An expectation-maximization-based interacting multiple model approach for cooperative driving systems

In this paper, we present a novel combined sensor registration and fusion approach for cooperative driving in intelligent transportation systems (ITSs). A realistic augmented registration and fusion-state space model in three dimensions is first developed for dissimilar sensors. In order to have unbiased sensor registration parameter estimates, the expectation-maximization (EM) algorithm is incorporated with the extended Kalman filter (EKF) to give simultaneous state and parameter estimates. Furthermore, the interacting multiple model (IMM) filter is introduced here for collaborative driving in order to deal with the jumping model problem occurred in different vehicles driving status. To evaluate the registration and fusion performance, a new recursive relationship is derived theoretically for computing the posterior Cramer-Rao bound (PCRB). It is shown by simulation that the proposed EM-IMM-EKF method has a more robust estimation performance than the conventional approach. The performance is furthermore verified by comparing the mean square error with the PCRB.