Physiological indicators of driver workload during car-following scenarios and takeovers in highly automated driving

Vishnu Radhakrishnan; Natasha Merat; Tyron Louw; Rafael Cirino Gonçalves; Guilhermina Torrao; Wei Lyu; Pablo Puente Guillen; Michael G. Lenné

doi:10.1016/j.trf.2022.04.002

Physiological indicators of driver workload during car-following scenarios and takeovers in highly automated driving

Vishnu Radhakrishnan^*, Natasha Merat, Tyron Louw, Rafael Cirino Gonçalves, Guilhermina Torrao, Wei Lyu, Pablo Puente Guillen, Michael G. Lenné

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

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.

Original language	English
Pages (from-to)	149-163
Number of pages	15
Journal	Transportation Research Part F: Traffic Psychology and Behaviour
Volume	87
Early online date	18 Apr 2022
DOIs	https://doi.org/10.1016/j.trf.2022.04.002
Publication status	Published - May 2022

Bibliographical note

© 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/)

Funding Information:
This study was supported by funding from the European Commission Horizon 2020 program under the project L3Pilot, grant agreement number 723051. The main author’s PhD is co-funded by Seeing Machines Pty Ltd, Canberra, Australia.

Keywords

Car-following
Electrodermal activity (EDA)
Heart-rate variability (HRV)
Highly automated driving (HAD)
Psychophysiology
Workload

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10.1016/j.trf.2022.04.002Licence: CC BY 4.0

1-s2.0-S1369847822000705-main
© 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/).
Final published version, 3.85 MBLicence: CC BY 4.0

Cite this

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title = "Physiological indicators of driver workload during car-following scenarios and takeovers in highly automated driving",

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.",

keywords = "Car-following, Electrodermal activity (EDA), Heart-rate variability (HRV), Highly automated driving (HAD), Psychophysiology, Workload",

author = "Vishnu Radhakrishnan and Natasha Merat and Tyron Louw and Gon{\c c}alves, {Rafael Cirino} and Guilhermina Torrao and Wei Lyu and {Puente Guillen}, Pablo and Lenn{\'e}, {Michael G.}",

note = "{\textcopyright} 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/) Funding Information: This study was supported by funding from the European Commission Horizon 2020 program under the project L3Pilot, grant agreement number 723051. The main author{\textquoteright}s PhD is co-funded by Seeing Machines Pty Ltd, Canberra, Australia. ",

year = "2022",

month = may,

doi = "10.1016/j.trf.2022.04.002",

language = "English",

volume = "87",

pages = "149--163",

journal = "Transportation Research Part F: Traffic Psychology and Behaviour",

issn = "1369-8478",

publisher = "Elsevier",

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T1 - Physiological indicators of driver workload during car-following scenarios and takeovers in highly automated driving

AU - Radhakrishnan, Vishnu

AU - Merat, Natasha

AU - Louw, Tyron

AU - Gonçalves, Rafael Cirino

AU - Torrao, Guilhermina

AU - Lyu, Wei

AU - Puente Guillen, Pablo

AU - Lenné, Michael G.

N1 - © 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/) Funding Information: This study was supported by funding from the European Commission Horizon 2020 program under the project L3Pilot, grant agreement number 723051. The main author’s PhD is co-funded by Seeing Machines Pty Ltd, Canberra, Australia.

PY - 2022/5

Y1 - 2022/5

N2 - 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.

AB - 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.

KW - Car-following

KW - Electrodermal activity (EDA)

KW - Heart-rate variability (HRV)

KW - Highly automated driving (HAD)

KW - Psychophysiology

KW - Workload

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DO - 10.1016/j.trf.2022.04.002

M3 - Article

AN - SCOPUS:85128302045

SN - 1369-8478

VL - 87

SP - 149

EP - 163

JO - Transportation Research Part F: Traffic Psychology and Behaviour

JF - Transportation Research Part F: Traffic Psychology and Behaviour

ER -

Physiological indicators of driver workload during car-following scenarios and takeovers in highly automated driving

Abstract

Bibliographical note

Keywords

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