Hydra-MDP++: Advancing End-to-End Driving via Expert-Guided Hydra-Distillation
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arXiv
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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866916653952925696 |
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| author | Li, Kailin Li, Zhenxin Lan, Shiyi Xie, Yuan Zhang, Zhizhong Liu, Jiayi Wu, Zuxuan Yu, Zhiding Alvarez, Jose M. |
| author_facet | Li, Kailin Li, Zhenxin Lan, Shiyi Xie, Yuan Zhang, Zhizhong Liu, Jiayi Wu, Zuxuan Yu, Zhiding Alvarez, Jose M. |
| contents | Hydra-MDP++ introduces a novel teacher-student knowledge distillation framework with a multi-head decoder that learns from human demonstrations and rule-based experts. Using a lightweight ResNet-34 network without complex components, the framework incorporates expanded evaluation metrics, including traffic light compliance (TL), lane-keeping ability (LK), and extended comfort (EC) to address unsafe behaviors not captured by traditional NAVSIM-derived teachers. Like other end-to-end autonomous driving approaches, \hydra processes raw images directly without relying on privileged perception signals. Hydra-MDP++ achieves state-of-the-art performance by integrating these components with a 91.0% drive score on NAVSIM through scaling to a V2-99 image encoder, demonstrating its effectiveness in handling diverse driving scenarios while maintaining computational efficiency. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_12820 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Hydra-MDP++: Advancing End-to-End Driving via Expert-Guided Hydra-Distillation Li, Kailin Li, Zhenxin Lan, Shiyi Xie, Yuan Zhang, Zhizhong Liu, Jiayi Wu, Zuxuan Yu, Zhiding Alvarez, Jose M. Computer Vision and Pattern Recognition Hydra-MDP++ introduces a novel teacher-student knowledge distillation framework with a multi-head decoder that learns from human demonstrations and rule-based experts. Using a lightweight ResNet-34 network without complex components, the framework incorporates expanded evaluation metrics, including traffic light compliance (TL), lane-keeping ability (LK), and extended comfort (EC) to address unsafe behaviors not captured by traditional NAVSIM-derived teachers. Like other end-to-end autonomous driving approaches, \hydra processes raw images directly without relying on privileged perception signals. Hydra-MDP++ achieves state-of-the-art performance by integrating these components with a 91.0% drive score on NAVSIM through scaling to a V2-99 image encoder, demonstrating its effectiveness in handling diverse driving scenarios while maintaining computational efficiency. |
| title | Hydra-MDP++: Advancing End-to-End Driving via Expert-Guided Hydra-Distillation |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2503.12820 |