The Role of World Models in Shaping Autonomous Driving: A Comprehensive Survey
It addresses the need for a structured review of DWM methods to aid researchers and practitioners in autonomous driving, but it is incremental as it synthesizes existing work rather than introducing novel findings.
This survey tackles the problem of understanding and categorizing Driving World Models (DWM) for autonomous driving by providing a comprehensive overview of recent progress, including categorization of approaches, datasets, and metrics, without presenting new experimental results.
Driving World Model (DWM), which focuses on predicting scene evolution during the driving process, has emerged as a promising paradigm in pursuing autonomous driving. These methods enable autonomous driving systems to better perceive, understand, and interact with dynamic driving environments. In this survey, we provide a comprehensive overview of the latest progress in DWM. We categorize existing approaches based on the modalities of the predicted scenes and summarize their specific contributions to autonomous driving. In addition, high-impact datasets and various metrics tailored to different tasks within the scope of DWM research are reviewed. Finally, we discuss the potential limitations of current research and propose future directions. This survey provides valuable insights into the development and application of DWM, fostering its broader adoption in autonomous driving. The relevant papers are collected at https://github.com/LMD0311/Awesome-World-Model.