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English(EN) FLAT: Smoothing the Rugged Landscape for Learnable, Sample-Efficient Traffic Calibration

新的FLAT方法提高了数字孪生样本高效的交通校准性能

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了FLAT，一种用于数字孪生中样本高效交通校准的新颖方法。该方法平滑了参数误差景观，使得目标可以从有限数量的样本中学习。FLAT将优化策略与采样位置相结合，利用了八维行为指纹和退火置信下界获取策略。在六个真实交通场景中进行了测试，FLAT-GP表现出优越的性能，实现了最低的平均行为误差，并优于几种基线方法。 AI

排序理由 该集群包含一篇研究论文，详细介绍了一种新的交通校准方法。[lever_c_demoted from research: ic=1 ai=0.4]

在 arXiv cs.LG 阅读 →

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新的FLAT方法提高了数字孪生样本高效的交通校准性能

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该集群包含一篇研究论文，详细介绍了一种新的交通校准方法。[lever_c_demoted from research: ic=1 ai=0.4]
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报道来源 [1]

  1. arXiv cs.LG TIER_1 English(EN) · Haopeng Deng, Thomas He, Dayuan Wang ·

    FLAT: Smoothing the Rugged Landscape for Learnable, Sample-Efficient Traffic Calibration

    arXiv:2610.04337v2 Announce Type: replace-cross Abstract: Calibrating microscopic traffic models for digital twins is an expensive black-box optimization problem: tuning car-following and lane-changing parameters requires a full simulation run, affording only a tight budget per r…

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