Road Scanner: A Road State Scanning Approach Based on Machine Learning Techniques
Source abstract
Abstract
The state of roads may sometimes be difficult to perceive due to intense climate conditions, absence of road signs, or simply human inattention, which may be harmful to both vehicles and drivers. The automatic monitoring of the road states represents a promising solution to warn drivers about the status of a road in order to protect them from injuries or accidents. In this paper, we present a novel application for data collection regarding road states. Our application entitled “Road Scanner” allows onboard users to tag four types of segments in roads: smooth, bumps, potholes, and others. For each tagged segment the application records multimodal data using the embedded sensors of a smartphone. The collected data concerns mainly vehicle accelerations, angular rotations, and geographical positions recorded by the accelerometer, the gyroscope, and the GPS sensor, respectively, of a user phone. Moreover, a medium-size dataset was built and machine learning models were applied to detect the right label for the road segment. Overall, the results were very promising since the SVM classifier (Support Vector Machines) has recorded an accuracy rate of 88.05%.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Infrastructure Maintenance and Monitoring · Automated Road and Building Extraction · Gait Recognition and Analysis
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Management Information Systems and Production Management, College of Business and Economics, Qassim University, P.O. Box 6640, Buraidah 51452, Saudi Arabia
FCSIT, Al-Baha University, Al-Baha 65528, Saudi Arabia
ReDCAD Laboratory, University of Sfax, Sfax 3038, Tunisia
Department of Digital Transformation, STC Solutions, Riyadh 12641, Saudi Arabia
References (44)
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