In this study, a driver assistance system that uses a network model based on deep learning technology was developed. It has forward collision warning and lane-mark recognition features. The application uses a webcam to capture forward images, which are transferred to a computer in which object recognition has been implemented. The system information is displayed on smart glasses through the network as an augmented reality image. You Only Look Once (YOLO) real-time object detection (tiny YOLOv2) was used as the main architecture to reduce the network complexity and enhance computing efficiency. During the training process, K-means was used to select the anchor box from each dataset. This enabled the size of the predicted box to be determined as a reference to enhance efficiency. This system makes it possible for the driver of a vehicle to learn about the movements and positions of vehicles ahead with respect to distance and lane marks. This reduces the chance of collisions as well as the violations of traffic regulations and improves driving safety.