@article{ref1,
title="3-D data processing to extract vehicle trajectories from roadside LiDAR data",
journal="Transportation research record",
year="2018",
author="Sun, Yuan and Xu, Hao and Wu, Jianqing and Zheng, Jianying and Dietrich, Kurt M.",
volume="2672",
number="45",
pages="14-22",
abstract="High-resolution vehicle data including location, speed, and direction is significant for new transportation systems, such as connected-vehicle applications, micro-level traffic performance evaluation, and adaptive traffic control. This research developed a data processing procedure for detection and tracking of multi-lane multi-vehicle trajectories with a roadside light detection and ranging (LiDAR) sensor. Different from existing methods for vehicle onboard sensing systems, this procedure was developed specifically to extract high-resolution vehicle trajectories from roadside LiDAR sensors. This procedure includes preprocessing of the raw data, statistical outlier removal, a Least Median of Squares based ground estimation method to accurately remove the ground points, vehicle data clouds clustering, a principle component-based oriented bounding box method to estimate the location of the vehicle, and a geometrically-based tracking algorithm. The developed procedure has been applied to a two-way-stop-sign intersection and an arterial road in Reno, Nevada. The data extraction procedure has been validated by comparing tracking results and speeds logged from a testing vehicle through the on-board diagnostics interface. This data processing procedure could be applied to extract high-resolution trajectories of connected and unconnected vehicles for connected-vehicle applications, and the data will be valuable to practices in traffic safety, traffic mobility, and fuel efficiency estimation.<p /> <p>Language: en</p>",
language="en",
issn="0361-1981",
doi="10.1177/0361198118775839",
url="http://dx.doi.org/10.1177/0361198118775839"
}