A Geometric Model based 2D LiDAR/Radar Sensor Fusion for Tracking Surrounding Vehicles

Abstract

Abstract This paper presents a novel and efficient sensor fusion system for tracking multiple moving vehicles over Light Detection and Ranging (LiDAR) and Radar in autonomous vehicles. What is important in the sensor fusion using LiDAR and Radar is how well they utilize the characteristics of each sensor. The proposed fusion system improves the estimating accuracy and maximum perception distance for the target vehicles by utilizing LiDAR which has high distance accuracy and Radar which has wide Field of View (FOV) and observability of relative speed. In addition, multiple hypothesis tracking (MHT) based track management and extended Kalman filtering (EKF) based filtering enable multitarget tracking with less computational complexity than particle filter-based methods. Three measurement models are designed according to the types of measurement assigned to each track, and the tracks were updated optimally according to each model. The proposed fusion system is evaluated through the real vehicle test with RT-range.