The introduction of Automated Vehicles (AVs) into the transportation network is expected to improve system performance, but the impacts of AVs in mixed traffic streams have not been clearly studied. As AV’s market penetration increases, the interactions between conventional vehicles and AVs are inevitable but by no means clear. This study aims to create new knowledge by quantifying the behavioral changes caused when conventional human-driven vehicles follow AVs and investigating the impact of these changes (if any) on safety and the environment. This study analyzes data obtained from a field experiment by Texas A&M University to evaluate the effects of AVs on the behavior of a following human-driver. The dataset is comprised of nine drivers that attempted to follow 5 speed-profiles, with two scenarios per profile. In scenario one, a human-driven vehicle follows an AV that implements a human driver speed profile (base). In scenario two, the human-driven vehicle follows an AV that executes an AV speed profile. In order to evaluate safety, these scenarios are compared using time-to-collision (TTC) and several other driving volatility measures. Likewise, fuel consumption and emissions are used to investigate environmental impacts. Overall, the results show that AVs in mixed traffic streams can induce behavioral changes in conventional vehicle drivers, with some beneficial effects on safety and the environment. On average, a driver that follows an AV exhibits lower driving volatility in terms of speed and acceleration, which represents more stable traffic flow behavior and lower crash risk. The analysis showed a remarkable improvement in TTC as a result of the notably better speed adjustments of the following vehicle (i.e., lower differences in speeds between the lead and following vehicles) in the second scenario. Furthermore, human-driven vehicles were found to consume less fuel and produce fewer emissions on average when following an AV.

有望将自动驾驶汽车（AVs）引入交通网络，以改善系统性能，但是尚未明确研究自动驾驶汽车对混合交通流的影响。随着视听设备市场渗透率的提高，传统车辆与视听设备之间的相互作用是不可避免的，但还不清楚。这项研究旨在通过量化常规人类驾驶车辆跟随自动驾驶汽车时引起的行为变化并调查这些变化（如果有）对安全和环境的影响来创造新知识。这项研究分析了得克萨斯州A＆M大学从现场实验中获得的数据，以评估AV对跟随驾驶员行为的影响。数据集由九个驱动程序组成，这些驱动程序尝试遵循5个速度配置文件，每个配置文件有两个方案。在方案一中 人工驾驶的车辆遵循实现人类驾驶员速度曲线（基本）的AV。在场景二中，人类驾驶的车辆跟随执行AV速度曲线的AV。为了评估安全性，使用碰撞时间（TTC）和其他几种驾驶波动性度量对这些情况进行了比较。同样，燃料消耗和排放也用于调查环境影响。总体而言，结果表明，混合交通流中的AV可以导致常规车辆驾驶员的行为发生变化，对安全性和环境产生一些有益影响。平均而言，跟随AV的驾驶员在速度和加速度方面表现出较低的驾驶波动性，这表示更稳定的交通流行为和较低的碰撞风险。分析表明，由于第二种情况下跟随车辆的速度调节明显更好（即，领先和跟随车辆之间的速度差较小），TTC有了显着改善。此外，人们发现，驾驶自动驾驶汽车时，人用车辆平均消耗更少的燃料并产生更少的排放物。