Enhanced cooperative car-following traffic model with the combination of V2V and V2I communication

Abstract

Vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication are emerging components of intelligent transport systems (ITS) based on which vehicles can drive in a cooperative way and, hence, significantly improve traffic flow efficiency. However, due to the high vehicle mobility, the unreliable vehicular communications such as packet loss and transmission delay can impair the performance of the cooperative driving system (CDS). In addition, the downstream traffic information collected by roadside sensors in the V2I communication may introduce measurement errors, which also affect the performance of the CDS.The goal of this paper is to bridge the gap between traffic flow modelling and communication approaches in order to build up better cooperative traffic systems. To this end, we aim to develop an enhanced cooperative microscopic (car-following) traffic model considering V2V and V2I communication (or V2X for short), and investigate how vehicular communications affect the vehicle cooperative driving, especially in traffic disturbance scenarios. For these purposes, we design a novel consensus-based vehicle control algorithm for the CDS, in which not only the local traffic flow stability is guaranteed, but also the shock waves are supposed to be smoothed. The IEEE 802.11p, the defacto vehicular networking standard, is selected as the communication protocols, and the roadside sensors are deployed to collect the average speed in the targeted area as the downstream traffic reference. Specifically, the imperfections of vehicular communication as well as the measured information noise are taken into account. Numerical results show the efficiency of the proposed scheme. This paper attempts to theoretically investigate the relationship between vehicular communications and cooperative driving, which is needed for the future deployment of both connected vehicles and infrastructure (i.e. V2X).

A Review of Sensing and Communication, Human Factors, and Controller Aspects for Information-Aware Connected and Automated Vehicles

“…Hence, it is very important to incorporate the centralized controller and distributed controller via V2X communication. There are several papers taking advantages of the V2I communication on top of the typical V2V-based controller designs in terms of the higher computation capability [188] and richer set of environmental information [189]. A unified V2V and V2I hybrid control structure should be further exploited.…”

Stability analysis on a dynamical model of route choice in a connected vehicle environment

“…Jia and Ngoduy (2016) applied V2I algorithm to a microscopic model combined with the CV algorithm, and proved the models stability using a non-linear stability test, the Lyapunov theorem. …”

“…Among these technologies, researchers have focused on vehicle control strategies, communication with other vehicles, vehicle management, and connection to other advanced technologies. Traffic engineers, however, focused on appropriately revised microscopic traffic flow or lane-changing models to reflect the CV environment (Jia and Ngoduy, 2016;Talebpour and Mahmassani, 2016). Researchers were also interested in the accuracy, privacy, and effectiveness of radio communication between vehicles or high occupancy vehicles (HOV) such as bus or truck as well as connection with traffic signal controller using V2I (Bauza and Gozalvez, 2013;Knorr, et al, 2012).…”

“…Subsequently, Lee (1995), Ghali (1995), Meneguzzer (1996), He, Guo, and Liu (2010), Liu and Smith (2015), Smith (2015), Smith and Watling (2016), and other numerous researchers used, developed, and modified the Lyapunov theorem in the stability test of the dynamical model of route choice. Zhang, Ge, and Hang (2000), Boyer, Blandin, and Wynter (2015), and Jia and Ngoduy (2016) used the Lyapunov theorem to prove the stability of microscopic models (ACC, CACC, V2X), and routing strategies.…”

Controlling a real-world intersection with connected vehicle information provided by CAMs (Cooperative Awareness Messages)

“…Platooning minimizes gaps between vehicles and thus reduces passing times of vehicles over intersections. Speed advices can be used to let vehicles pass an intersection without stopping, leading to reduced time losses and emissions due to braking and accelerating [3] [4] [5].…”