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SafeDrive: Hybrid Recommendation System Architecture for Early Safety Predication Using Internet of Vehicles.

Rayan Nouh1, Madhusudan Singh2, Dhananjay Singh3

  • 1Institute of Consulting and Research Studies, Umm Al-Qura University, Mecca 18135, Saudi Arabia.

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Summary

SafeDrive enhances road safety in the Internet of Vehicles (IoV) by analyzing driver behavior to predict risks. This dynamic driver profile (DDP) system improves driving patterns and provides early warnings for a safer transportation environment.

Keywords:
ITSIoVdeep learningdriving behavior and road safetyrecommender system

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Area of Science:

  • Intelligent Transportation Systems (ITS)
  • Internet of Vehicles (IoV)
  • Machine Learning for Driver Behavior Analysis

Background:

  • The Internet of Vehicles (IoV) represents a significant advancement in Intelligent Transportation Systems (ITS).
  • Accurate driver behavior analysis is crucial for improving road safety and preventing accidents in IoV environments.
  • Existing systems often lack dynamic profiling capabilities to adapt to individual driver risk patterns.

Purpose of the Study:

  • To propose SafeDrive, a novel dynamic driver profile (DDP) system utilizing a hybrid recommendation approach.
  • To identify and analyze individual driver risk patterns based on historical data and behavioral parameters.
  • To enhance road safety through improved driving behavior and proactive risk prediction in IoV.

Main Methods:

  • Development of a hybrid recommendation system for creating dynamic driver profiles (DDP).
  • Analysis of synthetic datasets (1500 drivers) including profile information, risk parameters, and likelihood.
  • Utilized historical violation/accident records categorized into high, medium, low, and no-risk levels for score prediction.
  • Applied various error calculation methods to evaluate the hybrid recommendation system's classification accuracy.

Main Results:

  • The proposed hybrid recommendation system demonstrated high accuracy in classifying driver data based on multiple criteria.
  • Evaluated results indicate significant potential for improving driver behavior through personalized risk assessment.
  • The system effectively predicts current and future driver risk scores, enabling proactive safety measures.

Conclusions:

  • SafeDrive provides a robust framework for dynamic driver profiling, enhancing safety in IoV environments.
  • The system contributes to a safer ecosystem for vehicles, pedestrians, and road objects through continuous monitoring.
  • Accurate accident prediction and reduced system complexity are key benefits, minimizing latency in IoV operations.