High-accurate vehicle trajectory extraction and denoising from roadside LIDAR sensors

In recent years, Light Detection and Ranging (LIDAR) has been widely used in the Intelligent Traffic System due to its high-precision data acquisition capabilities. It has great potential for accurately detecting and tracking vehicle trajectories on the roadside. This study aims to develop a novel methodological framework for accurate vehicle trajectory extraction from roadside LIDAR. The proposed method starts by designing a detection area, followed by the application of statistical filtering, density clustering, and a random sample consensus (RANSAC) segmentation algorithm to eliminate the background point cloud. Subsequently, a modified density-based spatial clustering of applications with a noise algorithm (BSO-DBSCAN) is employed to detect the vehicle based on a beetle swarm optimization algorithm. An oriented bounding box (OBB) is used to obtain vehicle position, length, and width. A modified trajectory association method based on the Kalman filtering algorithm is proposed to address vehicle occlusion. LIDAR is deployed to obtain vehicle operation data in the expressway work zone, and the performance of the proposed method is tested using an unmanned aerial vehicle (UAV). The experimental results demonstrate that the proposed method successfully extracts high-precision vehicle trajectories. Specif-ically, the vehicle recognition accuracy increased by 12.7% in MAPE and 14.9% in RMSE compared to DBSCAN. The trajectory tracking accuracy increased by 13.5%, and the number of ID switching (ID-SW) was reduced by 271 times compared to SORT. The vehicle trajectory data extracted in this study provides a foundation for traffic characteristic analysis and traffic modeling. The extracted data can be downloaded from the following GitHub: https://github.com/gao0628/Dataset.

Automated summary

This study proposes a novel method for accurate vehicle trajectory extraction from roadside LIDAR. The method utilizes statistical filtering, density clustering, and a beetle swarm optimization algorithm to eliminate background noise, detect vehicles, and obtain vehicle position and size. Experimental results show that the proposed method achieves higher accuracy in vehicle recognition and trajectory tracking compared to traditional methods.