Y. Bar-shalom and X. Li, Multitarget-multisensor tracking: principles and techniques, 1995.

S. Blackman and R. Popoli, Design and analysis of modern tracking systems, Artech House, 1999.

S. Blackman, Multiple-target Tracking with Radar Applications. Radar Library, 1986.

M. Boumediene, Evidential data association: Benchmark of belief assignment models, International Conference on Advanced Electrical Engineering, 2019.

M. Boumediene, J. P. Lauffenburger, J. Daniel, and C. Cudel, Coupled detection , association and tracking for traffic sign recognition, IEEE Intelligent Vehicle Symposium, pp.1402-1407, 2014.
URL : https://hal.archives-ouvertes.fr/hal-01123469

M. Boumediene, J. P. Lauffenburger, J. Daniel, C. Cudel, and A. Ouamri, Multi-roi association and tracking with belief functions: Application to traffic sign recognition, IEEE Transactions on Intelligent Transportation Systems, vol.15, issue.6, pp.2470-2479, 2014.
URL : https://hal.archives-ouvertes.fr/hal-01123461

B. Cobb and P. Shenoy, On the plausibility transformation method for translatin belief function models to probability models, IJAR, vol.41, issue.3, 2006.

F. Cuzzolin, On the properties of the intersection probability, Annals of Mathematics and AI, 2007.

A. P. Dempster, Upper and lower probabilities induced by a multiple valued mapping, Ann. Math. Statistics, vol.38, pp.325-339, 1967.

T. Denoeux, N. El-zoghby, V. Cherfaoui, and A. Jouglet, Optimal object association in the dempster-shafer framework, IEEE Transactions on Cybernetics, vol.44, issue.22, pp.2521-2531, 2014.
URL : https://hal.archives-ouvertes.fr/hal-01127786

J. Dezert and F. Smarandache, A new probabilistic transformation of belief mass assignment, International Conference on Information Fusion (FUSION), pp.1410-1417, 2008.
URL : https://hal.archives-ouvertes.fr/hal-00304319

T. E. Fortmann, Y. Bar-shalom, and M. Scheffe, Sonar tracking of multiple targets using joint probabilistic data association, IEEE Journal of Oceanic Engineering, vol.8, issue.3, pp.173-184, 1983.

A. Geiger, P. Lenz, and R. Urtasun, Are we ready for autonomous driving? the kitti vision benchmark suite, Conference on Computer Vision and Pattern Recognition (CVPR), 2012.

,

D. Gruyer, S. Demmel, V. Magnier, and R. Belaroussi, Multi-hypotheses tracking using the dempster-shafer theory, application to ambiguous road context. Information Fusion, vol.29, pp.40-56, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01466126

D. Gruyer and V. Berge-cherfaoui, Multi-objects association in perception of dynamical situation, Proceedings of the Fifteenth Conference on Uncertainty in Artificial Intelligence, pp.255-262, 1999.

S. Hachour, F. Delmotte, and M. David, Comparison of credal assignment algorithms in kinematic data tracking context, Information Processing and Management of Uncertainty, 2014.

J. P. Lauffenburger and M. Boumediene, Adaptive credal multi-target assignment for conflict resolving, International Conference on Information Fusion (FUSION), pp.1578-1584, 2016.

J. P. Lauffenburger, J. Daniel, and M. Boumediene, Traffic sign recognition: Benchmark of credal object association algorithms, International Conference on Information Fusion (FUSION). pp, pp.1-7, 2014.
URL : https://hal.archives-ouvertes.fr/hal-01123466

M. Li, X. Lu, Q. Zhang, and Y. Deng, Multiscale probability transformation of basic probability assignment, Mathematical Problems in Engineering, 2014.

D. Mercier, E. Lefèvre, and D. Jolly, Object association with belief functions, an application with vehicles, Information Sciences, vol.181, issue.24, pp.5485-5500, 2011.

W. Pan and H. Yang, New methods of transforming belief functions to pignistic probability functions in evidence theory, International Workshop on Intelligent Systems and Applications, 2009.

D. Reid, An algorithm for tracking multiple targets, IEEE Trans. on Automatic Control, vol.24, issue.6, pp.843-854, 1979.

M. Rombaut, Decision in multi-obstacle matching process using Dempster-Shafer's theory, International Conference on Advances in Vehicle Control and Safety, pp.63-68, 1998.

C. Royère, D. Gruyer, and V. Cherfaoui, Data association with believe theory, International Conference on Information Fusion (FUSION), 2000.

G. Shafer, A mathematical theory of evidence, 1976.

P. Smets, The combination of evidence in the transferable belief model, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.12, pp.447-458, 1990.

P. Smets and R. Kennes, The transferable belief model, Artificial Intelligence, vol.66, issue.2, pp.191-234, 1994.
URL : https://hal.archives-ouvertes.fr/hal-01185821

P. Smets, Decision making in the tbm: the necessity of the pignistic transformation, International Journal on Approximate Reasoning, vol.38, pp.133-147, 2005.

P. Smets, Analyzing the combination of conflicting belief functions, Information Fusion, vol.8, issue.4, pp.387-412, 2007.

J. Sudano, Pignistic probability transforms for mixes of low-and high-probability events, International Conference on Information Fusion, 2001.

J. Sudano, The system probability information content (pic) relationship to contributing components, combining independent multi-source beliefs, hybrid and pedigree pignistic probabilities, International Conference on Information Fusion, 2002.