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A Fast and Robust Lane Detection Method Based on Semantic Segmentation and Optical Flow Estimation.

Sheng Lu1, Zhaojie Luo1, Feng Gao2

  • 1School of Advanced Manufacturing Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China.

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|January 12, 2021
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Summary
This summary is machine-generated.

This study introduces a fast and robust lane detection method for autonomous driving by integrating semantic segmentation and optical flow networks. The approach enhances lane detection performance in complex scenarios while maintaining high speed.

Keywords:
automated drivingcoordinate mappinglane detectionoptical flow estimationsemantic segmentation

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Area of Science:

  • Computer Vision
  • Robotics
  • Artificial Intelligence

Background:

  • Lane detection is crucial for autonomous driving systems.
  • Existing methods face trade-offs between speed and robustness in complex scenarios.

Purpose of the Study:

  • To develop a lane detection method that is both fast and robust.
  • To improve lane detection performance in sophisticated driving conditions.

Main Methods:

  • A hybrid approach combining a semantic segmentation network for key frames and an optical flow estimation network for non-key frames.
  • Utilizing Density-Based Spatial Clustering of Applications with Noise (DBSCAN) for lane discrimination.
  • Implementing a mapping technique to convert pixel coordinates to the camera coordinate system for lane curve fitting.

Main Results:

  • The proposed method achieves up to a three-fold speedup for semantic segmentation networks.
  • Accuracy degradation is minimal, with a maximum drop of 2%.
  • Lane curve fitting in the camera coordinate system resulted in a feedback error of 3% in optimal conditions.

Conclusions:

  • The integrated semantic segmentation and optical flow method offers a significant improvement in speed and robustness for lane detection.
  • This approach effectively addresses the limitations of existing fast yet less accurate or slow yet robust lane detection techniques.
  • The method provides reliable lane curve feedback essential for autonomous driving applications.