Multi hypotheses tracking with nonholonomic motion models using lidar measurements

Abstract

This paper presents an implementation of the Multiple Hypothesis Tracking (MHT) algorithm in the Advanced Driver Assistance Systems (ADAS) context.The proposed algorithmuses laser data received from two front mounted sensors on a mobile vehicle. The algorithm was tested with simulated and real world data and shown to obtain a very good performance. Nonholonomic motion models were used to model the movement of road agents instead of the more traditional constant velocity/ acceleration models. The use of the nonholonomic motion models allows to obtain not only the linear velocity, but also the steering angle of vehicles, improving this way the future prediction and handling of occlusions. The MHT algorithm possesses some well-known critical disadvantages due to its complexity and computational growth, in this work we circumvent these limitations in order to achieve real time performance in real work conditions.