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.
As an important stage in the development of autonomous driving, mixed traffic conditions, consisting of connected autonomous vehicles (CAVs) and human-driven vehicles (HDVs), have attracted more and more attention. In fact, the randomness of human-driven vehicles (HDV) is the largest challenge for connected autonomous vehicles (CAV) to make reasonable decisions, especially in lane change scenarios. In this paper, we propose the problem of lane change decisions for CAV in low visibility and mixed traffic conditions for the first time. First, we consider the randomness of HDV in this environment and construct a finite state machine (FSM) model. Then, this study develops a partially observed Markov decision process (POMDP) for describing the problem of lane change. In addition, we use the modified deep deterministic policy gradient (DDPG) to solve the problem and get the optimal lane change decision in this environment. The reward designing takes the comfort, safety and efficiency of the vehicle into account, and the introduction of transfer learning accelerates the adaptation of CAV to the randomness of HDV. Finally, numerical experiments are conducted. The results show that, compared with the original DDPG, the modified DDPG has a faster convergence velocity. The strategy learned by the modified DDPG can complete the lane change in most of the scenarios. The comparison between the modified DDPG and the rule-based decisions indicates that the modified DDPG has a stronger adaptability to this special environment and can grasp more lane change opportunities.
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