Asynchronous Data Fusion for Vehicle Tracking Using MMW Radar and Magnetic Sensor in Tunnel

Sensor fusion plays an increasingly important role in real-time traffic perception using roadside sensing devices because the use of single type of sensors often fail to deliver satisfactory performance in certain harsh environment. This paper investigates asynchronous data fusion for real-time vehicle tracking with inaccurate and randomly delayed measurements from millimeter-wave (MMW) radars and magnetic sensors in tunnel environment. We first propose a multisensor data association algorithm to assign the measurements of MMW radar and magnetic sensors to a particular vehicle. A tracking algorithm is then designed to asynchronously update the current vehicle states with randomly delayed magnetic sensor measurements. The proposed algorithm is implemented in the Xianfengding Tunnel, Jiangxi Province, China. Experiments validate the proposed method's accuracy using real data. The method and collected data form the basis of a real-time digital twin system to support advanced traffic management. The fusion results and measurement dataset are available at https://github.com/futianxuan/data.