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Determining the steering direction in critical situations: A decision tree-based method.

Huajian Zhou1, Zhihua Zhong1,2, Manjiang Hu3

  • 1State Key Laboratory of Automotive Safety and Energy, School of Vehicle and Mobility, Tsinghua University, Beijing, China.

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Summary
This summary is machine-generated.

Driver steering direction in critical situations is influenced by vehicle angle differences, speed, and driver age. This study models evasive steering behavior using decision trees for safer intelligent vehicle development.

Keywords:
Decision of steering directionNASS-CDS data setcritical situationsdecision tree classifierevasive maneuvers

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Area of Science:

  • Traffic safety research
  • Human-computer interaction
  • Vehicle dynamics

Background:

  • Driver decision-making in critical situations is complex.
  • Evasive steering maneuvers are crucial for accident avoidance.
  • Systematic analysis of factors influencing steering direction is limited.

Purpose of the Study:

  • To analyze and model drivers' decision of steering direction (DSD) in critical traffic situations.
  • To identify key factors influencing DSD in both intersection-related and non-intersection-related scenarios.
  • To develop predictive models for driver steering behavior.

Main Methods:

  • Utilized National Automotive Sampling System - General Estimates System (NASS-CDS) data (1995-2015).
  • Employed decision tree (DT) classifiers for modeling DSD, with 10-fold cross-validation and grid search optimization.
  • Conducted variable importance analysis to identify key influencing factors.

Main Results:

  • Developed two DT models for DSD with test accuracies of 84.6% (intersection-related) and 79.2% (non-intersection-related).
  • Angle difference between vehicles (DIFFANGLE) was the most significant factor for DSD in both models.
  • Other key factors included vehicle speed (SPEED), driver age (AGE), pre-event movement (PREMOVE), and traffic flow (TRAFFLOW).

Conclusions:

  • Decision tree models effectively analyze and predict driver steering direction behavior using NASS-CDS data.
  • Identified critical variables influencing evasive steering, enhancing understanding of lateral movement behavior.
  • Findings support the application of these models in developing advanced driver-assistance systems and intelligent vehicles.