The article provides a comprehensive and critical analysis of the current state of the art in autonomous vehicles (AV), investigating the radical transformations brought about by the integration of advanced technologies in the industrial and road sectors. The paper explores the evolution of control architectures, summarizing the standardized SAE classifications and the critical role of driver assistance systems (ADAS). A central component of this review is the comparative evaluation of modern sensor systems (LiDAR, GNSS-RTK, inertial units and video cameras), emphasizing current data fusion methodologies for improving the perception of the driving environment. At the same time, the paper analyzes recent contributions regarding scenario modeling in virtual environments and obstacle localization and detection algorithms. Finally, the article explores the potential of artificial intelligence by applying reinforcement learning (RL) in trajectory planning and autonomous control, providing a perspective on future research directions necessary to increase the safety and efficiency of autonomous mobility. This synthesis provides researchers and practitioners with an overview of current challenges and emerging technological solutions.