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Eye Tracking During A Complex Aviation Task For Insights Into Information Processing
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Lane-change intention prediction using eye-tracking technology: A systematic review.

Yunxian Pan1, Qinyu Zhang1, Yifan Zhang1

  • 1Center for Psychological Sciences, Zhejiang University, Hangzhou, Zhejiang Province, PR China.

Applied Ergonomics
|May 2, 2022
PubMed
Summary

This study reviews driver lane-change intention (DLCI) prediction using eye-tracking. Eye-tracking data aids DLCI prediction, but optimal feature extraction and machine learning methods require further research.

Keywords:
Advanced driver assistance systemDriver lane change intentionEye trackingMachine learningSystematic review

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Area of Science:

  • Human-Computer Interaction
  • Automotive Safety
  • Machine Learning

Background:

  • Driver lane-change intention (DLCI) prediction is crucial for advanced driver-assistance systems (ADAS).
  • Eye-tracking technology offers a promising, non-invasive method for capturing driver's cognitive and behavioral states.
  • Existing research has explored various aspects of DLCI prediction, but a consolidated view of best practices and future directions is needed.

Purpose of the Study:

  • To systematically review and synthesize the literature on driver lane-change intention prediction using eye-tracking technologies.
  • To identify current best practices in terms of input features, data processing, and prediction models.
  • To outline future research directions and practical applications for enhancing intelligent vehicle safety.

Main Methods:

  • A systematic literature review was conducted across five academic databases.
  • Forty relevant articles were selected, coded, and analyzed in-depth.
  • The review focused on input features, feature extraction, prediction time windows, labeling methods, and machine learning algorithms.

Main Results:

  • Eye-tracking data, combined with other sources, shows significant potential for DLCI prediction.
  • Key challenges include defining optimal time windows for feature extraction and selecting/evaluating appropriate machine learning algorithms.
  • Current literature provides a foundation but lacks standardization in methodologies.

Conclusions:

  • Eye-tracking is a valuable data source for driver lane-change intention prediction.
  • Further research is needed to optimize feature engineering and machine learning model development for real-world applications.
  • Standardized methodologies are essential for advancing the reliability and effectiveness of DLCI prediction systems in intelligent vehicles.