Summary of Exploring the Interplay Between Video Generation and World Models in Autonomous Driving: a Survey, by Ao Fu et al.
Exploring the Interplay Between Video Generation and World Models in Autonomous Driving: A Survey
by Ao Fu, Yi Zhou, Tao Zhou, Yi Yang, Bojun Gao, Qun Li, Guobin Wu, Ling Shao
First submitted to arxiv on: 5 Nov 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computer Vision and Pattern Recognition (cs.CV); Robotics (cs.RO)
GrooveSquid.com Paper Summaries
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary World models and video generation are crucial technologies for enhancing the robustness and reliability of autonomous driving systems. This paper investigates the relationship between these two technologies, focusing on their structural parallels in diffusion-based models, which contribute to accurate and coherent simulations of driving scenarios. It examines leading works such as JEPA, Genie, and Sora, highlighting different approaches to world model design and the lack of a universally accepted definition. The survey discusses key evaluation metrics like Chamfer distance for 3D scene reconstruction and Fréchet Inception Distance (FID) for assessing generated video content quality. By analyzing their interplay, it identifies critical challenges and future research directions, emphasizing the potential to advance autonomous driving system performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Autonomous driving systems need better situational awareness and decision-making capabilities. This paper looks at how world models and video generation can help. World models simulate real-world environments, while video generation produces realistic videos. By studying diffusion-based models and leading works like JEPA, Genie, and Sora, researchers can improve simulations and optimize world models for different tasks. The study also talks about metrics like Chamfer distance and Fréchet Inception Distance (FID). This helps us understand how these technologies can make autonomous vehicles safer and more reliable. |
Keywords
» Artificial intelligence » Diffusion
