Abstract
Vehicle speed is a key variable for the calibration, validation, and improvement of traffic emission and air quality models. Lidar technologies have significant potential in vehicle tracking by scanning the surroundings in 3-D frequently, hence can be used as traffic flow monitoring sensors for accurate vehicle counting and speed estimation. However, the characteristics of lidar-based vehicle tracking and speed estimation, such as attainable accuracy, remain as open questions. This research therefore proposes a tracking framework from roadside lidar to detect and track vehicles with the aim of accurate vehicle speed estimation. Within this framework, on-road vehicles are first detected from the observed point clouds, after which a centroid-based tracking flow is implemented to obtain initial vehicle transformations. A tracker, utilizing the unscented Kalman Filter and joint probabilistic data association filter, is adopted in the tracking flow. Finally, vehicle tracking is refined through an image matching process to improve the accuracy of estimated vehicle speeds. The effectiveness of the proposed approach has been evaluated using lidar data obtained from two different panoramic 3-D lidar sensors, a RoboSense RS-LiDAR-32 and a Velodyne VLP-16, at a traffic light and a road intersection, respectively, in order to account for real-world scenarios. Validation against reference data obtained by a test vehicle equipped with accurate positioning systems shows that more than 94% of vehicles could be detected and tracked, with a mean speed accuracy of 0.22 m/s.
Highlights
C ITIES are facing increasing challenges in traffic management and air pollution induced by heavy traffic
Vehicles are detected via a three-step procedure, tracked by Unscented Kalman Filter (UKF) and joint probabilistic data association filter (JPDAF), which takes the centroid of the cluster as the vehicle position, resulting in biases in vehicle speeds due to the incompleteness of the scanned clusters
Vehicle clusters were detected from the raw point clouds using a three-step schema in the first instance
Summary
C ITIES are facing increasing challenges in traffic management and air pollution induced by heavy traffic. Emissions from on-road vehicles are widely regarded to be the main source of air pollution in urban areas [1]. The key input data source to air quality models is usually generated from vehicle emission models, which is supported by traffic data. Using better traffic flow representations is fundamental to improving. Manuscript received April 30, 2020; revised July 17, 2020 and August 6, 2020; accepted September 13, 2020. Date of publication September 18, 2020; date of current version September 30, 2020.
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