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

Abstract

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.