Radhakrishnan, V, Merat, N orcid.org/0000-0003-4140-9948, Louw, T orcid.org/0000-0001-6577-6369 et al. (5 more authors) (2022) Physiological indicators of driver workload during car-following scenarios and takeovers in highly automated driving. Transportation Research Part F: Traffic Psychology and Behaviour, 87. pp. 149-163. ISSN 1369-8478
Abstract
This driving simulator study, conducted as a part of Horizon2020-funded L3Pilot project, investigated how different car-following situations affected driver workload, within the context of vehicle automation. Electrocardiogram (ECG) and electrodermal activity (EDA)-based physiological metrics were used as objective indicators of workload, along with self-reported workload ratings. A total of 32 drivers were divided into two equal groups, based on whether they engaged in a non-driving related task (NDRT) during automation (SAE Level 3) or monitored the drive (SAE Level 2). Drivers in both groups were exposed to two counterbalanced experimental drives, lasting ∼ 18 min each, of Short (0.5 s) and Long (1.5 s) Time Headway conditions during automated car-following (ACF), which was followed by a takeover that happened with or without a lead vehicle. Results showed that driver workload due to the NDRT was significantly higher than both monitoring the drive during ACF and manual car-following (MCF). Furthermore, the results indicated that a lead vehicle maintain a shorter THW can significantly increase driver workload during takeover scenarios, potentially affecting driver safety. This warrants further research into understanding safe time headway thresholds to be maintained by automated vehicles, without placing additional cognitive or attentional demands on the driver. Our results indicated that ECG and EDA signals are sensitive to variations in workload, which warrants further investigation on the value of combining these two signals to assess driver workload in real-time, to help future driver monitoring systems respond appropriately to the limitations of the driver, and predict their performance in the driving task, if and when they have to resume manual control of the vehicle after a period of automated driving.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2022 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). |
Keywords: | Workload; Car-following; Psychophysiology; Heart-rate variability (HRV); Electrodermal activity (EDA); Highly automated driving (HAD) |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Environment (Leeds) > Institute for Transport Studies (Leeds) > ITS: Safety and Technology (Leeds) |
Funding Information: | Funder Grant number Innovate UK fka Technology Strategy Board (TSB) 84063-528134 |
Depositing User: | Symplectic Publications |
Date Deposited: | 28 Jun 2022 16:42 |
Last Modified: | 25 Jun 2023 23:02 |
Status: | Published |
Publisher: | Elsevier BV |
Identification Number: | 10.1016/j.trf.2022.04.002 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:188437 |