Modeling car-following behavior is fundamental to microscopic traffic simulation, yet traditional deterministic models often fail to capture the full extent of variability and unpredictability in human driving. While many modern approaches incorporate context-aware inputs (e.g., spacing, speed, relative speed), they frequently overlook structured stochasticity that arises from latent driver intentions, perception errors, and memory effects -- factors that are not directly observable from context alone. To fill the gap, this study introduces an interpretable stochastic modeling framework that captures not only context-dependent dynamics but also residual variability beyond what context can explain. Leveraging deep neural networks integrated with nonstationary Gaussian processes (GPs), our model employs a scenario-adaptive Gibbs kernel to learn dynamic temporal correlations in acceleration decisions, where the strength and duration of correlations between acceleration decisions evolve with the driving context. This formulation enables a principled, data-driven quantification of uncertainty in acceleration, speed, and spacing, grounded in both observable context and latent behavioral variability. Comprehensive experiments on the naturalistic vehicle trajectory dataset collected from the German highway, i.e., the HighD dataset, demonstrate that the proposed stochastic simulation method within this framework surpasses conventional methods in both predictive performance and interpretable uncertainty quantification. The integration of interpretability and accuracy makes this framework a promising tool for traffic analysis and safety-critical applications.

翻译：车辆跟驰行为建模是微观交通仿真的基础，然而传统的确定性模型往往无法充分捕捉人类驾驶行为的变异性和不可预测性。尽管许多现代方法引入了上下文感知输入（如车距、速度、相对速度），但它们常常忽略了由潜在驾驶员意图、感知误差和记忆效应产生的结构化随机性——这些因素仅从上下文中无法直接观测。为填补这一空白，本研究提出了一种可解释的随机建模框架，该框架不仅能捕捉上下文依赖的动态特性，还能捕获超出上下文可解释范围的残差变异性。通过将深度神经网络与非平稳高斯过程（GPs）相结合，我们的模型采用场景自适应的吉布斯核来学习加速度决策中的动态时间相关性，其中加速度决策间相关性的强度和持续时间随驾驶场景演化。该框架基于可观测上下文和潜在行为变异性，实现了对加速度、速度和车距不确定性的原则性数据驱动量化。在德国高速公路采集的自然车辆轨迹数据集（即HighD数据集）上进行综合实验表明，该框架内提出的随机仿真方法在预测性能和可解释不确定性量化方面均优于传统方法。可解释性与准确性的结合使该框架成为交通分析和安全关键应用的有力工具。