Determination of road vehicle motion model by constrained multiple model filtering procedures
Abstract
The paper presents a solution based on traditional methods for manoeuvring detection in road traffic. The method works in a multiple model structure with Kálmán filters and particle filters. Each manoeuver is defined and fitted into the elementary filters using different state constraints so that a unique filter is associated with each manoeuver. The multiple model structure evaluates the accuracy of the estimation of each filter and accepts the manoeuver associated with the better performing filter as current. The efficiency of the procedure is demonstrated in a simulated traffic situation where the observed object was examined from the perspective of the observation vehicle.
References
X. R. Li and V. P. Jilkov, “Survey of maneuvering target tracking. part v. multiple-model methods,” IEEE Transactions on Aerospace and Electronic Systems, vol. 41, no. 4, pp. 1255–1321, 2005. DOI: https://doi.org/fn8tfr
H. A. Blom and Y. Bar-Shalom, “The interacting multiple model algorithm for systems with markovian switching coefficients,” IEEE transactions on Automatic Control, vol. 33, no. 8, pp. 780–783, 1988. DOI: https://doi.org/cwbf76
E. Mazor, A. Averbuch, Y. Bar-Shalom, and J. Dayan, “Interacting multiple model methods in target tracking: a survey,” IEEE Transactions on aerospace and electronic systems, vol. 34, no. 1, pp. 103–123, 1998. DOI: https://doi.org/c9b8j9
Y. Bar-Shalom, X. R. Li, and T. Kirubarajan, Estimation with applications to tracking and navigation: theory algorithms and software. John Wiley & Sons, 2004. DOI: https://doi.org/dfqbsw
Z. Messaoudi, A. Ouldali, and M. Oussalah, “Comparison of interactive multiple model particle filter and interactive multiple model unscented particle filter for tracking multiple manoeuvring targets in sensors array,” in Cybernetic Intelligent Systems (CIS), 2010 IEEE 9th International Conference on. IEEE, 2010, pp. 1–6. DOI: https://doi.org/bbrxrf
M. Zhang and W. Chen, “Variable structure multiple model particle filter for maneuvering radar target tracking,” in Microwave and Millimeter Wave Technology (ICMMT), 2010 International Conference on. IEEE, 2010, pp. 1754-1757. DOI: https://doi.org/cvvkz2
Törő, O., Bécsi, T., Aradi, S. and Gáspár, P., 2018. Imm bernoulli filter for cooperative object tracking in road traffic. IFACPapersOnLine, 51(9), pp.355-360. DOI: https://doi.org/gt4j
Törő, O., Bécsi, T., Aradi, S. and Gáspár, P., 2018. IMM Bernoulli Gaussian Particle Filter. IFAC-PapersOnLine, 51(22), pp.274-279. DOI: https://doi.org/gt4k
D. Simon, Optimal state estimation : Kalman, H_∞ and nonlinear approaches. Hoboken, N.J: Wiley-Interscience, 2006. DOI: https://doi.org/bmzttv
Teixeira, B. O., Chandrasekar, J., Tôrres, L. A., Aguirre, L. A., & Bernstein, D. S. (2009). State estimation for linear and non-linear equality-constrained systems. International Journal of Control, 82(5), 918-936. DOI: https://doi.org/bfwshc
O. Törő, T. Bécsi, and S. Aradi, “Design of lane keeping algorithm of autonomous vehicle,” Periodica Polytechnica Transportation Engineering, vol. 44, no. 1, pp. 60–68, 2016. DOI: https://doi.org/ggfvvq
Z. Zhao, B. Huang, and F. Liu, “Constrained particle filtering methods for state estimation of nonlinear process,” AIChE Journal, vol. 60, no. 6, pp. 2072–2082, 2014. DOI: https://doi.org/gt4m
Chen, Z., 2003. Bayesian filtering: From Kalman filters to particle filters, and beyond. Statistics, 182(1), pp.1-69. DOI: https://doi.org/bkztpw
A. A. Saucan, T. Chonavel, C. Sintes, and J. M. L. Caillec, “Interacting multiple model particle filters for side scan bathymetry,” in 2013 MTS/IEEE OCEANS - Bergen, June 2013, pp. 1–5. DOI: https://doi.org/gt4n
The journal provides immediate open access to its content on the principle that making research freely available to the public supports a greater global exchange of knowledge. There are no Open Access (OA) fees or charges (APCs) charged to either authors or readers, i.e. the articles can be used for non-commercial purposes, without changes or modifications.
All articles published in the journal are licensed under CC-BY-NC-ND 4.0 license.
The names and email addresses entered in this journal site will be used exclusively for the stated purposes of this journal and will not be made available for any other purpose or to any other party and will not be used for any other purpose or be accessible to any other party.
