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    Area of Science:

    • Robotics and Artificial Intelligence
    • Computer Vision
    • Machine Learning

    Background:

    • Automated driving functions, particularly free space detection (FSD), often rely on convolutional neural networks (CNNs).
    • Limited or non-diverse training datasets for CNNs can compromise the safety of autonomous ground vehicles (AGVs), especially in unstructured environments.
    • Existing methods struggle with robustness and adaptability in varied, real-world conditions.

    Purpose of the Study:

    • To develop an autonomous ground vehicle (AGV) capable of seamless indoor/outdoor navigation for realistic data collection.
    • To introduce a self-evolving free space detection (FSD) framework utilizing online active machine learning (ML) and sensor fusion.
    • To enhance the robustness and reduce data dependency for FSD in autonomous driving systems.

    Main Methods:

    • An AGV was engineered for multimodal data stream collection across diverse indoor and outdoor settings.
    • A self-evolving FSD framework was implemented, integrating online active ML paradigms with sensor data fusion.
    • Image data was cross-validated against a reliable ultrasound data stream, followed by sensor fusion for enhanced robustness.

    Main Results:

    • The proposed framework demonstrated superior performance compared to the state-of-the-art DeepLabV3+ semantic segmentation model.
    • The self-evolving capability enabled the system to learn free space dynamically, reducing the need for extensive pre-existing datasets.
    • Sensor fusion, particularly with ultrasound data, significantly improved the reliability and accuracy of free space classification.

    Conclusions:

    • The developed AGV and self-evolving FSD framework offer a robust solution for autonomous navigation challenges.
    • Active ML and sensor fusion significantly enhance FSD accuracy and reduce reliance on large, curated datasets.
    • This approach paves the way for safer and more adaptable autonomous driving systems in complex, real-world scenarios.