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 https://github.com/woxihuanjiangguo/Hydra-MDP

Hydra-MDP 是一种使用多个教师模型的新范例，使用来自人类和基于规则的教师的知识蒸馏来训练学生模型，具有多头解码器以学习适应各种评估指标的多样化轨迹候选，该方法在 Navsim 挑战中获得了第一名，并在各种驾驶环境和条件下显著提高了泛化能力。