[NeurIPS 2024] TopoLogic: An Interpretable Pipeline for Lane Topology Reasoning on Driving Scenes

🔥New Topology Reasoning work TopoPoint is released.

• Production from Institute of Computing Technology, Chinese Academy of Sciences.
• Primary contact: Yanping Fu ( fuyanping23s@ict.ac.cn ) or/and Xinyuan Liu.

TL;DR

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.

Updates

• [2025.5.26] 🔥New work TopoPoint is released.
• [2024.10.6] Code and Model are released.
• [2024.9.26] TopoLogic is accepted by NeurIPS 2024.
• [2024.5.23] TopoLogic paper is released at arXiv.

Table of Contents

• TopoLogic: An Interpretable Pipeline for Lane Topology Reasoning on Driving Scenes
  • Table of Contents
  • Model Zoo
  • Main Results
    • Results on OpenLane-V2 subset-A val
    • Results on OpenLane-V2 subset-B val
  • Prerequisites
  • Installation
  • Prepare Dataset
  • Train and Evaluate
    • Train
    • Evaluate
  • Citation
  • Related resources

Model Zoo

Method	Backbone	Epoch	Dataset	OLS	Version	Config	Download
TopoLogic	ResNet-50	24	subset-A	44.1	OpenLane-V2-v2.1.0	config	ckpt / log

Main Results

The result is based on the v1.0.0 OpenLane-V2 devkit and metrics.

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
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

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

• 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 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/[work_dir_name].

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.

@inproceedings{fu2024topologic,
 author = {Fu, Yanping and Liao, Wenbin and Liu, Xinyuan and Xu, Hang and Ma, Yike and Zhang, Yucheng and Dai, Feng},
 booktitle = {Advances in Neural Information Processing Systems},
 pages = {61658--61676},
 title = {TopoLogic: An Interpretable  Pipeline for Lane Topology Reasoning on Driving Scenes},
 volume = {37},
 year = {2024}
}

@misc{fu2025topopoint,
      title={TopoPoint: Enhance Topology Reasoning via Endpoint Detection in Autonomous Driving}, 
      author={Yanping Fu and Xinyuan Liu and Tianyu Li and Yike Ma and Yucheng Zhang and Feng Dai},
      year={2025},
      eprint={2505.17771},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2505.17771}, 
}

Related resources

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

• TopoPoint
• TopoNet
• Openlane-V2
• BEVFormer