Lane-change intention prediction using eye-tracking technology: A systematic review

Yunxian PAN, Qinyu ZHANG, Yifan ZHANG, Xianliang GE, Xiaoqing GAO, Shiyan YANG, Jie XU*

*Corresponding author for this work

Research output: Journal PublicationsReview articleOther Review

20 Citations (Scopus)

Abstract

The aim of this study is to identify the best practices and future research directions for driver lane-change intention (DLCI) prediction using eye-tracking technologies based on a systematic literature review. We searched five academic literature databases and then conducted an in-depth review, structured coding, and analysis of 40 relevant articles. The literature on DLCI prediction is summarized in terms of input features, feature extraction and prediction time windows, labeling methods, and machine learning algorithms. The results show that eye tracking data features along with other data sources can be useful inputs for the prediction of DLCI. Major challenges in this line of research include determining the optimal time window for feature extraction and developing and evaluating the appropriate machine learning algorithm. Suggestions for future research and practice for DLCI prediction in intelligent vehicles are discussed.
Original languageEnglish
Article number103775
JournalApplied Ergonomics
Volume103
Early online date29 Apr 2022
DOIs
Publication statusPublished - Sept 2022
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2022 Elsevier Ltd

Funding

This research was funded by the National Natural Science Foundation of China (grant numbers: 31800931 and T2192931).

Keywords

  • Advanced driver assistance system
  • Driver lane change intention
  • Eye tracking
  • Machine learning
  • Systematic review

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