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A dynamic method to predict driving risk on sharp curves using multi-source data.

Yongfeng Ma1, Fan Wang1, Shuyan Chen1

  • 1Jiangsu Key Laboratory of Urban ITS, School of Transportation, Southeast University, Nanjing 211189, China; Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, Southeast University, Nanjing 211189, China.

Accident; Analysis and Prevention
|July 23, 2023
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Summary
This summary is machine-generated.

Predicting driving risk on sharp curves using connected vehicle data is crucial for safety. A new LSTM model effectively identifies high-risk driving behavior, outperforming other algorithms.

Keywords:
Clustering algorithmDriving riskDynamic predictionLong short-term memory (LSTM) algorithmSharp curve

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Area of Science:

  • Road safety engineering
  • Intelligent transportation systems
  • Machine learning applications in automotive safety

Background:

  • Sharp curves pose significant traffic accident risks, especially under adverse driving conditions.
  • Real-time risk prediction on curved roads is essential for enhancing driver safety in connected vehicles.
  • Existing methods may not adequately capture the dynamic nature of driving risks on curves.

Purpose of the Study:

  • To develop a dynamic, real-time method for predicting driving risk on sharp curves.
  • To fuse multiple data sources, including driver maneuvering, vehicle kinematics, and physiological data, for comprehensive risk assessment.
  • To evaluate the performance of a Long Short-Term Memory (LSTM) network model against other machine learning algorithms for risk prediction.

Main Methods:

  • A driving simulation experiment was conducted on six curves with varying radii and directions.
  • Driver maneuvering, vehicle kinematic, and driver physiological data were collected from 55 participants.
  • Data were segmented, and critical lateral acceleration was used as a risk index, classified into low, medium, and high levels using K-means clustering.
  • An LSTM network model was developed to predict risk levels using fused data, with optimal lookback and delay windows determined.
  • LSTM model performance was compared against Random Forest, XGBoost, and LightGBM algorithms.

Main Results:

  • The proposed LSTM-based model demonstrated superior performance in predicting dangerous driving behavior on sharp curves.
  • The optimal window combination for the LSTM model was identified as a 20m lookback and 20m delay.
  • The LSTM model achieved F1-scores of 84.8% for medium risk and 86.0% for high risk, outperforming comparative algorithms.
  • The multi-source data fusion approach significantly outperformed models using only vehicle kinematics data.

Conclusions:

  • The developed dynamic, real-time risk prediction method using LSTM is effective for sharp curves in connected vehicle environments.
  • The LSTM model, leveraging fused multi-source data, offers a robust solution for predicting driving risks.
  • This approach can significantly contribute to the development of real-time prediction and warning systems for intelligent connected vehicles.