Moving Object Detection and Tracking Based on Interaction of Static Obstacle Map and Geometric Model-Free Approach for Urban Autonomous Driving

Detection and tracking of moving objects (DATMO) in an urban environment using Light Detection and Ranging (LiDAR) is a major challenge for autonomous vehicles due to sparse point cloud, multiple moving directions, various traffic participants, and computational load. To address the complexity of this issue, this study presents a novel model-free approach for DATMO using 2D LiDAR implemented on autonomous vehicles. The approach has been used to classify moving points in the point cloud using the predicted Static Obstacle Map (SOM) generated via interaction between Geometric Model-Free Approach (GMFA) and SOM, and estimates the state of each moving object via GMFA. The motion of each point represented by the state of moving objects updates the SOM. The interaction between GMFA and SOM estimates the correspondence between consecutive point clouds in real-time. The proposed approach has been evaluated via RT range and labeled dataset. The accuracy of estimation of the yaw angle and the velocity of a moving vehicle has been quantitatively evaluated using the RT-range. The performance is significantly improved compared with the geometric model-based tracking (MBT). The estimation of the yaw angle, which has a significant effect on the cut-in/cut-out intention of the target vehicle, is shown to be remarkably improved. Based on the evaluation of the labeled dataset, false-positive and false-negative features are suppressed more than MBT.