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Sensor-Based Extraction Approaches of In-Vehicle Information for Driver Behavior Analysis.

Beomjun Kim1, Yunju Baek1

  • 1School of Computer Science and Engineering, Pusan National University, Busan 46241, Korea.

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|September 16, 2020
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Summary
This summary is machine-generated.

This study introduces a novel system for automatically extracting proprietary vehicle data from CAN frames. The method accurately identifies driver actions like braking and steering, crucial for driver behavior analysis.

Keywords:
controller area networkin-vehicle sensorreverse engineeringvehicle state estimationvehicular information

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Area of Science:

  • Automotive Engineering
  • Data Science
  • Sensor Technology

Background:

  • Modern vehicles collect standardized data via Controller Area Network (CAN) systems.
  • Extracting proprietary vehicle data (e.g., brake, steering) for driver behavior analysis presents challenges.
  • Existing methods require complex electronic control unit identifier analysis and data interpretation.

Purpose of the Study:

  • To develop an automated system for extracting proprietary in-vehicle information from CAN frames.
  • To correlate sensor data with desired information for accurate extraction.
  • To enable detailed driver behavior analysis through enhanced data acquisition.

Main Methods:

  • Vehicle driving status estimation using Inertial Measurement Unit (IMU) and Global Positioning System (GPS) data via threshold, random forest, and LSTM techniques.
  • Segmentation of CAN frames based on estimated driving status.
  • Scoring and selection of CAN frame segments using a distance matching technique for similarity assessment.

Main Results:

  • Driving condition estimation accuracy achieved 84.20%.
  • Proprietary in-vehicle information extraction accuracy reached 82.31%.
  • The system demonstrated feasibility for automatic proprietary data extraction in real-world urban driving.

Conclusions:

  • The proposed system effectively automates the extraction of critical proprietary vehicle data.
  • Accurate driving status estimation is key to successful information extraction from CAN frames.
  • This approach offers a viable solution for advanced driver behavior analysis and automotive research.