## 1 Introduction  
One of the countries with the highest traffic in the world is India. In 2018, there were approximately 467,000 motor vehicle accidents in India, with 70% of them being attributed to human error. Autonomous vehicles, with their ability to sense their surroundings, can decrease the number of accidents on the road, making transportation safer and more convenient for seniors and others who do not have to constantly rely on drivers. Level 0 (No Automation) to Level 5 are the different levels of autonomy for vehicles (Full Automation). Level 0 describes the typical non-automated vehicle, while Level 1 and 2 are driver-assisted vehicles. Level 3 and 4 represent conditional and high automation, respectively, where the driver is only required in certain circumstances. Level 5 vehicles are completely autonomous, requiring no human intervention [1]. As the automotive industry continues to advance, intelligent automobiles are becoming increasingly automated, moving from driver-assisted vehicles to fully autonomous ones. These advancements bring incremental improvements, such as structures that help humans maintain constant speed, stay in their lane, and recognize obstacles. As a result, one of the most significant challenges facing autonomous or self-driving vehicles is road detection. The study into self-driving cars heavily depends on road segmentation, which can be accomplished in several ways. Camera images, using RGB data, vanishing points, and edges, can perform road segmentation, but this method is susceptible to errors due to weather, light, and shadow conditions. LiDAR sensors can still detect road areas using 3D PC images from top, front, and distance views, but it cannot utilize color data. Hence, even a small height difference could result in inaccurate distinction between the road and the roadside. To overcome these limitations, the LiDAR and camera data fusion technique was developed by calibrating both the camera and LiDAR, and by displaying the LiDAR-detected cloud on the camera-captured image. This combination of exceptional data sets facilitates accurate road area division using LiDAR and camera information [2]. A comprehensive evaluation, using two standard benchmark datasets, provides insights into the suitability of various approaches. Autonomous navigation in modern driving systems relies on sensors' abilities to detect and classify both fixed and moving objects, which in turn aids in forecasting the future state of the environment, preventing accidents, and organizing routes and schedules. By separating moving from static objects in 3D LiDAR PC data, moving object segmentation (MOS) techniques enhance environment awareness, localization, and future state prediction. While the network model is sophisticated and the MOS job is computationally demanding, it is crucial to fulfil the real-time processing needs of autonomous vehicle applications [3]. One of the areas of robotics that is expanding the quickest is automation. Giving robots the capacity to navigate on their own is crucial given the ever-increasing interest in automating procedures for both performance and privacy. For the autonomous navigation of mobile robots, a variety of sensor technologies, navigational system types, and data fusion techniques are available. The capacity to recognize things in their path and enable them to move about freely with a minimal issue of collision is a crucial feature of mobile autonomous robots. Other vehicles (such as forklifts and truck trailers) may not be spotted, even when using laser scanners designed for outdoor usage, potentially resulting in crashes. Under these challenging circumstances, 3D PCs recorded using a depth camera might be utilized to lessen this issue. In a brief streaming sequence, 3D coordinates and reflection intensities may be produced from more than 10,000 points of data using 3D PCs. PCs have clear 3D spatial structures that also contain reflection elements on the surface of objects, unlike RGB pictures, which are their main benefits [4].