Time-varying system identification via maximum a posteriori estimation and its application to driver steering models

Modern automotive technologies try to predict the driver’s intention in order to control the vehicle effectively. However mathematical models describing the driver’s steering behavior with sufficient accuracy are not available. The difficulties arise from the time-varying properties of the driver’s behavior under rapidly changing traffic conditions. In this paper, a time-varying system identification method using maximum a posteriori estimation is proposed. An efficient iterative procedure is presented for maximizing the posterior probability of the parameters conditioning on observed data. Then it is applied to the experimental driving data, and the driver’s time-varying steering models are identified and analyzed. The results indicate that the time-varying model reduces the output estimation errors significantly. Moreover, changes of driving strategies are observed from the identified models after drivers drive for a period of time.