The integration of computer vision and deep learning techniques has revolutionized the automotive industry, paving the way for the development of intelligent vehicle systems. This paper proposes a comprehensive framework for designing an intelligent vehicle system capable of advanced perception and decision-making tasks. Leveraging computer vision algorithms, the system processes real-time visual data from onboard cameras, LiDAR, and other sensors to extract rich environmental information. Deep learning models are employed for object detection, classification, and semantic segmentation, enabling the vehicle to accurately recognize and interpret its surroundings. Key components of the proposed system include convolutional neural networks (CNNs) for detecting and tracking objects, recurrent neural networks (RNNs) for modeling temporal dependencies in dynamic environments, for data augmentation and synthesis. By continuously analyzing the environment and predicting future scenarios, the intelligent vehicle system can make informed decisions about navigation, collision avoidance, and route planning. Furthermore, the system is designed to adapt and learn from real-world driving experiences, improving its performance over time.