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Graph-Embedded Lane Detection.

Pingping Lu, Shaobing Xu, Huei Peng

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    |February 10, 2021
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    Summary

    This study introduces a novel graph-embedded approach for robust lane detection, especially in complex road scenarios like merges and splits. The method effectively extracts lane features and infers geometry without strict assumptions, outperforming existing systems.

    Area of Science:

    • Computer Vision
    • Robotics
    • Artificial Intelligence

    Background:

    • Accurate lane detection remains a significant challenge in autonomous driving, particularly on road segments with complex topologies (e.g., lane merges, splits, highway ramps).
    • Existing methods often rely heavily on specific annotated lane data or make strong geometric assumptions that limit their applicability.

    Purpose of the Study:

    • To develop a novel and robust lane detection and inference system capable of handling complex road topologies.
    • To reduce the dependency on meticulously annotated lane datasets for training.
    • To provide a lane detection solution that does not rely on rigid geometric assumptions.

    Main Methods:

    • A two-part approach: 1) A learning-based low-level lane feature extraction algorithm trained across heterogeneous open-source semantic segmentation datasets (Cityscape, Vistas, Apollo).

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  • 2) A graph-embedded lane inference algorithm that models lane geometry and topology using detected lane nodes, avoiding strong geometric constraints.
  • Lane parameters are inferred via efficient graph-based searching and calculation in the world coordinate system.

    • Main Results:

    • The proposed method demonstrates effective lane feature extraction and inference across diverse and complex road scenarios.
    • Validation on both open-source and custom datasets shows promising performance.
    • On-vehicle experiments indicate favorable results compared to the Mobileye EyeQ2 system.

    Conclusions:

    • The novel graph-embedded solution offers a robust and adaptable approach to lane detection in challenging driving environments.
    • The method's ability to leverage heterogeneous datasets and avoid strong geometric assumptions enhances its practical applicability.
    • The system shows competitive performance, suggesting its potential for real-world autonomous driving systems.