This paper develops a stochastic and unifying framework to examine variability in car-following (CF) dynamics of commercial automated vehicles (AVs) and its direct relation to traffic-level dynamics. The asymmetric behavior (AB) model by Chen at al. (2012a) is extended to accommodate a range of CF behaviors by AVs and compare with the baseline of human-driven vehicles (HDVs). The parameters of the extended AB (EAB) model are calibrated using an adaptive sequential Monte Carlo method for Approximate Bayesian Computation (ABC-ASMC) to stochastically capture various uncertainties including model mismatch resulting from unknown AV CF logic. The estimated posterior distributions of the parameters reveal significant differences in CF behavior (1) between AVs and HDVs, and (2) across AV developers, engine modes, and speed ranges, albeit to a lesser degree. The estimated behavioral patterns and simulation experiments further reveal mixed platoon dynamics in terms of traffic throughout reduction and hysteresis.

翻译：本文构建了一个随机且统一的框架，研究商用自动驾驶汽车（AVs）跟驰（CF）动力学的变异性及其与交通层面动力学的直接关系。Chen等人（2012a）提出的非对称行为（AB）模型得到扩展，以容纳自动驾驶汽车的一系列跟驰行为，并与人类驾驶车辆（HDVs）的基准进行对比。采用自适应序贯蒙特卡洛近似贝叶斯计算方法（ABC-ASMC）对扩展AB（EAB）模型的参数进行校准，以随机方式捕捉各种不确定性，包括因未知AV跟驰逻辑导致的模型失配。参数的后验分布估计揭示了跟驰行为在以下两方面的显著差异：（1）自动驾驶汽车与人类驾驶车辆之间，（2）不同自动驾驶汽车开发商、发动机模式及速度范围之间（尽管程度较小）。估计的行为模式及仿真实验进一步揭示了混合队列在交通流吞吐量降低和滞后现象方面的动力学特性。