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Method for detecting rough road index using iot sensor fusion and v2v interaction for efficient road infrastructure
Akhilesh Kumar Singh1, Damodar Reddy Edla1
1CSE, NIT Goa, Cuncolim, Goa, India.
Methodsx
|July 14, 2025
Summary
This study introduces an IoT multi-sensor fusion hardware model for real-time road condition assessment. It enhances transportation safety and efficiency by identifying clean or rough road surfaces using advanced sensor fusion and V2V communication.
Area of Science:
- Intelligent Transportation Systems
- Internet of Things (IoT)
- Sensor Fusion Technology
Background:
- Modern transportation systems require enhanced efficiency, safety, and road quality assessment.
- Vehicle-to-Vehicle (V2V) communication is crucial for advanced navigation and safety features.
Purpose of the Study:
- To propose a novel IoT-based multi-sensor fusion hardware model for real-time road surface condition identification.
- To enhance road safety and navigation through integrated IoT and sensor fusion techniques.
Main Methods:
- Integration of Internet of Things (IoT) technologies and sensor fusion.
- Utilizing Vehicle-to-Vehicle (V2V) communication with Dedicated Short Range Communication (DSRC).
- Developing a multi-sensor fusion hardware model combining LiDAR, accelerometer, and ultrasonic sensors.
Main Results:
- Real-time collection and processing of road surface data.
- Classification of road surfaces into 'clean' (flat) or 'rough' (bumpy) categories based on a smoothness index.
- Successful identification of road anomalies with high accuracy.
Conclusions:
- The proposed IoT multi-sensor fusion model offers a novel approach to intelligent transportation systems.
- The system significantly improves road safety and navigation, particularly for autonomous vehicles.
- This method addresses existing gaps in high-accuracy road anomaly detection.
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