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Artificial Intelligence (AI) and Machine Learning (ML) play a critical role in the development of autonomous vehicles (AVs), forming the backbone of the systems that enable vehicles to perceive, decide, and act without human intervention. In the context of Smart Urban Energy Systems and Smart Transportation Systems, AVs offer potential for revolutionizing urban mobility, enhancing safety, reducing energy consumption, and optimizing traffic flow. AI and ML technologies drive the sensors, decision-making algorithms, and real-time responses that define autonomous mobility. Their evolution will continue to impact how AVs interact with infrastructure, other vehicles, and road users, improving performance and safety across the transportation ecosystem. At the core of an autonomous vehicle is its ability to understand and interpret its environment through perception systems. These include cameras, radar, LiDAR, ultrasonic sensors, and GPS. AI, particularly through deep learning, enables the interpretation of massive volumes of data from these sensors. Convolutional Neural Networks (CNNs), a form of deep learning, are widely used in image recognition and object detection tasks, allowing AVs to recognize road signs, pedestrians, cyclists, lane markings, and other vehicles with high accuracy. ML algorithms train on diverse driving scenarios to improve their understanding and responses to complex environments, including adverse weather, nighttime driving, and unpredictable human behavior. Decision-making and control are other key components powered by _______ ____ ____ _____ _______ __________ _________.
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