Dataset Safety in Autonomous Driving: Requirements, Risks, and Assurance
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arXiv
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| Auteurs principaux: | , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866910127019261952 |
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| author | Abbaspour, Alireza Patil, Tejaskumar Balgonda Kiran, B Ravi Mohr, Russel Yogamani, Senthil |
| author_facet | Abbaspour, Alireza Patil, Tejaskumar Balgonda Kiran, B Ravi Mohr, Russel Yogamani, Senthil |
| contents | Dataset integrity is fundamental to the safety and reliability of AI systems, especially in autonomous driving. This paper presents a structured framework for developing safe datasets aligned with ISO/PAS 8800 guidelines. Using AI-based perception systems as the primary use case, it introduces the AI Data Flywheel and the dataset lifecycle, covering data collection, annotation, curation, and maintenance. The framework incorporates rigorous safety analyses to identify hazards and mitigate risks caused by dataset insufficiencies. It also defines processes for establishing dataset safety requirements and proposes verification and validation strategies to ensure compliance with safety standards. In addition to outlining best practices, the paper reviews recent research and emerging trends in dataset safety and autonomous vehicle development, providing insights into current challenges and future directions. By integrating these perspectives, the paper aims to advance robust, safety-assured AI systems for autonomous driving applications. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_08439 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Dataset Safety in Autonomous Driving: Requirements, Risks, and Assurance Abbaspour, Alireza Patil, Tejaskumar Balgonda Kiran, B Ravi Mohr, Russel Yogamani, Senthil Artificial Intelligence Dataset integrity is fundamental to the safety and reliability of AI systems, especially in autonomous driving. This paper presents a structured framework for developing safe datasets aligned with ISO/PAS 8800 guidelines. Using AI-based perception systems as the primary use case, it introduces the AI Data Flywheel and the dataset lifecycle, covering data collection, annotation, curation, and maintenance. The framework incorporates rigorous safety analyses to identify hazards and mitigate risks caused by dataset insufficiencies. It also defines processes for establishing dataset safety requirements and proposes verification and validation strategies to ensure compliance with safety standards. In addition to outlining best practices, the paper reviews recent research and emerging trends in dataset safety and autonomous vehicle development, providing insights into current challenges and future directions. By integrating these perspectives, the paper aims to advance robust, safety-assured AI systems for autonomous driving applications. |
| title | Dataset Safety in Autonomous Driving: Requirements, Risks, and Assurance |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2511.08439 |