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原文传递 Impact of Autonomous Vehicles on the Car-Following Behavior of Human Drivers

题名：	Impact of Autonomous Vehicles on the Car-Following Behavior of Human Drivers
正文语种：	eng
作者：	Ruixuan Zhang;Sara Masoud;Neda Masoud
作者单位：	Dept. of Civil and Environmental Engineering Univ. of Michigan Ann Arbor 2350 Hayward St. Ann Arbor MI 48109 Dept. of Advanced Engineering Isuzu Technical Center of America 46401 Commerce Center Dr. Plymouth MI 48170;Dept. of Industrial and Systems Engineering Wayne State Univ. 4815 4th St. Detroit MI 48201;Dept. of Civil and Environmental Engineering Univ. of Michigan Ann Arbor 2350 Hayward St. Ann Arbor MI 48109
关键词：	Autonomous vehicle-human driver interactions; Car-following behavior
摘要：	The past few years have been witness to an increase in autonomous vehicle (AV) development and testing. However, even with a fully developed and comprehensively tested AV technology, AVs are anticipated to share the roadway network with human drivers for the unforeseeable future. In such a mixed environment, we use naturalistic driving data from the next generation simulation (NGSIM) and Lyft Level 5 (Lyft L5) prediction data sets to investigate whether the existence of AVs influences the car-following behavior of human drivers. We use time headway time series as a proxy to capture the car-following behavior of human drivers. We then develop a nested fixed model to find possible changes in behavior when human drivers are following different types of vehicles (i.e., human-driven vehicles or AVs). The factors included in this model are the platoon structure (a legacy vehicle following a legacy vehicle, and a legacy vehicle following an autonomous vehicle), road type (freeway and urban), time period (morning and afternoon), lane (right, middle, and left), and the source of the data (NGSIM and Lyft L5). Results indicate a statistically significant difference between the car-following behavior of drivers when they follow a human-driven vehicle compared to an AV. This change in the car-following behavior of drivers is manifested in the form of a reduction in the mean and variance of time headways when human drivers follow an AV. These findings can bridge the gap between anticipated and real-world impacts of AVs on traffic streams and roadway capacity, providing meaningful insights on the influence of AVs on the driving behavior of humans in a naturalistic driving environment.
出版年：	2023
期刊名称：	Journal of Transportation Engineering
卷：	149
期：	3
页码：	04022152.1-04022152.10