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 .