Update README.md

Author: Mrmoore98

README.md

@@ -1,5 +1,5 @@
 # VectorMapNet_code
-**VectorMapNet: End-to-end Vectorized HD Map Learning**
+**VectorMapNet: End-to-end Vectorized HD Map Learning** ICML 2023
 
 This is the official codebase of VectorMapNet
 
@@ -10,7 +10,7 @@ This is the official codebase of VectorMapNet
 **[[Paper](https://arxiv.org/pdf/2206.08920.pdf)] [[Project Page](https://tsinghua-mars-lab.github.io/vectormapnet/)]**
 
 **Abstract:**
-Autonomous driving systems require a good understanding of surrounding environments, including moving obstacles and static High-Definition (HD) semantic maps. Existing methods approach the semantic map problem by offline manual annotations, which suffer from serious scalability issues. More recent learning-based methods produce dense rasterized segmentation predictions which do not include instance information of individual map elements and require heuristic post-processing that involves many hand-designed components, to obtain vectorized maps. To that end, we introduce an end-to-end vectorized HD map learning pipeline, termed VectorMapNet. VectorMapNet takes onboard sensor observations and predicts a sparse set of polylines primitives in the bird's-eye view to model the geometry of HD maps. Based on this pipeline, our method can explicitly model the spatial relation between map elements and generate vectorized maps that are friendly for downstream autonomous driving tasks without the need for post-processing. In our experiments, VectorMapNet achieves strong HD map learning performance on nuScenes dataset, surpassing previous state-of-the-art methods by 14.2 mAP. Qualitatively, we also show that VectorMapNet is capable of generating comprehensive maps and capturing more fine-grained details of road geometry. To the best of our knowledge, VectorMapNet is the first work designed toward end-to-end vectorized HD map learning problems.
+Autonomous driving systems require High-Definition (HD) semantic maps to navigate around urban roads. Existing solutions approach the semantic mapping problem by offline manual annotation, which suffers from serious scalability issues.  Recent learning-based methods produce dense rasterized segmentation predictions to construct maps. However, these predictions do not include instance information of individual map elements and require heuristic post-processing to obtain vectorized maps. To tackle these challenges, we introduce an end-to-end vectorized HD map learning pipeline, termed VectorMapNet. VectorMapNet takes onboard sensor observations and predicts a sparse set of polylines in the bird's-eye view. This pipeline can explicitly model the spatial relation between map elements and generate vectorized maps that are friendly to downstream autonomous driving tasks. Extensive experiments show that VectorMapNet achieve strong map learning performance on both nuScenes and Argoverse2 dataset, surpassing previous state-of-the-art methods by 14.2 mAP and 14.6mAP. Qualitatively, VectorMapNet is capable of generating comprehensive maps and capturing fine-grained details of road geometry. To the best of our knowledge, VectorMapNet is the first work designed towards end-to-end vectorized map learning from onboard observations. 
 
 **Questions/Requests:** 
 Please file an [issue](https://github.com/Tsinghua-MARS-Lab/vecmapnet/issues) or send an email to [Yicheng]([email protected]).
@@ -19,11 +19,12 @@ Please file an [issue](https://github.com/Tsinghua-MARS-Lab/vecmapnet/issues) or
 ## Bibtex
 If you found this paper or codebase useful, please cite our paper:
 ```
-@article{liu2022vectormapnet,
-    title={VectorMapNet: End-to-end Vectorized HD Map Learning},
-    author={Liu, Yicheng and Wang, Yue and Wang, Yilun and Zhao, Hang},
-    journal={arXiv preprint arXiv:2206.08920},
-    year={2022}
+@inproceedings{liu2022vectormapnet,
+        title={VectorMapNet: End-to-end Vectorized HD Map Learning},
+        author={Liu, Yicheng and Yuantian, Yuan and Wang, Yue and Wang, Yilun and Zhao, Hang},
+        booktitle={International conference on machine learning},
+        year={2023},
+        organization={PMLR}
     }
 ```