This book aims to provide a comprehensive exploration of the integration of machine learning and deep learning algorithms into the field of autonomous vehicles and advanced driver assistance systems. It also highlights the use of various sensing technologies such as LiDAR, radar, cameras, and ultrasonic sensors. This book presents machine learning techniques relevant to autonomous systems, with a focus on deep learning, neural networks, and reinforcement learning, providing readers with a solid understanding of these foundational concepts. It further includes real-world applications, offering insights into how these cutting-edge techniques are being employed by industry leaders and startups to improve the perception capabilities of autonomous vehicles. It is primarily written for senior undergraduates, graduate students, and academic researchers in the fields of electrical engineering, electronics and communications engineering, computer engineering, and automotive engineering.