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Visual Evaluation for Autonomous Driving.

Yijie Hou, Chengshun Wang, Junhong Wang

    IEEE Transactions on Visualization and Computer Graphics
    |November 1, 2021
    PubMed
    Summary
    This summary is machine-generated.

    Evaluating autonomous driving systems is complex. This new visual method helps experts understand system performance by analyzing data from all components, identifying specific issues for improvement.

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    Area of Science:

    • Artificial Intelligence
    • Computer Vision
    • Robotics

    Background:

    • Autonomous driving systems rely on complex AI for navigation and control.
    • Current evaluation methods provide a single score, hindering detailed performance analysis.
    • Experts need granular insights into component contributions for system improvement.

    Purpose of the Study:

    • To develop a visual evaluation method for autonomous driving systems.
    • To enable detailed analysis of individual component performance.
    • To facilitate understanding of factors influencing overall system scores.

    Main Methods:

    • Collaborated with domain experts to design a visual analytics workflow.
    • Integrated a mathematical evaluation model with adjustable parameters.
    • Considered data from perception, prediction, planning, control, and comfort modules.
    • Developed an interactive system showing dynamic score changes and contributing factors.

    Main Results:

    • The visual analytics system provides an overview score and dynamic score animations.
    • Experts can interactively explore specific components and time periods.
    • The method reveals contributing factors to both overall and component-level performance.
    • Identified problematic areas within individual autonomous driving components.

    Conclusions:

    • The proposed visual evaluation method enhances understanding of autonomous driving system performance.
    • Domain experts can gain in-depth insights and pinpoint specific issues.
    • The system is applicable to autonomous driving simulations and various evaluation scenarios.
    • Confirmed usefulness and efficiency through expert feedback and simulation cases.