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Adapting the California Vehicle Code into a Machine-Readable Database to Improve Legal Compliance in Automated Driving Systems

Abstract

The proliferation of automated driving systems in the contemporary vehicular landscape has prompted an increased focus on proper vehicle operation from all stakeholders, including the general public, law enforcement agencies, policy makers, and autonomous vehicle designers. As these automated driving systems advance in complexity and responsibility, they challenge established norms on how the rules of the road are conveyed and interpreted, especially across various jurisdictions and driving cultures. In response to this challenge, this paper introduces an innovative framework for the conversion of text-based vehicle codebooks, currently enshrined in legal terminology dictating human driver behavior, into a machine-readable database conducive to the decision-making algorithms inherent in automated driving systems. Moreover, with this proposed methodology, existing vehicle regulations can be analyzed for ambiguity and coverage, allowing legislators and autonomous vehicle designers to focus efforts on improving the automated driving legal landscape.

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