The increasing adoption of Advanced Driver Assistance Systems (ADAS) and intelligent transportation technologies has significantly improved vehicle perception capabilities. Modern smart vehicles employ deep learning models capable of detecting and classifying surrounding objects in real time; however, the effective communication of this information to human drivers remains a critical challenge. Excessive visual, auditory, or symbolic feedback may increase cognitive load and reduce the overall effectiveness of driver assistance systems. This paper proposes a human-centered framework that combines Edge Artificial Intelligence (Edge AI) and Augmented Reality (AR) interfaces to enhance real-time situational understanding in smart vehicles. The proposed approach utilizes lightweight deep learning object detection models deployed on edge computing platforms to identify vehicles, pedestrians, cyclists, road signs, and potential hazards directly within the vehicle environment. Detection results are subsequently integrated into an AR visualization layer designed to present contextual information intuitively within the driver's field of view. The framework emphasizes explainability by transforming complex perception outputs into spatially aligned visual cues that support rapid decision-making while minimizing distraction. Attention is given to the visualization of uncertainty, object prioritization, and dynamic risk assessment. The proposed architecture leverages recent advances in computer vision, embedded AI, and immersive technologies to create a seamless interaction between vehicle perception systems and human operators. A proof-of-concept implementation is presented using a YOLO-based object detection pipeline integrated with an AR visualization environment. Preliminary experimental scenarios illustrate the potential benefits of the proposed approach for hazard recognition, driver awareness, and trust in automated vehicle systems. We discuss performance considerations, usability aspects, and future integration within next-generation intelligent vehicles.