Summary of Drivegpt: Scaling Autoregressive Behavior Models For Driving, by Xin Huang et al.
DriveGPT: Scaling Autoregressive Behavior Models for Driving
by Xin Huang, Eric M. Wolff, Paul Vernaza, Tung Phan-Minh, Hongge Chen, David S. Hayden, Mark Edmonds, Brian Pierce, Xinxin Chen, Pratik Elias Jacob, Xiaobai Chen, Chingiz Tairbekov, Pratik Agarwal, Tianshi Gao, Yuning Chai, Siddhartha Srinivasa
First submitted to arxiv on: 19 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); 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 DriveGPT is a machine learning model designed for autonomous driving that approaches driving as a sequential decision-making problem. The transformer-based model predicts future agent states in an autoregressive fashion, allowing it to scale up its parameters and training data by orders of magnitude. This enables the exploration of scaling properties in terms of dataset size, model parameters, and compute. DriveGPT is evaluated across different scales through both quantitative metrics and qualitative examples, including closed-loop driving in complex real-world scenarios. Additionally, it outperforms state-of-the-art baselines and exhibits improved performance when pretraining on a large-scale dataset. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary DriveGPT is a new way to think about autonomous driving. It’s like a super smart computer that can make decisions really fast. We teach this computer by giving it lots of data, which helps it learn how to drive well. This computer is special because it can get better at driving just by having more data and processing power. We tested DriveGPT in different situations and found that it works really well. It’s even better than other models when we train it on a huge amount of data. |
Keywords
» Artificial intelligence » Autoregressive » Machine learning » Pretraining » Transformer
