Over the past several decades, the development of technologies and the production of autonomous vehicles have enhanced the need for intelligent intersection management systems. Subsequently, growing interest in studying the traffic management of autonomous vehicles at intersections has been evident, which indicates a critical need to conduct a systematic literature review on this topic. This paper offers a systematic review of the proposed methodologies for intelligent intersection management systems and presents the remaining research gaps and possible future research approaches. We consider both pure autonomous vehicle traffic and mixed traffic at four-way signalized and unsignalized intersection(s). We searched for articles published from 2008 to 2019, and identified 105 primary studies. We applied the thematic analysis method to analyze the extracted data, which led to the identification of four main classes of methodologies, namely rule-based, optimization, hybrid, and machine learning methods. We also compared how well the methods satisfy their goals, namely efficiency, safety, ecology, and passenger comfort. This analysis allowed us to determine the primary challenges of the presented methodologies and propose new approaches in this area.INDEX TERMS Autonomous vehicle, intelligent intersection management system, mixed traffic, vehicleto-infrastructure (V2I) communication, vehicle-to-vehicle (V2V) communication.

With increasingly rapid advances in the field of producing modern and autonomous vehicles, the need for intelligent traffic management systems, which take advantage of the vehicle's abilities to sense and communicate, has increased. A considerable amount of literature has been published on managing traffic that includes only autonomous vehicles. However, changing all vehicles to autonomous versions is a longterm process. In the near future, traffic will be a mixture of human-driven and autonomous vehicles. To date, few studies have investigated mixed traffic in intelligent management systems. The main objective of this research is to study the possibility of using a vehicle-mounted camera to sense and collect the required traffic data of the surrounding vehicles in mixed traffic. To achieve this, a vehicle with a monocular camera is used to collect image information for detecting and counting the vehicles in different lanes and estimating their distance and speed on the defined route. The results indicate that our proposed image processing algorithms can acquire the information needed for intelligent traffic management systems.

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A primary concern of intelligent traffic management systems (ITMS) is to collect necessary traffic data. Vehicle position is one of the most important data types to manage traffic effectively. Most current approaches to localize modern vehicles (MVs) fall into three categories. The first category uses standalone reference stations, such as the wide-area augmentation system (WAAS), which are expensive modules. The second category uses multiple expensive localization sensors such as the global positioning system (GPS), global navigation satellite system (GNSS), and inertial measurement unit (IMU). However, such expensive solutions may not be applicable in all vehicles, impacting generalizability. The third category is a software-based approach. As opposed to the abovementioned approaches using expensive hardware, the third category uses software, such as map-matching techniques, to augment noisy localization sensors. In this study, we investigated map-matching software in some case studies and found that it cannot locate the vehicle effectively if the positional data are collected by a low-cost and too noisy GPS receiver. Therefore, this paper analyzes and highlights the impact of GPS receiver's noise in applying self-localization. It also proposes a new methodology by integrating cross-GPS validation, interpolation/best fit, and map-matching techniques to localize a vehicle in the presence of GPS signal noise and investigate it in real traffic data from a metropolitan area. Our proposed methodology is able to identify the more accurate GPS receiver dynamically, by considering the fixed distance between the two GPS receivers. Our evaluations indicate that the proposed methodology can significantly improve vehicle self-localization performance.