| --- |
| license: apache-2.0 |
| language: |
| - en |
| tags: |
| - autonomous-driving |
| - end-to-end-driving |
| - diffusion-model |
| - trajectory-planning |
| - uncertainty-estimation |
| - navsim |
| pretty_name: Mimir Data |
| size_categories: |
| - 100M<n<1G |
| --- |
| |
| # 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**](https://arxiv.org/pdf/2512.07130) |
| > |
| > 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 |
|
|
| <div align="center"> |
| <img src="./assets/teaser.png" width="75%" /> |
| </div> |
| |
| **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](https://github.com/ZebinX/Mimir-Uncertainty-Driving). |
|
|
| --- |
|
|
| ## 📦 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 |
|
|
| <div align="center"> |
| <img src="./assets/navhard.png" width="49%" /> |
| <img src="./assets/navtest.png" width="49%" /> |
| |
| </div> |
|
|
| Please refer to the [paper](https://arxiv.org/pdf/2512.07130) 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 |
