Liability Allocation and Safety Verification Standards for AI Perception Failures: A Regulatory Framework for Deep Learning Models in Autonomous Driving Perception Systems

• Abey city

  Lautech

  Author

The integration of deep learning models into autonomous driving perception systems has introduced significant safety and liability challenges, particularly when perception failures lead to accidents. Traditional liability frameworks, structured around driver negligence and product liability at the time of sale, are ill-suited for systems where software continues to evolve post-deployment through over-the-air updates and where perception algorithms exhibit probabilistic behavior. This study proposes a comprehensive regulatory framework that integrates safety verification standards with liability allocation mechanisms specifically designed for AI perception failures. Using a design-based research methodology, we synthesize existing standards including ISO 26262, ISO/PAS 8800, and the emerging Safety Critical Labs AI requirements framework, and develop a risk-stratified liability model informed by prospect theory. The proposed framework achieves 89.4% accuracy in predicting liability assignment across 1,250 simulated accident scenarios involving perception failures. Key findings demonstrate that a dual-layer architecture—comprising runtime verification monitors based on Signal Temporal Logic specifications and a tiered liability allocation matrix—effectively addresses the information asymmetry between manufacturers, operators, and accident victims. The framework's operational design domain classification system enables prospective safety assurance while maintaining 92.3% precision in distinguishing between system design defects and operational edge cases. This research provides actionable guidance for policymakers, system designers, and insurers navigating the complex intersection of AI reliability and legal accountability in autonomous driving.

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