## 1 Introduction  
The literature review conducted in this paper reports a comprehensive analysis of the latest advances in perception and scanning technology related to autonomous forklift systems. This paper offers a complete evaluation of the present state-of-the-art techniques and developments in machine vision for independent forklifts. It encompasses various aspects such as the identification of objects, localization, mapping and path planning, emphasizing the significant challenges and recent advancements in each area. Additionally, emerging technologies such as deep learning, sensor fusion and real-time perception are deliberated in the perspective of their impact on boosting the capabilities of independent forklifts. Currently, autonomous vehicles are a hot topic as this era is currently witnessing a great deal of development in the field of self-driving that might possess the capability to revolutionize our lives in terms of safety, reliability and efficiency [1]. Transporting and delivering goods to storage places has been made easy by these promising vehicles, resulting in improvement in productivity.  
Over the last two decades, there have been exceptional and unprecedented trends in the field of self-driving cars. The idea of self-driving cars first appeared in the 1920s, when the possibility of making an autonomous vehicle began to be seriously considered, as explained by [PERSON] et al. as in [2]. It also discussed the functioning of autonomous vehicles.The first promising endeavors of autonomous vehicles, although of course not fully autonomous, were in the 1950s, with the initial autonomous vehicles emerging in the 1980s through the efforts of Carnegie Mellon University's Navlab [3].As the field progressed, multiple prominent companies and research centers became actively involved in its development. The automotive industry is presently advancing sensor-based solutions to enhance vehicle safety. These systems, known as Advanced Driver Assistance Systems (ADASs), utilize an array of sophisticated sensors as confirmed in [4].  
In this paper, a review of different types of sensors such as Time of Flight (ToF) and LiDARs has been presented to illustrate the principle of sensor use in autonomous vehicles, and to equip the reader with the basic knowledge necessary to understand this technology. Additionally, this paper will explain the fundamentals of modern advancements in the key features of self-driving technology. Self-driving forklift trucks are employed in warehouses to move goods between different locations. These autonomous vehicles have already had a considerable effect on logistics operations, with most current self-driving forklifts depending on advanced sensors, as well as vision and Geographic Information System (GIS)-based guidance systems. In addition, the scope of this paper also considers automated forklifts with multi-level load actuation in warehouse cracking systems, which require vertical lifting capabilities to operate effectively. This excludes more basic floor-level automated guided vehicles (AGVs) that do not perform vertical lifting into cracking, as they serve different operational purposes.  
This paper is structured as follows: Section 1 introduces the focus and objectives of the research, providing an overview of the current advancements in autonomous forklifts. Section 2 reviews related work, highlighting key academic contributions and solutions for developing autonomous vehicle prototypes. Section 3 explains the architecture of autonomous vehicle components, detailing their perception, localization, planning and control systems. Section 4 focuses on machine vision and object detection techniques, emphasizing models used in autonomous forklifts and deep learning-based detection methods, respectively. Section 5 examines industry applications and major manufacturers of autonomous forklifts. Finally, the discussion and conclusions are presented in Section 6 and in Section 7 respectively.