An integrated framework for traffic monitoring using intelligent radar sensing technology
Traffic congestion and road safety remain major challenges for modern transportation systems, particularly in complex highway environments with high traffic volumes, tunnels, and adverse visibility conditions. Traditional monitoring methods, such as inductive loop detectors and video cameras, often suffer from high maintenance costs, limited adaptability to environmental conditions, and reduced accuracy under challenging weather or lighting conditions. To address these limitations, this paper proposes an integrated traffic monitoring framework based on omnidirectional millimeter-wave radar, high-definition road cameras, and high-precision mapping services. The proposed framework consists of a sensing layer, a data layer, and a strategy layer, enabling multi-source data acquisition, radar–camera–map data fusion, lane-level vehicle trajectory reconstruction, traffic state classification, and unsafe driving behavior identification. Field tests were conducted on the HeBa and LanHai highway sections in Guangxi Province. The results showed that the proposed system achieved high accuracy for vehicle counting and lane-change estimation, including 99.63% accuracy for car counting in the HeBa section and 98.65% accuracy for bus counting in the LanHai section. In addition, the system effectively identified unsafe driving behaviors, including stopping on the highway, wrong-way driving, slow-speed driving, and unsafe lane changes, with F1 scores ranging from 95.65% to 100% in the tested scenarios. These results demonstrate that the proposed integrated radar-based framework provides reliable lane-level traffic monitoring and safety risk identification under complex highway conditions.