AI Safety Assurance for Automated Vehicles: A Survey on Research, Standardization, Regulation
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Abstract (arXiv)
Assuring safety of artificial intelligence (AI) applied to safety-critical systems is of paramount importance. Especially since research in the field of automated driving shows that AI is able to outperform classical approaches, to handle higher complexities, and to reach new levels of autonomy. At the same time, the safety assurance required for the use of AI in such safety-critical systems is still not in place. Due to the dynamic and far-reaching nature of the technology, research on safeguarding AI is being conducted in parallel to AI standardization and regulation. The parallel progress necessitates simultaneous consideration in order to carry out targeted research and development of AI systems in the context of automated driving. Therefore, in contrast to existing surveys that focus primarily on research aspects, this paper considers research, standardization and regulation in a concise way. Accordingly, the survey takes into account the interdependencies arising from the triplet of research, standardization and regulation in a forward-looking perspective and anticipates and discusses open questions and possible future directions. In this way, the survey ultimately serves to provide researchers and safety experts with a compact, holistic perspective that discusses the current status, emerging trends, and possible future developments.
Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). First posted on arXiv 25 Apr 2025.
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Citing papers per year: 2025: 3; 2026: 2.
5 papers.
- 2025Security Risks of Agentic Vehicles: A Systematic Analysis of Cognitive and Cross-Layer Threatscited by 6
- 2025Large Language Models for Crash Detection in Video: A Survey of Methods, Datasets, and Challengescited by 3
- 2025A Concept for Efficient Scalability of Automated Driving Allowing for Technical, Legal, Cultural, and Ethical Differencescited by 0
- 2026Toward a Fully Autonomous, AI-Native Particle Acceleratorcited by 0
- 2026AESOP: Adversarial Execution-path Selection to Overload Deep Learning Pipelinescited by 0
- 2026AESOP: Adversarial Execution-path Selection to Overload Deep Learning Pipelines9 May 2026
- 2026Toward a Fully Autonomous, AI-Native Particle Accelerator19 Feb 2026
- 2025Security Risks of Agentic Vehicles: A Systematic Analysis of Cognitive and Cross-Layer Threats18 Dec 2025
- 2025A Concept for Efficient Scalability of Automated Driving Allowing for Technical, Legal, Cultural, and Ethical Differences24 Jul 2025
- 2025Large Language Models for Crash Detection in Video: A Survey of Methods, Datasets, and Challenges2 Jul 2025