Radhakrishnan, V., Louw, T., Gonçalves, R.C. et al. (3 more authors) (2023) Using pupillometry and gaze-based metrics for understanding drivers’ mental workload during automated driving. Transportation Research Part F: Traffic Psychology and Behaviour, 94. pp. 254-267. ISSN 1369-8478
Abstract
This Horizon2020-funded driving simulator-based study on automated driving investigated the effect of different car-following scenarios, and takeover situations, on drivers’ mental workload, as measured by eye tracking-based metrics of pupil diameter and self-reported workload ratings. This study incorporated a mixed design format, with 16 drivers recruited for the SAE Level 2 (L2; SAE International, 2021) automation group, who were asked to monitor the driving and road environment during automation, and 16 drivers in the Level 3 (L3) automation group, who engaged in a non-driving related task (NDRT; Arrows task) during automation. Drivers in each group undertook two experimental drives, lasting about 18 min each. To manipulate perceived workload, difficulty of the driving task was controlled by incorporating a lead vehicle which maintained either a Short (0.5 s) or Long (1.5 s) Time Headway (THW) condition during automated car-following (ACF). Each ACF session was followed by a subsequent request to takeover, which happened either in the presence or absence of a lead vehicle. Results from standard deviation of pupil diameter values indicated that drivers’ mental workload levels fluctuated significantly more when monitoring the drive during L2 ACF, compared to manual car-following (MCF). Additionally, we found that drivers’ mental workload, as indicated by their mean pupil diameter, increased steeply around takeovers, and was further exacerbated by the presence of a lead vehicle during the takeovers, especially in the Short THW condition, for both groups. Pupil diameter was found to be sensitive to subtle variations in mental workload, and closely resembled the trend seen in self-reported workload ratings. Further research is warranted to assess the feasibility of using eye-tracking-based metrics along with other physiological sensors, especially in real-world settings, to understand whether they can be used as real-time indicators of drivers’ mental workload, in future driver state monitoring systems.
Metadata
Item Type: | Article |
---|---|
Authors/Creators: |
|
Copyright, Publisher and Additional Information: | © 2023 The Author(s). This is an open access article under the terms of the Creative Commons Attribution License (CC-BY 4.0), which permits unrestricted use, distribution and reproduction in any medium, provided the original work is properly cited. |
Keywords: | Mental Workload, Car-following, Takeovers, Pupil diameter, Gaze, Visual attention |
Dates: |
|
Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Environment (Leeds) > Institute for Transport Studies (Leeds) |
Funding Information: | Funder Grant number EU - European Union 723051 |
Depositing User: | Symplectic Publications |
Date Deposited: | 27 Mar 2025 10:37 |
Last Modified: | 27 Mar 2025 10:37 |
Status: | Published |
Publisher: | Elsevier |
Identification Number: | 10.1016/j.trf.2023.02.015 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:224921 |