Detection and Tracking of Moving Objects for Automotive Driver Assistance System

A method derived from the adaptive Sampling Importance Resampling particle filter is proposed to solve vehicle detection and tracking problems in laser range finder data. The originality of this approach lies in a joint detection and tracking of the objects. To this end, the solution is based on a matched filter which uses a predefined vehicle model. The non-linearity of the state equations is tackled by sequential Monte Carlo methods which are here the basis of our solution. A central point here is to calculate the weights of the matched particle filter, according to the vehicle model. The proposed approach is then applied to synthetic data from a road scenario. The efficiency of the method is shown in terms of estimation accuracies and detection.