Detection of critical driving situations based on wheel-ground contact normal forces

To develop an effective vehicle control system, it is needed to estimate vehicle motions accurately, and the estimation of reliable road frictions is one of the most important steps to achieve this goal. In the absence of commercially available transducers to measure the friction coefficient directly, various types of estimation methods have been investigated in the past. Most models in the literature usually use low degrees of freedom. Also, these models have different values from the real vehicle motions and have a difficulty to adapt with new technologies. In this paper, a new estimation process is proposed to estimate tire forces and vehicle state histories, that is, longitudinal and lateral velocities, angular velocity and rolling radius of wheels, and side slip, pitch and roll angle based on extended Kalman filter, and road friction coefficient based on a recursive least squares method. These methods use the measurements from currently available standard sensors. For such estimation, a fourteen degree-of-freedom nonlinear vehicle model was developed. The estimated results are compared with the results obtained from CarSim using the same parameter values to verify the proposed model.