Hydra-MDP: End-to-end Multimodal Planning with Multi-target Hydra-Distillation
<|reference_start|>Hydra-MDP: End-to-end Multimodal Planning with Multi-target Hydra-Distillation: 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}.<|reference_end|>
@article{li2024hydra-mdp:,
    title={Hydra-MDP: End-to-end Multimodal Planning with Multi-target
  Hydra-Distillation},
    author={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},
    journal={arXiv preprint arXiv:2406.06978},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.06978},
    primaryClass={cs.CV}
}