Multimodel state estimation in road traffic using constrained filtering

The paper presents multimodel state estimation with constrained filtering applicable in a road traffic situation. The state to be estimated describes the motion of a car observed from the ego-vehicle. Multiple motion modes of the target vehicle are predefined and each is associated with a suitable constraining method. Both Kalman and particle filters are used to perform the estimations. The constrained filters are implemented in the Interacting Multiple Model structure. The outputs are the actual state of the observed vehicle and the motion mode that is in effect at every moment with an associated likelihood value. The performance of the proposed method is evaluated in a simulated environment.