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An Interpretable Pipeline for Lane Topology Reasoning on Driving Scenes
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# TopoLogic: An Interpretable Pipeline for Lane Topology Reasoning on Driving Scenes [![arXiv](https://img.shields.io/badge/arXiv-2304.05277-479ee2.svg)](https://arxiv.org/abs/2405.14747) [![OpenLane-V2](https://img.shields.io/badge/GitHub-OpenLane--V2-blueviolet.svg)](https://github.com/OpenDriveLab/OpenLane-V2) ![method](figs/pipeline.png "Model Architecture")

This repository contains the source code of TopoLogic, An Interpretable Pipeline for Lane Topology Reasoning on Driving Scenes.

TopoLogic is the first to employ an interpretable approach for lane topology reasoning. TopoLogic fuses the geometric distance of lane line endpoints mapped through a designed function and the similarity of lane query in a high-dimensional semantic space to reason lane topology. Experiments on the large-scale autonomous driving dataset OpenLane-V2 benchmark demonstrate that TopoLogic significantly outperforms existing methods in topology reasoning in complex scenarios.

Table of Contents

Main Results

Results on OpenLane-V2 subset-A val

We provide results on Openlane-V2 subset-A val set.

Method Backbone Epoch SDMap DETl TOPll DETt TOPlt OLS
STSU ResNet-50 24 × 12.7 0.5 43.0 15.1 25.4
VectorMapNet ResNet-50 24 × 11.1 0.4 41.7 6.2 20.8
MapTR ResNet-50 24 × 8.3 0.2 43.5 5.8 20.0
MapTR* ResNet-50 24 × 17.7 1.1 43.5 10.4 26.0
TopoNet ResNet-50 24 × 28.6 4.1 48.6 20.3 35.6
TopoLogic ResNet-50 24 × 29.9 18.6 47.2 21.5 41.6
SMERF ResNet-50 24 √ 33.4 7.5 48.6 23.4 39.4 15.4
TopoLogic ResNet-50 24 √ 34.4 23.4 48.3 24.4 45.1

The result of TopoLogic is from this repo.

Results on OpenLane-V2 subset-B val

Method Backbone Epoch DETl TOPll DETt TOPlt OLS
TopoLogic ResNet-50 24 25.9 15.1 54.7 15.1 39.6 21.6

The result is based on the updated v2.1.0 OpenLane-V2 devkit and metrics.
The result of TopoLogic is from this repo.

Method Backbone Epoch DETl TOPll DETt TOPlt OLS
TopoLogic ResNet-50 24 29.9 23.9 47.2 25.4 44.1

Prerequisites

Installation

We recommend using conda to run the code.

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

# (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 other required packages.

pip install -r requirements.txt

Prepare Dataset

Following OpenLane-V2 repo to download the data and run the preprocessing code.

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/toponet.

cd TopoLogic
mkdir work_dirs

./tools/dist_train.sh 8 [work_dir_name] [--autoscale-lr]

Evaluate

You can set --show to visualize the results.

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

Citation

If this work is helpful for your research, please consider citing the following BibTeX entry.

@misc{fu2024topologic,
      title={TopoLogic: An Interpretable Pipeline for Lane Topology Reasoning on Driving Scenes}, 
      author={Yanping Fu and Wenbin Liao and Xinyuan Liu and Hang xu and Yike Ma and Feng Dai and Yucheng Zhang},
      year={2024},
      eprint={2405.14747},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}

Related resources

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