A Systematic Literature Review of Autonomous and Connected Vehicles in Traffic Management

Abstract

The emergence of autonomous vehicles and the advancement of technology over the past several decades has increased the demand for intelligent intersection management systems. Since there has been increased interest in researching how autonomous vehicles manage traffic at junctions, a thorough literature analysis is urgently needed. This study discovered peer-reviewed publications published between 2012 and 2022 in the most prestigious libraries to address this problem. After that, 100 primary studies were identified, and the chosen literature was subjected to systematic analysis. According to the findings, there are four primary categories of approaches, i.e., rule-based, optimization, hybrid, and machine learning procedures, which are used to achieve diverse driving objectives, including efficacy, safety, ecological, and passenger ease. The analyses illustrate the many attributes, limits, and views of the current solutions. This analysis enables the provision of potential future difficulties and directions in this study area.