Hydra-MDP：基于多目标 Hydra 蒸馏的端到端多模态规划
Hydra-MDP: End-to-end Multimodal Planning with Multi-target Hydra-Distillation
AI总结：
Hydra-MDP 通过多教师知识蒸馏与多头解码器实现端到端多模态规划，在 Navsim 挑战赛中获得第一名，显著提升了跨驾驶环境的泛化能力。
摘要：
我们提出 Hydra-MDP，一种在教师-学生模型中采用多个教师的新范式。该方法利用来自人类教师和基于规则的教师的知识蒸馏来训练学生模型，该模型具有多头解码器，以学习针对各种评估指标量身定制的多样化轨迹候选。借助基于规则的教师的知识，Hydra-MDP 以端到端的方式学习环境如何影响规划，而不是诉诸不可微分的后处理。该方法在 Navsim 挑战赛中获得第 1 名，展示了在不同驾驶环境和条件下泛化能力的显著提升。更多详情请访问 https://github.com/NVlabs/Hydra-MDP。
英文摘要：
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}.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
