D. Koller, J. Weber, T. Huang, J. Malik, G. Ogasawarea et al., Towards robust automatic traffic scene analysis in real-time, Proceedings of the 33rd Conference on Decision and Control, Lake Buena Vista, FL (USA), pp.3776-3781, 1994.

K. Kyriakopoulos and G. Saridis, An integrated collision prediction and avoidance scheme for mobile robots in non-stationary environments, Proceedings of the IEEE Int. Conf. on Robotics and Automation, pp.194-199, 1992.

M. Bennewitz, W. Burgard, and S. Thrun, Learning motion patterns of persons for mobile service robots, Proceedings 2002 IEEE International Conference on Robotics and Automation (Cat. No.02CH37292), pp.3601-3606, 2002.
DOI : 10.1109/ROBOT.2002.1014268

S. Osentoski, V. Manfredi, and S. Mahadevan, Learning hierarchical models of activity, 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (IEEE Cat. No.04CH37566), 2004.
DOI : 10.1109/IROS.2004.1389465

E. Kruse, R. Gusche, and F. M. Wahl, Acquisition of statistical motion patterns in dynamic environments and their application to mobile robot motion planning, Proceedings of the 1997 IEEE/RSJ International Conference on Intelligent Robot and Systems. Innovative Robotics for Real-World Applications. IROS '97, pp.713-717, 1997.
DOI : 10.1109/IROS.1997.655089

D. Vasquez and T. Fraichard, Motion prediction for moving objects: a statistical approach, IEEE International Conference on Robotics and Automation, 2004. Proceedings. ICRA '04. 2004, pp.3931-3936, 2004.
DOI : 10.1109/ROBOT.2004.1308883
URL : https://hal.archives-ouvertes.fr/inria-00182066

H. Bui, S. Venkatesh, and G. West, Policy recognition in the abstract hidden markov models, Journal of Artificial Intelligence Research, vol.17, pp.451-499, 2002.

L. R. Rabiner, A tutorial on hidden markov models and selected applications in speech recognition Readings in speech recognition, pp.267-296, 1990.

B. Fritzke, A growing neural gas network learns topologies, Advances in Neural Information Processing Systems, 1995.

L. Baum and T. Petrie, Statistical Inference for Probabilistic Functions of Finite State Markov Chains, The Annals of Mathematical Statistics, vol.37, issue.6, 1966.
DOI : 10.1214/aoms/1177699147

N. Dempster, A. Laird, and D. Rubin, Maximum likelihood from incomplete data via the EM algorithm, Journal of the Royal Statistical Society, vol.9, issue.1, pp.1-38, 1977.

Y. Singer and M. K. Warmuth, Training algorithms for hidden markov models using entropy based distance functions, Advances in Neural Information Processing Systems 9, NIPS. Denver, CO (USA), pp.641-647, 1996.

R. M. Neal and G. E. Hinton, A new view of the EM algorithm that justifies incremental, sparse and other variants, Learning in Graphical Models, pp.355-368, 1998.

A. J. Viterbi, Error bounds for convolutional codes and an asymptotically optimum decoding algorithm, IEEE Transactions on Information Theory, vol.13, issue.2, pp.260-269, 1967.
DOI : 10.1109/TIT.1967.1054010

Y. Linde, A. Buzo, and R. Gray, An Algorithm for Vector Quantizer Design, IEEE Transactions on Communications, vol.28, issue.1, pp.84-95, 1980.
DOI : 10.1109/TCOM.1980.1094577

T. Kohonen, Self-Organizing Maps, ser. Springer Series in Information Sciences, 1995.

M. Martinetz and K. J. Schulten, A " neural-gas " network learns topologies, Proceedings of International Conference on Articial Neural Networks, pp.397-402, 1991.

G. Carpenter, S. Grossberg, and D. Rosen, Fuzzy ART: an adaptive resonance algorithm for rapid, stable classification of analog patterns, IJCNN-91-Seattle International Joint Conference on Neural Networks, pp.411-420, 1991.
DOI : 10.1109/IJCNN.1991.155368

A. Guttman, R-trees: A dynamic index structure for spatial searching, SIGMOD Conference, pp.47-57, 1984.

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, issue.2, pp.174-188, 2002.
DOI : 10.1109/78.978374