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Dynamic road section risk identification model based on connected in-vehicle data.

Yongjian Zhang1,2, Ge Yang3, Tian Xie4

  • 1School of Civil Engineering, Central South University of Forestry & Technology, Changsha, China.

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Summary
This summary is machine-generated.

This study introduces an onboard unit and Bayesian network models to identify road risks using Advanced Driver Assistance Systems (ADAS) data. The models effectively assess road segment risks by analyzing driving behaviors and near-crash events.

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Area of Science:

  • Road Safety Engineering
  • Transportation Data Science
  • Artificial Intelligence in Transportation

Background:

  • Advanced Driver Assistance Systems (ADAS) generate valuable data for understanding driving behavior.
  • Identifying dynamic road section risks is crucial for improving traffic safety.
  • Existing methods may not fully capture the complex interplay between driving behavior and road risks.

Purpose of the Study:

  • To develop a dynamic system for identifying road section risks.
  • To analyze the relationship between driving behavior and road risk across different road types (urban, expressway, freeway).
  • To create predictive models for road safety using real-world driving data.

Main Methods:

  • Designed an onboard unit to collect dynamic driving behavior data from ADAS-equipped vehicles.
  • Segmented roads into urban, expressway, and freeway categories using defined criteria.
  • Utilized Bayesian network (BN) models, DBSCAN clustering, and near-crash event analysis (braking deceleration, time to collision).

Main Results:

  • Developed separate BN models for urban roads, expressways, and freeways.
  • Successfully identified and classified near-crash events by severity.
  • Matched driving behavior data and weighted near-crash events to assess road segment risk levels.
  • Models demonstrated high sensitivity to observed data nodes.

Conclusions:

  • The proposed system effectively identifies road section risks using dynamic driving data.
  • Bayesian network models provide a robust framework for analyzing driving behavior and road safety.
  • This approach offers a promising method for real-time road risk assessment and proactive safety measures.