The Role of World Models in Shaping Autonomous Driving: A Comprehensive Survey
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arXiv
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| Auteurs principaux: | , , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866917237379563520 |
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| author | Tu, Sifan Zhou, Xin Liang, Dingkang Jiang, Xingyu Zhang, Yumeng Li, Xiaofan Bai, Xiang |
| author_facet | Tu, Sifan Zhou, Xin Liang, Dingkang Jiang, Xingyu Zhang, Yumeng Li, Xiaofan Bai, Xiang |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_10498 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | The Role of World Models in Shaping Autonomous Driving: A Comprehensive Survey Tu, Sifan Zhou, Xin Liang, Dingkang Jiang, Xingyu Zhang, Yumeng Li, Xiaofan Bai, Xiang Computer Vision and Pattern Recognition 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. |
| title | The Role of World Models in Shaping Autonomous Driving: A Comprehensive Survey |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2502.10498 |