Traffic participants classification based on 3D radio detection and ranging point clouds

Abstract

Traffic participant classification is critical in autonomous driving perception. Millimetre wave radio detection and ranging (RADAR) is a cost-effective and robust means of performing this task in adverse traffic scenarios such as inclement weather (e.g. fog, snow, and rain) and poor lighting conditions. Compared to commercial two-dimensional RADAR, the new generation of three-dimensional (3D) RADAR can obtain height information about targets as well as their dense point clouds, greatly improving target classification capabilities. This study proposes a multi-objective classification method for traffic participants based on 3D RADAR point clouds. First, a 22-dimensional feature vector of the 3D RADAR point cloud distribution was extracted to describe the shape, discrete, Doppler, and reflection intensity features of the targets. Then, dynamic and static datasets containing five classes of targets were produced, creating a 10k frame. Extensive experiments were conducted to build machine learning classifiers. The experimental results show that the trained classifiers can achieve over 92% classification accuracy when the targets are classified into five groups and over 95% classification accuracy when the targets are classified into four groups. The proposed method can guide the design of safer and more efficient intelligent driving systems.