From data-compliance to model-introspection: Challenges in AV rule compliance monitoring

Abstract

Autonomous vehicles (AVs) are expected to comply with traffic laws, ensure safety, and provide transparent explanations of their decisions. Achieving these goals requires monitoring architectures that pro- cess large volumes of sensor, control, and contextual data. While real-time perception and decision-making are functionally indispensable, storing and using this data for auditing or improvement raises unresolved legal and technical challenges. Data protection regulations—such as the GDPR—mandate that personal data processing be limited to what is strictly necessary for specified purposes (Art. 5(1)(b), (c), and (e)). Yet, in practice, what counts as “necessary” remains ambiguous. This tension gives rise to the data-justification gap: the lack of systematic methods to determine which logged data is both sufficient to support compliance assessments and minimal under data protection constraints. At the same time, aligning formalized rules with their legal intent poses a separate but interrelated challenge—the alignment problem. Legal norms are often ambiguous or context-dependent, and existing monitoring frameworks rarely guarantee that formal specifications faithfully reflect legal meaning. This paper outlines a research agenda for bridging these gaps. We propose an integrated approach com- bining formal methods, legal reasoning, and runtime monitoring to develop data-justification frameworks. Such frameworks would enable developers to generate interpretable rule formalizations, synthesize minimally sufficient monitors, and justify data collection in a transparent and legally defensible manner.