Accurate calibration of car-following models is essential for understanding human driving behaviors and implementing high-fidelity microscopic simulations. This work proposes a memory-augmented Bayesian calibration technique to capture both uncertainty in the model parameters and the temporally correlated behavior discrepancy between model predictions and observed data. Specifically, we characterize the parameter uncertainty using a hierarchical Bayesian framework and model the temporally correlated errors using Gaussian processes. We apply the Bayesian calibration technique to the intelligent driver model (IDM) and develop a novel stochastic car-following model named memory-augmented IDM (MA-IDM). To evaluate the effectiveness of MA-IDM, we compare the proposed MA-IDM with Bayesian IDM in which errors are assumed to be i.i.d., and our simulation results based on the HighD dataset show that MA-IDM can generate more realistic driving behaviors and provide better uncertainty quantification than Bayesian IDM. By analyzing the lengthscale parameter of the Gaussian process, we also show that taking the driving actions from the past five seconds into account can be helpful in modeling and simulating the human driver's car-following behaviors.

翻译：对跟车模型进行精确校准对于理解人类驾驶行为及实现高保真微观仿真至关重要。本文提出一种记忆增强的贝叶斯校准技术，以同时捕获模型参数的不确定性以及模型预测与观测数据之间随时间相关的行为偏差。具体而言，我们利用分层贝叶斯框架刻画参数不确定性，并采用高斯过程对时间相关误差进行建模。我们将该贝叶斯校准技术应用于智能驾驶员模型（IDM），并开发了一种名为记忆增强IDM（MA-IDM）的新型随机跟车模型。为了评估MA-IDM的有效性，我们将所提出的MA-IDM与假设误差独立同分布的贝叶斯IDM进行对比。基于HighD数据集的仿真结果表明，MA-IDM能够生成更真实的驾驶行为，并提供比贝叶斯IDM更优的不确定性量化。通过分析高斯过程的长度尺度参数，我们还发现考虑过去五秒内的驾驶行为有助于对人类驾驶员的跟车行为进行建模与仿真。