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.

中文翻译：

---

路边激光雷达的车辆跟踪和速度估计

车速是校准、验证和改进交通排放和空气质量模型的关键变量。激光雷达技术通过频繁地以 3D 方式扫描周围环境，在车辆跟踪方面具有巨大潜力，因此可用作交通流量监测传感器，用于准确的车辆计数和速度估计。然而，基于激光雷达的车辆跟踪和速度估计的特性，例如可达到的精度，仍然是悬而未决的问题。因此，本研究提出了一种基于路边激光雷达的跟踪框架来检测和跟踪车辆，以实现准确的车速估计。在此框架内，首先从观察到的点云中检测道路车辆，然后实施基于质心的跟踪流程以获得初始车辆变换。一个追踪者，在跟踪流程中采用无迹卡尔曼滤波器和联合概率数据关联滤波器。最后，通过图像匹配过程细化车辆跟踪，以提高估计车辆速度的准确性。已使用从两个不同的全景 3-D 激光雷达传感器（RoboSense RS-LiDAR-32 和 Velodyne VLP-16）获得的激光雷达数据评估了所提出方法的有效性，分别在红绿灯和道路交叉口处，以便以考虑现实世界的场景。对配备精确定位系统的测试车辆获得的参考数据进行验证表明，可以检测和跟踪超过 94% 的车辆，平均速度精度为 0.22 m/s。车辆跟踪通过图像匹配过程进行细化，以提高估计车辆速度的准确性。已使用从两个不同的全景 3-D 激光雷达传感器（RoboSense RS-LiDAR-32 和 Velodyne VLP-16）获得的激光雷达数据评估了所提出方法的有效性，分别在红绿灯和道路交叉口处，以便以考虑现实世界的场景。对配备精确定位系统的测试车辆获得的参考数据进行验证表明，可以检测和跟踪超过 94% 的车辆，平均速度精度为 0.22 m/s。车辆跟踪通过图像匹配过程进行细化，以提高估计车辆速度的准确性。已使用从两个不同的全景 3-D 激光雷达传感器（RoboSense RS-LiDAR-32 和 Velodyne VLP-16）获得的激光雷达数据评估了所提出方法的有效性，分别在红绿灯和道路交叉口处，以便以考虑现实世界的场景。对配备精确定位系统的测试车辆获得的参考数据进行验证表明，可以检测和跟踪超过 94% 的车辆，平均速度精度为 0.22 m/s。RoboSense RS-LiDAR-32 和 Velodyne VLP-16，分别在红绿灯和道路交叉口处，以考虑现实世界的场景。对配备精确定位系统的测试车辆获得的参考数据进行验证表明，可以检测和跟踪超过 94% 的车辆，平均速度精度为 0.22 m/s。RoboSense RS-LiDAR-32 和 Velodyne VLP-16，分别在红绿灯和道路交叉口处，以考虑现实世界的场景。对配备精确定位系统的测试车辆获得的参考数据进行验证表明，可以检测和跟踪超过 94% 的车辆，平均速度精度为 0.22 m/s。