Cloud-Based Road Safety for Real-Time Vehicle Rash Driving Alerts with Random Forest Algorithm

The cloud-based road safety technology introduced in this research improves real-time vehicle behavior monitoring and warns for rash driving. It uses cloud computing to gather and interpret data from onboard car sensors and other traffic monitoring equipment. Powerful machine learning technology the...

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Bibliographic Details
Published in2024 3rd International Conference for Innovation in Technology (INOCON) pp. 1 - 6
Main Authors M, Presitha Aarthi, Reddy, Chilukala Mahender, Anbarasi, A., Mohankumar, N., M.V, Ishwarya, Murugan, S.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.03.2024
Subjects
Accident Prevention
Alert Generation
Cloud computing
Cloud Infrastructure
Driving Parameters
Machine learning algorithms
Pattern Identification
Radio frequency
Real-time systems
Road safety
Technological innovation
Transportation
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Summary:The cloud-based road safety technology introduced in this research improves real-time vehicle behavior monitoring and warns for rash driving. It uses cloud computing to gather and interpret data from onboard car sensors and other traffic monitoring equipment. Powerful machine learning technology the Random Forest (RF) algorithm analyses this data intelligently. The RF algorithm is trained on a varied array of driving metrics to detect rash driving tendencies. Real-time data streams may be processed efficiently and scalable on the cloud, enabling speedy decision-making for rash driving detection. A network sends alerts to authorities and nearby vehicles. The system is adaptable to different road conditions, resistant to sensor data noise, and able to enhance rash driving detection accuracy via model updates. Cloud architecture makes the system available worldwide. With its focus on real-time notifications utilizing the RF algorithm, this cloud-based road safety system may reduce the hazards of reckless driving and avoid accidents.
DOI:10.1109/INOCON60754.2024.10511316