Summary of Dyronet: Dynamic Routing and Low-rank Adapters For Autonomous Driving Streaming Perception, by Xiang Huang et al.
DyRoNet: Dynamic Routing and Low-Rank Adapters for Autonomous Driving Streaming Perception
by Xiang Huang, Zhi-Qi Cheng, Jun-Yan He, Chenyang Li, Wangmeng Xiang, Baigui Sun
First submitted to arxiv on: 8 Mar 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Multimedia (cs.MM)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 Dynamic Routing Network (DyRoNet) is a novel framework designed for low-latency and high-accuracy perception in autonomous driving systems. It integrates pre-trained branch networks fine-tuned for distinct environmental conditions, leveraging a speed router module to route input data to the most suitable branch. This approach overcomes conventional models’ limitations in adapting to diverse driving conditions while balancing performance and efficiency. Experimental evaluations demonstrate DyRoNet’s adaptability and significant performance enhancements across scenarios. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary DyRoNet is a new way for cars to see the road without getting stuck. Right now, self-driving cars can only do their job well if they’re in one specific kind of weather or situation. This paper introduces a new system that lets them work better in lots of different situations. It does this by using special “branch networks” that are trained just for certain kinds of weather or roads. Then, it decides which branch to use based on what the car is seeing. This makes the whole system way more accurate and efficient. |
