Liability Determination for Smart Vehicle Accidents: Based on Black Box and Smart Vehicle Class Assessment

With the rapid evolution of intelligent vehicle technology, autonomous driving systems have undeniably boosted traffic efficiency. Nevertheless, they have concurrently muddied the waters of accident liability determination. In the Current research lacks clarity in defining responsible entities, unifying technical standards, and ensuring data reliability, while lagging legal frameworks exacerbate industry uncertainties. This study focuses on accident liability determination for intelligent vehicles, leveraging an integration of black box technology and an autonomous driving level assessment system. Through case analysis (e.g., the Xu case in China), technical standard interpretation like GB44497-2024, and systematic framework construction, the research explores data-driven and legally coordinated approaches for liability allocation. Findings reveal that black box data serves as critical evidence but faces limitations in extreme weather, while the proposed L1-L5 assessment system clarifies responsibilities among manufacturers, software providers, and drivers. The study recommends refining product liability laws, developing tailored insurance products, and enhancing collaborative governance among governments, industries, and the public. Future research should address data reliability under extreme conditions and compatibility of international legal standards.