Centroid Estimation of Leading Target Vehicle Based on Decision Trees

Automotive radar application is a focus in active traffic safety research activities. And accurate lateral position estimation from the leading target vehicle through radar is of great interest. This paper presents a method based on the regression tree, which estimates the rear centroid of leading target vehicle with a long range FLR (Forward Looking Radar) of limited resolution with multiple radar detections distributed on the target vehicle. Hours of radar log data together with reference value of leading vehicle's lateral offset are utilized both as training data and test data as well. A ten-fold cross validation is applied to evaluate the performance of the generated regression trees together with fused decision forest for each percentage of the training data. As a result, compared with the current approach which calculates the mean of lateral offset, the regression tree and decision forest approach yield more accurate position estimation of the lateral offset from a single leading target vehicle with radar multiple detections.