Journal of Institute of Control, Robotics and Systems (제어로봇시스템학회논문지)

Institute of Control, Robotics and Systems (제어로봇시스템학회)
권호 표지
DOI QR코드

DOI QR Code

Neighboring Vehicle Maneuver Detection using IMM Algorithm for ADAS

지능형 운전보조시스템을 위한 IMM 기법을 이용한 전방차량 거동추정기법

  • Jung, Sun-Hwi (Department of Automotive Engineering, Kookmin University) ;
  • Lee, Woon-Sung (Department of Automotive Engineering, Kookmin University) ;
  • Kang, Yeonsik (Department of Automotive Engineering, Kookmin University)
  • 정선휘 (국민대학교 자동차전문대학원) ;
  • 이운성 (국민대학교 자동차전문대학원) ;
  • 강연식 (국민대학교 자동차전문대학원)
  • Received : 2013.01.25
  • Accepted : 2013.06.10
  • Published : 2013.08.01
Download PDF
⟨ Previous Next ⟩

Abstract

In today's automotive industry, there exist several systems that help drivers reduce the possibility of accidents, such as the ADAS (Advanced Driver Assistance System). The ADAS helps drivers make correct and quick decisions during dangerous situations. This study analyzed the performance of the IMM (Interacting Multiple Model) method based on multiple Kalman filters using the data acquired from a driving simulator. An IMM algorithm is developed to identify the current discrete state of neighboring vehicles using the sensor data and the vehicle dynamics. In particular, the driving modes of the neighboring vehicles are classified by the cruising and maneuvering modes, and the transition between the states is modeled using a Markovian switching coefficient. The performance of the IMM algorithm is analyzed through realistic simulations where a target vehicle executes sudden lane change or acceleration maneuver.

Keywords

References

  1. "The Impact of Driver inattention on Near-Crach Risk: An Analysis Using the 100-Car Naturalistic Driving Study Data,"2006 NHSTA report
  2. R. A. Singer, "Estimating optimal tracking filter performance for manned maneuvering targets," IEEE Trans. on Aerospace and Electronic Systems, vol. AES-6, pp. 473-483, Jul. 1970. https://doi.org/10.1109/TAES.1970.310128
  3. R. J. McAulay and E. Denlinger, "A decision-direted adaptive tracker," IEEE Trans. on Aerospace and Electronic Systems, vol. AES-9, no. 4, pp. 123-145, May 1989.
  4. Y. T. Chan and A. G. C. Hu, "A Kalman filter based tracking scheme with input estimation," IEEE Trans. on Aerospace and Electronic Systems, vol. AES-15, pp. 237-244, Mar. 1979. https://doi.org/10.1109/TAES.1979.308710
  5. Y. Bar-Shalom and K. Birmiwal, "Variable dimension filter for maneuvering target tracking," IEEE Trans. on Aerospace and Electronic Systems, vol. AES-18, pp. 621-629, Sep. 1982. https://doi.org/10.1109/TAES.1982.309274
  6. H. A. P. Blom and Y. Bar-Shalom, "The interacting multiple model algorithm for systems with Markovian switching coefficients," IEEE Trans. on Automatic Control, vol. 33. pp. 780-783, Aug. 1988. https://doi.org/10.1109/9.1299
  7. X. R. Li and Y. Bar-Shalom, "Design of an interacting multiple model algorithm for air traffic control tracking," IEEE Trans. on Control Systems Technology, vol. 1. pp. 186-194, Sep. 1993. https://doi.org/10.1109/87.251886
  8. S. M. Aly, "Extended Kalman filtering and interacting multiple model for tracking maneuvering targets in sensor networks," Intelligent Solutions in Embedded Systems 7th Workshop, pp. 149-156, 2009.
  9. Y.-S. Kim and K.-S. Hong, "An IMM algorithm for tracking maneuvering vehicles in an adaptive cruise control environment," International Journal of Control, Automation and Systems, vol. 2. pp. 310-318, 2004.
  10. S. K. Park, J. P. Hwang, E. Kim, and H.-J. Kang, "Vehicle tracking using a microwave radar for situation awareness," A Journal of International Federation of Automatic Control, vol. 18, pp. 383-395, 2010.
  11. K. C. Jo, K. Y. Chu, K. Y. Lee, and M. H. Sunwoo, "Integration of multiple vehicle models with an imm filter for vehicle localization," Proc. of IEEE Intelligent Vehicle Symposium, 2010.
  12. F. Liu, J. Sparbert, and C. Stiller, "IMMPDA vehicle tracking system using asynchronous sensor fusion of radar and vision,"Proc. of IEEE Intelligent Vehicle Symposium, 2008.
  13. Y. S. Kang, "A lidar-based decision-making method for road boundary detection using multiple Kalman filters," IEEE Trans. on Industrial Electronics, vol. 59, pp. 4360-4368, Nov. 2012. https://doi.org/10.1109/TIE.2012.2185013
  14. D. Musicki, "Tracking in clutter using IMM-IPDA-based algorithms," IEEE Trans. on Aerospace and Electronic Systems, vol. 44. pp. 111-126, Jan. 2008. https://doi.org/10.1109/TAES.2008.4516993
  15. C. E. Seah, "Algorithm for performance analysis of the IMM algorithm," IEEE Trans. on Aerospace and Electronic Systems, vol. 47. pp. 1114-1124, Apr. 2011. https://doi.org/10.1109/TAES.2011.5751246
  16. W. D. Blair, "Tracking maneuvering targets with an interacting multiple model particle filter containing exponentially-correlated acceleration models," System Theory, Proceedings Twenty-Third Southeastern Symposium, pp. 224-228, 1991.
  17. J. R. Layne, "Adaptive interacting multiple model tracking of maneuvering targets," Digital Avionics System Conference, vol. 1, pp. 5.3-16-23, 1997.
  18. N. Yang, "Performance test of interacting multiple model particle filter for maneuvering target location," Measuring Technology and Mechatronics Automation, pp. 224-228, 1991.Mechatronics
  19. D. S. Caveney, "Multiple target tracking in the adaptive cruise control environment using multiple models and probabilistic data association," M. S. Thesis, University of California, Berkeley, U. S. A., 1999.
  20. J. Lim, J, Lee, and C. Park, "An integrated fault detection and isolation method for sensors and actuators of LEO satellite," Journal of Institute of Control, Robotics and Systems (in Korean), vol. 17, no. 11, pp. 1117-1124, 2011. https://doi.org/10.5302/J.ICROS.2011.17.11.1117

Cited by

  1. Multiple Vehicle Recognition based on Radar and Vision Sensor Fusion for Lane Change Assistance vol.21, pp.2, 2015, https://doi.org/10.5302/J.ICROS.2015.14.9007
  2. Determination of Driving States using the Driving Characteristics Index vol.21, pp.3, 2015, https://doi.org/10.5302/J.ICROS.2015.14.9013