Artificial Intelligence has experienced remarkable development in recent years, and Deep Learning has emerged as one of the most influential technologies driving innovation across numerous fields. One of its most important applications is the development of autonomous vehicles, which represent an advanced integration of artificial intelligence, computer vision, sensing technologies, and intelligent control systems. Deep Learning is a branch of Machine Learning that relies on multi-layer artificial neural networks capable of learning complex patterns from large amounts of data. This capability makes deep learning particularly suitable for autonomous driving systems, where vehicles must continuously analyze complex and rapidly changing environments. Autonomous vehicles depend on multiple sources of information, including cameras, radar, positioning systems, and other sensors. Deep learning models can process the information generated by these systems and transform raw sensor data into meaningful representations of the surrounding environment. Computer vision is one of the most significant applications of deep learning in autonomous vehicles. Advanced neural networks can help vehicles recognize pedestrians, other vehicles, bicycles, traffic signs, road markings, and lane boundaries. This information is essential for understanding the driving environment and supporting safe navigation. Object detection is another critical function. Deep learning algorithms can identify and classify multiple objects in real time, allowing the vehicle to distinguish between a pedestrian crossing the road, a parked vehicle, a traffic signal, or an unexpected obstacle. Accurate and rapid detection is essential because the vehicle may need to respond within seconds or fractions of a second. Deep learning is also used for lane detection and road understanding. By analyzing images captured by cameras, intelligent systems can identify lane boundaries and provide information that supports steering and lane-keeping decisions. Sensor fusion further improves environmental perception by combining information from different sensing technologies. Instead of depending on a single source, autonomous driving systems can integrate camera, radar, and other sensor data to produce a more comprehensive understanding of surrounding conditions. Another important application is predicting the behavior of road users. An autonomous vehicle must not only identify where pedestrians and vehicles are currently located but also estimate how they may move in the immediate future. Deep learning models can analyze movement patterns and support predictions about possible trajectories. Deep learning also contributes to motion planning and decision support. Autonomous systems must continuously determine whether the vehicle should accelerate, slow down, stop, change lanes, or respond to unexpected situations. These decisions require rapid analysis of multiple environmental variables. In vehicles that still require occasional human intervention, deep learning can also support driver-monitoring systems. By analyzing eye movement, facial features, and head position, intelligent systems may identify signs of fatigue, distraction, or reduced attention and provide appropriate warnings. Despite its considerable potential, deep learning in autonomous driving faces significant challenges. Poor weather conditions, unusual road environments, and rare events can affect the reliability of perception systems. Models must therefore be trained and evaluated across diverse scenarios rather than relying only on performance under ideal conditions. Cybersecurity is another major concern because increasingly connected vehicles depend on software, communication systems, and digital data. Protecting autonomous driving systems against unauthorized access and cyber threats is essential for ensuring safety and public trust.