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The drivability plays an important role for marketability and competitiveness of passenger car in meeting some customer requirements, which directly affects the driving experience and the desire of purchasing. In this paper, a framework of objective drivability evaluation with multi-source information fusion for passenger car is proposed. At first, according to vehicle powertrain system and optimization theory, certain vehicle performances, which are closely related to objective drivability are analyzed, including vehicle longitudinal acceleration, vehicle speed, engine torque, engine speed, gear position, accelerator pedal, brake signal and voltage signal. Then, combined with the evaluation criterion of signal-to-noise ratio (SNR), mean error (ME), root mean squared error (RMSE) and signal smoothness (SS), a de-noising method is developed for the drivability evaluation information.

Path tracking is one of the critical technologies in the autonomous vehicle. Its performance may be seriously affected by disturbance resulting from unpredictable environment like changes in road friction coefficient and parameter uncertainty such as cornering stiffness and mass caused by errors of measurement. Besides, since the vehicle system consisting of many systems is an extremely complex nonlinear system, it is almost impossible for us to establish a precise model of a vehicle especially when it is moving. These inevitable factors influence the control accuracy and even threaten the stability and safety of the vehicle system. This paper proposed a promising solution to this problem, robust MPC (Model Predictive Control) combined with the optimal preview controller for path tracking problems of an autonomous vehicle. The state space model in tracking error variables of a passenger vehicle used for path tracking application is established.