Enhancing Vehicle Sensing for Traffic Safety and Mobility Performance Improvements Using Roadside LiDAR Sensor Data

Zhang, Guohui; Zhou, Shanglian; University of Alaska Fairbanks. Center for Safety Equity in Transportation (CSET); University of Hawaii at Manoa. Department of Civil and Environmental Engineering

Advanced Search

Select up to three search categories and corresponding keywords using the fields to the right. Refer to the Help section for more detailed instructions.

Search our Collections & Repository

Advanced Search
Custom Query

All these words:

For very narrow results

This exact word or phrase:

When looking for a specific result

Any of these words:

Best used for discovery & interchangable words

None of these words:

Recommended to be used in conjunction with other fields

Language:

Dates

Publication Date Range:

to

Document Data

Title:

Document Type:

Library

Collection:

Series:

People

Author:

Clear All

Query Builder

Query box

Clear All

For additional assistance using the Custom Query please check out our Help Page

i

Enhancing Vehicle Sensing for Traffic Safety and Mobility Performance Improvements Using Roadside LiDAR Sensor Data

2024-06-30
By Zhang, Guohui ; Zhou, Shanglian

[PDF-6.30 MB]

Select the Download button to view the document

This document is over 5mb in size and cannot be previewed

English

Details You May Also Like

Details:

Creators:

Zhang, Guohui ; Zhou, Shanglian

Zhang, Guohui ; Zhou, Shanglian Less -
Corporate Creators:

University of Alaska Fairbanks. Center for Safety Equity in Transportation (CSET) ; University of Hawaii at Manoa. Department of Civil and Environmental Engineering

University of Alaska Fairbanks. Center for Safety Equity in Transportation (CSET) ; University of Hawaii at Manoa. Department of Civil and Environmental Engineering Less -
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

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

[+]

Computer Vision Deep Learning Indigenous Peoples Laser Radar Performance Roadside Roadside LiDAR Sensors Traffic Object Recognition Traffic Safety
Publication/ Report Number:

INE/CSET 24.06
Resource Type:

Tech Report
Geographical Coverage:

Alaska ; Washington ; Idaho ; Hawaii

Alaska ; Washington ; Idaho ; Hawaii Less -
Edition:

Final Research Report
Corporate Publisher:

University of Alaska Fairbanks. Center for Safety Equity in Transportation (CSET)
Abstract:

Recent technological advancements in computer vision algorithms and data acquisition devices have greatly facilitated research activities towards enhancing traffic sensing for traffic safety performance improvements. Significant research efforts have been devoted to developing and deploying more effective technologies to detect, sense, and monitor traffic dynamics and rapidly identify crashes in in Rural, Isolated, Tribal, or Indigenous (RITI) communities. As a new modality for 3D scene perception, Light Detection and Ranging (LiDAR) data have gained increasing popularity for traffic perception, due to its advantages over conventional RGB data, such as being insensitive to varying lighting conditions. In the past decade, researchers and professionals have extensively adopted LiDAR data to promote traffic perception for transportation research and applications. Nevertheless, a series of challenges and research gaps are yet to be fully addressed in LiDAR-based transportation research, such as the disturbance of adverse weather conditions, lack of roadside LiDAR data for deep learning analysis, and roadside LiDAR-based vehicle trajectory prediction. In this technical report, we focus on addressing these research gaps and proposing a series of methodologies to optimize deep learning-based feature recognition for roadside LiDAR-based traffic object recognition tasks. The proposed methodologies will help transportation agencies monitor traffic flow, identify crashes, and develop timely countermeasures with improved accuracy, efficiency, and robustness, and thus enhance traffic safety in RITI communities in the States of Alaska, Washington, Idaho, and Hawaii.
Format:

PDF
Collection(s):

University Transportation Centers
Main Document Checksum:

[+]

urn:sha256:49b16b0fe1d55ab2480e865a76b73f1fd6d1c91480856c8c5ef6042e18d6cb69
Download URL:

https://rosap.ntl.bts.gov/view/dot/75513/dot_75513_DS1.pdf
File Type:

No Additional Files

More +

Enhancing Vehicle Sensing for Traffic Safety and Mobility Performance Improvements Using Roadside LiDAR Sensor Data

Details:

Supporting Files

You May Also Like