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

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.