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Vehicle Trajectory Tracking Using Adaptive Kalman Filter from Roadside Lidar
| 题名： | Vehicle Trajectory Tracking Using Adaptive Kalman Filter from Roadside Lidar |
| 正文语种： | eng |
| 作者： | Qi Zhang;Nischal Bhattarai;Hong Chen;Hao Xu;Hongchao Liu |
| 作者单位： | College of Civil and Architectural Engineering North China Univ. of Science and Technology Tangshan 063210 China Doctoral Researcher Tangshan Key Laboratory of Air-Ground Intelligent Transportation North China Univ. of Science and Technology Tangshan 063210 China;Dept. of Civil Construction and Environmental Engineering Texas Tech Univ. Lubbock TX 79409;College of Transportation Engineering Chang'an Univ. Xi'an 710064 China;Dept. of Civil and Environmental Engineering Univ. of Nevada Reno NV 89557;Dept. of Civil Construction and Environmental Engineering Texas Tech Univ. Lubbock TX 79409 |
| 关键词： | Vehicle trajectory tracking; Roadside lidar; Adaptive Kalman filter; Trajectory smoothing |
| 摘要： | Recently, roadside lidar sensors have been adopted as a reliable measure to extract high-resolution vehicle trajectory data from the field. The trajectory-level data can be extracted from roadside lidar detection using a series of data processing algorithms such as background filtering, object clustering, object classification, and object tracking. However, the results from current methods are associated with trajectory fluctuations, indicating that vehicle trajectory optimization remains a challenge. Previous studies used traditional Kalman filters to optimize vehicle trajectories, but there remains room for improvement in the smoothing effect. This paper addresses the issue by presenting an effective method for trajectory smoothing using the adaptive Kalman filter. The proposed method demonstrates two significant highlights: a multipoint matching algorithm for velocity estimation, and a robust adaptive strategy for Kalman filter. The performance of the proposed method was found to be satisfactory after evaluation using field data collected at a site in Reno, Nevada. Additionally, the method can be implemented under simple prior assumptions, making it user-friendly for real-world applications. |
| 出版年： | 2023 |
| 期刊名称： | Journal of Transportation Engineering |
| 卷： | 149 |
| 期： | 6 |
| 页码： | 04023043.1-04023043.10 |
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