Model based supervision of lateral vehicle dynamics

An approach for supervision of vehicle dynamics is presented which may be used for intelligent vehicle control and state monitoring. In particular, an on-board process parameter estimation was implemented which allows one to compute the physical coefficients of lateral vehicle models and their changes during operation. In addition, tire- and velocity-dependent look-up tables in presently used vehicle models were replaced by feedforward neural networks. In the phase of driving state monitoring, a set of these hybrid models-each of them trained for a special driving situation-predict the vehicle motion as a result of the actual steering angle and velocity. In a further step, suitable classification algorithms were used to detect the actual driving state by processing the residual output.