Abstract
Adverse weather conditions have a significant impairment on the safety, mobility, and efficiency of highway networks. Dense fog is considered the most dangerous within the adverse weather conditions. As to improve the traffic flow throughput and driving safety in dense fog weather condition on highway, this paper uses a mathematical modeling method to study and control the fleet mixed with human-driven vehicles (HDVs) and connected automatic vehicles (CAVs) in dense fog environment on highway based on distributed model predictive control algorithm (DMPC), along with considering the car-following behavior of HDVs driver based on cellular automatic (CA) model. It aims to provide a feasible solution for controlling the mixed flow of HDVs and CAVs more safely, accurately, and stably and then potentially to improve the mobility and efficiency of highway networks in adverse weather conditions, especially in dense fog environment. This paper explores the modeling framework of the fleet management for HDVs and CAVs, including the state space model of CAVs, the car-following model of HDVs, distributed model predictive control for the fleet, and the fleet stability analysis. The state space model is proposed to identify the status of the feet in the global state. The car-following model is proposed to simulate the driver behavior in the fleet in local. The DMPC-based model is proposed to optimize rolling of the fleet. Finally, this paper used the Lyapunov stability principle to analyze and prove the stability of the fleet in dense fog environment. Finally, numerical experiments were performed in MATLAB to verify the effectiveness of the proposed model. The results showed that the proposed fleet control model has the ability of local asymptotic stability and global nonstrict string stability.
Highlights
Adverse weather conditions have a significant impact on the safety, mobility, and efficiency of highway networks [1]
As the connected automatic vehicles (CAVs) will be first practically applied in the highway environment, based on the research and development programs of governments and enterprises [22], this paper attempts to propose a fleet control model to manage the mixed flow of CAVs and human-driven vehicles (HDVs) passing through the dense fog environment safely on the highway. e main work of this paper is as follows: (1) A cellular automatic- (CA-) based HDV following model is proposed to analyze the motion characteristics of HDVs in the mixed flow of HDVs and CAVs, along with considering the following behavior of drivers in dense fog weather conditions
(2) A fleet control model based on the distributed model predictive control (DMPC) algorithm is proposed by using a mathematical modeling method to manage the HDVs and CAVs within the fleet
Summary
Adverse weather conditions have a significant impact on the safety, mobility, and efficiency of highway networks [1]. As the CAVs will be first practically applied in the highway environment, based on the research and development programs of governments and enterprises [22], this paper attempts to propose a fleet control model to manage the mixed flow of CAVs and HDVs passing through the dense fog environment safely on the highway. (1) A cellular automatic- (CA-) based HDV following model is proposed to analyze the motion characteristics of HDVs in the mixed flow of HDVs and CAVs, along with considering the following behavior of drivers in dense fog weather conditions. (2) A fleet control model based on the distributed model predictive control (DMPC) algorithm is proposed by using a mathematical modeling method to manage the HDVs and CAVs within the fleet It attempts to provide a feasible solution for controlling the fleet to improve the mobility and efficiency of highway network in adverse weather conditions, especially in dense fog environment.
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