Abstract
This study explores the advancements and challenges of AI-powered self-driving cars, specifically in the context of urban planning, traffic management, and transportation systems. It investigates the technological components of autonomous vehicles, including computer vision, machine learning algorithms, sensor fusion, and real-time decision-making systems. The research further delves into the training and learning procedures, focusing on the use of large datasets, deep neural networks, and reinforcement learning to continuously enhance driving capabilities through interaction with the environment. The goal is to assess the potential of AI to improve road safety, transit efficiency, and individual mobility, while acknowledging the obstacles that need to be overcome for widespread adoption and societal trust.
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