Summary of Drivegenvlm: Real-world Video Generation For Vision Language Model Based Autonomous Driving, by Yongjie Fu et al.
DriveGenVLM: Real-world Video Generation for Vision Language Model based Autonomous Driving
by Yongjie Fu, Anmol Jain, Xuan Di, Xu Chen, Zhaobin Mo
First submitted to arxiv on: 29 Aug 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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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 The proposed DriveGenVLM framework combines denoising diffusion probabilistic models (DDPM) with vision language models (VLMs) to generate realistic driving videos and understand them. This is achieved by training a DDPM model on the Waymo open dataset, evaluating its quality using the Fréchet Video Distance (FVD) score, and providing narrations through Efficient In-context Learning on Egocentric Videos (EILEV). The generated videos can improve traffic scene understanding, navigation, and planning capabilities in autonomous driving. By leveraging advanced AI models like VLMs, DriveGenVLM takes a significant step forward in addressing complex challenges. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new way to make self-driving cars smarter by creating fake videos of real-life driving scenarios using special computer models called Vision Language Models (VLMs). They train these models with real video data from the Waymo open dataset and test their quality. The goal is to create videos that are so realistic, they can be used to help self-driving cars understand what’s happening around them, make better decisions, and improve navigation. |
Keywords
» Artificial intelligence » Diffusion » Scene understanding
