README.md

August 1, 2025 · View on GitHub

FASTopoWM: Fast-Slow Lane Segment Topology Reasoning with Latent World Models

Yiming Yang1,2 , Hongbin Lin1,2, Yueru Luo1,2, Suzhong Fu1,2, Chao Zheng3, Xinrui Yan3, Shuqi Mei3, Kun Tang3, Shuguang Cui2,1, Zhen Li2,1

1 FNii-Shenzhen 2 SSE, CUHK-Shenzhen, 3 T Lab, Tencent

arXiv

This repository is built upon LaneSegNet.

Motivation

vis1

Framework

vis1

Visualizations

The visualization results demonstrate that our predictions maintain robust temporal consistency, reflected in the stable alignment of lane segment coordinates and topological structures as the ego vehicle moves.

vis3

vis4

Prerequisites

  • 4 x 40G memory A100 GPUs or 4 x 32G memory V100 GPUs (for batch size = 2)

Prepare Dataset

Following OpenLane-V2 repo to download the Image and the Map Element Bucket data. Run the following script to collect data for this repo.

cd TopoStreamer
mkdir data

ln -s {Path to OpenLane-V2 repo}/data/OpenLane-V2 ./data/
python ./tools/data_process.py
python ./tools/tracking/dist_track.sh

After setup, the hierarchy of folder data is described below:

data/OpenLane-V2
├── train
|   └── ...
├── val
|   └── ...
├── test
|   └── ...
├── data_dict_subset_A_train_lanesegnet.pkl
├── data_dict_subset_A_val_lanesegnet.pkl
├── data_dict_subset_A_train_lanesegnet_gt_tracks.pkl
├── data_dict_subset_A_val_lanesegnet_gt_tracks.pkl
├── ...

Installation

We recommend using conda to run the code.

conda create -n fastopowm python=3.8 -y
conda activate fastopowm

# (optional) If you have CUDA installed on your computer, skip this step.
conda install cudatoolkit=11.1.1 -c conda-forge

pip install torch==1.9.0+cu111 torchvision==0.10.0+cu111 torchaudio==0.9.0 -f https://download.pytorch.org/whl/torch_stable.html

Install mm-series packages.

pip install mmcv-full==1.5.2 -f https://download.openmmlab.com/mmcv/dist/cu111/torch1.9.0/index.html
pip install mmdet==2.26.0
pip install mmsegmentation==0.29.1
pip install mmdet3d==1.0.0rc6

Install other required packages.

pip install -r requirements.txt

Train

We recommend using 4 GPUs for training. The training logs will be saved to work_dirs/stream.

mkdir -p work_dirs/stream
./tools/dist_train.sh 4 && ./tools/dist_train_stage2.sh 4

Evaluate

./tools/dist_test.sh 4

For per frame visualization, you can run:

./tools/dist_test.sh 4 --show

We acknowledge all the open-source contributors for the following projects to make this work possible: