Journal of KIISE (정보과학회 논문지)

Korean Institute of Information Scientists and Engineers (한국정보과학회)
권호 표지
DOI QR코드

DOI QR Code

Modeling and Analysis of Vehicle Detection Using Roadside Ultrasonic Sensors in Wireless Sensor Networks

WSN 기반 노변 초음파 센서를 이용한 차량인식에 대한 모델링 및 분석

  • 조영태 (강원대학교 컴퓨터정보통신공학과) ;
  • 정인범 (강원대학교 컴퓨터정보통신공학과)
  • Received : 2014.03.28
  • Accepted : 2014.08.08
  • Published : 2014.10.15
⟨ Previous Next ⟩

Abstract

To address the problems of existing traffic information acquisition systems such as high cost and low scalability, wireless sensor networks (WSN)-based traffic information acquisition systems have been studied. WSN-based systems have many benefits including high scalability and low maintenance cost. Recently, various sensors are studied for traffic surveillance based on WSN, such as magnetic, acoustic, and accelerometer sensors. However, ultrasonic sensor based systems have not been studied. There are many issues for WSN-based systems, such as battery driven operation and low computing power. Thus, power saving methods and specific algorithms with low complexity are necessary. In this paper, we introduce optimal methodologies for power saving of ultrasonic sensors based on the modeling and analysis in detail. Moreover, a new vehicle detection algorithm for low complexity using ultrasonic data is presented. The proposed methodologies are implemented in a tiny microprocessor. The evaluation results show that our algorithm has high detection accuracy.

기존 교통정보 수집시스템의 낮은 확장성과 높은 비용 등의 문제를 해결하기 위해 WSN 기반의 교통정보 수집 시스템이 최근 많이 연구되고 있다. WSN 기반 시스템은 기존 시스템에 비해 높은 확장성과 낮은 유지보수 비용을 가지는 장점이 있다. 최근에는 자기장센서, 가속도센서, 음향센서 등 다양한 센서를 이용하여 WSN 기반 교통정보수집 시스템 연구가 진행되고 있다. 하지만, 초음파센서를 이용한 WSN 기반 시스템은 아직 연구가 부족한 실정이다. WSN 기반 시스템은 배터리 동작, 낮은 컴퓨팅 파워와 같은 고유 특징으로 인해 에너지절약과 알고리즘의 경량화가 필요하다. 본 논문에서는 상세 모델링과 분석을 통해 도로환경에 따른 초음파 센서의 이상적인 에너지절약 방법을 제시한다. 또한 초음파 데이터를 이용한 차량인식 알고리즘의 경량화를 위한 방법을 제시한다. 제안된 방법은 초소형 마이크로프로세서에 실제 구현되었으며 성능평가를 통해 높은 차량인식 정확성을 보임을 확인하였다.

Keywords

Acknowledgement

Supported by : 한국연구재단

References

  1. R. L. Gordon, R. A. Reiss, H. Haenel, E. R. Case, R. L. French, A. Mohaddes, R. Wolcott, Traffic Control Systems Handbook, FHWA-SA-95-032, Federal Highway Administration, U.S. Department of Transportation, 1996.
  2. P. McGowen, M. Sanderson, "Accuracy of Pneumatic Road Tube Counters," Proc. of the 2011 Western District Annual Meeting, pp. 1-17, 2011.
  3. L. E. Y. Mimbela, L. A. Klein, A Summary of Vehicle Detection and Surveillance Technologies used in Intelligent Transportation Systems, The Vehicle Detector Clearinghouse, 2007.
  4. J. Wang, M. Wu, "An Overview of Research on Weigh-in-motion System," Proc. of the 5th World Congress on Intelligent Control and Automation, pp. 5241-5244, 2004.
  5. A. Duzdar, G. Kompa, "Applications Using A Low-Cost Baseband Pulsed Microwave Radar Sensor," Proc. of the Instrumentation and Measurement Technology Conference, pp. 239-243, 2001.
  6. J. Kranig, E. Minge, C. Jones, Field Test of Monitoring of Urban Vehicle Operations Using Non-Intrusive Technologies, FHWA-PL-97-018, Federal Highway Administration, U.S. Department of Transportation, 1997.
  7. V. Kastrinaki, M. Zervakis, K. Kalaitzakis, "A survey of video processing techniques for traffic applications," Image and Vision Computing, Vol. 21, No. 4, pp. 359-381, 2003. https://doi.org/10.1016/S0262-8856(03)00004-0
  8. N. Ushio, T. Shimizu, "Loop vs Ultrasonic in Chicago: Ultrasonic vehicle detector field test isolating diffused reflection and enduring harsh environment," Proc. of the 5th World Congress on Intelligent Transport Systems, 1998.
  9. J. F. Forren, D. Jaarsma, "Traffic Monitoring by Tire Noise," Proc. of the IEEE Conference on Intelligent Transportation System, pp. 177-182, 1997.
  10. S. Y. Cheung, P. Varaiya, Traffic Surveillance by Wireless Sensor Networks: Final Report, California PATH Research Report, UCB-ITS-PRR-2007-4, 2007.
  11. W. Ma, D. Xing, A. McKee, R. Bajwa, C. Flores, B. Fuller, P. Varaiya, "A Wireless Accelerometer-Based Automatic Vehicle Classification Prototype Systems," IEEE Transactions on Intelligent Transportation Systems, Vol. 15, No. 1, pp. 104-111, 2014. https://doi.org/10.1109/TITS.2013.2273488
  12. B. Barbagli, G. Manes, R. Facchini, A. Manes, "Acoustic Sensor Network for Vehicle Traffic Monitoring," Proc. of the 1st International Conference on Advances in Vehicular Systems, Technologies and Applications, pp. 1-6, 2012.
  13. FHWA. Traffic detector handbook, FHWA-HRT-06-108, 2006.
  14. FHWA. Effects of Raising and Lowering Speed Limits, Report No. FHWA-RD-92-084, 1992.
  15. MNDOT. Available: http://iris.dot.state.mn.us(accessed 2014, Mar. 11)
  16. Crossbow Technology. Available: http://www.xbow.com (accessed 2014, Mar. 11)
  17. Devantech Ltd. Available: http://www.robot-electronics.co.uk (accessed 2014, Mar. 11)
  18. TinyOS Alliance. Available: http://www.tinyos.net (accessed 2014, Mar. 12)