Order Statistic Estimation With Application to Tracking in Autonomous Driving
Kaipei Yang, Yaakov Bar‐Shalom, Peter Willett, Shawn D. Hunt · IEEE Transactions on Aerospace and Electronic Systems · 2022
In this work, we study the order statistic estimation and provide a simple solution to lidar bounding box (BB) centroid estimation for an autonomous vehicle. The estimated BB centroid and its uncertainty will be used in object tracking as measurement and measurement noise variance; the latter is commonly not available from the sensor manufacturer, and is needed for data association and target state estimation. The detected and clustered sensor observations for a single target, on one axis, are assumed to have 1) a triangular density or 2) a uniform (rectangular) density. These densities constitute the likelihood function (LF) of the corresponding support (the interval where they are nonzero) boundaries. Estimators are proposed for the LF support boundary parameter estimation and the centroid coordinates are given by the centers of the boundaries. Experiments using real data are carried out to show the performance of the proposed methods for autonomous driving applications. A comparison with the max–min average approach shows the superiority of the proposed algorithm.