Citation

Kircher K, Ahlstrom C. Transp. Res. F Traffic Psychol. Behav. 2024; 100: 243-253.

Copyright

(Copyright © 2024, Elsevier Publishing)

DOI

10.1016/j.trf.2023.12.003

PMID

unavailable

Abstract

Eye tracking is a common tool to assess drivers' attentional state, either in real-time with the goal to prevent incidents, or offline, to understand underlying processes. While seemingly objective, eye tracking data can be coded and interpreted in different ways, which can have substantial effects on the results. The objective of this paper is to highlight and discuss the possibilities and limitations of three different approaches to code glance data: the direction-based encoding, the target-based approach, and the purpose-based approach. The direction-based coding scheme describes glances relative to the direction of travel. The target-based approach classifies the glance targets into different categories. The purpose-based approach needs additional layers of information to deduce the reason for the glance. This information encompasses road layout, traffic rules, and the presence and relevance of other traffic. Data from a field study with 23 participants driving an instrumented vehicle on an urban route was used to illustrate differences between the three methods. The results showed that the coding approach clearly affected the interpretation of the measured glance data. A purely target-based approach is limited by its inability to account for spare visual capacity and that also the absence of a target constitutes valuable information, and a purely direction-based approach does not account for the need to scan areas located off forward. The purpose-based approach requires contextual information that can be cumbersome to integrate. Regardless of the approach used, additional layers of contextual information increase understanding and interpretability, potentially at the cost of increased complexity. The three approaches are suitable for different contexts and their feasibility also depends on the availability of additional data. A key message is that context awareness improves the accuracy of driver attention monitoring and inattention identification.

Language: en