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arXiv · 2503.12820

Hydra-MDP++: Advancing End-to-End Driving via Expert-Guided Hydra-Distillation

Kailin Li·Zhenxin Li·Shiyi Lan·Yuan Xie·Zhizhong Zhang·Jiayi Liu·Zuxuan Wu·Zhiding Yu·Jose M. Alvarez
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Abstract

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.

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Kailin Li, Zhenxin Li, Shiyi Lan, Yuan Xie, Zhizhong Zhang, Jiayi Liu, Zuxuan Wu, Zhiding Yu, Jose M. Alvarez. 2025-03-17. Hydra-MDP++: Advancing End-to-End Driving via Expert-Guided Hydra-Distillation. https://arxiv.org/abs/2503.12820

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