  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. Code will
be available at \url{https://github.com/NVlabs/Hydra-MDP}.
