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http://dx.doi.org/10.5391/JKIIS.2015.25.2.186

MCMC Particle Filter based Multiple Preceeding Vehicle Tracking System for Intelligent Vehicle  

Choi, Baehoon (School of Electrical and Electronic Engineering, Yonsei University)
An, Jhonghyun (School of Electrical and Electronic Engineering, Yonsei University)
Cho, Minho (School of Electrical and Electronic Engineering, Yonsei University)
Kim, Euntai (School of Electrical and Electronic Engineering, Yonsei University)
Publication Information
Journal of the Korean Institute of Intelligent Systems / v.25, no.2, 2015 , pp. 186-190 More about this Journal
Abstract
Intelligent vehicle plans motion and navigate itself based on the surrounding environment perception. Hence, the precise environment recognition is an essential part of self-driving vehicle. There exist many vulnerable road users (e.g. vehicle, pedestrians) on vehicular driving environment, the vehicle must percept all the dynamic obstacles accurately for safety. In this paper, we propose an multiple vehicle tracking algorithm using microwave radar. Our proposed system includes various special features. First, exceptional radar measurement model for vehicle, concentrated on the corner, is described by mixture density network (MDN), and applied to particle filter weighting. Also, to conquer the curse of dimensionality of particle filter and estimate the time-varying number of multi-target states, reversible jump markov chain monte carlo (RJMCMC) is used to sampling step of the proposed algorithm. The robustness of the proposed algorithm is demonstrated through several computer simulations.
Keywords
Intelligent Vehicle; Multiple Target Tracking (MTT); Markov Chain Monte Carlo (MCMC); Particle Filter; Mixture Density Network (MDN);
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Times Cited By KSCI : 4  (Citation Analysis)
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1 M. Montemerlo, J. Becker, S. Bhat, H. Dahlkamp, D. Dolgov, S. Ettinger, D. Haehnel, T. Hilden, G. Hoffmann, B. Huhnke, D. Johnston, S. Klumpp, D. Langer, A. Levandowski, J. Levinson, J. Marcil, D. Orenstein, J. Paefgen, I. Penny, A. Petrovskaya, M. Pflueger, G. Stanek, D. Stavens, A. Vogt, and S. Thrun, "Junior : The Stanford Entry in the Urban Challenge," Journal of Field Robotics, vol. 25, no. 9, pp. 569-597, Sep. 2008.   DOI
2 Y. Bar-Shalom, F. Daum, and J. Huang, "The Probabilistic Data Association Filter," IEEE Control Systems Magazine, vol. 29, no. 6, pp. 82-100, Dec. 2009.   DOI   ScienceOn
3 M. S. Arulampalam, S. Maskell, N. Gordon, and T. Clapp, "A tutorial on particle filters for online nonlinear/ non-Gaussian Bayesian tracking," IEEE Transactions on Signal Processing, vol. 50, no. 2, pp. 174-188, 2002.   DOI
4 M.-J. Lee, T.-S. Jin, G.-H. Hwang, "A Study on Image Segmentation and Tracking based on Fuzzy Method," Journal of The Korean Institute of Intelligent Systems, vol. 17, no. 1, pp.368-373, Jun. 2007.
5 M. Lee, J. Han, C. Jang, "Information Fusion of Cameras and Laser Radars for Perception Systems of Autonomous Vehicles," Journal of The Korean Institute of Intelligent Systems, vol. 23, no. 1, pp. 35-45, Feb. 2013.   DOI   ScienceOn
6 B. Kim, B. Choi, J. An, H. Lee, and E. Kim, "Prediction of Centerlane Violation for vehicle in opposite direction using Fuzzy Logic and Interacting Multiple Model," Journal of The Korean Institute of Intelligent Systems, vol. 23, no. 5, pp. 444-450, Oct. 2013.   DOI
7 W.-H. Cho, J. Park, "Nonlinear Approximations Using Modified Mixture Density Networks," Journal of The Korean Institute of Intelligent Systems, vol. no. 14, vol. 7, pp. 847-851, Dec. 2004.
8 Y.-C. Lim, D. Kim, and C.-H. Lee, "MCMC Particle Filter-based Vehicle Tracking Method Using Multiple Hypotheses and Appearance Model," in Proceedings of the 2013 IEEE Intelligent Vehicles Symposium (IV), 2013, no. Iv, pp. 1131-1136.