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.

Abstract
 1. 서 론
 2. CNN 기반의 운전자 안전/위험 상태 인지시스템
  2.1 응시 영역 설정
  2.2 이미지 데이터베이스의 작성
  2.3 컨볼루션 신경망 (CNN) 구조 설정
 3. 실험결과
 4. 결 론
 References

• 미아오 쉬(School of Robot Engineeing, Kyungpook National University, Daegu, Korea) | Xu Miao
• 이현순(School of Mechanical Engineeing, Kyungpook National University Daegu, Korea) | Hyun-Soon Lee
• 강보영(School of Mechanical Engineeing, Kyungpook National University) | Bo-Yeong Kang Corresponding author