The detailed vehicle trajectories collected by various advanced sensors (e.g., LiDARs, radars, digital cameras) form an essential foundation for in-depth analysis of connected and automated transportation systems, in which lane division of vehicle trajectories is an essential step. Rather than high-definition maps, trajectory-based methods have attracted increasing attention for their merits of low cost and adaptability to map changes in a timely manner. However, they require a priori geometric information about roads which is usually unavailable in practice. To address this problem, we propose a method solely based on trajectory data to derive geometric road features for dividing lanes of trajectories collected in connected environments. The proposed method consists of two stages: (i) to extract the lane lines of the road, the direction of the road centerline is calculated as the weighted arithmetic average of trajectory directions, and an iterative process can keep updating the feature points of road centerline and lane lines based on kernel density estimation of trajectories; (ii) trajectories are divided into lanes by constructing a polygon based on the extracted lane lines. We conducted a field experiment to collect high-precision vehicle trajectories using roadside LiDAR devices and created a high-definition digital map at an urban road without signalized intersection in the city of Chengdu. The experiment based on the empirical data demonstrates the proposed method can accurately divide lanes for 97.83% of trajectory points. In addition, a series of sensitivity analyses show that the proposed method is robust to the noise in trajectory data and the error of calibrating the roadside LiDAR devices.


    Access

    Download

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Trajectory-Based Method for Dividing Lanes of Vehicle Trajectories Collected by Roadside LiDAR


    Additional title:

    Transportation Research Record: Journal of the Transportation Research Board


    Contributors:
    Cao, Peng (author) / Wang, Yiming (author) / Liu, Xiaobo (author)


    Publication date :

    2023-06-06




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



    Vehicle Trajectory Tracking Using Adaptive Kalman Filter from Roadside Lidar

    Zhang, Qi / Bhattarai, Nischal / Chen, Hong et al. | ASCE | 2023


    3-D Data Processing to Extract Vehicle Trajectories from Roadside LiDAR Data

    Sun, Yuan / Xu, Hao / Wu, Jianqing et al. | Transportation Research Record | 2018


    Automatic Identification of Vehicle Partial Occlusion in Data Collected by Roadside LiDAR Sensors

    Zhao, Junxuan / Xu, Hao / Zhang, Yibin et al. | Transportation Research Record | 2022


    IMAGE-BASED VEHICLE TRACKING FROM ROADSIDE LIDAR DATA

    J. Zhang / W. Xiao / B. Coifman et al. | DOAJ | 2019

    Free access

    Automatic Vehicle Classification using Roadside LiDAR Data

    Wu, Jianqing / Xu, Hao / Zheng, Yichen et al. | Transportation Research Record | 2019