系统梳理世界模型在自动驾驶中的进展与挑战
The Role of World Models in Shaping Autonomous Driving: A Comprehensive Survey
- 按预测场景模态分类，涵盖视频、点云等五类方法
- 对比主流方法在生成与驾驶任务上的表现差异
- 适合关注自动驾驶规划与环境建模的研究者
驾驶世界模型（DWM）聚焦于预测驾驶过程中的场景演化，已成为实现自动驾驶的有前景范式。它使自动驾驶系统能更好感知、理解并交互于动态驾驶环境。本文全面综述了DWM领域的最新进展：首先梳理基于主流模拟器、高影响力数据集和多维度评估指标构建的DWM生态体系；其次按预测场景模态（视频、点云、占据、潜在特征、交通地图）对现有方法进行分类，并总结其在自动驾驶研究中的具体应用；此外，呈现代表性方法在生成与驾驶任务中的性能表现；最后讨论当前研究的局限性并提出未来方向。本综述为DWM的发展与应用提供重要参考，推动其在自动驾驶中的更广泛应用。相关论文列表见https://github.com/LMD0311/Awesome-World-Model。
原文摘要 · Abstract (English)
The Driving World Model (DWM), which focuses on predicting scene evolution during the driving process, has emerged as a promising paradigm in the pursuit of autonomous driving (AD). DWMs enable AD 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. First, we review the DWM ecosystem, which is constructed using mainstream simulators, high-impact datasets, and various metrics that evaluate DWMs across multiple dimensions. We then categorize existing approaches based on the modalities of the predicted scenes, including video, point cloud, occupancy, latent feature, and traffic map, and summarize their specific applications in AD research. In addition, the performance of representative approaches across generating and driving tasks is presented. 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 AD. The relevant papers are collected at https://github.com/LMD0311/Awesome-World-Model.
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