides a
detailed analysis on the importance of cybersecurity audits, the need for
explainability in AI decision-making processes and protocols for assessing the
robustness and ethical behaviour of predictive systems in AVs. The paper
identifies significant challenges and suggests future directions for research
and development of AI in AV technology, highlighting the need for
multidisciplinary expertise.