An Asymmetric-Anticipation Car-following Model in the Era of Autonomous-Connected and Human-Driving Vehicles

Herein, we explored the impact of anticipation and asymmetric driving behavior on vehicle’s position, velocity, acceleration, energy consumption, and exhaust emissions of CO, HC, and NOx in mixed traffic flow. We present an asymmetric-anticipation car-following model (AAFVD) considering the motion information from two direct preceding vehicles (i.e., human-driving (HD) and autonomous and connected (AC) vehicles platoon) via wireless data transmission. The linear stability approach was used to evaluate the properties of the AAFVD model. Our simulations revealed that the drivers’ anticipation factor using the motion information from two direct preceding vehicles in connected vehicles environment can effectively improve traffic flow stability. The vehicle’s departure and arrival process while passing through a signal lane with a traffic light considering the anticipation and asymmetric driving behavior, and the motion information from two direct preceding vehicles was explored. Our numerical results demonstrated that the AAFVD model can decrease the velocity fluctuations, energy consumption, and exhaust emissions of vehicles in mixed traffic flow system

Driver’s Anticipation and Memory Driving Car-Following Model

We developed a new car-following model to investigate the effects of driver anticipation and driver memory on traffic flow. The changes of headway, relative velocity, and driver memory to the vehicle in front are introduced as factors of driver’s anticipation behavior. Linear and nonlinear stability analyses are both applied to study the linear and nonlinear stability conditions of the new model. Through nonlinear analysis a modified Korteweg-de Vries (mKdV) equation was constructed to describe traffic flow near the traffic near the critical point. Numerical simulation shows that the stability of traffic flow can be effectively enhanced by the effect of driver anticipation and memory. The starting and breaking process of vehicles passing through the signalized intersection considering anticipation and driver memory are presented. All results demonstrate that the AMD model exhibit a greater stability as compared to existing car-following models

Environmental Analyses of Delayed-Feedback Control Effects in Continuum-Traffic Flow of Autonomous Vehicles

Connected and Autonomous Vehicles are predicted to drive in a platoon with the aid of communication technologies to increase traffic flow efficiency while improving driving comfort, safety, fuel consumption, and exhaust emissions. However, some vehicles in a group may face communication failures. Such potential risks may even worsen the efficiency and safety of traffic flow and increase fuel consumption and exhaust emissions. Therefore, there is a need to propose an alternative scheme to control traffic flow effectively through vehicle-based information without the aid of communication technologies. In this paper, a deterministic acceleration model was developed considering the sensor’s detection range to capture the underlying process of a car following the dynamics of autonomous vehicles. A delayed-feedback control was proposed based on the current and previous states of throttle angle to increase traffic flow stability and improve fuel consumption and exhaust emissions without the aid of communication technologies. Numerical simulations were carried out to study the impact of sensor detection range on micro-driving behavior and explore the effect of the proposed delayed-feedback control on the fuel consumption and exhaust emissions of autonomous vehicles in large-scale traffic flow. The numerical results certified that using delayed feedback with proper gains and delay time improved the total fuel consumption and exhaust emissions of autonomous vehicles

Integrated-Hybrid Framework for Connected and Autonomous Vehicles Microscopic Traffic Flow Modelling

In this study, a novel traffic flow modeling framework is proposed considering the impact of driving system and vehicle mechanical behavior as two different units on the traffic flow. To precisely model the behavior of Connected and Autonomous (CA) vehicles, three submodels are proposed as a novel microscopic traffic flow framework, named Integrated-Hybrid (IH) model. Focusing on the realization of the car following behavior of CA vehicles, the driving system (vehicle control system) and the vehicle mechanical system are modeled separately and linked by throttle and brake actuators model. +e IH model constitutes the key part of the Full Velocity Difference (FVD) model considering the mechanical capability of vehicles and dynamic collision avoidance strategies to ensure the safety of following distance between two consecutive vehicles. Linear stability conditions are derived for each model and developing methodology for each submodel is discussed. Our simulations revealed that the IH model successfully generates velocity and acceleration profiles during car following maneuvers and throttle angle/brake information in connected vehicles environment can effectively improve traffic flow stability. +e vehicles’ departure and arrival process while passing through a signal-lane with a traffic light considering the anticipation driving behavior and throttle angle/brake information of direct leading vehicle was explored. Our numerical results demonstrated that the IH model can capture the velocity fluctuations, delay times, and kinematic waves efficiently in traffic flow

A viscous continuum traffic flow model based on the cooperative car‐following behaviour of connected and autonomous vehicles

Abstract Connected and Autonomous Vehicles (CAVs) can receive various information from surrounding vehicles through Vehicle‐to‐Everything (V2X) communication technologies and adjust their car‐following behaviour accordingly. Although several studies have evaluated the impact of CAVs on traffic flow stability in a small segment of networks, most approaches are focused on their specific applications considering the trajectory information, and there is a lack of studies analyzing the impact of CAVs on a large‐scale network. This paper proposes a novel viscous continuum traffic model considering the anticipation of space headway, the throttle angle, and brake torque information during cooperative car‐following. The methods employed to develop the new car‐following model and its counterpart continuum traffic model have been described. The linear and non‐linear stability analyses of the newly developed model have been conducted to obtain the critical stability factors in small perturbations. Numerical simulations have been carried out to investigate the effect of the anticipation, the throttle angle, and brake torque information on traffic stability, fuel consumption, and exhaust emissions. The numerical results reveal that the anticipation of space headway and the transmission of the throttle angle and brake torque information during cooperative car‐following manoeuvres can improve the traffic flow stability and reduce fuel consumption and emissions