Update README.md

README.md

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 ---
 license: mit
+tags:
+  - autonomous-driving
+  - motion-planning
+  - flow-matching
+  - generative-model
+  - navsim
+library_name: pytorch
+pipeline_tag: other
 ---
+
+# FlowR2A: Learning Reward-to-Action Distribution for Multimodal Driving Planning
+
+FlowR2A is a generative multimodal driving planner that learns the **reward-conditioned action distribution** $p(a \mid r)$ with **flow matching**. Instead of treating simulation-based rewards as *discriminative targets* (as in scoring-based planners), FlowR2A reframes them as *generative conditions*, unifying the dense supervision of scoring-based methods with the dynamic proposal generation of anchor-based methods in a single model. This forces the planner to internalize how an action relates to its outcomes in safety, progress, comfort, and rule compliance.
+
+- 📄 **Paper:** FlowR2A: Learning Reward-to-Action Distribution for Multimodal Driving Planning
+- 🌐 **Project page:** https://lixirui142.github.io/flowr2a-project-page/
+- 💻 **Code:** https://github.com/lixirui142/FlowR2A
+
+## Model Description
+
+FlowR2A consists of four components:
+
+1. **Perception Encoder** — a Transfuser backbone (multi-view camera + BEV LiDAR) producing scene and agent tokens.
+2. **Reward Encoder** — embeds simulation reward signals (safety, progress, comfort, rule compliance) into a condition vector injected via adaptive layer norm; supports classifier-free guidance through reward dropout.
+3. **Flow-based Action Decoder** — a transformer with self-attention over trajectory points and cross-attention to scene tokens, conditioned on reward + time embeddings via AdaLN, trained with a velocity-matching loss over dense action–reward pairs.
+4. **Mode Selector** — a lightweight transformer that scores generated proposals, trained with online simulation labeling.
+
+## Checkpoint
+
+| File | Description |
+|------|-------------|
+| `flowr2a_s2.ckpt` | Stage-2 checkpoint, including all components. |
+
+## Results
+
+State-of-the-art closed-loop performance on the NAVSIM `navtest` benchmarks (lightweight backbone).
+
+**NAVSIM v1**
+
+| Setting | NC | DAC | TTC | Comf. | EP | **PDMS** |
+|---|---|---|---|---|---|---|
+| Single proposal | 98.6 | 97.3 | 95.3 | 100 | 84.9 | **90.0** |
+| 60 proposals | 98.8 | 98.0 | 96.0 | 100 | 90.1 | **92.8** |
+
+**NAVSIM v2**
+
+| NC | DAC | DDC | TLC | EP | TTC | LK | HC | EC | **EPDMS** |
+|---|---|---|---|---|---|---|---|---|---|
+| 98.9 | 98.1 | 99.1 | 99.7 | 91.5 | 98.5 | 95.0 | 98.3 | 65.2 | **88.9** |
+
+## Usage
+
+See the [GitHub repository](https://github.com/lixirui142/FlowR2A) for setup, the NAVSIM data pipeline, and inference instructions. Download the checkpoint with:
+
+```python
+from huggingface_hub import hf_hub_download
+
+ckpt = hf_hub_download(repo_id="lixirui142/FlowR2A", filename="flowr2a_s2.ckpt")
+```
+
+## Citation
+
+```bibtex
+@article{flowr2a2026,
+  title   = {FlowR2A: Learning Reward-to-Action Distribution for Multimodal Driving Planning},
+  author  = {Li, Xirui and Liu, Zhe and Ye, Xiaoqing and Han, Wenhua and Pan, Yifeng and Han, Junyu and Zhao, Hengshuang},
+  journal = {arXiv preprint},
+  year    = {2026}
+}
+```
+
+## License
+
+Released under the MIT License.