Improving Time-To-Collision Estimation by IMM Based Kalman Filter

In a CAS system, the distance and relative velocity between front and host vehicles are estimated to calculate time-to-collision (TTC). The distance estimates by different methods will certainly include noise which should be removed to ensure the accuracy of TTC calculations. Kalman filter is a good tool to filter such type of noise. Nevertheless, Kalman filter is a model based filter, which means a correct model is important to get the good filtering results. Usually, a vehicle is either moving with a constant velocity (CV) or constant acceleration (CA) maneuvers. This means the distance data between front and host vehicles can be described by either constant velocity or constant acceleration model. In this paper, first, CV and CA models are used to design two Kalman filters and an interacting multiple model (IMM) is used to dynamically combine the outputs from two filters. In detail, IMM technique is used to estimate the mode probabilities for CV and CA based Kalman filters and mix the two filter results based on the mode probabilities. Then the motion equation is used to calculate the TTC based on the distance and velocity estimates obtained from the IMM based filter. The experimental results on both simulated and real estimated distance data are found to be satisfactory and indicate that the proposed algorithm does improve the signal-to-noise ratio of distance data.