Integration of multiple vehicle models with an IMM filter for vehicle localization

A vehicle localization system can be extremely useful for intelligent transformation systems (ITS) such as advanced driver assistance systems (ADASs), emergency vehicle notification systems, and collision avoidance systems. To optimize the performance of vehicle localization systems, localization algorithms that analyze multi-sensor data processed using a Kalman filter have been developed. However, a Kalman filter with a single process model cannot guarantee the accuracy of localization under various driving conditions, because the single vehicle model does not cover all driving situations. Therefore, we present a position estimation algorithm based on an interacting multiple model (IMM) filter that uses two kinds of vehicle models: a kinematic vehicle model and a dynamic vehicle model. While the kinematic vehicle model is suitable for low-speed and low-slip driving conditions, the dynamic vehicle model is more appropriate for high-speed and high-slip situations. The IMM filter integrates the estimates from a kinematic vehicle model based on an extended Kalman filter (EKF) and estimates from a dynamic vehicle model based on EKF to improve localization accuracy. The developed estimation algorithm was verified by simulation using a commercial vehicle model. The simulation results show that the estimates of vehicle position by the algorithm presented in this study are accurate under a wide range of driving conditions.