On the necessity of adaptive eye movement classification in conditionally automated driving scenarios

Christian Braunagel, David Geisler, Wolfgang Stolzmann, Wolfgang Rosenstiel, Enkelejda Kasneci

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

12 Scopus citations

Abstract

Algorithms for eye movement classification are separated into threshold-based and probabilistic methods. While the parameters of static threshold-based algorithms usually need to be chosen for the particular task (task-individual), the probabilistic methods were introduced to meet the challenge of adjusting automatically to multiple individuals with different viewing behaviors (inter-individual). In the context of conditionally automated driving, especially while the driver is performing various secondary tasks, these two requirements of task-and inter-individuality fuse to an even greater challenge. This paper shows how the combination of task-and interindividual differences influences the viewing behavior of a driver during conditionally automated drives and that state-of-the-art algorithms are not able to sufficiently adapt to these variances. To approach this challenge, an extended version of a Bayesian online learning algorithm is introduced, which is not only able to adapt its parameters to upcoming variances in the viewing behavior, but also has real-time capability and lower computational overhead. The proposed approach is applied to a large-scale driving simulator study with 74 subjects performing secondary tasks while driving in an automated setting. The results show that the eye movement behavior of drivers performing different secondary tasks varies significantly while remaining approximately consistent for idle drivers. Furthermore, the data shows that only a few of the parameters used for describing the eye movement behavior are responsible for these significant variations indicating that it is not necessary to learn all parameters in an online-fashion.

Original languageEnglish
Title of host publicationProceedings - ETRA 2016
Subtitle of host publication2016 ACM Symposium on Eye Tracking Research and Applications
EditorsStephen N. Spencer
PublisherAssociation for Computing Machinery
Pages19-26
Number of pages8
ISBN (Electronic)9781450341257
DOIs
StatePublished - 14 Mar 2016
Externally publishedYes
Event9th Biennial ACM Symposium on Eye Tracking Research and Applications, ETRA 2016 - Charleston, United States
Duration: 14 Mar 201617 Mar 2016

Publication series

NameEye Tracking Research and Applications Symposium (ETRA)
Volume14

Conference

Conference9th Biennial ACM Symposium on Eye Tracking Research and Applications, ETRA 2016
Country/TerritoryUnited States
CityCharleston
Period14/03/1617/03/16

Keywords

  • Automated analysis methods
  • Eye movements and cognition
  • Machine learning methods and algorithms

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