https://orcid.org/0000-0002-4222-9861
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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
5 Microsoft Common Objects in Context