M. Munz, M. Mählisch, and K. Dietmayer, Generic Centralized Multi Sensor Data Fusion based on Probabilistic Sensor and Environment Models for Driving Assistance Systems, IEEE Intelligent Transportation Systems Magazine, vol.2, issue.1, 2010.

F. Fayad and V. Cherfaoui, Object-level Fusion and Confidence Management in a Multi-sensor Pedestrian Tracking System, Proc. of the IEEE Int. Conf. on Multisensor Fusion and Integration for Intelligent Systems, 2008.

M. Mählish, R. Hering, W. Ritter, and K. Dietmayer, Heterogenous Fusion of Video, LIDAR, and ESP Data for Automotive ACC Vehicle Tracking, Proc. of the IEEE Int. Conf. on Multisensor Fusion and Integration for Intelligent Systems, 2006.

T. Gindele, S. Brechtel, J. Schröder, and R. Dillmann, Bayesian Occupancy grid Filter for dynamic environments using prior map knowledge, 2009 IEEE Intelligent Vehicles Symposium, 2009.
DOI : 10.1109/IVS.2009.5164357

C. Coué, C. Pradalier, C. Laugier, T. Fraichard, and P. Bessì-ere, Bayesian Occupancy Filtering for Multitarget Tracking: An Automotive Application, Int. J. Robotics Research, issue.1, 2006.

M. K. Tay, K. Mekhnacha, C. Chen, M. Yguel, and C. Laugier, An efficient formulation of the Bayesian occupation filter for target tracking in dynamic environments, International Journal of Vehicle Autonomous Systems, vol.6, issue.1/2, pp.155-171, 2008.
DOI : 10.1504/IJVAS.2008.016483
URL : https://hal.archives-ouvertes.fr/inria-00182089

H. P. Moravec and A. Magazine, Sensor Fusion in Certainty Grids for Mobile Robots, 1988.
DOI : 10.1007/978-3-642-74567-6_19

K. Mekhnacha, Y. Mao, D. Raulo, and C. Laugier, Bayesian Occupancy Filter based " Fast Clustering-Tracking " Algorithm, IEEE/RSJ Int. Conf. on Intelligent Robots and Systems, 2008.
URL : https://hal.archives-ouvertes.fr/inria-00336356

C. Tay, Analysis of Dynamics Scenes: Application to Driving Assistance, 2009.

I. E. Paromtchik, C. Laugier, M. Perrollaz, M. Yong, A. N-`-egre et al., The ArosDyn project: Robust analysis of dynamic scenes, 2010 11th International Conference on Control Automation Robotics & Vision, 2010.
DOI : 10.1109/ICARCV.2010.5707333
URL : https://hal.archives-ouvertes.fr/inria-00539999

M. Perrollaz, R. Labayrade, R. Gallen, D. M. Aubert, J. Perrollaz et al., A Three Resolution Framework for Reliable Road Obstacle Detection Using Stereovision Using Obstacle and Road Pixels in the Disparity Space Computation of Stereo-vision based Occupancy Grids, Proc. of the IAPR MVA Conf Proc. of the IEEE Int. Conf. on Intelligent Transportation Systems, 2007.

G. Welch and G. Bishop, An Introduction to the Kalman Filter