Browse > Article
http://dx.doi.org/10.5302/J.ICROS.2010.16.7.639

Radar and Vision Sensor Fusion for Primary Vehicle Detection  

Yang, Seung-Han (아주대학교 기계공학부)
Song, Bong-Sob (아주대학교 기계공학부)
Um, Jae-Young (현대자동차 전자개발센터)
Publication Information
Journal of Institute of Control, Robotics and Systems / v.16, no.7, 2010 , pp. 639-645 More about this Journal
Abstract
This paper presents the sensor fusion algorithm that recognizes a primary vehicle by fusing radar and monocular vision data. In general, most of commercial radars may lose tracking of the primary vehicle, i.e., the closest preceding vehicle in the same lane, when it stops or goes with other preceding vehicles in the adjacent lane with similar velocity and range. In order to improve the performance degradation of radar, vehicle detection information from vision sensor and path prediction predicted by ego vehicle sensors will be combined for target classification. Then, the target classification will work with probabilistic association filters to track a primary vehicle. Finally the performance of the proposed sensor fusion algorithm is validated using field test data on highway.
Keywords
vehicle detection; sensor fusion; target classification; path prediction;
Citations & Related Records

Times Cited By SCOPUS : 1
연도 인용수 순위
  • Reference
1 E. Richter, R. Schubert, and G. Wanielik, "Radar and vision based data fusion - advanced filtering techniques for a multi object vehicle tracking system," IEEE Intelligent Vehicles Symposium, pp. 120-125, June 2008.
2 Y. Tan, F. Han, and F. Ibrahim, "A radar guided vision system for vehicle validation and vehicle motion characterization," IEEE Intelligent Transportation Systems Conference, pp. 1059-1066, Oct. 2007.
3 S. Denasi and G. Quaglia, "Obstacle detection using a deformable model of vehicles," IEEE Intelligent Vehicles Symposium, pp. 145-150, May 2001.
4 G. Alessandretti, A. Broggi, and P. Cerri, "Vehicle and guard rail detection using radar and vision data fusion," IEEE Transactions on Intelligent Trasportation Systems, vol. 8, pp. 95-105, May 2001.
5 유재형, 한영준, 한헌수, "수직 Haar-like Feature를 이용한 실시간 자동차 검출 알고리즘," 음성통신 및 신호처리 학술대회 논문집, pp. 163-166, June 2009.
6 C.-F. Lin, A. Galip Ulsoy, and D. J. LeBlanc, "Vehicle dynamics and external disturbance estimation for vehicle path prediction," IEEE Transaction on Control Systems Technology, vol. 8, no. 3, May 2000.
7 D. Simon, "Kalman filtering," Embedded Systems Programming, vol. 14, no. 6, pp. 72-79, 2001.
8 R. Mobus and U. Kolbe, "Multi-target multi-object tracking, sensor fusion of radar and infrared," IEEE Intelligent Vehicles Symposium, pp. 732-737, June 2004.
9 S.-G. Yang, B.-S. Song, J.-Y. Um, and S.-H. Jo, "Sensor fusion for relevant vehicle detection and tracking," KSAE 2009 Annual Conference, pp. 92-97, Sep. 2009.
10 D.-H. Yi, H.-J. Kang, and J.-Y. Hwang, "A multi-target tracking system implementation uuing 24GHz short range radar for ACC Stop&Go system," KSAE 2008 Annual Conference, pp. 495-495, Nov. 2008.
11 S. Blackman and R. Popoli, "Design and analysis of modern tracking system," Artech House, 1999.
12 F. Liu, J. Sparbert, and C. Stiller, "IMMPDA vehicle tracking system using asynchronous sensor fusion of radar and vision," IEEE Intelligent Vehicles Symposium, June 2008.