为自动驾驶数据集安全构建系统框架，防范因数据缺陷引发的潜在风险。
Dataset Safety in Autonomous Driving: Requirements, Risks, and Assurance
- 提出AI数据飞轮与全生命周期管理方法，覆盖数据采集到维护全过程。
- 通过安全分析识别数据不足带来的风险，并制定验证策略保障合规性。
- 适合自动驾驶安全团队、AI系统开发者及标准制定者参考。
数据集完整性是人工智能系统安全与可靠性的基础，尤其在自动驾驶领域至关重要。本文基于ISO/PAS 8800指南，构建了一个结构化框架，用于开发符合安全要求的数据集。以AI感知系统为主要应用场景，引入AI数据飞轮与数据生命周期管理机制，涵盖数据采集、标注、清洗与维护等环节。框架整合了严格的安全分析流程，识别因数据不足引发的潜在危害并提出缓解措施。同时，明确数据集安全需求的建立方法，并提出验证与确认策略，确保符合安全标准。此外，论文综述了近期研究进展与新兴趋势，剖析当前挑战与未来方向。通过融合多视角观点，旨在推动自动驾驶应用中具备鲁棒性与安全保障的AI系统发展。
原文摘要 · Abstract (English)
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.
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