Increasingly, commercial vehicles are equipped with automated vehicle (AV) features such as adaptive cruise control systems. The AV feature can automatically control the headway between the current vehicle and the preceding vehicle in an adaptive manner. The automatic control may lead to significantly different car- following motions compared with those of human-driven vehicles, which challenges the applicability of classic traffic flow theory to emerging road traffic with AVs. To investigate the impacts of commercial AVs on traffic flow, this paper proposes a general methodology that combines both empirical experiments and theoretical models to construct a fundamental diagram (FD), i.e., the foundation for traffic flow theory for AV traffic. To demonstrate the empirical experiment settings, we collected high-resolution trajectory data with multiple commercial AVs following one another in a platoon with different headway settings. The field experiment results revealed that the traditional triangular FD structure remains applicable to describe the traffic flow characteristics of AV traffic. Further, by comparing the FDs between AVs and human-driven vehicles, it was found that although the shortest AV headway setting can significantly improve road capacity, other headway settings may decrease road capacity compared with existing human-driven-vehicle traffic. It was also found that headway settings may affect the stability of traffic flow, which has been revealed by theoretical studies but was first verified by empirical AV data. With these findings, mixed traffic flow FDs were derived by incorporating different headway settings and AV penetration rates. The method proposed in this paper, including experiment designs, data collection approaches, traffic flow characteristics analyses, and mixed traffic flow FD construction approaches, can serve as a methodological foundation for studying future mixed traffic flow features with uncertain and evolving AV technologies.

越来越多的商用车辆配备了自动车辆 (AV) 功能，例如自适应巡航控制系统。AV 功能可以自适应地自动控制当前车辆与前车之间的车距。与人类驾驶车辆相比，自动控制可能会导致跟车运动明显不同，这对经典交通流理论在新兴道路交通中的 AV 应用提出了挑战。为了研究商用自动驾驶汽车对交通流的影响，本文提出了一种将经验实验和理论模型相结合的通用方法来构建基本图（FD），即自动驾驶汽车交通流量理论的基础。为了演示经验实验设置，我们收集了高分辨率轨迹数据，其中多个商用 AV 在具有不同车头设置的排中相互跟随。现场实验结果表明，传统的三角形FD结构仍然适用于描述AV交通的交通流特征。此外，通过比较 AV 和人类驾驶车辆之间的 FD，发现虽然最短的 AV 车距设置可以显着提高道路通行能力，但与现有的人类驾驶车辆交通相比，其他车距设置可能会降低道路通行能力。还发现车头时距设置可能会影响交通流的稳定性，这已被理论研究揭示，但首先由经验 AV 数据验证。有了这些发现，混合交通流量 FDs 是通过结合不同的车头时距设置和 AV 渗透率得出的。本文提出的方法，包括实验设计、数据收集方法、交通流特征分析和混合交通流 FD 构建方法，可以作为研究未来混合交通流特征的方法论基础，其中包含不确定和不断发展的 AV 技术。