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Related Experiment Video

Updated: Jan 11, 2026

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
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Graph attention-driven relation network for 3D lane detection.

Yanji Jiang1, Yingyang Zhang1, Jiayu Bi2

  • 1Liaoning Technical University, Huludao, 125105, Liaoning, China.

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|November 19, 2025
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Summary

This study introduces Graph-RMNet, a novel graph attention-based 3D lane detection method. It significantly improves autonomous driving systems by accurately identifying lanes, even when occluded or missing.

Keywords:
3D lane detection3D positional encodingAutonomous drivingGraph attention networkRelation modelling

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Area of Science:

  • Computer Vision
  • Robotics
  • Artificial Intelligence

Background:

  • Lane detection is critical for autonomous driving safety.
  • Current methods face challenges with occluded, missing, or complex lane structures.
  • A robust 3D lane detection method is needed to enhance perception.

Purpose of the Study:

  • To propose Graph-RMNet, a graph attention-based 3D lane detection network.
  • To improve the understanding of 3D lane distribution and inter-lane relationships.
  • To enhance the robustness of lane detection in challenging real-world scenarios.

Main Methods:

  • Developed a 3D positional query generation strategy combining 3D spatial and 2D image features.
  • Designed a dual-path relation module with graph attention mechanisms for spatial and categorical lane relation modeling.
  • Implemented an adaptive graph structure to capture complex inter-lane interactions.

Main Results:

  • Graph-RMNet achieved an F-Score of 63.2% on the OpenLane dataset.
  • Achieved an F-Score of 80.63% on the ONCE-3DLanes dataset, outperforming existing algorithms.
  • Demonstrated significant robustness in detecting occluded and missing lanes.

Conclusions:

  • Graph-RMNet effectively addresses limitations in current 3D lane detection methods.
  • The proposed method enhances the comprehension of 3D lane topology and semantics.
  • Graph-RMNet offers a promising solution for reliable autonomous driving perception systems.