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How Could Generative AI Support Compliance with the EU AI Act? A Review for Safe Automated Driving Perception

Mert Keser, Youssef Shoeb, Alois Knoll

TL;DR

This paper surveys how generative AI can support compliance with the EU AI Act in autonomous driving perception. It maps regulatory requirements to generative AI capabilities across data generation, world models, and multimodal reasoning (VQA, planning), and proposes concrete strategies for transparency, accuracy, and run-time monitoring. The authors offer a structured framework to categorize GenAI applications in AD and outline actionable pathways for aligning advancements with legal mandates, while noting open challenges such as hallucinations and computational demands. The work emphasizes that, with careful integration and ongoing research, generative AI can bolster safety, explainability, and regulatory trust in AD perception systems.

Abstract

Deep Neural Networks (DNNs) have become central for the perception functions of autonomous vehicles, substantially enhancing their ability to understand and interpret the environment. However, these systems exhibit inherent limitations such as brittleness, opacity, and unpredictable behavior in out-of-distribution scenarios. The European Union (EU) Artificial Intelligence (AI) Act, as a pioneering legislative framework, aims to address these challenges by establishing stringent norms and standards for AI systems, including those used in autonomous driving (AD), which are categorized as high-risk AI. In this work, we explore how the newly available generative AI models can potentially support addressing upcoming regulatory requirements in AD perception, particularly with respect to safety. This short review paper summarizes the requirements arising from the EU AI Act regarding DNN-based perception systems and systematically categorizes existing generative AI applications in AD. While generative AI models show promise in addressing some of the EU AI Acts requirements, such as transparency and robustness, this review examines their potential benefits and discusses how developers could leverage these methods to enhance compliance with the Act. The paper also highlights areas where further research is needed to ensure reliable and safe integration of these technologies.

How Could Generative AI Support Compliance with the EU AI Act? A Review for Safe Automated Driving Perception

TL;DR

This paper surveys how generative AI can support compliance with the EU AI Act in autonomous driving perception. It maps regulatory requirements to generative AI capabilities across data generation, world models, and multimodal reasoning (VQA, planning), and proposes concrete strategies for transparency, accuracy, and run-time monitoring. The authors offer a structured framework to categorize GenAI applications in AD and outline actionable pathways for aligning advancements with legal mandates, while noting open challenges such as hallucinations and computational demands. The work emphasizes that, with careful integration and ongoing research, generative AI can bolster safety, explainability, and regulatory trust in AD perception systems.

Abstract

Deep Neural Networks (DNNs) have become central for the perception functions of autonomous vehicles, substantially enhancing their ability to understand and interpret the environment. However, these systems exhibit inherent limitations such as brittleness, opacity, and unpredictable behavior in out-of-distribution scenarios. The European Union (EU) Artificial Intelligence (AI) Act, as a pioneering legislative framework, aims to address these challenges by establishing stringent norms and standards for AI systems, including those used in autonomous driving (AD), which are categorized as high-risk AI. In this work, we explore how the newly available generative AI models can potentially support addressing upcoming regulatory requirements in AD perception, particularly with respect to safety. This short review paper summarizes the requirements arising from the EU AI Act regarding DNN-based perception systems and systematically categorizes existing generative AI applications in AD. While generative AI models show promise in addressing some of the EU AI Acts requirements, such as transparency and robustness, this review examines their potential benefits and discusses how developers could leverage these methods to enhance compliance with the Act. The paper also highlights areas where further research is needed to ensure reliable and safe integration of these technologies.
Paper Structure (17 sections, 3 figures, 4 tables)

This paper contains 17 sections, 3 figures, 4 tables.

Table of Contents

  1. Introduction
  2. BACKGROUND
  3. Generative AI and Autonomous Driving
  4. Image Generation
  5. Text Generation
  6. Generative Models in Autonomous Driving
  7. Data Generation
  8. Planning & Decision Making
  9. Question Answering
  10. Evaluation & Benchmark
  11. Planning
  12. Perception Safety in EU AI Act
  13. Transparency
  14. Accuracy & Robustness
  15. Run-Time Monitoring
  16. ...and 2 more sections

Figures (3)

  • Figure 1: Categorisation of Generative Models in Autonomous Driving
  • Figure 2: Generating diverse driving scenarios with, e.g., DIDEX niemeijer2024generalization
  • Figure 3: The event finder flowchart shows that multimodal LLMs can be utilized to identify events during the deployment. After the deployment, the object detector can be retrained, or the event can be recorded.