Summary of Gaussianad: Gaussian-centric End-to-end Autonomous Driving, by Wenzhao Zheng et al.
GaussianAD: Gaussian-Centric End-to-End Autonomous Driving
by Wenzhao Zheng, Junjie Wu, Yao Zheng, Sicheng Zuo, Zixun Xie, Longchao Yang, Yong Pan, Zhihui Hao, Peng Jia, Xianpeng Lang, Shanghang Zhang
First submitted to arxiv on: 13 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Robotics (cs.RO)
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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 This paper proposes a Gaussian-centric end-to-end autonomous driving (GaussianAD) framework for 3D scene representation and decision-making. The approach initializes the scene with uniform 3D Gaussians, which are then refined using surrounding-view images to create a comprehensive yet efficient representation. Sparse convolutions enable fast 3D perception, including object detection and semantic map construction. The framework predicts 3D flows with dynamic semantics, allowing for ego trajectory planning and future scene forecasting. The GaussianAD is trained end-to-end with optional perception labels, demonstrating effectiveness on tasks like motion planning, occupancy prediction, and 4D forecasting on the nuScenes dataset. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper makes self-driving cars better by using special mathematical shapes called Gaussians to understand what’s happening in a scene. It takes pictures from many angles and uses them to create a detailed picture of the world. Then it uses this information to decide where to go next. This is done all at once, without needing extra help or training. The results are really good on a big dataset used by self-driving car researchers. |
Keywords
» Artificial intelligence » Object detection » Semantics
