mmTraffic: Live In-car Traffic Monitoring using mmWave Sensing

Traffic monitoring is crucial for managing urban transportation systems and ensuring road safety. Traditional methods, such as cameras and inductive loop sensors, have limitations in adverse weather conditions or high traffic densities. In this work, we propose a novel approach for live, in-car traffic monitoring using mmWave radar sensing. Our method utilizes mmWave range-doppler and range-azimuth heatmaps as input for a 2D convolutional neural network (2D-CNN) to perform live traffic monitoring. The classification model determines the presence of traffic and classifies the number of vehicles as well as the type of vehicle (two-wheeler, four-wheeler, etc.). We evaluate our approach using real-world mmWave radar data and demonstrate its effectiveness in accurately monitoring traffic conditions in various scenarios.