Enhanced Multiple DBSCAN Algorithm for Traffic Detection Using mmWave Radar

Abstract

The ability to robustly and effectively detect and classify road objects is vital to an all-purpose traffic monitoring system. Recent development in mmWave radar technologies offers improved range and resolution at an affordable price, making it an ideal candidate for Intelligent Transportation System (ITS) applications. Modern mmWave radars output 3D detection point clouds representing moving objects. The Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm is a popular method for clustering radar point clouds. However, our study found that several variations of DBSCAN perform less than expected in a road and intersection scene. To address this, we propose an Enhanced Multiple DBSCAN algorithm tailored specifically for traffic monitoring applications, which aims to improve detection performance using radar point cloud data. By using adaptive parameters, the Enhanced Multiple DBSCAN algorithm addresses the problem of reducing cluster size over distance. Additionally, a modified Non-Maximum Suppression (NMS) variation is included to address missed detections when merging results from multiple DBSCANs. Our Enhanced Multiple DBSCAN achieves over 90% precision in detecting road objects, the best result among all tested methods. The algorithms proposed and evaluated in this study provide a valuable reference for modern radar ITS applications.