FPGA-Based Real-Time Multi-Class Vehicle Classification Using mmWave Radar

The present study introduces FPGA(Field-Programmable Gate Array)-based Real-Time Multi-Class Vehicle Classification using Millimeter wave Radar (mmWave radar), which overcomes the limitations of conventional sensors such as LiDAR and cameras, which are sensitive to adverse weather and lighting conditions. On a hardware-software platform, the implementation of multi-class vehicle classification demonstrated its effectiveness. Within the realm of multi-class vehicle classification applications, the FPGA-based PYNQ-ZU (Python Productivity for Zynq) serves as an efficient embedded architecture. The reliability and accuracy of this method are improved, rendering it a promising solution for autonomous vehicles and advanced driver assistance systems (ADAS) in a variety of driving scenarios. We employed 3D point cloud data produced by mmWave radar via a PC, then transformed it into 2D point cloud images by top-view filtration methods. This method demonstrated greater efficacy in feature extraction with VGG-16. Multiple machine learning models were employed for classification tasks on both hardware and software platforms, achieving 100% accuracy with the Random Forest (RF) algorithm.