Cooperative Adaptive Cruise Control and exhaust emission evaluation under heterogeneous connected vehicle network environment in urban city

doi:10.1016/j.jenvman.2019.109975

Journal of Environmental Management

Volume 256, 15 February 2020, 109975

https://doi.org/10.1016/j.jenvman.2019.109975 Get rights and content

Highlights

•
An extended car following model considering the manual vehicles effect and automatic vehicles co-exist is proposed.
•
The switch variables is introduced and we designs the collaborative controller for the mix traffic flow.
•
The sufficient conditions to our designed controller existence is obtained by applying the Lyapunov function theory.
•
The validity of our designed controller to traffic jams suppression and exhaust emissions reduction is confirmed.

Abstract

With the development of information communication and artificial intelligence, the ICV (intelligent connected vehicle) will inevitably play an important part in future urban transport system. In this paper, we study the car following behaviour under the heterogeneous ICV environment. The time to receive information varies from vehicle to vehicle, since the manual vehicles and autonomous vehicles co-exist on the road. By introducing time-varying lags function, a new car following model is proposed, and the cooperative control strategy of this model is studied. Based on Lyapunov function theory and linear matrix inequality (LMI) approach, the sufficient condition that the existence of the feedback controller is given, which makes the closed-loop system asymptotically stable under mixed traffic flow environment. That is to say, traffic congestion phenomenon under heterogeneous traffic flow can be effectively suppressed, and the feedback controller gain matrix can be obtained via solving linear matrix inequality. Finally, by simulation the method is verified effective in alleviating traffic congestions and reducing fuel consumption and exhaust emissions. It could be a useful reference to Cooperative Vehicle Infrastructure System and Smart City.

Introduction

Recently, with the economic growth, more and more motorized cars are running on urban roads (Zhang et al., 2017a, Zhang et al., 2017b). This exacerbates traffic congestion and brings many related environmental issues, such as noise pollution, air pollution, and energy consumption. Governments are eager to find ways to alleviate traffic congestion effectively. In the past few decades, people tried to use traffic organization and traffic demand management (TDM) to alleviate traffic congestion, yet the effect is not significant. Recently, under the impetus of the electronic information and wireless communication technology, the internet of things (IoT) technology has rapidly developed, making it possible to use car networking technology to alleviate traffic congestion (Knorr and Schreckenberg, 2012). Vehicle network, as the core of the Internet of things, makes real-time interconnection of vehicle-vehicle (V2V) and vehicle-infrastructure (V2I) possible to be implemented. Based on the IoT technology, drivers can obtain the specific information of surrounding vehicles in real time, such as speed, headway etc., and thereby achieve communication with other vehicles. Because this technology can effectively improve the current deteriorating traffic environment, scholars conduct researches focused on this technology (Tang et al., 2014, You et al., 2015, Zhang et al., 2017a, Zhang et al., 2017b).

With the development of artificial intelligence (AI) and automatic technology, the autonomous vehicles (AV) will inevitably become the mainstream of future cars, scholars has focused on the topic of the traffic flow of the ACC vehicles (Nilsson et al., 2016). It's found that based on the differences of all vehicles, the traffic performance of the road didn't realized the optimum under each vehicles regards as the separate individuals. To solve this problem, the CACC (Cooperative Adaptive Cruise Control) arises. Considering the key factors such as current acceleration, safety deceleration, space headway, and relative speed, a new CACC control logic is proposed by Van Arem et al. (2006). Wang et al. (2014) take advantage of the model predictive control to improve the Van Arem et al. (2006). In order to investigated the effect of the market penetration rate of ACC vehicles and the platoon sizes, a simulation framework based on VISSIM considering ACC vehicles was proposed by Wang et al. (2013), and the results show that urban traffic status is effective improved.

Though the car-following models in connected-vehicle network environments have attracted a number of scholars' attention (Wu et al., 2011, Tang et al., 2013, Ahn et al., 2014, Zhai and Wu, 2018a, Zhai and Wu, 2018b, Sun et al., 2018, Francesco et al., 2018), there are still some limitations in current researches. Firstly, these previous works did not consider the influence of the difference of receiving information in conventional human-driven vehicles (also called manual vehicles). The manual vehicles receive information mainly through the driver's observation of the outside world. Responses to external stimuli vary from driver to driver, while some drivers have radar or infrared technology giving assistance to observation. AVs can communicate with other car via WIFI or local network, for which they also called intelligent connected vehicle (ICV). For the same stimulus, different ways of receiving external information would result in different response time. In this paper, we mainly use the lag function to represent the various response time. Secondly, considering the complexity of car networking and driverless technology, the public accept new technologies with varying degrees, which means the popularity of technology will take a long time. In other words, for a long time, there are both manual vehicles and ICVs running on the same traffic infrastructure. This paper mainly studied the car-following problem in this kind of heterogeneous traffic flow.

In order to solve these problems, the mechanism of vehicle intelligent control under the “semi-unicom status” has been studied. Before the start of the study, we first consider manual vehicles as controller failures. We assume the vehicles include the AVs implementing networking communication and the conventional human-driven vehicles not implementing the networking communication. Compared with the AVs, the manual vehicles require longer reflection time. For convenient, by treating the different receiver pattern as different lag, we introduce the lag function to represent the differences between these two different types of vehicles (AVs and manual vehicles). In this way, we propose an improved car-following model with multiple time lags. Based on the Lypunov function mentioned by Zhai and Liu (2016), the sufficient condition, where the designed feedback controller exists, is presented, and the heterogeneous traffic flow model under the designed controller satisfied the asymptotical stability. Then the controller is proved to be obtained by solving LMI. Last, simulation tests verified the effectiveness of the controller in both suppressing traffic oscillation and reducing the emission of CO₂. The findings will give some inspirations for future study of AV driving strategy and environmental management in heterogeneous traffic flow with conventional manual vehicles and AVs.

The structure of the paper is as follows: Section 2 introduces the improved car-following model; Section 3 is the stability analysis of the car-following model; Section 4 presents the design of the feedback controller; Section 5 is the simulation test results. Conclusions and future works are in Section 6.

Section snippets

Improved car-following model with lags

Here, we improve car-following systems with multiple-lags from the one proposed by Zhai and Liu (2016). Parts of the formulas are similar, but for the sake of the integrity of the model description, we still present the whole model as: ${\dot{v}}_{i} (t) = a_{i} (F (h_{i} (t - τ_{1} (t))) - v_{i} (t - τ_{2} (t)))$ ${\dot{h}}_{i} (t) = v_{i - 1} (t) - v_{i} (t)$ where: $h_{i} (t) > 0$ stands for the space headway of the adjacent vehicle $i$ and $i - 1$ at time $t$ , i indicates the ID number of the vehicles, and $i = 1, 2,..., N$ , $v_{i} (t)$ stands for the speed of vehicle $i$ at time $t$ , $a_{i}$

Linear stability analysis

Suppose the leading car moving at the speed of v0, then the steady state of Eq. (1) would be: ${[v_{i}^{*}, h_{i}^{*}]}^{T} = {[v_{0}, F^{- 1} (v_{0})]}^{T}$

Let the error variable ${\bar{h}}_{i} (t)$ , ${\bar{v}}_{i} (t)$ mentioned by Zhai and Wu (2019), the error dynamic equation would be: ${\dot{\bar{v}}}_{i} (t) = α_{i} (F ({\bar{h}}_{i} (t - τ_{1} (t))) - {\bar{v}}_{i} (t - τ_{2} (t)))$ ${\dot{\bar{h}}}_{i} (t) = {\bar{v}}_{i - 1} (t) - {\bar{v}}_{i} (t)$ and the variable ${\bar{v}}_{i} (t - τ_{2} (t))$ , $F ({\bar{h}}_{i} (t - τ_{1} (t)))$ are consistent with Zhai and Wu (2019).

Let $\bar{h} (t) = [{\bar{h}}_{1} (t), {\bar{h}}_{2} (t), ... {\bar{h}}_{n} (t)], \bar{v} (t) = [{\bar{v}}_{1} (t), {\bar{v}}_{2} (t), ... {\bar{v}}_{n} (t)]$ , then Eq. (6) is: $\dot{\bar{v}} (t) = A (F (\bar{h} (t - τ_{1} (t))) - \bar{v} (t - τ_{2} (t)))$ $\dot{\bar{h}} (t$

Design of the feedback controller

With the rise of AI, more and more countries try to take advantage of computing machines replace manual operation. This principle mainly uses on-board sensors to sense the surrounding environment of vehicles, and adjusts the vehicle's own speed in real time according to road conditions and other vehicle positions and speeds. Compared with manual vehicles, autonomous driving technology can effectively abandon some drivers' bad driving habits, which effectively reduce traffic accidents, and make

Simulation tests

In this section, the vehicles following behaviours under different situations are analysed. Through the simulation tests we verify the feasibility and effectiveness of the method.

Conclusion and future works

Considering different receiver pattern as different time-varying lags and regarding the manual operations as the actuator existing failure constraints, an improved car-following model is proposed. By the theory of Lyapunov function, the sufficient condition of the traffic flow stability is given. That means traffic congestion can be avoided, and when the above stability conditions are not met, a new feedback controller is designed, and the detailed design steps are given. By the controller,

Declaration of competing interest

The authors declare that there is no conflict of interests regarding the publication of this article.

Acknowledgment

This work was partially supported by the Science and Technology Planning Project of Guangdong Province (Grant Nos. 2017A040405021), National Natural Science Foundation of China (Grant Nos. 51408237, 51808151, 51775565), Fundamental Research Funds for the Central Universities (Grant No.18LGPY83), and the Fundamental Research Funds for Guangdong Communication Polytechnic(20181014).

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¹: These authors contributed equally to this work.

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Research articleCooperative Adaptive Cruise Control and exhaust emission evaluation under heterogeneous connected vehicle network environment in urban city

Highlights

Abstract

Introduction

Section snippets

Improved car-following model with lags

Linear stability analysis

Design of the feedback controller

Simulation tests

Conclusion and future works

Declaration of competing interest

Acknowledgment

J. Environ. Manag.

Physica A

J. Environ. Manag.

Physica A

Measurement

Compos. B Eng.

Transp. Res. C Emerg. Technol.

Transp. Res. C Emerg. Technol.

Transp. Res. Part D-Transport. Environ.

Expert Syst. Appl.

Estimating vehicle fuel consumption and emissions based on instantaneous speed and acceleration levels

Transp. Eng. J. ASCE

Green roof and photovoltaic panel integration: effects on plant and arthropod diversity and electricity production

J. Environ. Manag.

Energy efficiency and reduction of CO2 emissions from campsites management in a protected area

J. Environ. Manag.

Car-sharing services: an annotated review

Sust. Cities Soc.

Research article
Cooperative Adaptive Cruise Control and exhaust emission evaluation under heterogeneous connected vehicle network environment in urban city