Research on World Models for Connected Automated Driving: Advances, Challenges, and Outlook

Connected Autonomous Vehicles (CAVs) technology holds immense potential for enhancing traffic safety and efficiency; however, its inherent complexity presents significant challenges for conventional autonomous driving. World Models (WMs), an advanced deep learning paradigm, offer an innovative approach to address these CAV challenges by learning environmental dynamics and precisely predicting future states. This survey systematically reviews the advancements of WMs in connected automated driving, delving into the key methodologies and technological breakthroughs across six core application domains: cooperative perception, prediction, decision-making, control, human–machine collaboration, and scene generation. Furthermore, this paper critically analyzes the current limitations of WMs in CAV scenarios, particularly concerning multi-source heterogeneous data fusion, physical law mapping, long-term temporal memory, and cross-scenario generalization capabilities. Building upon this analysis, we prospectively outline future research directions aimed at fostering the development of more robust, efficient, and interpretable WMs. Ultimately, this work aims to provide a crucial reference for constructing safe, efficient, and sustainable connected automated driving systems.