RNGDet++: Road Network Graph Detection by Transformer With Instance Segmentation and Multi-Scale Features Enhancement

Zhenhua Xu, Yuxuan Liu, Yuxiang Sun, Ming Liu, Lujia Wang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

4 Citations (Scopus)


The road network graph is a critical component for downstream tasks in autonomous driving, such as global route planning and navigation. In the past years, road network graphs are usually annotated by human experts manually, which is time-consuming and labor-intensive. To annotate road network graphs effectively and efficiently, automatic algorithms for road network graph detection are demanded. Most existing methods either adopt a post-processing step on semantic segmentation maps to produce road network graphs, or propose graph-based algorithms to directly predict the graphs. However, these works suffer from hard-coded algorithms and inferior performance. To enhance the previous state-of-the-art (SOTA) method RNGDet, we add an instance segmentation head to better supervise the training, and enable the network to leverage multi-scale features of the backbone. Since the new proposed approach is improved from RNGDet, we name it RNGDet++. Experimental results show that our RNGDet++ outperforms baseline methods in terms of almost all evaluation metrics on two large-scale public datasets.

Original languageEnglish
Pages (from-to)2991-2998
Number of pages8
JournalIEEE Robotics and Automation Letters
Issue number5
Publication statusPublished - 1 May 2023


  • autonomous driving
  • imitation learning
  • Road network graph detection
  • robotics

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Biomedical Engineering
  • Human-Computer Interaction
  • Mechanical Engineering
  • Computer Vision and Pattern Recognition
  • Computer Science Applications
  • Control and Optimization
  • Artificial Intelligence


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