FPGA-Based In-Vehicle Occupancy Detection Using mmWave Radar With Mexican Hat Wavelet Transform

Abstract

A demonstration of the implementation of vehicle occupancy detection on hardware-software is shown in this letter. For the purpose of validating applications for vehicle occupancy detection, a hardware field programmable gate array (FPGA) platform, also known as Python productivity for zynq ultrascale+ MPSoC (PYNQ-ZU), is a feasible embedded architecture. Automatic in-car occupancy monitoring is an important technology in modern transportation, with major implications for safety, energy efficiency, and smart vehicle management. One of the primary benefits of millimeter wave (mmWave) radar is its ability to accurately detect the number and location of vehicle occupants, mmWave radar ensures robust detection under all lighting and weather conditions. In our research, the proposed approach was applied to point cloud images. Following the generation of 3-D point cloud images, two filters, top-view (TV), and front-view (FV), were used to improve vehicle occupancy detection. These filters transformed 3-D images into 2-D ones. TV filter was found to be more effective than the FV filter. After filtering the 2-D images, Mexican Hat Wavelet Transform (MHWT) was used to extract features from them. Four machine learning methods were then used to determine vehicle seat occupancy, with logistic regression (LR) and support vector machine producing the highest results, with an accuracy of 98%. In comparison to existing methods, the proposed approach, which utilizes mmWave radar, TV Filter, MHWT, FPGA (PYNQ-ZU), and LR, was determined to significantly improve the accuracy of vehicle occupancy detection.