Currently, autonomous driving is advancing along two mainstream paths: the "individual-vehicle intelligence, vehicle‒cloud collaboration" path led by Europe and the United States, and the "vehicle-to-infrastructure cooperation, vehicle‒road‒cloud integration" path with Chinese characteristics. Both paths have their district features and advantages. This study outlines the development trajectory and industrial progress of autonomous driving, highlighting its evolution from conventional approaches based on rules and deep learning toward a direction driven by world models. Propelled by end-to-end models and vision‒language‒action integration, autonomous driving is advancing into a new era of cognitive intelligence. Leveraging its system advantages, China has established an overall architecture for vehicle‒road‒cloud integration, preliminarily forming an intelligent transportation solution tailored to China's national conditions. Looking ahead, with the deep integration of the vehicle‒road‒cloud architecture with emerging technologies such as large language models and generative artificial intelligence, autonomous driving will enter a new stage characterized by autonomous intelligence and human‒machine collaboration. Furthermore, the study analyzes the key issues and future development paths for autonomous driving from the dimensions of technological innovation, education, national policies, international perspectives, industrial status, and development trends, and proposes recommendations including strengthening independent innovation in core technologies, accelerating the intelligent development of infrastructure, improving the industrial support system, and establishing social mechanisms for industrial development.