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README.md

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----
-license: apache-2.0
----
+---
+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
+
+**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 |
+
+> Each `*_naviunc/` directory contains `navi.npy` (navigation points) and `unc.npy` (uncertainty estimates).
+
+
+
+## 📊 Results
+
+### Navtest (NAVSIM v1)
+
+| Method | NC↑ | DAC↑ | TTC↑ | EP↑ | PDMS↑ |
+|:---|:---:|:---:|:---:|:---:|:---:|
+| GoalFlow‡ | 98.2 | 96.4 | 93.8 | 82.6 | 87.9 |
+| DiffusionDrive | 98.2 | 96.2 | 94.7 | 82.2 | 88.1 |
+| **Mimir** | **98.2** | **97.5** | 94.6 | **83.6** | **89.3** |
+
+### Navhard (NAVSIM v2)
+
+| Method | EPDMS↑ |
+|:---|:---:|
+| GoalFlow† | 28.9 |
+| DiffusionDrive† | 29.1 |
+| **Mimir** | **34.6** |
+
+Please refer to the [paper](https://arxiv.org/pdf/2512.07130) for full comparison tables.
+
+---
+
+## 📄 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

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