Dataset Safety in Autonomous Driving: Requirements, Risks, and Assurance

A new framework addresses dataset safety for autonomous driving AI systems by aligning with ISO/PAS 8800 guidelines. The paper establishes structured processes for data collection, annotation, curation, and maintenance while proposing verification strategies to mitigate risks from dataset insufficiencies in perception systems.

The autonomous driving industry faces a critical bottleneck: AI perception systems depend entirely on dataset quality, yet standardized safety frameworks for dataset integrity remain underdeveloped. This paper tackles that gap by proposing a comprehensive lifecycle management approach grounded in existing safety standards, directly addressing a weakness that has hindered regulatory approval of autonomous vehicles globally.

Dataset failures have already caused documented incidents in deployed AI systems, from biased training data compromising fairness to incomplete datasets missing edge cases that trigger failures. The automotive industry, bound by functional safety standards like ISO 26262, has long struggled to extend these requirements to machine learning components. This framework bridges that divide by introducing the AI Data Flywheel concept, which treats data as a managed asset requiring hazard identification, risk mitigation, and continuous validation—similar to hardware safety assurance practices.

For developers and manufacturers, this framework reduces liability exposure by establishing defensible processes for dataset governance. Regulators benefit from clearer benchmarks for evaluating autonomous vehicle safety claims, potentially accelerating certification timelines. Insurance providers gain better tools for risk assessment. The emphasis on verification and validation strategies signals that data governance is becoming a competitive differentiator in autonomous vehicle programs.

Looking forward, standardization bodies will likely formalize guidelines based on research like this into regulatory requirements. The pace of AV deployment may correlate with how thoroughly manufacturers adopt dataset safety practices. Emerging challenges include scaling validation across diverse geographic conditions and handling adversarial data contamination—areas where further research will determine practical implementation feasibility.

• →Dataset integrity frameworks aligned with ISO/PAS 8800 are emerging as essential prerequisites for autonomous vehicle safety certification.
• →The AI Data Flywheel concept establishes lifecycle processes covering collection, annotation, curation, and maintenance as managed safety activities.
• →Hazard analysis and risk mitigation specific to dataset insufficiencies require verification and validation strategies comparable to hardware safety assurance.
• →Standardized dataset safety practices reduce manufacturer liability while enabling regulators to evaluate autonomous vehicle safety claims more consistently.
• →Dataset governance is becoming a competitive differentiator in autonomous vehicle development and a potential bottleneck for market deployment.