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Related Experiment Video

Updated: Sep 12, 2025

Evaluating the Effect of Roadside Parking on a Dual-Direction Urban Street

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Published on: January 20, 2023

SGPLane: Efficient lane detection via sampled grid points for autonomous driving.

Xuewei Tang¹, Mengmeng Yang¹, Kun Jiang¹

¹School of Vehicle and Mobility, Tsinghua University, Beijing 100084, China.

Fundamental Research

|August 8, 2025

Summary

This study introduces SGPLane, a novel approach for autonomous driving lane detection using sampled grid points. This method enhances accuracy and efficiency, overcoming limitations of previous unstable post-processing techniques.

Keywords:

Autonomous driving Autonomous vehicle navigation Lane detection Road scene understanding Vehicle perception

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Area of Science:

Computer Vision
Robotics
Artificial Intelligence

Background:

Lane detection is crucial for autonomous driving systems.
Existing methods often rely on complex post-processing, leading to instability.
A gap exists between raw camera input and reliable lane information for downstream applications.

Purpose of the Study:

To propose a novel lane detection representation and a simplified network architecture.
To eliminate the need for complicated post-processing in lane detection.
To achieve accurate and efficient end-to-end lane detection.

Main Methods:

Lane lines are represented using sampled grid points.
A network, termed SGPLane, is designed for this specific lane format.
The network employs regression and confidence branches for end-to-end detection.

Main Results:

SGPLane achieves 96.84% accuracy on the Tusimple dataset with high FPS.
The model demonstrates 76.85% accuracy on a real-world dataset.
The proposed method outperforms previous lane detection techniques.

Conclusions:

The sampled grid point representation offers an effective approach for lane detection.
SGPLane provides a simple yet powerful end-to-end solution for autonomous driving.
The method shows strong performance on both benchmark and real-world driving data.