# A Systematic Literature Review of Autonomous and Connected Vehicles in Traffic Management
## 1 Introduction  
With population growth, there are more and more automobiles on the roadways. Peak hours in nearly all extensive metropolitan regions are incredibly congested. A simple road repair project or an unfortunate accident might cause a significant delay [1]. Each year, 1.35 million individuals lose their lives in traffic accidents [2]. Thus, traffic safety has been one of the critical problems investigated in intelligent transportation systems (ITS), as significant accidents entail the loss of life and devastation of infrastructures [3]. Fortunately, cutting-edge information and communications technology have enabled notable safety features to be included [4, 5]. As a result, driverless vehicles are a practical option that might improve road traffic flow while also increasing driving safety and trip comfort [6]. The Society of Automotive Engineers, generally termed as SAE, categorized autonomous vehicles (AVs) at levels 4 and 5--also known as self-driving vehicles--which can function with no human intervention.  
In addition to being self-reliant, connected and autonomous vehicles (CAVs) can interact with other nearby vehicles, the infrastructure, and even cloud services to share data on the traffic and infrastructure (such as the status of traffic signals or the existence of a downstream queue) [7]. Although connected vehicles (CVs) may communicate with their surroundings, a human driver controls them. Due to the numerous potential sociological and financial advantages of autonomous driving, there has been a substantial increase in research interest over the past two decades. Autonomous vehicles (AVs) promise lower carbon emissions than conventional vehicles, while also enhancing the economy, efficiency, and safety [8]. Unfortunately, making high-level decisions in AVs is difficult because of the complex and dynamic traffic environment, particularly in mixed traffic coexisting with other car drivers. Road intersections, interchanges, and roundabouts are particularly prone to accidents because collisions happen when two or more vehicles approach the exact location simultaneously.  
Intersections, interchanges, and roundabouts have been widely used in the traffic infrastructure to boost traffic safety, since they do so while also requiring less maintenance and higher traffic flow [9]. For these considerations, numerous researchers used a variety of traffic management strategies that were included in AVs and CVs to reduce the likelihood of accidents. Recently, the usage of blockchain technology has increased in the transportation sector [10]. Moreover, traditional Adaptive Cruise Control (ACC), Three-traffic-Phase Adaptive Cruise Control (TPACC), and others have been recently employed [11]. Therefore, it is essential to evaluate the literature already in existence, particularly those related to the methods used to control the application of autonomous vehicle traffic flow. This is crucial to map out pertinent articles and academic works in an organized manner to find the answers offered for AVs and CVs.  
This research paper aims to highlight existing research on the many types of AV and CV methodologies used in traffic management, along with suggested fixes and purposes methods to avoid junction conflicts, increase traffic harmonization, and reach environmental/social objectives. The goal is to offer a community-driven beginning for a better investigation of creating traffic management strategies that investigate suitable solutions regarding various traffic circumstances. To do this, the study will critically evaluate recent works and research on autonomous vehicles, using our observations to create innovative approaches.