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Leveraging Artificial Intelligence and Fleet Sensor Data towards a Higher Resolution Road Weather Model
Toon Bogaerts1, Sylvain Watelet2, Niko De Bruyne3
1IDLab-Faculty of Applied Engineering, University of Antwerp-IMEC, Sint-Pietersvliet 7, 2000 Antwerp, Belgium.
Sensors (Basel, Switzerland)
|April 12, 2022
Summary
Real-time road condition monitoring using vehicle sensors and AI improves driver safety. This system forecasts weather, providing timely warnings for hazardous driving conditions.
Area of Science:
- Environmental Science
- Transportation Engineering
- Computer Science
Background:
- Adverse road weather conditions significantly impact driver safety.
- Real-time monitoring of road conditions is crucial for proactive safety measures.
Purpose of the Study:
- To develop and validate a real-time road condition monitoring and forecasting system.
- To leverage artificial intelligence for enhanced data processing and accuracy.
Main Methods:
- Equipping a vehicle fleet with sensors for continuous data collection.
- Utilizing a physical road weather model for condition forecasting.
- Applying artificial intelligence for sensor calibration and image-based weather information retrieval.
Main Results:
- Demonstrated AI's effectiveness in calibrating sensor data and extracting weather insights from images.
- Validated the road weather model by comparing its output with road weather station forecasts.
Conclusions:
- The proposed approach enables continuous, real-time monitoring and forecasting of road weather conditions.
- AI integration optimizes data processing, enhancing the reliability of road safety warnings.
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