Testing autonomous vehicles and AI: perspectives and challenges from cybersecurity, transparency, robustness and fairness

This study aims to comprehensively explore the complexities of integrating Artificial Intelligence (AI) into Autonomous Vehicles (AVs), examining the challenges introduced by AI components and their impact on testing procedures. The research focuses on essential requirements for trustworthy AI, incl...

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Autores: Fernández Llorca, David, Hamon, Ronan, Junklewitz, Henrik, Grosse, Kathrin, Kunze, Lars, Seiniger, Patrick, Swaim, Robert, Reed, Nick, Alahi, Alexandre, Gómez, Emilia, Sánchez, Ignacio, Kriston, Akos
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2025
País:España
Institución:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
Repositorio:Recercat. Dipósit de la Recerca de Catalunya
OAI Identifier:oai:recercat.cat:10230/71821
Acceso en línea:http://hdl.handle.net/10230/71821
http://dx.doi.org/10.1186/s12544-025-00732-x
Access Level:acceso abierto
Palabra clave:Autonomous vehicles
Trustworthy AI
Testing
Vehicle Regulations
Cybersecurity
Transparency
Explainability
Robustness
Fairness
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spelling Testing autonomous vehicles and AI: perspectives and challenges from cybersecurity, transparency, robustness and fairnessFernández Llorca, DavidHamon, RonanJunklewitz, HenrikGrosse, KathrinKunze, LarsSeiniger, PatrickSwaim, RobertReed, NickAlahi, AlexandreGómez, EmiliaSánchez, IgnacioKriston, AkosAutonomous vehiclesTrustworthy AITestingVehicle RegulationsCybersecurityTransparencyExplainabilityRobustnessFairnessThis study aims to comprehensively explore the complexities of integrating Artificial Intelligence (AI) into Autonomous Vehicles (AVs), examining the challenges introduced by AI components and their impact on testing procedures. The research focuses on essential requirements for trustworthy AI, including cybersecurity, transparency, robustness, and fairness. We first analyse the role of AI at the most relevant operational layers of AVs, and discuss the implications of the EU’s AI Act on AVs, highlighting the importance of the concept of a safety component. Using an expert opinion-based methodology, involving an interdisciplinary workshop with 21 academics and a subsequent in-depth analysis by a smaller group of experts, this study provides a state-of-the-art overview of the current landscape of vehicle regulation and standards, including ex-ante, post-hoc, and accident investigation processes, highlighting the need for new testing methodologies for both Advanced Driver Assistance Systems (ADAS) and Automated Driving Systems (ADS). The study also provides a detailed analysis of cybersecurity audits, explainability in AI decision-making processes and protocols for assessing the robustness and ethical behaviour of predictive systems in AVs. The analysis highlights significant challenges and suggests future directions for research and development of AI in AV technology, emphasising the need for multidisciplinary expertise. The study’s conclusions have relevant implications for the development of trustworthy AI systems, vehicle regulations, and the safe deployment of AVs.Springer202520252025info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfapplication/pdfhttp://hdl.handle.net/10230/71821http://dx.doi.org/10.1186/s12544-025-00732-xhttp://hdl.handle.net/10230/71821reponame:Recercat. Dipósit de la Recerca de Catalunyainstname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)InglésEuropean Transport Research Review. 2025 Jul 30;17(1):38© European Union 2025. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.http://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:recercat.cat:10230/718212026-05-29T05:05:01Z
dc.title.none.fl_str_mv Testing autonomous vehicles and AI: perspectives and challenges from cybersecurity, transparency, robustness and fairness
title Testing autonomous vehicles and AI: perspectives and challenges from cybersecurity, transparency, robustness and fairness
spellingShingle Testing autonomous vehicles and AI: perspectives and challenges from cybersecurity, transparency, robustness and fairness
Fernández Llorca, David
Autonomous vehicles
Trustworthy AI
Testing
Vehicle Regulations
Cybersecurity
Transparency
Explainability
Robustness
Fairness
title_short Testing autonomous vehicles and AI: perspectives and challenges from cybersecurity, transparency, robustness and fairness
title_full Testing autonomous vehicles and AI: perspectives and challenges from cybersecurity, transparency, robustness and fairness
title_fullStr Testing autonomous vehicles and AI: perspectives and challenges from cybersecurity, transparency, robustness and fairness
title_full_unstemmed Testing autonomous vehicles and AI: perspectives and challenges from cybersecurity, transparency, robustness and fairness
title_sort Testing autonomous vehicles and AI: perspectives and challenges from cybersecurity, transparency, robustness and fairness
dc.creator.none.fl_str_mv Fernández Llorca, David
Hamon, Ronan
Junklewitz, Henrik
Grosse, Kathrin
Kunze, Lars
Seiniger, Patrick
Swaim, Robert
Reed, Nick
Alahi, Alexandre
Gómez, Emilia
Sánchez, Ignacio
Kriston, Akos
author Fernández Llorca, David
author_facet Fernández Llorca, David
Hamon, Ronan
Junklewitz, Henrik
Grosse, Kathrin
Kunze, Lars
Seiniger, Patrick
Swaim, Robert
Reed, Nick
Alahi, Alexandre
Gómez, Emilia
Sánchez, Ignacio
Kriston, Akos
author_role author
author2 Hamon, Ronan
Junklewitz, Henrik
Grosse, Kathrin
Kunze, Lars
Seiniger, Patrick
Swaim, Robert
Reed, Nick
Alahi, Alexandre
Gómez, Emilia
Sánchez, Ignacio
Kriston, Akos
author2_role author
author
author
author
author
author
author
author
author
author
author
dc.subject.none.fl_str_mv Autonomous vehicles
Trustworthy AI
Testing
Vehicle Regulations
Cybersecurity
Transparency
Explainability
Robustness
Fairness
topic Autonomous vehicles
Trustworthy AI
Testing
Vehicle Regulations
Cybersecurity
Transparency
Explainability
Robustness
Fairness
description This study aims to comprehensively explore the complexities of integrating Artificial Intelligence (AI) into Autonomous Vehicles (AVs), examining the challenges introduced by AI components and their impact on testing procedures. The research focuses on essential requirements for trustworthy AI, including cybersecurity, transparency, robustness, and fairness. We first analyse the role of AI at the most relevant operational layers of AVs, and discuss the implications of the EU’s AI Act on AVs, highlighting the importance of the concept of a safety component. Using an expert opinion-based methodology, involving an interdisciplinary workshop with 21 academics and a subsequent in-depth analysis by a smaller group of experts, this study provides a state-of-the-art overview of the current landscape of vehicle regulation and standards, including ex-ante, post-hoc, and accident investigation processes, highlighting the need for new testing methodologies for both Advanced Driver Assistance Systems (ADAS) and Automated Driving Systems (ADS). The study also provides a detailed analysis of cybersecurity audits, explainability in AI decision-making processes and protocols for assessing the robustness and ethical behaviour of predictive systems in AVs. The analysis highlights significant challenges and suggests future directions for research and development of AI in AV technology, emphasising the need for multidisciplinary expertise. The study’s conclusions have relevant implications for the development of trustworthy AI systems, vehicle regulations, and the safe deployment of AVs.
publishDate 2025
dc.date.none.fl_str_mv 2025
2025
2025
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10230/71821
http://dx.doi.org/10.1186/s12544-025-00732-x
http://hdl.handle.net/10230/71821
url http://hdl.handle.net/10230/71821
http://dx.doi.org/10.1186/s12544-025-00732-x
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv European Transport Research Review. 2025 Jul 30;17(1):38
dc.rights.none.fl_str_mv http://creativecommons.org/licenses/by/4.0/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Springer
publisher.none.fl_str_mv Springer
dc.source.none.fl_str_mv reponame:Recercat. Dipósit de la Recerca de Catalunya
instname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
instname_str Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
reponame_str Recercat. Dipósit de la Recerca de Catalunya
collection Recercat. Dipósit de la Recerca de Catalunya
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,812429

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