提升自动驾驶仿真中车辆与交通流的精准度，实现全自动校准。
A simulation platform calibration method for automated vehicle evaluation: accurate on both vehicle level and traffic flow level
- 通过自动化校准，精准还原车辆间交互行为。
- 交互复现准确率提升83.53%，校准效率提高76.75%。
- 兼顾车辆级与交通流级精度，适合仿真测试研发人员。
仿真测试是评估自动驾驶车辆（AV）的核心方法。为确保其可靠性，必须精确复现自动驾驶车辆与背景交通之间的交互，这依赖于有效的校准。然而，现有校准方法在实现这一目标上常显不足。为此，本文提出一种仿真平台校准方法，可同时保证车辆级和交通流级的高精度。该方法具备车辆-车辆交互校准、精度保障、效率提升及流程化校准能力。与无校准基线及当前最优方法对比，本方法使交互复现准确率提升83.53%，校准效率提升76.75%，并在车辆级与交通流级指标上综合改善51.9%。整个校准过程完全自动化，无需人工干预。
原文摘要 · Abstract (English)
Simulation testing is a fundamental approach for evaluating automated vehicles (AVs). To ensure its reliability, it is crucial to accurately replicate interactions between AVs and background traffic, which necessitates effective calibration. However, existing calibration methods often fall short in achieving this goal. To address this gap, this study introduces a simulation platform calibration method that ensures high accuracy at both the vehicle and traffic flow levels. The method offers several key features:(1) with the capability of calibration for vehicle-to-vehicle interaction; (2) with accuracy assurance; (3) with enhanced efficiency; (4) with pipeline calibration capability. The proposed method is benchmarked against a baseline with no calibration and a state-of-the-art calibration method. Results show that it enhances the accuracy of interaction replication by 83.53% and boosts calibration efficiency by 76.75%. Furthermore, it maintains accuracy across both vehicle-level and traffic flow-level metrics, with an improvement of 51.9%. Notably, the entire calibration process is fully automated, requiring no human intervention.
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