AI 中文总结
FLAT通过八维行为指纹平滑目标地形并结合退火下置信界采样，实现样本高效的微观交通校准，在六个真实场景中以更少仿真超越多种基线。
AI 中文摘要
为数字孪生校准微观交通模型是一个昂贵的黑箱优化问题：调整跟车和换道参数需要完整的仿真运行，每次重新校准窗口仅有紧张的预算。匹配原始轨迹会产生崎岖的目标函数，稀疏代理模型无法学习，将顺序采集退化为近乎随机的探测。我们提出FLAT，它将优化什么与下一步在哪里采样相结合。一个八维行为指纹平滑了参数误差地形，使目标函数可从几十个样本中学习；退火下置信界（LCB）采集则将每个剩余运行花费在最能减少误差的地方。代理模型可在高斯过程（GP）、随机森林（RF）或多层感知机（MLP）集成之间互换，并接入相同的LCB循环。在六个异构真实场景中，FLAT-GP实现了最低的场景平均行为误差，在匹配预算下，对SPSA、GA和CMA-ES赢得6/6个场景，对TPE赢得5/6个场景。一些基线需要多达4.4倍的仿真才能匹配。消融实验显示，目标函数选择平均改变最终行为误差81%，移除顺序LCB使六场景均值提高20%，代理模型选择最多改变4.2%，确认增益源于目标几何和顺序分配，而非代理容量。
英文摘要
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 recalibration window. Matching raw trajectories yields a rugged objective that sparse surrogates cannot learn, reducing sequential acquisition to near-random probing. We present FLAT, which couples what to optimize with where to sample next. An eight-dimensional behavioral fingerprint smooths the parameter-error landscape, making the objective learnable from a few dozen samples; annealed lower-confidence-bound (LCB) acquisition then spends each remaining run where it most reduces error. The surrogate, interchangeable among a Gaussian process (GP), random forest (RF), or multi-layer-perceptron (MLP) ensemble, plugs into the same LCB loop. Across six heterogeneous real-world scenes, FLAT-GP achieves the lowest scene-averaged behavioral error, winning 6/6 scenes against SPSA, GA, and CMA-ES and 5/6 against TPE under the matched budget. Some baselines need up to 4.4 times more simulations to match. Ablations show objective choice shifts final behavioral error by 81% on average, removing sequential LCB raises the six-scene mean by 20%, and surrogate choice shifts it by at most 4.2%, confirming gains trace to objective geometry and sequential allocation rather than surrogate capacity.
Comments9 pages, 28 figures. Code and cache: https://github.com/QianyHP/FLAT-calibration
