Summary of Openemma: Open-source Multimodal Model For End-to-end Autonomous Driving, by Shuo Xing et al.
OpenEMMA: Open-Source Multimodal Model for End-to-End Autonomous Driving
by Shuo Xing, Chengyuan Qian, Yuping Wang, Hongyuan Hua, Kexin Tian, Yang Zhou, Zhengzhong Tu
First submitted to arxiv on: 19 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG); Robotics (cs.RO)
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 This paper proposes OpenEMMA, an open-source end-to-end framework for autonomous driving based on Multimodal Large Language Models (MLLMs). Building on recent advancements in inference computing, the authors demonstrate significant improvements compared to the baseline by incorporating Chain-of-Thought reasoning. The framework achieves effectiveness, generalizability, and robustness across various challenging driving scenarios, offering a more efficient and effective approach to autonomous driving. By leveraging diverse MLLMs, OpenEMMA outperforms existing fine-tuning methods that require substantial resources. The paper’s contributions include the development of an open-source framework for end-to-end autonomous driving systems and the application of Chain-of-Thought reasoning to MLLMs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a new way for cars to drive themselves using special language models. It uses complex computer processing to make good decisions while driving. Right now, making these language models work with self-driving cars is hard because it needs lots of computers, data, and money. The researchers created a new system called OpenEMMA that makes this process easier and better. They tested it on different scenarios and showed it can do well even when things get tricky. |
Keywords
» Artificial intelligence » Fine tuning » Inference
