MapBEVPrediction

This repository contains the official implementation of Accelerating Online Mapping and Behavior Prediction via Direct BEV Feature Attention published in ECCV 2024.

Getting Started

• Environment Setup
• Prepare Dataset
• Mapping Train and Eval
• Merge Map and Trajectory Dataset
• Trajectory Train and Eval
• Visualization

Results

Mapping checkpoints are here. Trajectory prediction checkpoints are here.

Dataset

I have uploaded all datasets (complete) for MapTR, StreamMapNet, MapTRv2 and MapTRv2 CL. They are around 500GB each (StreamMapNet is around 200GB). You can download them through AWS S3. They are located at

• AWS Bucket Name: s3://mapbevprediction
• Region: us-east-2

I will try to transfer them to Hugging Face in the future. AWS is a bit expensive.

Dataset Structure is as follows:

mapbevprediction
├── stream_bev/
├── maptr_bev/
├── maptrv2_bev/
│   ├── mini_val/
│   |   ├── data/
│   |   |   ├── scene-{scene_id}.pkl
│   ├── train/
│   ├── val/
├── maptrv2_cent_bev/

Catalog

• Visualization Code
• Code release
  • MapTR
  • MapTRv2
  • StreamMapNet
  • HiVT
  • DenseTNT
• Untested version released + Instructions
• Initialization

Citation

If you found this repository useful, please consider citing our work:

@Inproceedings{GuSongEtAl2024,
  author    = {Gu, Xunjiang and Song, Guanyu and Gilitschenski, Igor and Pavone, Marco and Ivanovic, Boris},
  title     = {Accelerating Online Mapping and Behavior Prediction via Direct BEV Feature Attention},
  booktitle = {European Conference on Computer Vision (ECCV)},
  year      = {2024}
}

This codebase is built using our prior work, if your found this helpful, please also consider citing:

@Inproceedings{GuSongEtAl2024,
  author    = {Gu, Xunjiang and Song, Guanyu and Gilitschenski, Igor and Pavone, Marco and Ivanovic, Boris},
  title     = {Producing and Leveraging Online Map Uncertainty in Trajectory Prediction},
  booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year      = {2024}
}

License

This repository is licensed under Apache 2.0.