Using Intelligent Cooperative System for Travel Flow Management in Autonomous Vehicle Networks

This paper describes the use of the intelligent cooperative system for autonomous vehicle network to manage the ravel flow in the urban roads. The intelligent cooperative system aims to reduce the amount of traffic congestion in road networks, and their negative effects, such as delays, waiting time, driver stress, air and noise pollution, and the blocking of emergency vehicles. The proposed algorithm is very successful for two reasons. The intelligent cooperative system in autonomous vehicle network uses performance analysis based on statistical measurement error to improve the accuracy of the forecast model, and the ability to communicate with others vehicles to update its local information based on quality of experience (QoE). The term QoE is defined and relates to how end users perceive the quality of an application or service. QoE is a new element that can play an important role in improving road traffic congestion, especially under abnormal conditions. The data collection to assess driver satisfaction was based on a questionnaire given to drivers, on road traffic management, and on channel demand data in base stations.