Vehicle Tracking and Speed Estimation From Roadside Lidar

• Published in: IEEE journal of selected topics in applied earth observations and remote sensing Vol. 13; pp. 5597 - 5608
• Main Authors: Zhang, Jiaxing, Xiao, Wen, Coifman, Benjamin, Mills, Jon P.
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: 3-D lidar; Accuracy; Air quality; Atmospheric modeling; Calibration; Centroids; Estimation; Feature extraction; Kalman filters; Laser radar; Lidar; Positioning systems; Roadsides; Sensors; Smart city; Test vehicles; Three dimensional models; Three-dimensional displays; Tracking; Traffic flow; traffic monitoring; Traffic signals; Traffic speed; urban sensing; Vehicle detection; Vehicle emissions; Vehicles; Velocity
• ISSN: 1939-1404; 2151-1535
• DOI: 10.1109/JSTARS.2020.3024921

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.