Update README.md

Author: rb93dett

README.md

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 - VAD implicitly and explicitly utilizes the vectorized scene information to improve planning safety, via query interaction and vectorized planning constraints.
 - VAD achieves SOTA end-to-end planning performance, outperforming previous methods by a large margin. Not only that, because of the vectorized scene representation and our concise model design, VAD greatly improves the inference speed, which is critical for the real-world deployment of an autonomous driving system.
 
+## Results
+- Open-loop planning results on [nuScenes](https://github.com/nutonomy/nuscenes-devkit). See the [paper](https://arxiv.org/abs/2303.12077) for more details.
+
+| Method | L2 (m) 1s | L2 (m) 2s | L2 (m) 3s | Col. (%) 1s | Col. (%) 2s | Col. (%) 3s | FPS |
+| :---: | :---: | :---: | :---: | :---:| :---: | :---: | :---: |
+| ST-P3 | 1.33 | 2.11 | 2.90 | 0.23 | 0.62 | 1.27 | 1.6 |
+| UniAD | 0.48 | 0.96 | 1.65 | **0.05** | 0.17 | 0.71 | 1.8 |
+| VAD-Tiny | 0.20 | 0.38 | 0.65 | 0.10 | 0.12 | 0.27 | **16.8** |
+| VAD-Base | **0.17** | **0.34** | **0.60** | **0.07** | **0.10** | **0.24** | 4.5 |
+
 ## Catalog
 - [ ] Code & Checkpoints Release
 - [x] Initialization
 
-## Acknowledgement
-VAD is based on the following projects: [mmdet3d](https://github.com/open-mmlab/mmdetection3d), [detr3d](https://github.com/WangYueFt/detr3d), [BEVFormer](https://github.com/fundamentalvision/BEVFormer) and [MapTR](https://github.com/hustvl/MapTR). Many thanks to their excellent contributions to the community.
-
-
 ## Contact
 If you have any questions or suggestions about this repo, please feel free to contact us ([email protected], [email protected]).
 
 ## Citation
-
 If you find VAD is useful in your research or applications, please consider giving us a star 🌟 and citing it by the following BibTeX entry.
 
 ```BibTeX
@@ -50,3 +55,9 @@ If you find VAD is useful in your research or applications, please consider givi
   year={2023}
 }
 ```
+
+## License
+All code in this repository is under the [Apache License 2.0](https://www.apache.org/licenses/LICENSE-2.0).
+
+## Acknowledgement
+VAD is based on the following projects: [mmdet3d](https://github.com/open-mmlab/mmdetection3d), [detr3d](https://github.com/WangYueFt/detr3d), [BEVFormer](https://github.com/fundamentalvision/BEVFormer) and [MapTR](https://github.com/hustvl/MapTR). Many thanks to their excellent contributions to the community.