AI & ML
National and State-Level Approaches to Automated Vehicle Certification: A Comparative Framework for State Operational Governance
arXiv cs.RO0 views
arXiv:2611.08681v1 Announce Type: new Abstract: Automated vehicle (AV) deployment in the United States is expanding without a unified federal certification framework, leading to substantial variation in how states authorize and oversee AV testing and deployment. This paper examines how U.S. states regulate Society of Automotive Engineers (SAE) Level 4-5 automated driving systems (ADS), identifies common certification approaches, compares them with international practices, and evaluates the implications for North Carolina's future regulatory strategy. Publicly available state statutes, administrative regulations, agency guidance, and federal policy documents were reviewed. Ten states, selected to capture the range of regulatory approaches, were analyzed using a structured framework covering operational scope, governance and oversight, federal interaction, and implementation outcomes. Certification practices in the European Union, China, Japan, and Australia were reviewed for comparative context. Three state certification models were identified, reflecting increasing regulatory involvement: baseline legal compliance, administrative self-certification with ongoing oversight, and permit-based authorization requiring formal agency review before deployment. North Carolina currently aligns with the baseline compliance model. International frameworks are converging toward scenario-based testing, operational-design-domain-specific approval, safety-case documentation, cybersecurity assurance, and lifecycle oversight, although direct transferability to the U.S. is limited. The paper develops a three-model typology for comparing state AV certification approaches and integrates state practices with international frameworks, providing a consistent basis for evaluating differences in regulatory involvement, governance complexity, and institutional responsibilities across jurisdictions.
Source:https://arxiv.org/abs/2611.08681PreviousForPC: Force-Aware Whole-Body Control for Precise Humanoid Cart PushingNextVICTR: Value-Informed In-Context Learning for Vision-Language-Action Models
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