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Related Concept Videos

Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

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Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence...
193

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Real-Time Driving Behavior Identification Based on Multi-Source Data Fusion.

Yongfeng Ma1, Zhuopeng Xie1, Shuyan Chen1

  • 1Jiangsu Key Laboratory of Urban ITS, School of Transportation, Southeast University, Nanjing 211189, China.

International Journal of Environmental Research and Public Health
|January 11, 2022
PubMed
Summary
This summary is machine-generated.

Integrating driver expression with vehicle data improves real-time driving behavior identification. This approach enhances driver monitoring and risk prediction, outperforming traditional methods using only kinematic data.

Keywords:
data fusiondriver expression dataonline car-hailingreal-time driving behavior identificationstacked long short-term memory networktime window

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Area of Science:

  • Transportation science
  • Artificial intelligence
  • Human-computer interaction

Background:

  • Traditional driving behavior identification relies on single data sources, limiting accuracy.
  • Real-time driver monitoring and risk prediction are crucial for safety.

Purpose of the Study:

  • To develop an innovative method for real-time driving behavior identification by integrating driver expression data.
  • To compare the effectiveness of a stacked Long Short-Term Memory (S-LSTM) network with other algorithms and data sources.

Main Methods:

  • Collected 12-day online car-hailing driving data, including vehicle kinematics and driver expressions.
  • Constructed a stacked Long Short-Term Memory (S-LSTM) network using integrated data to identify five driving behaviors.
  • Compared S-LSTM against Artificial Neural Network (ANN) and XGBoost, utilizing ten sliding time windows.

Main Results:

  • Integrating driver expression data with kinematic data improved the average F1 score by 0.041 compared to using kinematic data alone.
  • The S-LSTM algorithm demonstrated superior performance over ANN and XGBoost for driving behavior identification.
  • An optimal time window length of 3.5 seconds yielded the highest average F1 score of 0.877.

Conclusions:

  • The proposed method effectively enhances real-time driving behavior identification by incorporating driver expression.
  • This integrated approach supports advanced driving pattern analysis and the development of Advanced Driving Assistance Systems (ADAS).