The Journal of The Korea Institute of Intelligent Transport Systems (한국ITS학회 논문지)

The Korea Institute of Intelligent Transport Systems (한국ITS학회)
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Development of Vehicle and/or Obstacle Detection System using Heterogenous Sensors

이종센서를 이용한 차량과 장애물 검지시스템 개발 기초 연구

  • 장정아 (한국전자통신연구원 자동차/조선 IT융합연구부) ;
  • 이기룡 (아주대학교 일반대학원 전자공학과) ;
  • 곽동용 (한국전자통신연구원 자동차/조선 IT융합연구부)
  • Received : 2012.05.11
  • Accepted : 2012.09.05
  • Published : 2012.10.31
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Abstract

This paper proposes the new object detection system with two laser-scanners and a camera for classifying the objects and predicting the location of objects on road street. This detection system could be applied the new C-ITS service such as ADAS(Advanced Driver Assist System) or (semi-)automatic vehicle guidance services using object's types and precise position. This study describes the some examples in other countries and feasibility of object detection system based on a camera and two laser-scanners. This study has developed the heterogenous sensor's fusion method and shows the results of implementation at road environments. As a results, object detection system at roadside infrastructure is a useful method that aims at reliable classification and positioning of road objects, such as a vehicle, a pedestrian, and obstacles in a street. The algorithm of this paper is performed at ideal condition, so it need to implement at various condition such as light brightness and weather condition. This paper should help better object detection and development of new methods at improved C-ITS environment.

본 연구는 도로 위의 객체를 분류하고 그 위치를 추정하기 위해 카메라와 레이저스캐너를 이용한 이종센서 검지 시스템의 연구를 다루고 있다. 이러한 도로인프라에서의 검지시스템은 ADAS(Advanced Driver Assist System) 및 (반)자동제어 서비스 등의 새로운 C-ITS 서비스에서 요구되는 객체의 위치 정보를 검지할 수 있다. 본 연구에서는 국외 관련 사례를 살펴보고, 카메라와 레이저스캐너를 이용한 검지시스템의 가능성을 살펴보았다. 그 후 이종센서 처리 알고리즘을 제안하고, 실 도로환경에서 몇 가지 도로상황 시나리오를 설정하여 시험검증을 실시하였다. 그 결과 이종센서 검지시스템으로 차량, 보행자 및 기타 장애물에 대한 검지 및 위치 추정에 대하여 비교적 용이하게 이용될 수 있음을 확인할 수 있었다. 본 연구의 경우 매우 이상적인 조건에서 실험이 실시되었으며, 조도, 날씨 등의 외부환경 조건의 변화에 따른 알고리즘의 평가가 필요하다. 이러한 연구는 향후 미래의 C-ITS 환경 하에서 객체 검지 기술로 활용될 수 있을 것으로 기대한다.

Keywords

References

  1. H. Zhao, J. Cui and H. Zha, "Sensing an intersection using a network of laser scanners and video cameras", IEEE Intelligent Transportation Systems Magazine, vol. 1, no. 2, pp.31-37, 2009.
  2. L. Alexander, P. Cheng, A. Gorjestani, A. Menon, B. Newstom, C. Shankwitz, and M. Donath, "The minnesota mobile intersection surveillance system", Proc. of IEEE Intelligent Transportation Systems Conference, pp.139-144, 2006.
  3. L. A. Klein, "Sensor Technologies and Data Requirements for ITS", Artech House, 2001.
  4. F. Ahlers, and Ch. Stimming, "Laserscanner based cooperative Pre-data-fusion", in Proc. IEEE in Intelligent Vehicles Symposium, pp.1187-1190, 2008.
  5. M. Tsogas, N. Floudas, P. Lytrivis, A. Amditis, and A. Polychronopoulos, "Combined lane and road attributes extraction by fusion data from digital map, laser scanner and camera", Information Fusion, vol. 12, pp.28-36, 2011. https://doi.org/10.1016/j.inffus.2010.01.005
  6. M. Jokela, M. Kutila, J. Laitnen, F. Ahlers, N. Hautiere, and T. Schendzielorz, "Optical road monitoring of the future smart roads - preliminary results", World Academy of Science, Engineering and Technology, vol. 34, pp.52-57, 2007.
  7. R. Labayrade, C. Royere, D. Gruyer, and D. Aubert, "Cooperative fusion for multi-obstacles detection with use of stereovision and laser scanner", Autonomous Robots, vol. 19, pp.11-140, 2005.
  8. N. Kaempchen and K. C. J. Dietmayer, "Fusion of laserscanner and video for advanced driver assistance systems", in Proc.11th World Congress on Intelligent Transportation Systems, Nagoya, Japan, 2004.
  9. S. Wender and K. C. J. Dietmayer, "A feature level fusion approach for object classification", in Proc. IEEE Intelligent Vehicles Symposium, Istanbul, Turkey, 2007.
  10. A. Soloviev, and M. M. Miller, "Navigation in difficult environments: Multi-sensor fusion techniques", RTO-EN-SET-116, Nato Otan Science and Technology Organization publication, 2011.
  11. C. h. Cuan, J. W. Gong, Y. D. Chen, and H. Y. Chen, "An application of data fusion combining laser scanner and vision in real-time driving environment recognition system", in Proc. 8th International Conference on Machine Learning and Cybernetics, Baoding, 12-15 July 2009.
  12. H. Cramer, U. Scheunert, and G. Wanielik, "Multi sensor data fusion using a generalized feature model applied to different types of extended road objects," in Proc. 7th International Conference on Information Fusion. Stockholm, Schweden, June 2004.
  13. A. Fod, A. Howard, and M.J. Mataric, "Laser-based people tracking", in Proc. IEEE Robotics, pp.3025-3029, 2003. J. Blanco, W. Burgard, R. Sanz, and J.L. Fernandez, "Fast face detection for mobile robots by integrating laser range data with vision", in Proc .IEEE Robotics, 2003.
  14. J. Cui, H. Zha, H. Zhao, and R. Shibasaki, "Multi-modal tracking of people using laser scanners and video camera", Image and vision computing, vol. 26, pp.240-252, 2008. https://doi.org/10.1016/j.imavis.2007.05.005
  15. 장정아, 곽동용, 임동선, "자동차-IT서비스를 위한 인프라기반 이종센서의 융합기술연구", 2011년도 한국융합소프트웨어학회 추계학술발표대회 논문집, 제1권 1호, 2011.
  16. E. Waltz and J. Llinas, "Multisensor data fusion norwood", MA: Artech House, 1990.
  17. N. E. Faouzi, H. Leung, and A. Kurian, "Data fusion in intelligent transportation systems: progress and challenges- a survey", Information Fusion, vol. 12, pp.4-10, 2011. https://doi.org/10.1016/j.inffus.2010.06.001