In this paper, we propose Intelligent Driver Assistance System (I-DAS) for driver safety. The proposed system recognizes safety and danger status by analyzing blind spots that the driver cannot see because of a large angle of head movement from the front. Most studies use image pre-processing such as face detection for collecting information about the driver's head movement. This not only increases the computational complexity of the system, but also decreases the accuracy of the recognition because the image processing system dose not use the entire image of the driver's upper body while seated on the driver's seat and when the head moves at a large angle from the front. The proposed system uses a convolutional neural network to replace the face detection system and uses the entire image of the driver's upper body. Therefore, high accuracy can be maintained even when the driver performs head movement at a large angle from the frontal gaze position without image pre-processing. Experimental result shows that the proposed system can accurately recognize the dangerous conditions in the blind zone during operation and performs with 95% accuracy of recognition for five drivers.

The trailer system offers efficiency of transportation capability. However, it is difficult to control the backward motion. It is an open loop unstable problem. To solve this problem, we are proposed the driver assistance system. Driver assistance system assists a driver to control the backward motion of trailer system as if forward motion. A driver only secure the rear view of last passive trailer, and select the control input to drive the last passive trailer. The driver assistance system converts the control input of the driver into velocity and steering angle of the vehicle using the inverse kinematics. It is possible by electronic control input devices and the rear view camera. Effectiveness of driving assistance system is verified by the simulation and the experiments.