TITLE:
Development and Evaluation of Intersection-Based Turning Movement Counts Framework Using Two Channel LiDAR Sensors
AUTHORS:
Ravi Jagirdar, Joyoung Lee, Dejan Besenski, Min-Wook Kang, Chaitanya Pathak
KEYWORDS:
Vehicle Trajectory Construction, Two Channel LiDAR, Turning Movement Counts, RTMS, Smart Cities, LiDAR
JOURNAL NAME:
Journal of Transportation Technologies,
Vol.13 No.4,
September
1,
2023
ABSTRACT: This paper presents vehicle localization and tracking methodology to
utilize two-channel LiDAR data for turning movement counts. The proposed
methodology uniquely integrates a K-means clustering technique, an inverse
sensor model, and a Kalman filter to obtain the final trajectories of an
individual vehicle. The objective of applying K-means clustering is to robustly
differentiate LiDAR data generated by pedestrians and multiple vehicles to
identify their presence in the LiDAR’s field of view (FOV). To localize the
detected vehicle, an inverse sensor model was used to calculate the accurate
location of the vehicles in the LiDAR’s FOV with a known LiDAR position. A
constant velocity model based Kalman filter is defined to utilize the localized
vehicle information to construct its trajectory by combining LiDAR data from
the consecutive scanning cycles. To test the accuracy of the proposed
methodology, the turning movement data was collected from busy intersections
located in Newark, NJ. The results show that the proposed method can
effectively develop the trajectories of the turning vehicles at the
intersections and has an average accuracy of 83.8%. Obtained R-squared value
for localizing the vehicles ranges from 0.87 to 0.89. To measure the accuracy
of the proposed method, it is compared with previously developed methods that
focused on the application of multiple-channel LiDARs. The comparison shows
that the proposed methodology utilizes two-channel LiDAR data effectively which
has a low resolution of data cluster and can
achieve acceptable accuracy compared to multiple-channel LiDARs and therefore
can be used as a cost-effective measure for large-scale data collection
of smart cities.