Credible microscopic traffic simulation requires car-following models that capture both the average response and the substantial variability observed across drivers and situations. However, most data-driven calibrations remain deterministic, producing a single best-fit parameter vector and offering limited guidance for uncertainty-aware prediction, risk-sensitive evaluation, and population-level simulation. Bayesian calibration addresses this gap by inferring a posterior distribution over parameters, but per-trajectory sampling methods such as Markov chain Monte Carlo (MCMC) are computationally infeasible for modern large-scale naturalistic driving datasets. This paper proposes an active simulation-based inference framework for scalable car-following model calibration. The approach combines (i) a residual-augmented car-following simulator with two alternatives for the residual process and (ii) an amortized conditional density estimator that maps an observed leader--follower trajectory directly to a driver-specific posterior over model parameters with a single forward pass at test time. To reduce simulation cost during training, we introduce a joint active design strategy that selects informative parameter proposals together with representative driving contexts, focusing simulations where the current inference model is most uncertain while maintaining realism. Experiments on the HighD dataset show improved predictive accuracy and closer agreement between simulated and observed trajectory distributions relative to Bayesian calibration baselines, with convergence and ablation studies supporting the robustness of the proposed design choices. The framework enables scalable, uncertainty-aware driver population modeling for traffic flow simulation and risk-sensitive transportation analysis.

翻译：可信的微观交通仿真需要能够捕捉平均响应以及在驾驶员和情境间观察到的显著变异性的跟驰模型。然而，大多数数据驱动的标定方法仍是确定性的，仅产生单一的最佳拟合参数向量，对于不确定性感知预测、风险敏感评估和群体级仿真的指导有限。贝叶斯标定通过推断参数的后验分布来弥补这一差距，但针对每条轨迹的采样方法（如马尔可夫链蒙特卡洛）对于现代大规模自然驾驶数据集在计算上不可行。本文提出了一种用于可扩展跟驰模型标定的主动仿真推断框架。该方法结合了（i）一个残差增强的跟驰仿真器（提供两种残差过程替代方案）与（ii）一个摊销条件密度估计器，该估计器在测试时通过单次前向传播，将观测到的主车-从车轨迹直接映射到特定驾驶员的模型参数后验分布。为降低训练期间的仿真成本，我们引入了一种联合主动设计策略，该策略同时选择信息丰富的参数提议和具有代表性的驾驶情境，将仿真集中在当前推断模型最不确定的区域，同时保持真实性。在HighD数据集上的实验表明，相较于贝叶斯标定基线方法，所提框架在预测精度上有所提升，且仿真与观测轨迹分布之间的一致性更佳，收敛性研究和消融实验支持了所提设计选择的鲁棒性。该框架为交通流仿真和风险敏感交通分析实现了可扩展的、不确定性感知的驾驶员群体建模。