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State of the Art

A review on pavement data acquisition and analytics tools using autonomous vehicles

, , ORCID Icon, &
Pages 914-940 | Received 25 Jan 2023, Accepted 11 Jul 2023, Published online: 22 Jul 2023
 

Abstract

Nowadays, road maintenance is a crucial planning task for all road authorities across the world, making them spend vast amounts of money on identification and rehabilitation programmes every year. Thus, many researchers, road engineers, and decision-makers have turned to a system called pavement management system, the critical step of which is pavement inspection. So far, several methods have been proposed for pavement distress data collection and evaluation. These methods have also evolved with the advancement of technology and intelligent transportation systems. Today, researchers have acknowledged autonomous vehicles as a sophisticated tool monitoring right of way for appropriate operating; however, little attention has been paid to using collected pavement condition data for pavement management. This study reviews the technologies and algorithms proposed so far to investigate the feasibility of using the data regularly collected by these vehicles to evaluate pavement conditions. We hope our paper paves the way for further research.

Acknowledgments

No funding was received to assist with the preparation of this manuscript.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Glossary of Terms

AV=

Autonomous Vehicle

CV=

Connected Vehicle

ITS=

Intelligent Transportation Systems

V2V=

Vehicle-to-Vehicle

V2I=

Vehicle-to – the Internet

V2R=

Vehicle-to-Road Infrastructure

SAE=

Society of Automotive Engineers

LiDAR=

Light Detection And Ranging

NCHRP=

National Cooperative Highway Research Programme

LED=

Light Emitting Diode

LDR=

Light Dependant Resistor

IP=

Image Processing

CNN=

Convolutional Neural Network

YOLO=

You Only Look Once

LTPP=

Long Term Pavement Performance

FHWA=

Federal Highway Administration

XML=

Extensible Markup Language

ASPRS=

American Society for Photogrammetry and Remote Sensing

IMU=

Inertial Measurement Unit

VMT=

Vehicle Miles Travelled

ML=

Machine Learning

RL=

Reinforcement Learning

SVM=

Support Vector Machine

UAV=

Unmanned Aerial Vehicle

ANN=

Artificial Neural Networks

Adam=

Adaptive Moment Estimation

SGD=

Stochastic Gradient Descent

DL=

Deep Learning

ROC=

Receiver Operating Characteristics

SSD=

Single Shot Detector

WT=

Wavelet Transformation

RADA=

Road Anomaly Detection Algorithm

RACA=

Road Anomaly Characterisation Algorithm

FPR=

False Positive Rate

Notes

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