The Markov property serves as a foundational assumption in most existing work on vehicle driving behavior, positing that future states depend solely on the current state, not the series of preceding states. This study validates the Markov properties of vehicle trajectories for both Autonomous Vehicles (AVs) and Human-driven Vehicles (HVs). A statistical method used to test whether time series data exhibits Markov properties is applied to examine whether the trajectory data possesses Markov characteristics. t test and F test are additionally introduced to characterize the differences in Markov properties between AVs and HVs. Based on two public trajectory datasets, we investigate the presence and order of the Markov property of different types of vehicles through rigorous statistical tests. Our findings reveal that AV trajectories generally exhibit stronger Markov properties compared to HV trajectories, with a higher percentage conforming to the Markov property and lower Markov orders. In contrast, HV trajectories display greater variability and heterogeneity in decision-making processes, reflecting the complex perception and information processing involved in human driving. These results have significant implications for the development of driving behavior models, AV controllers, and traffic simulation systems. Our study also demonstrates the feasibility of using statistical methods to test the presence of Markov properties in driving trajectory data.

翻译：马尔可夫性质是大多数现有车辆驾驶行为研究的基础假设，它假定未来状态仅取决于当前状态，而与之前的状态序列无关。本研究验证了自动驾驶车辆（AVs）和人类驾驶车辆（HVs）轨迹的马尔可夫性质。我们应用一种用于检验时间序列数据是否呈现马尔可夫性质的统计方法，来考察轨迹数据是否具备马尔可夫特征。此外，还引入了t检验和F检验来刻画AVs与HVs之间马尔可夫性质的差异。基于两个公开的轨迹数据集，我们通过严格的统计检验，研究了不同类型车辆马尔可夫性质的存在性及其阶数。我们的研究结果表明，与HV轨迹相比，AV轨迹通常表现出更强的马尔可夫性质，其符合马尔可夫性质的百分比更高，且马尔可夫阶数更低。相比之下，HV轨迹在决策过程中表现出更大的变异性和异质性，反映了人类驾驶中涉及的复杂感知与信息处理。这些结果对驾驶行为模型、自动驾驶控制器以及交通仿真系统的开发具有重要意义。我们的研究也证明了使用统计方法来检验驾驶轨迹数据中马尔可夫性质存在性的可行性。