Artificial Intelligence (AI) has evolved into a defining technology of the modern era, profoundly transforming the global automotive industry. One of its most remarkable applications is the development of autonomous or self-driving vehicles that can sense their surroundings and navigate without human intervention. These vehicles integrate advanced AI models, machine learning (ML), and deep learning (DL) algorithms to process environmental data, recognize patterns, and make real-time driving decisions. The objective of this paper is to present a comprehensive survey of AI-driven self-driving car technologies, their system architecture, sensor networks, perception models, and control mechanisms. The study explores the evolution of intelligent transportation systems, emphasizing computer vision, sensor fusion, neural networks, and decision-making frameworks. Additionally, it examines the ethical, legal, and social challenges associated with autonomous vehicles, as well as future research opportunities. This paper concludes that AI-powered self-driving cars hold the potential to revolutionize mobility, enhance road safety, and contribute to sustainable urban development.
U et al. (Thu,) studied this question.