Le développement des systèmes intelligents pour contrôler la stabilité du véhicule et éviter les accidents routier est au cœur de la recherche automobile. L'expansion de ces systèmes intelligents à l'application réelle exige une estimation précise de la dynamique du véhicule dans des environnements diverses (dévers et pente). Cette exigence implique principalement trois problèmes : ⅰ), extraire des informations non mesurées à partir des capteurs faible coût; ⅱ), rester robuste et précis face aux les perturbations incertaines causées par les erreurs de mesure ou de la méconnaissance de l'environnement; ⅲ), estimer l'état du véhicule et prévoir le risque d'accident en temps réel. L’originalité de cette thèse par rapport à l’existant, consiste dans le développement des nouveaux algorithmes, basés sur des nouveaux modèles du véhicule et des différentes techniques d'observation d'état, pour estimer des variables ou des paramètres incertains de la dynamique du véhicule en temps réel. La première étape de notre étude est le développement de nouveaux modèles pour mieux décrire le comportement du véhicule dans des différentes situations. Pour minimiser les erreurs de modèle, un système d'estimation composé de quatre observateurs est proposé pour estimer les forces verticales, longitudinales et latérales par pneu, ainsi que l'angle de dérive. Trois techniques d'observation non linéaires (EKF, UKF et PF) sont appliquées pour tenir compte des non-linéarités du modèle. Pour valider la performance de nos observateurs, nous avons implémenté en C++ des modules temps-réel qui, embarqué sur le véhicule, estiment la dynamique du véhicule pendant le mouvement. / Enhancing road safety by developing active safety system is the general purpose of this thesis. A challenging task in the development of active safety system is to get accurate information about immeasurable vehicle dynamics states. More specifically, we need to estimate the vertical load, the lateral frictional force and longitudinal frictional force at each wheel, and also the sideslip angle at center of gravity. These states are the key parameters that could optimize the control of vehicle's stability. The estimation of vertical load at each tire enables the evaluation of the risk of rollover. Estimation of tire lateral forces could help the control system reduce the lateral slip and prevent the situation like spinning and drift out. Tire longitudinal forces can also greatly influence the performance of vehicle. The sideslip angle is one of the most important parameter to control the lateral dynamics of vehicle. However, in the current market, very few safety systems are based on tire forces, due to the lack of cost-effective method to get these information. For all the above reasons, we would like to develop a perception system to monitor these vehicle dynamics states by using only low-cost sensor. In order to achieve this objective, we propose to develop novel observers to estimate unmeasured states. However, construction of an observer which could provide satisfactory performance at all condition is never simple. It requires : 1, accurate and efficient models; 2, a robust estimation algorithm; 3, considering the parameter variation and sensor errors. As motivated by these requirements, this dissertation is organized to present our contribution in three aspects : vehicle dynamics modelization, observer design and adaptive estimation. In the aspect of modeling, we propose several new models to describe vehicle dynamics. The existent models are obtained by simplifying the vehicle motion as a planar motion. In the proposed models, we described the vehicle motion as a 3D motion and considered the effects of road inclination. Then for the vertical dynamics, we propose to incorporate the suspension deflection to calculate the transfer of vertical load. For the lateral dynamics, we propose the model of transfer of lateral forces to describe the interaction between left wheel and right wheel. With this new model, the lateral force at each tire can be calculated without sideslip angle. Similarly, for longitudinal dynamics, we also propose the model of transfer of longitudinal forces to calculate the longitudinal force at each tire. In the aspect of observer design, we propose a novel observation system, which is consisted of four individual observers connected in a cascaded way. The four observers are developed for the estimation of vertical tire force, lateral tire force and longitudinal tire force and sideslip angle respectively. For the linear system, the Kalman filter is employed. While for the nonlinear system, the EKF, UKF and PF are applied to minimize the estimation errors. In the aspect of adaptive estimation, we propose the algorithms to improve sensor measurement and estimate vehicle parameters in order to stay robust in presence of parameter variation and sensor errors. Furthermore, we also propose to incorporate the digital map to enhance the estimation accuracy. The utilization of digital map could also enable the prediction of vehicle dynamics states and prevent the road accidents. Finally, we implement our algorithm in the experimental vehicle to realize real-time estimation. Experimental data has validated the proposed algorithm.