ArticleScientific reports2024
Evaluation of data representation techniques for vibration based road surface condition classification.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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Who cites it
5 citing papers in PubMed.
- Driving-Context Classification from Wearable Physiological and Motion Signals During Real-World Driving.Sensors (Basel, Switzerland) · 2026Article
- Real-Time Edge Computing for Road Surface Classification Using Multi-IMU Data and a Hybrid CNN-LSTM Classification Model.Sensors (Basel, Switzerland) · 2026Article
- Vehicle-as-a-Sensor Approach for Urban Track Anomaly Detection.Sensors (Basel, Switzerland) · 2025Article
- Effective deep learning aided vehicle classification approach using Seismic Data.Scientific reports · 2025Article
- RoadDiffBox: Automatic Road Distress Diagnosis through Controlled Image Generation and Semi-Supervised Learning.Research (Washington, D.C.) · 2025Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The accurate classification of road surface conditions plays a vital role in ensuring road safety and effective maintenance. Vibration-based techniques have shown promise in this domain, leveraging the unique vibration signatures generated by vehicles to identify different road conditions. In this study, we focus on utilizing vehicle-mounted vibration sensors to collect road surface vibrations and comparing various data representation techniques for classifying road surface conditions into four classes: normal road surface, potholes, bad road surface, and speedbumps. Our experimental results reveal that the combination of multiple data representation techniques results in higher performance, with an average accuracy of 93.4%. This suggests that the integration of deep neural networks and signal processing techniques can produce a high-level representation better suited for challenging multivariate time series classification issues.
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