전자공학회논문지 IE

The Institute of Electronics and Information Engineers (대한전자공학회)
권호 표지

Drowsy Driving Detection Algorithm Using a Steering Angle Sensor And State of the Vehicle

조향각센서와 차량상태를 이용한 졸음운전 판단 알고리즘

  • 문병준 (대덕대학교 자동차학부) ;
  • 연규봉 (자동차부품연구원 지능형자동차기술연구본부) ;
  • 이순걸 (경희대학교 기계공학과) ;
  • 홍승표 (한경대학교 디자인학부) ;
  • 남상엽 (국제대학교 IT계열) ;
  • 김동한 (경희대학교 전자전파공학과)
  • Received : 2012.04.16
  • Accepted : 2012.06.05
  • Published : 2012.06.25
Download PDF
⟨ Previous Next ⟩

Abstract

An effective drowsy driver detection system is needed, because the probability of accident is high for drowsy driving and its severity is high at the time of accident. However, the drowsy driver detection system that uses bio-signals or vision is difficult to be utilized due to high cost. Thus, this paper proposes a drowsy driver detection algorithm by using steering angle sensor, which is attached to the most of vehicles at no additional cost, and vehicle information such as brake switch, throttle position signal, and vehicle speed. The proposed algorithm is based on jerk criterion, which is one of drowsy driver's steering patterns. In this paper, threshold value of each variable is presented and the proposed algorithm is evaluated by using acquired vehicle data from hardware in the loop simulation (HILS) through CAN communication and MATLAB program.

졸음운전은 사고발생 확률이 높고, 사고 발생 시 심각도가 높기 때문에 효율적인 졸음운전 판단 시스템이 필요하다. 그러나 생체 신호나 비전을 이용한 졸음운전 판단시스템은 비용 측면에서 활용되기가 어렵다. 이에 본 논문에서는 추가적인 비용 없이 대부분의 차량에 기본 장착되어 있는 조향각 센서(steering angle sensor)와 차량정보(brake switch, throttle position signal, vehicle speed)를 이용하여 졸음운전자의 조향패턴 중 하나인 저킹 판단을 이용한 졸음운전 판단 알고리즘을 제안한다. 본 연구에서는 각 변수의 임계값을 제시하고, HILS(Hardware in the Loop Simulation)에서 CAN을 통해 취득한 차량의 데이터와 Matlab 프로그램을 이용하여 알고리즘을 평가한다.

Keywords

References

  1. Traffic Accident Analysis Center, Traffic Accident Statistical Analysis 2009, KoROAD, 2009.
  2. Hee-Sub Rim, Eun-Bi Jeong, Cheol Oh, Kyeong-Pyo Kang, Detection of Unsafe Zigzag Driving Maneuvers using a Gyro Sensor , The Journal of The Korea Institute of Intelligent Transport Systems, pp42-54, 2011.
  3. Han-Woo Ko, Woan-Kyu Lee, Chang-Ho Kim, Arousal monitoring and Analysis System To Assess Driver Drowsiness, KSAE 1995 Annual Conference, pp474-480, 1995.
  4. Han-Woo Ko, Woan-Kyu Lee, Chang-Ho Kim, Arousal monitoring and Analysis System To Assess Driver Drowsiness - Detection of eye blinking speed - , KSAE 1995 Annual Conference, pp481-486, 1995.
  5. H.W.Ko, Y.H. Kim, Analysis and Determination of Arousal Level Control to Prevent Drowsy Driving, KSAE 1997 Annual Conference, pp 1171-1176, 1997.
  6. Sunwoong Kim, Sejin Park, Youngsin Lee, Development of Evaluation Method of Driver`s Fatigue by Physiological Signal , KSAE 2003 Annual Conference, pp 1208-1212, 2003.
  7. Tom Pilutti, A. Galip Ulsoy, Identification of Driver State for Lane-Keeping Tasks, IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS　PART A: SYSTEMS AND HUMANS, VOL. 29, NO. 5, 1999.
  8. Paul Smith, Mubarak Shah, Niels da Vitoria Lobo, Determining Driver Visual Attention With One Camera, IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, VOL. 4, NO. 4, 2003.
  9. JiHong Min, Jung-Chul Kim, Kicheon Hong, Implementation of Drowsiness Driving Warning System based on Eyes Detection and Pupil Tracking, Proceedings of KFIS Autumn Conference 2005, Vol 15, No 2, pp 249-252, 2005.
  10. Mu-Chun Su, Chao-Yueh Hsiung, De-Yuan Huang, A Simple Approach to Implementing a System for Monitoring Driver Inattention, IEEE International Conference on Systems, Man, and Cybernetics, 2006.
  11. Zhibo Guo , Huajun Liu, Qiong Wang and Jingyu Yang, A Fast Algorithm of Face Detection for Driver Monitoring, Proceedings of the Sixth International Conference on Intelligent Systems Design and Applications, 2006.
  12. Zutao Zhang, Jia-shu Zhang, Driver Fatigue Detection Based Intelligent Vehicle Control, The 18th International Conference on Pattern Recognition, 2006.
  13. Qiong Wang, Jingyu Yang, Mingwu Ren, Yujie Zheng, Driver Fatigue Detection: A Survey, Proceedings of the 6th World Congress on Intelligent Control and Automation, 2006.
  14. Cheol-Soo Bae, Behavior Recognition using Facial Feature Information, Journal of Korean Institute of Information Technology, Vol 5, No 3, pp 82-88, 2007.
  15. Nanae Michida, Hiroshi Okiyama, Kazuo Nishikawa and Takahide Nouzawa, A Study of Drivers' Fatigue Mechanisms during Long Hour Driving, SAE 2001 World Congress, 2001.
  16. Hong J. Eoh, Min K. Chung, Seong-Han Kim, Electroencephalographic study of drowsiness in simulated driving with sleep deprivation, International Journal of Industrial Ergonomics, pp. 307-320, 2005.
  17. Byung-Chan Chang, Jung-Eun Lim, Hae-Jin Kim, and Bo-Hyeok Seo, A study of classification of the level of sleepiness for the drowsy driving prevention, 2007 SICE Annual Conference, 2007.
  18. Ju-taek Oh, Sang-yong Lee, Young-sam Kim, Analysis of Car controls and Perclos by Normal and Fatigue driving, Korean Society of Road Engineers, Vol 10, No 4, pp 127-138, 2008.
  19. Hwan Hu, Sung-Soo Park, Woon-Sung Lee, Development of a Driver Status Monitoring System Using a Driving Simulator, KSAE 2009 Annual Conference, pp. 1986-1993, 2009.
  20. Hwan Hu, Development of a Drowsy Driving Detection System , Graduate School of Automotive Engineering, Kookmin University, 2010
  21. ISO 17361, Intelligent transport systems - Lane departure warning systems - Performance requirements and test procedures, INTERNATIONAL STANDARD ISO, 2007-02-01
  22. Youngki Lee, Seokwon Yeom, Drowsy driver warning with eye recognition, IEEK Fall Conference 2010, pp 329-330, 2010.