Citation

Cao P, Wang Y, Liu X. Transp. Res. Rec. 2024; 2678(3): 816-828.

Copyright

(Copyright © 2024, Transportation Research Board, National Research Council, National Academy of Sciences USA, Publisher SAGE Publishing)

DOI

10.1177/03611981231182987

PMID

unavailable

Abstract

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.

Language: en