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Driving behavior analysis and classification by vehicle OBD data using machine learning.

Raman Kumar1, Anuj Jain1

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Summary
This summary is machine-generated.

Classifying driver behavior using machine learning models accurately identifies driving patterns. This technology analyzes vehicle data to improve driving efficiency and safety.

Keywords:
Analysis of driving behavior (ADB) Body control unit (BCU)Controller area network (CAN)Data acquisition systems (DAS)Engine control unit (ECU)Information and communication technology (ICT)Internet of things (IoT)Keyword protocol (KWP)On-board diagnostic (OBD)Society of automotive engineers (SAE)

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Area of Science:

  • Automotive Engineering
  • Data Science
  • Machine Learning

Background:

  • The transportation sector seeks to enhance performance and reduce operational costs.
  • Driver behavior significantly impacts fuel consumption and emissions, necessitating driver pattern classification.
  • Modern vehicles are equipped with sensors providing extensive operational data.

Purpose of the Study:

  • To develop a machine learning-based technique for classifying driver behavior.
  • To analyze vehicle performance data for driver pattern identification.
  • To provide insights for improving driving efficiency and safety.

Main Methods:

  • Collected critical vehicle performance data (speed, RPM, load, etc.) via the On-Board Diagnostics (OBD-II) interface.
  • Employed machine learning algorithms including Support Vector Machine (SVM), AdaBoost, and Random Forest.
  • Classified driver behavior into ten categories based on fuel consumption, steering stability, velocity stability, and braking patterns.

Main Results:

  • Achieved high classification accuracy: SVM (99%), AdaBoost (99%), and Random Forest (100%).
  • Successfully classified drivers into ten distinct behavioral categories.
  • Demonstrated the model's effectiveness in analyzing driving patterns using real-time sensor data.

Conclusions:

  • The proposed model offers an effective method for studying and classifying driver behavior.
  • Utilizing OBD-II data eliminates the need for additional sensors, providing a practical solution.
  • The classification system can provide feedback to drivers, promoting safer and more efficient driving habits.