世界自动驾驶模式：回顾与展望

Towards to general intelligentization of autonomous driving systems, the world models as a cognitive engine that internally models, infers, and predicts the environment, is becoming a critical technical pathway to break bottlenecks in traditional perception-decision paradigms and address long-tail scenarios. To synthesize the research progress and key issues of the world models in autonomous driving, and explore their technical routes for advancing the implementation of general intelligent driving, the research status and development trends in autonomous driving are reviewed. Firstly, the basic concept of world models and their core functionalities in autonomous driving are clarified, mainstream technical architectures are summarized, and the merits and drawbacks of various paradigms are comparatively analyzed. Secondly, the latest progress of world models in three key application directions are summarized including of future scene generation and understanding, end-to-end driving policy learning, and data-driven closed-loop simulation systems, and practical value in enhancing the system’ s forward-looking capabilities and interaction understanding is revealed. Thirdly, the evaluation metrics of world models and the application scopes of public datasets are organized, which lays a foundation for the subsequent analysis of their technical challenges. Overall, despite achieving phased breakthroughs in multi-scale spatiotemporal representation and complex scene generation, the world models still face the challenges in adhering to physical laws, safe and credible reasoning, long-term temporal stability, and lightweight deployment. Accordingly, it is suggested that future research should focus on efficient computing architectures, long-term generation consistency, uncertainty modeling, and self-supervised representation integrated with physical knowledge, so as to promote the effective function of world models in various traffic scenarios.