Opportunities, Challenges and Future Prospects of Artificial Intelligence in Autonomous Driving

Abstract

The rapid development of artificial intelligence technology has brought new momentum to the autonomous driving industry. The deep integration of these two fields has become a core direction for transformation in the transportation sector. Relying on core technologies such as environmental perception, decision-making and planning, and vehicle-infrastructure cooperation, artificial intelligence effectively enhances the operational efficiency and travel safety of autonomous vehicles. This paper takes the application of AI in the field of autonomous driving as its research object, briefly outlines the core principles of this technology's implementation, and analyzes the current development opportunities for autonomous driving by considering the scale of China's new energy vehicle industry, massive driving data, and policy and infrastructure advantages. The study finds that the large-scale popularization of autonomous driving is still constrained by multiple factors. These include not only technical bottlenecks such as poor adaptability to long-tail scenarios and unstable algorithmic decision-making in extreme situations, but also practical difficulties like lagging laws and regulations, ambiguous accident liability definitions, and imperfect data security and personal privacy protection systems. To address these issues, this paper, drawing on cutting-edge technological architectures, industry policy directions, and data management standards, proposes breaking through technical challenges via multimodal large models and Vehicle-Road-Cloud integrated architecture, and improving the governance system by revising traffic laws, unifying industry standards, and establishing a tiered data protection mechanism. The research results can provide theoretical reference and ideas for achieving safe and compliant large-scale commercial and civilian use in China.