Asynchronous Data Fusion With Randomly Delayed Measurements for Lane-Level Vehicle Tracking in Tunnel Environment

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 lane-level vehicle tracking with randomly delayed measurements and inaccurate detections from millimeter wave (MMW) radars and magnetic sensors in tunnels, where vehicle tracking with single type of sensors can not meet the requirements of reliable and accurate lane-level tracking due to inaccurate radar detections at far distances, noisy radar detections in tunnel environment, and missed or false vehicle detections by magnetic sensors. A multisensor data association algorithm is first designed to assign the measurements of MMW radar and magnetic sensors to a particular vehicle. A multi-lane estimation model is then developed, which employs Bayesian weight mixture filtering to fuse MMW radar and magnetic sensor measurements and to estimate the lane in which a vehicle is located. Finally, the proposed algorithm is implemented in a real environment - the Xianfengding Tunnel located in Jiangxi Province, China. Experiments are conducted to validate the accuracy of the proposed method using real data. The proposed method and the collected data are further integrated to establish a real-time digital twin system aimed at supporting advanced traffic management. The fusion results and the real radar measurement dataset of the tunnel are made available at https://github.com/futianxuan/data.