DriveGPT: Scaling Autoregressive Behavior Models for Driving
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arXiv
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| Main Authors: | , , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866909598507597824 |
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| author | Huang, Xin Wolff, Eric M. Vernaza, Paul Phan-Minh, Tung Chen, Hongge Hayden, David S. Edmonds, Mark Pierce, Brian Chen, Xinxin Jacob, Pratik Elias Chen, Xiaobai Tairbekov, Chingiz Agarwal, Pratik Gao, Tianshi Chai, Yuning Srinivasa, Siddhartha |
| author_facet | Huang, Xin Wolff, Eric M. Vernaza, Paul Phan-Minh, Tung Chen, Hongge Hayden, David S. Edmonds, Mark Pierce, Brian Chen, Xinxin Jacob, Pratik Elias Chen, Xiaobai Tairbekov, Chingiz Agarwal, Pratik Gao, Tianshi Chai, Yuning Srinivasa, Siddhartha |
| contents | We present DriveGPT, a scalable behavior model for autonomous driving. We model driving as a sequential decision-making task, and learn a transformer model to predict future agent states as tokens in an autoregressive fashion. We scale up our model parameters and training data by multiple orders of magnitude, enabling us to explore the scaling properties in terms of dataset size, model parameters, and compute. We evaluate DriveGPT across different scales in a planning task, through both quantitative metrics and qualitative examples, including closed-loop driving in complex real-world scenarios. In a separate prediction task, DriveGPT outperforms state-of-the-art baselines and exhibits improved performance by pretraining on a large-scale dataset, further validating the benefits of data scaling. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_14415 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | DriveGPT: Scaling Autoregressive Behavior Models for Driving Huang, Xin Wolff, Eric M. Vernaza, Paul Phan-Minh, Tung Chen, Hongge Hayden, David S. Edmonds, Mark Pierce, Brian Chen, Xinxin Jacob, Pratik Elias Chen, Xiaobai Tairbekov, Chingiz Agarwal, Pratik Gao, Tianshi Chai, Yuning Srinivasa, Siddhartha Machine Learning Artificial Intelligence Computer Vision and Pattern Recognition Robotics We present DriveGPT, a scalable behavior model for autonomous driving. We model driving as a sequential decision-making task, and learn a transformer model to predict future agent states as tokens in an autoregressive fashion. We scale up our model parameters and training data by multiple orders of magnitude, enabling us to explore the scaling properties in terms of dataset size, model parameters, and compute. We evaluate DriveGPT across different scales in a planning task, through both quantitative metrics and qualitative examples, including closed-loop driving in complex real-world scenarios. In a separate prediction task, DriveGPT outperforms state-of-the-art baselines and exhibits improved performance by pretraining on a large-scale dataset, further validating the benefits of data scaling. |
| title | DriveGPT: Scaling Autoregressive Behavior Models for Driving |
| topic | Machine Learning Artificial Intelligence Computer Vision and Pattern Recognition Robotics |
| url | https://arxiv.org/abs/2412.14415 |