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Mimir-Uncertainty-Driving

Name: Mimir-Uncertainty-Driving
Availability: InStock

uncertainty-estimation

navsim

2512.07130

apache-2.0

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Mimir: Hierarchical Goal-Driven Diffusion with Uncertainty Propagation for End-to-End Autonomous Driving

Mimir: Hierarchical Goal-Driven Diffusion with Uncertainty Propagation for End-to-End Autonomous Driving

Zebin Xing, Yupeng Zheng, Qichao Zhang, Zhixing Ding, Pengxuan Yang, Songen Gu, Zhongpu Xia, Dongbin Zhao

Institute of Automation, Chinese Academy of Sciences; China University of Geosciences; Fudan University

IEEE Robotics and Automation Letters (RAL), 2025

Mimir is a hierarchical goal-driven diffusion model for end-to-end autonomous driving. It improves upon GoalFlow by explicitly modeling goal uncertainty and accelerating goal inference, enabling more robust and efficient planning. With a ResNet-34 backbone, Mimir achieves 89.3 PDMS and 34.6 EPDMS, demonstrating strong performance in both accuracy and efficiency.

This repository contains the model weights and intermediate data files required for Mimir's evaluation and training. For the full codebase, please visit the main Mimir repository.

📦 Repository Structure

Path	Description
`ckpts/mimir_epoch94.ckpt`	Mimir planning model (~746MB)
`ckpts/mimir_unc_epoch99.ckpt`	Uncertainty modeling model (~724MB)
`goalflow_goal/`	GoalFlow goal points for `mimir_unc` training initialization (navhard/navtest/navtrain)
`goalflow_dac/`	Predicted DAC using GoalFlow (navhard/navtest/navtrain)
`navhard_naviunc/`	Navigation & uncertainty data for Navhard
`navstest_naviunc/`	Navigation & uncertainty data for Navtest
`navtrain_naviunc/`	Navigation & uncertainty data for Navtrain
`cluster_points_8192_.npy`	8192 cluster centers for goal point discretization
`frame_mapping_navhard.yaml`	Frame index mapping for Navhard
`global_pose_navhard.npy`	Global pose data for Navhard

📊 Results

Please refer to the paper for more details.

📄 Citation

If you find Mimir useful, please consider citing our paper:

bibtex

@ARTICLE{11282450,
  author={Xing, Zebin and Zheng, Yupeng and Zhang, Qichao and Ding, Zhixing and Yang, Pengxuan and Gu, Songen and Xia, Zhongpu and Zhao, Dongbin},
  journal={IEEE Robotics and Automation Letters}, 
  title={Mimir: Hierarchical Goal-Driven Diffusion With Uncertainty Propagation for End-to-End Autonomous Driving}, 
  year={2026},
  volume={11},
  number={2},
  pages={2178-2185},
  keywords={Uncertainty;Trajectory;Predictive models;Autonomous vehicles;Laser radar;Vocabulary;Planning;Feature extraction;Estimation;Artificial intelligence;Learning from demonstration;imitation learning;autonomous vehicle navigation},
  doi={10.1109/LRA.2025.3641129}}

Contact

If you have any questions or suggestions, please open an issue or contact: xzebin@bupt.edu.cn

XXXXing/Mimir-Uncertainty-Driving