الفهرس 
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Abstract
Mobile robots are capable to perform many tasks without human assistance. One of the major mobile robot applications is autonomous vehicle (A V). A V is a driverless system which can derive information about the environment from its on-board sensors, make decisions based on the information, and control it to meet the mission
requirements. AV can move in a real indoor and outdoor. When the AV is going to work in a real outdoor environment, the complexity of the environmental conditions increases, and several considerations have to be taken into account. Data fusion is the process of combining information from a number of different sources to provide a robust and complete description of an environment or process of interest. Data fusion has special significance in many applications especially in AV combined, fused to obtain information of
appropriate quality and integrity on which decisions can be made. The AV sensors are used to sense the required information with achieving the
accuracy and real time performance needed for significant vehicle autonomy.
In this research, the state estimation theory of data fusion technology is applied to estimate the vehicle parameters such as the lateral tire forces, sideslip angle and tire road friction coefficient. A 3-degree-of freedom vehicle model is used. A vehicle model contains Dugoff tire model in order to satisfy the required accuracy. Due to the nonlinear characteristic of tire-road behavior, physical and economic reasons, only a few of these states and parameters can actually be obtained by
measurement, thus online estimation is needed for the knowledge of these states. In
this research, a new technique of dual unscented Kalman filter which is Modified Unscented Kalman Filter (MDUKF) is proposed to estimate the vehicle parameters. MDUKF can be regarded as two extended Kalman filters operating and communicating simultaneously. Using the Matlab platform, the model is achieved and the estimation algorithm is done with the user interface in Carsim simulator, users can view full-vehicle model, < simulation and results. It is demonstrated the effectiveness of the MDUKF estimation approach. Results indicate that this approach is considered to be of potential benefit to estimate the autonomous vehicle parameters.