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arXiv · 2406.06978

Hydra-MDP: End-to-end Multimodal Planning with Multi-target Hydra-Distillation

Zhenxin Li·Kailin Li·Shihao Wang·Shiyi Lan·Zhiding Yu·Yishen Ji·Zhiqi Li·Ziyue Zhu·Jan Kautz·Zuxuan Wu·Yu-Gang Jiang·Jose M. Alvarez
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Abstract

We propose Hydra-MDP, a novel paradigm employing multiple teachers in a teacher-student model. This approach uses knowledge distillation from both human and rule-based teachers to train the student model, which features a multi-head decoder to learn diverse trajectory candidates tailored to various evaluation metrics. With the knowledge of rule-based teachers, Hydra-MDP learns how the environment influences the planning in an end-to-end manner instead of resorting to non-differentiable post-processing. This method achieves the $1^{st}$ place in the Navsim challenge, demonstrating significant improvements in generalization across diverse driving environments and conditions. More details by visiting \url{https://github.com/NVlabs/Hydra-MDP}.

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Zhenxin Li, Kailin Li, Shihao Wang, Shiyi Lan, Zhiding Yu, Yishen Ji, Zhiqi Li, Ziyue Zhu, Jan Kautz, Zuxuan Wu, Yu-Gang Jiang, Jose M. Alvarez. 2024-06-11. Hydra-MDP: End-to-end Multimodal Planning with Multi-target Hydra-Distillation. https://arxiv.org/abs/2406.06978

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