<p>The development of autonomous vehicles has garnered significant attention, mainly due to advancements in embedded systems and wireless communication technologies. Achieving fully automated vehicles requires addressing various challenges, notably the development of an autonomous lane change (ALC) system, a crucial component for ensuring safe and efficient driving. Lane Changing (LC) is a multifaceted task influenced by numerous factors, making it a focal point of extensive research. Reinforcement Learning (RL) is used for solving sequential decision-making problems. One instance of such a problem is driving in LC scenarios, which involve the execution of a sequence of LC decisions. This paper presents a comprehensive review of the progress toward ALC systems, with a particular focus on the use of RL. First, we give a background on critical aspects within the field, encompassing LC maneuvers and RL. Then, we conduct an in-depth examination of various studies in this area. By rigorously analyzing the findings and methodologies of these studies, we identify the essential components to consider and the practical aspects required for training LC driving models using RL. Future perspectives as well as current challenges are also covered in this review.</p>