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 journalArticlepeer-review


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 languageEnglish
Pages (from-to)149-163
Number of pages15
JournalTransportation Research Part F: Traffic Psychology and Behaviour
Early online date18 Apr 2022
Publication statusPublished - May 2022

Bibliographical note

© 2022 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (

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.


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


Dive into the research topics of 'Physiological indicators of driver workload during car-following scenarios and takeovers in highly automated driving'. Together they form a unique fingerprint.

Cite this