gwx18800155805 / lanesegnet

[ICLR 2024] Map Learning with Lane Segment for Autonomous Driving

LaneSegNet: Map Learning with Lane Segment Perception for Autonomous Driving

• 📬 Primary contact: Tianyu Li ( [email protected] )
• arXiv paper | OpenReview, ICLR 2024
• Blog TODO | Slides TODO

Highlights

🔥 We advocate Lane Segment as a map learning paradigm that seamlessly incorporates both map 🛣️ geometry and 🕸️ topology information.

🏁 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!

News

• [2023/12] LaneSegNet paper is available on arXiv. Code is also released!

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Overall pipeline of LaneSegNet

Table of Contents

• Model Zoo
• Prerequisites
• Installation
• Prepare Dataset
• Train and Evaluate
• License and Citation

Model Zoo

Model	Epoch	mAP	TOPlsls	Memory	Config	Download
LaneSegNet	24	33.5	25.4	9.4G	config	ckpt / log

Prerequisites

• Linux
• Python 3.8.x
• NVIDIA GPU + CUDA 11.1
• PyTorch 1.9.1

Installation

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

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.

🔔 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
├── train
|   └── ...
├── val
|   └── ...
├── test
|   └── ...
├── data_dict_subset_A_train_lanesegnet.pkl
├── data_dict_subset_A_val_lanesegnet.pkl
├── ...

Train and Evaluate

Train

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]

Evaluate

You can set --show to visualize the results.

./tools/dist_test.sh 8 [--show]

License and Citation

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}
}

Related resources

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

• Openlane-V2
• BEVFormer
• TopoNet
• PersFormer
• OpenLane