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Intelligent tires are envisioned to be an important part of the future vehicle control systems and the three dimensional wireless MEMS accelerometers embedded inside the tire stand out as a promising candidate for the development of intelligent tires. The first part of the paper focuses on accelerometer based tire sensors for the estimation of slip angle and tire/road friction coefficient. We use a simple tire finite element model to generate lateral, tangential and radial tire accelerations for a fixed load and slip angle. The profiles are validated by using experimental data. The simulated acceleration profiles are used for the estimation of slip angle and tire/road friction coefficient. We present the estimation algorithms, promising simulative results and output sensitivities studies focused on the effects of changes in normal load, tire pressure and vehicle velocity.

To provide a feasible transitional solution from all-by-human driving style to fully autonomous driving style, this paper proposed concept and its control algorithm of a robust lane-keeping ‘co-pilot’ system. In this a semi-autonomous system, Learning based Model Predictive Control (LBMPC) theory is employed to improve system's performance in target state tracking accuracy and controller's robustness. Firstly, an approximate LTI model which describes driver-vehicle-road closed-loop system is set up and real system's deviations from the LTI system resulted by uncertainties in the model are regarded as bounded disturbance. The LTI model and bounded disturbances make up a nominal model. Secondly, a time-varying model which is composed of LTI model and an ‘oracle’ component is designed to observe the possible disturbances numerically and it is online updated using Extended Kalman Filter (EKF).