- π¬ Primary contact: Tianyu Li ( [email protected] )
- arXiv paper | OpenReview, ICLR 2024
- Blog (Zhihu) | Slides TODO
π₯ We advocate Lane Segment as a map learning paradigm that seamlessly incorporates both map π£οΈ geometry and πΈοΈ topology information.
π Lane Segment and OpenLane-V2 Map Element Bucket
will serve as a track in the CVPR 2024 Autonomous Driving Challenge
. For further details, please stay tuned!
[2023/12]
LaneSegNet paper is available on arXiv. Code is also released!
Model | Epoch | mAP | TOPlsls | Memory | Config | Download |
---|---|---|---|---|---|---|
LaneSegNet | 24 | 33.5 | 25.4 | 9.4G | config | ckpt / log |
- Linux
- Python 3.8.x
- NVIDIA GPU + CUDA 11.1
- PyTorch 1.9.1
We recommend using conda to run the code.
conda create -n lanesegnet python=3.8 -y
conda activate lanesegnet
# (optional) If you have CUDA installed on your computer, skip this step.
conda install cudatoolkit=11.1.1 -c conda-forge
pip install torch==1.9.1+cu111 torchvision==0.10.1+cu111 -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
Following OpenLane-V2 repo to download the Image and the Map Element Bucket data. Run the following script to collect data for this repo.
π The Map Element Bucket
has been updated as of October 2023. Please ensure you download the most recent data!
cd LaneSegNet
mkdir data
ln -s {Path to OpenLane-V2 repo}/data/OpenLane-V2 ./data/
python ./tools/data_process.py
After setup, the hierarchy of folder data
is described below:
data/OpenLane-V2
βββ train
| βββ ...
βββ val
| βββ ...
βββ test
| βββ ...
βββ data_dict_subset_A_train_lanesegnet.pkl
βββ data_dict_subset_A_val_lanesegnet.pkl
βββ ...
We recommend using 8 GPUs for training. If a different number of GPUs is utilized, you can enhance performance by configuring the --autoscale-lr
option. The training logs will be saved to work_dirs/lanesegnet
.
cd LaneSegNet
mkdir -p work_dirs/lanesegnet
./tools/dist_train.sh 8 [--autoscale-lr]
You can set --show
to visualize the results.
./tools/dist_test.sh 8 [--show]
All assets and code are under the Apache 2.0 license unless specified otherwise.
If this work is helpful for your research, please consider citing the following BibTeX entry.
@inproceedings{li2023lanesegnet,
title={LaneSegNet: Map Learning with Lane Segment Perception for Autonomous Driving},
author={Li, Tianyu and Jia, Peijin and Wang, Bangjun and Chen, Li and Jiang, Kun and Yan, Junchi and Li, Hongyang},
booktitle={ICLR},
year={2024}
}
@inproceedings{wang2023openlanev2,
title={OpenLane-V2: A Topology Reasoning Benchmark for Unified 3D HD Mapping},
author={Wang, Huijie and Li, Tianyu and Li, Yang and Chen, Li and Sima, Chonghao and Liu, Zhenbo and Wang, Bangjun and Jia, Peijin and Wang, Yuting and Jiang, Shengyin and Wen, Feng and Xu, Hang and Luo, Ping and Yan, Junchi and Zhang, Wei and Li, Hongyang},
booktitle={NeurIPS},
year={2023}
}
We acknowledge all the open-source contributors for the following projects to make this work possible: