世界模型在塑造自动驾驶中的作用：全面综述
The Role of World Models in Shaping Autonomous Driving: A Comprehensive Survey
AI总结：
本文综述了驾驶世界模型在自动驾驶中的作用，分析了其生态系统、分类方法及性能表现，探讨了当前研究的局限性与未来发展方向。
摘要：
驾驶世界模型（DWM），专注于在驾驶过程中预测场景演变，已成为实现自动驾驶（AD）的有前景范式。DWMs使AD系统能够更好地感知、理解和与动态驾驶环境互动。在本次综述中，我们提供了DWM最新进展的全面概述。首先，我们回顾了DWM生态系统，该生态系统使用主流模拟器、高影响力数据集以及各种评估DWM的多维指标构建。然后，我们根据预测场景的模态对现有方法进行分类，包括视频、点云、占用、潜在特征和交通地图，并总结了它们在AD研究中的具体应用。此外，展示了代表性方法在生成和驾驶任务中的性能。最后，我们讨论了当前研究的潜在局限性，并提出了未来的研究方向。本次综述为DWM的发展和应用提供了有价值的见解，促进了其在AD中的更广泛应用。相关论文收集在https://github.com/LMD0311/Awesome-World-Model。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
