Future ground transportation is predicted to be enhanced, transformed, and revolutionized by autonomous vehicles (AV). It’s predicted that one-day intelligent automobiles will displace conventional ones, capable of autonomous decision-making and driving activities. Self-driving cars are outfitted with sensors to see and understand their immediate surroundings as well as the environment in the distance using more advanced communication technologies, such as 5G, to accomplish this goal. Local perception will still be a useful tool for short-range vehicle control in the interim, just like it is for humans. The car can reach its goal while maintaining a set of standards (security, energy conservation, reduction of congestion, leisure), but with the help of expanded perception, which enables anticipating of distant events. Despite significant advancements in sensor technologies over the past few years in the context of their usefulness to audiovisual systems and efficacy, it is not advised to rely solely on one sensor to perform any of the self-sufficient tasks related to driving because sensors continue to malfunction because of vibration, conditions outside, production flaws, or other variables. Several models of architecture have been put out as guides. To tackle this complexity, autonomous systems must be created, created, operated, and deployed. We provide a brief review of sensors and the fusion of sensors in self-driving cars in this study. We concentrated on the lens, radar detectors, and laser sensor integration from the main autonomous car sensors. The state-of-the-art in this field will be discussed, including occupancy grid mapping for navigating and location in changing circumstances, using both pictures and three-dimensional point cloud data, 3D object identification techniques are used, along with moving object tracking and tracking systems. It has been demonstrated that adding more sensors to a sensor fusion system improves both the performance and durability of the outcome. Additionally, using camera data for localization and mapping, which are typically handled by sonar and laser data, enhances the perception of the world as a whole. One of the fastest-growing fields in the self-driving car arena is sensor fusion because it is essential to autonomous systems as a whole.