Vehicle Trajectory Tracking Using Adaptive Kalman Filter from Roadside Lidar

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.

Automated summary

Recently, roadside lidar sensors have been utilized to extract high-resolution vehicle trajectory data from the field. However, the current methods for trajectory optimization still face challenges in terms of fluctuations. This paper proposes an effective method for trajectory smoothing using the adaptive Kalman filter, with a multipoint matching algorithm for velocity estimation and a robust adaptive strategy for the Kalman filter. The method shows satisfactory performance when evaluated using field data collected in Reno, Nevada, and it is user-friendly for real-world applications.