Platoon control and optimization for mixed traffic flow with consideration of realistic inter-vehicle communication

Abstract

As one key part of a smart city, intelligent transportation systems have attracted a lot of attention by virtue of the advanced management and monitoring systems with smart sensors and excellent information transformation based on Vehicle-to-X (or V2X) technology. Such emerging techniques provide a wealth of knowledge of the traffic conditions for all vehicles driving on a traffic signal-free network, which can reduce traffic congestions, fuel consumption, environmental footprints, and enhance safety. Considering the intelligence and safety of the vehicle design, connected automated vehicles (CAVs) have been addressed to provide an optimal choice for enabling users to better drive with smart decisions in the complex traffic environment. Notably, coordination and interaction problems of CAVs and estimation human drivers’ social behaviors are challenging issues due to the complex vehicle dynamics and unpredictable traffic conditions. Currently, no common data has been yet available for the calibration and validation of a platoon control and optimization model for a realistic traffic system, and most research has been only conducted from a theoretical point of view. This study seeks to develop a cooperative platoon control and optimization strategy for a platoon mixed with CAVs and human-driven vehicles in a realistic inter-vehicle communication environment, aiming to ensure system-level traffic flow smoothness and stability as well as individual vehicles’ mobility and safety. In addition, with consideration of human drivers and CAVs, we also focus on the development of a more general microscopic traffic model which enables cooperative driving behaviors. To this end, we will need to design the platoon model, consensus-based controller, and optimization method for the mixed traffic system considering intelligent traffic flow that consists of many platoons moving together. Finally, we will try to implement our model and algorithm in realistic traffic simulations such as SUMO and so on.