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Journal of Sensors
Volume 2016, Article ID 1963450, 8 pages
http://dx.doi.org/10.1155/2016/1963450
Research Article

Detection and Tracking of Road Barrier Based on Radar and Vision Sensor Fusion

Department of Mechanical Engineering, Ajou University, Suwon 16499, Republic of Korea

Received 19 June 2016; Accepted 16 August 2016

Academic Editor: Antonio Fernández-Caballero

Copyright © 2016 Taeryun Kim and Bongsob Song. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. C. Lundquist, L. Hammarstrand, and F. Gustafsson, “Road intensity based mapping using radar measurements with a probability hypothesis density filter,” IEEE Transactions on Signal Processing, vol. 59, no. 4, pp. 1397–1408, 2011. View at Publisher · View at Google Scholar · View at Scopus
  2. F. Breyer, C. Blaschke, B. Färber, J. Freyer, and R. Limbacher, “Negative behavioral adaptation to lane-keeping assistance systems,” IEEE Intelligent Transportation Systems Magazine, vol. 2, no. 2, pp. 21–32, 2010. View at Publisher · View at Google Scholar · View at Scopus
  3. T. Winkle, Autonomous Driving, Legal and Social Aspects, Springer, Berlin, Germany, 2016.
  4. C. Lundquist, U. Orguner, and F. Gustafsson, “Extended target tracking using polynomials with applications to road-map estimation,” IEEE Transactions on Signal Processing, vol. 59, no. 1, pp. 15–26, 2011. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  5. A. Polychronopoulos, A. Amditis, N. Floudas, and H. Lind, “Integrated object and road border tracking using 77 GHz automotive radars,” IEE Proceedings—Radar, Sonal and Navigation, vol. 151, no. 6, pp. 375–381, 2004. View at Publisher · View at Google Scholar
  6. G. Alessandretti, A. Broggi, and P. Cerri, “Vehicle and guard rail detection using radar and vision data fusion,” IEEE Transactions on Intelligent Transportation Systems, vol. 8, no. 1, pp. 95–105, 2007. View at Publisher · View at Google Scholar · View at Scopus
  7. J. Han, D. Kim, M. Lee, and M. Sunwoo, “Enhanced road boundary and obstacle detection using a downward-looking LIDAR sensor,” IEEE Transactions on Vehicular Technology, vol. 61, no. 3, pp. 971–985, 2012. View at Publisher · View at Google Scholar · View at Scopus
  8. K. R. S. Kodagoda, S. S. Ge, W. S. Wijesoma, and A. P. Balasuriya, “IMMPDAF approach for road-boundary tracking,” IEEE Transactions on Vehicular Technology, vol. 56, no. 2, pp. 478–486, 2007. View at Publisher · View at Google Scholar · View at Scopus
  9. R. Schubert, K. Schulze, and G. Wanielik, “Situation assessment for automatic lane-change maneuvers,” IEEE Transactions on Intelligent Transportation Systems, vol. 11, no. 3, pp. 607–616, 2010. View at Publisher · View at Google Scholar · View at Scopus
  10. R. Faragher, “Understanding the basis of the kalman filter via a simple and intuitive derivation,” IEEE Signal Processing Magazine, vol. 29, no. 5, pp. 128–132, 2012. View at Publisher · View at Google Scholar · View at Scopus
  11. H. Kim, B. Song, H. Lee, and H. Jang, “Multiple vehicle tracking and estimation for all-around perception,” in Proceedings of the 12th International Symposium on Advanced Vehicle Control (AVEC '14), pp. 480–485, Tokyo, Japan, September 2014.
  12. H.-T. Kim, O. Kwon, B. Song, H. Lee, and H. Jang, “Lane confidence assessment and lane change decision for lane-level localization,” in Proceedings of the 14th International Conference on Control, Automation and Systems (ICCAS '14), pp. 1448–1451, Seoul, Republic of Korea, October 2014. View at Publisher · View at Google Scholar · View at Scopus
  13. R. Möbus and U. Kolbe, “Multi-target multi-object tracking, sensor fusion of radar and infrared,” in Proceedings of the IEEE Intelligent Vehicles Symposium, pp. 732–737, IEEE, June 2004. View at Publisher · View at Google Scholar · View at Scopus