Hydra-MDP: End-to-end Multimodal Planning with Multi-target Hydra-Distillation
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

TL;DR
Hydra-MDP introduces a multi-teacher, end-to-end multimodal planning framework that distills knowledge from human and rule-based teachers, achieving state-of-the-art results in autonomous driving challenges.
Contribution
It presents a novel multi-teacher distillation approach with a multi-head decoder for diverse trajectory generation in autonomous driving.
Findings
Achieved 1st place in Navsim challenge.
Demonstrated improved generalization across environments.
Learned environment influence end-to-end without non-differentiable post-processing.
Abstract
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 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}.
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Taxonomy
TopicsAI-based Problem Solving and Planning · Robotic Path Planning Algorithms · Multi-Agent Systems and Negotiation
MethodsKnowledge Distillation
