DOI QR코드

DOI QR Code

Laser Scanner based Static Obstacle Detection Algorithm for Vehicle Localization on Lane Lost Section

차선 유실구간 측위를 위한 레이저 스캐너 기반 고정 장애물 탐지 알고리즘 개발

  • 서호태 (서울대학교 기계항공공학부) ;
  • 박성렬 (서울대학교 기계항공공학부) ;
  • 이경수 (서울대학교 기계항공공학부)
  • Received : 2017.04.24
  • Accepted : 2017.08.31
  • Published : 2017.09.30

Abstract

This paper presents the development of laser scanner based static obstacle detection algorithm for vehicle localization on lane lost section. On urban autonomous driving, vehicle localization is based on lane information, GPS and digital map is required to ensure. However, in actual urban roads, the lane data may not come in due to traffic jams, intersections, weather conditions, faint lanes and so on. For lane lost section, lane based localization is limited or impossible. The proposed algorithm is designed to determine the lane existence by using reliability of front vision data and can be utilized on lane lost section. For the localization, the laser scanner is used to distinguish the static object through estimation and fusion process based on the speed information on radar data. Then, the laser scanner data are clustered to determine if the object is a static obstacle such as a fence, pole, curb and traffic light. The road boundary is extracted and localization is performed to determine the location of the ego vehicle by comparing with digital map by detection algorithm. It is shown that the localization using the proposed algorithm can contribute effectively to safe autonomous driving.

Keywords

References

  1. Abdulhakam.AM. Assidiq, Othman O. khalifa, Md. Rafiqul Islam, 2008, "Real time lane detection for autonomous vehicles," IEEE Xplore, 10.1109/ICCCE.2008.4580573.
  2. Matthew Brown, Joseph Funke, Stephen Erlien, J. Christian Gerdes, 2017, "Safe Driving envelopes for path tracking in autonomous vehicles," Control Engineering Practice, Vol. 61, pp. 307-316. https://doi.org/10.1016/j.conengprac.2016.04.013
  3. Jongsang Suh, Beomjun Kim, Kyongsu Yi, 2016, "Stochastic predictive control based motion planning for lane change decision using a Vehicle Traffic Simulator" IEEE Xplore, 10.1109/ITEC-AP.2016.7513079.
  4. A. Kullack, I. Ehrenpfordt, K. Lemmer, "Lane departure prevention system based on behavioural control," IET Intelligent Transport Systems, Vol. 2, Issue 4, pp. 285-293. https://doi.org/10.1049/iet-its:20080031
  5. 채흥석, 정용환, 이명수, 신재곤, 이경수, 2017, "자율주행 안전성 평가 시나리오 개발 및 검증," 자동차안전학회지, Vol. 9, No.1, pp. 6-12
  6. Meng Zhang and Ke Liu, 2016, "Unmanned ground vehicle positioning system by GPS/ dead-reckoning/IMU sensor fusion," Advances in Engineering Research (AER), Vol. 117, pp. 737-747.
  7. Shivani Godha, 2017, "On-road obstacle detection system for driver assistance," Asia Pacific Journal of Engineering Science and Techonology, Vol. 3, No. 1, pp. 16-21.
  8. 김민우, 문상찬, 주다니, 이진기, 김병수, 이순걸, 2013, "영상에 의한 DGPS기반의 LDWS 오작동 주행 평가," KASE 부문 종합학술대회, pp. 929-933.
  9. Valentina Campanelli, Stephen M. Howell, Maury L. Hull, 2016, "Accuracy evaluation of a lowercost and four higher-cost laser scanners," Journal of Biomechanics, Vol. 49, Issue 1, pp. 127-131. https://doi.org/10.1016/j.jbiomech.2015.11.015
  10. Kang, Chul-Woo, Yoo, Young_Min, Park, Chan-Gook, 2008, "Performance Improvement of Attitude Estimation Using Modified Euler Angle Based Kalman Filter," Journal of Institute of Control, Robotics and Systems, Vol. 14, Issue 9, pp. 881-885. https://doi.org/10.5302/J.ICROS.2008.14.9.881