DyRoNet: Dynamic Routing and Low-Rank Adapters for Autonomous Driving Streaming Perception
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arXiv
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| Autores principales: | , , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Acceso en línea: | |
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| _version_ | 1866913612419825664 |
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| author | Huang, Xiang Cheng, Zhi-Qi He, Jun-Yan Li, Chenyang Xiang, Wangmeng Sun, Baigui |
| author_facet | Huang, Xiang Cheng, Zhi-Qi He, Jun-Yan Li, Chenyang Xiang, Wangmeng Sun, Baigui |
| contents | The advancement of autonomous driving systems hinges on the ability to achieve low-latency and high-accuracy perception. To address this critical need, this paper introduces Dynamic Routing Network (DyRoNet), a low-rank enhanced dynamic routing framework designed for streaming perception in autonomous driving systems. DyRoNet integrates a suite of pre-trained branch networks, each meticulously fine-tuned to function under distinct environmental conditions. At its core, the framework offers a speed router module, developed to assess and route input data to the most suitable branch for processing. This approach not only addresses the inherent limitations of conventional models in adapting to diverse driving conditions but also ensures the balance between performance and efficiency. Extensive experimental evaluations demonstrate the adaptability of DyRoNet to diverse branch selection strategies, resulting in significant performance enhancements across different scenarios. This work establishes a new benchmark for streaming perception and provides valuable engineering insights for future work. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_05050 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | DyRoNet: Dynamic Routing and Low-Rank Adapters for Autonomous Driving Streaming Perception Huang, Xiang Cheng, Zhi-Qi He, Jun-Yan Li, Chenyang Xiang, Wangmeng Sun, Baigui Computer Vision and Pattern Recognition Artificial Intelligence Multimedia The advancement of autonomous driving systems hinges on the ability to achieve low-latency and high-accuracy perception. To address this critical need, this paper introduces Dynamic Routing Network (DyRoNet), a low-rank enhanced dynamic routing framework designed for streaming perception in autonomous driving systems. DyRoNet integrates a suite of pre-trained branch networks, each meticulously fine-tuned to function under distinct environmental conditions. At its core, the framework offers a speed router module, developed to assess and route input data to the most suitable branch for processing. This approach not only addresses the inherent limitations of conventional models in adapting to diverse driving conditions but also ensures the balance between performance and efficiency. Extensive experimental evaluations demonstrate the adaptability of DyRoNet to diverse branch selection strategies, resulting in significant performance enhancements across different scenarios. This work establishes a new benchmark for streaming perception and provides valuable engineering insights for future work. |
| title | DyRoNet: Dynamic Routing and Low-Rank Adapters for Autonomous Driving Streaming Perception |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Multimedia |
| url | https://arxiv.org/abs/2403.05050 |