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http://dx.doi.org/10.5302/J.ICROS.2016.16.0060

A Study on IMM-PDAF based Sensor Fusion Method for Compensating Lateral Errors of Detected Vehicles Using Radar and Vision Sensors  

Jang, Sung-woo (The Department of Secured Smart Electric Vehicle Engineering, Kookmin University)
Kang, Yeon-sik (Department of Automotive Engineering, Kookmin University)
Publication Information
Journal of Institute of Control, Robotics and Systems / v.22, no.8, 2016 , pp. 633-642 More about this Journal
Abstract
It is important for advanced active safety systems and autonomous driving cars to get the accurate estimates of the nearby vehicles in order to increase their safety and performance. This paper proposes a sensor fusion method for radar and vision sensors to accurately estimate the state of the preceding vehicles. In particular, we performed a study on compensating for the lateral state error on automotive radar sensors by using a vision sensor. The proposed method is based on the Interactive Multiple Model(IMM) algorithm, which stochastically integrates the multiple Kalman Filters with the multiple models depending on lateral-compensation mode and radar-single sensor mode. In addition, a Probabilistic Data Association Filter(PDAF) is utilized as a data association method to improve the reliability of the estimates under a cluttered radar environment. A two-step correction method is used in the Kalman filter, which efficiently associates both the radar and vision measurements into single state estimates. Finally, the proposed method is validated through off-line simulations using measurements obtained from a field test in an actual road environment.
Keywords
sensor fusion; interacting multiple model; Kalman filter; probabilistic data association filter; automotive radar; vision sensor; tracking; active safety system; autonomous vehicle;
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Times Cited By KSCI : 4  (Citation Analysis)
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1 M. Blanco, J. Atwood, H. M. Vasquez, T. E. Trimble, V. L. Fitchett, J. Radlbeck, G. M. Fitch, S. M. Russell, C. A. Green, B. Cullinane, and J. F. Morgan, "Human factors evaluation of level 2 and level 3 automated driving concepts," NHTSA, Report, no. DOT HS 812 182, Aug. 2015.
2 H. A. P. Blom and Y. Bar-Shalom, "The interacting multiple model algorithm for systems with Markovian switching coefficients," IEEE Trans. on Automatic Control, pp. 780-783, Aug. 1988.
3 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.   DOI
4 R. Mobus and U. Kolbe "Multi-target multi-object tracking, sensor fusion of radar and infrared," 2004 IEEE Intelligent Vehicles Symposium, pp.732-737, Jun. 2004.
5 F. Liu, J. Sparbert, and C. Stiller, "IMMPDA vehicle tracking system using asynchronous sensor fusion of radar and vision," IEEE Intelligent Vehicles Symposium, Jun. 2008.
6 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, Jun. 2008.
7 H. Kim, B. Song, H. Lee, and H. Jang, "Multiple vehicle recognition based on radar and vision sensor fusion for lane change assistance," Journal of Institute of Control, Robotics and Systems Engineering (in Korean), vol. 21, no. 2, pp. 121-129, Feb. 2015.   DOI
8 A. Houles and Y. Bar-Shalom, "Multisensor tracking of maneuvering target in clutter," IEEE Transactions on aerospace and Electronic System, vol. AES-25, no. 2, Mar. 1989.
9 J. Choi, "Realtime on-road vehicle detection with optical flows and haar-like feature detector," A Final Report of a Course CS543, 2006.
10 P. Viola and M. Jones, "Rapid object detection using a boosted cascade of simple features," Proc. of Conference on Computer Vision and Pattern Recognition, 2001.
11 Y. Kang and D. S. Caveney, "Performance analysis of an IMMbased obstacle detection algorithm," Proc. of IMECE, 2004 ASME International Mechanical Engineering Conference and RD&D Expo, Anaheim, California, USA, Nov. 2004.
12 B. W. Ahn, J. W. Choi, and, T. L. Song, "A variable dimensional structure with probabilistic data association filter for tracking a maneuvering target in clutter environment," Journal of Institute of Control, Robotics and Systems Engineering (in Korean), vol. 9, no. 10, pp. 747-754, Oct. 2003.
13 Y. Bar-Shalom and T. E. Fortmann, "Tracking and data association," Academic Press, 1988.
14 S. Jung, W. Lee, and Y. Kang, "Neighboring vehicle maneuver detection using IMM algorithm for ADAS," Journal of Institute of Control, Robotics and Systems Engineering (in Korean), vol. 19, no. 8, pp. 747-754, 2013.
15 Y. Freund, R. Schapire, and N. Abe, "A short introduction to boosting," Journal of Japanese Society for Artificial Intelligence, vol. 14, no. 5, pp. 771-780, Sep. 1999.
16 J. Yoo and Y. Kang, "Performance analysis on the IMM-PDAF method for longitudinal and lateral maneuver detection using automotive radar measurements," Journal of Institute of Control, Robotics and Systems Engineering (in Korean), vol. 21, no. 3, pp. 224-232, Mar. 2015.   DOI
17 A. Marinik, R. Bishop, V. Fitchett, J. F. Morgan, T. E. Trimble, and M. Blanco, "Human factors evaluation of level 2 and level 3 automated driving concepts: concepts of operation," NHTSA, Report, no. DOT HS 812 044, Jul. 2014.