Research On Identification Of Dynamic Parameters For Intelligent Vehicle

As the prominence of traffic safety and efficiency, intelligent vehicle technology develops rapidly. To get the better driving, it's reasonable to control based on vehicle dynamics. Unfortunately, some parameters in dynamic model are difficult to determine or costly to measure. Research on identification of dynamic parameters for intelligent vehicle is stated in this paper.For the convenience of research work, an experiment platform based on smart vehicle is developed. The smart vehicle performs well in steering and speed property. After being integrated with embedded micro-controllers, camera and encoder, it have the ability of autonomous navigation.Then a localization method of the smart vehicle based on laser radar is given. This method combines the advantages of tangent angle histogram and XY histogram to pre-estimate the pose. Later, model registration and robust Iterative Closest Point (ICP) algorithm is designed to locate the smart vehicle. Finally, for compensating laser radar deficiencies, Extended Kalman Filter (EKF) algorithm is used for Laser Radar and Dead Reckoning data fusion so as to guarantee the accuracy of localization. Simulation and real experiments illustrate that the localization method has high robustness and accuracy.Based on the location result, preliminary and basic research work is done about identification of the smart vehicle. The parameter identification method is derived from the 2-DOF dynamic model. Contrast of simulation trace based on identified parameters and real trace based on localization indicates the feasibility of the method.