Investigation of LiDAR Sensing Technology to Improve Freeway Traffic Monitoring

Ritchie, Stephen G.; Tok, Andre; Li, Yiqiao; Allu, Koti R; Sun, Jared

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Investigation of LiDAR Sensing Technology to Improve Freeway Traffic Monitoring

2022-09-01
By Ritchie, Stephen G. ; Tok, Andre ; Li, Yiqiao

File Language:

English

Details

Creators:

Ritchie, Stephen G. ; Tok, Andre ; Li, Yiqiao ; Allu, Koti R ; Sun, Jared
Corporate Creators:

METRANS Transportation Center (Calif.) ; Pacific Southwest Region 9 UTC, University of Southern California
Corporate Contributors:

United States. Department of Transportation. University Transportation Centers (UTC) Program ; United States. Department of Transportation. Office of the Assistant Secretary for Research and Technology
Subject/TRT Terms:
Publication/ Report Number:

PSR-21-34
Resource Type:

Tech Report
Geographical Coverage:

United States
Edition:

Final Report
Corporate Publisher:

METRANS Transportation Center (Calif.)
Abstract:

LiDAR is an emerging technology that can provide detailed point-cloud measurements for accurate detection and characterization of objects. The cost of this technology has seen significant reduction in recent years with the scaling of production to meet the demands of wide-ranging applications such as autonomous vehicles, infrastructure inventory and topographic mapping, to name a few. Within the field of infrastructure-based traffic monitoring, recent studies have investigated the use of this sensor for advanced truck classification applications in side-fire orientation, as well as for motorized vehicle, bicycle and pedestrian detection at traffic intersections. This study explored the potential of LiDAR in traffic monitoring applications. These include the investigation in the feasibility of edge-side data processing of real-time LiDAR data, real-time detection of vehicle objects using a state-of-the-art object detection algorithm and the use of LiDAR to estimate microscopic vehicle trajectories within its field of view.
Format:

PDF
Funding:

69A3551747109CALTRANS TO 049
Download URL:

https://rosap.ntl.bts.gov/view/dot/67550/dot_67550_DS1.pdf
File Type:

[PDF - 2.67 MB]
Collection(s):

Autonomous Vehicles ; University Transportation Centers
Main Document Checksum:

urn:sha-512:72c88c3ab20d9f58e790f83afedef67a54bf9b705954f57a114087defef59778cbe0ca519d38d1b8ae749b09ea1d7b8b78c2afcdb28dd30702a12ac0e8fffaa4

File Language:

English

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