Testing autonomous vehicles and AI: perspectives and challenges from cybersecurity, transparency, robustness and fairness
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...
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| Veröffentlicht in: | arXiv.org 2024-02 |
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| creator | David Fernández Llorca Hamon, Ronan Junklewitz, Henrik Grosse, Kathrin Kunze, Lars Seiniger, Patrick Swaim, Robert Reed, Nick Alahi, Alexandre Gómez, Emilia Sánchez, Ignacio Kriston, Akos |
| description | 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. |
| format | Article |
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| identifier | EISSN: 2331-8422 |
| ispartof | arXiv.org, 2024-02 |
| issn | 2331-8422 |
| language | eng |
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| source | Freely Accessible Journals |
| subjects | Advanced driver assistance systems Artificial intelligence Cybersecurity R&D Research & development Robustness Test procedures |
| title | Testing autonomous vehicles and AI: perspectives and challenges from cybersecurity, transparency, robustness and fairness |
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