Abstract

utomated truck platooning is getting an increasing interest for its potentially beneficial effects on fuel consumption, driver workload, traffic flow efficiency, and safety. Nevertheless, one major challenge lies in the safe and comfortable transitions of control from the automated system back to the human drivers, especially when they have been inattentive during highly automated driving. In this study, we investigated truck drivers’ take-over response times after a system initiated request to take back control in non-critical truck platooning scenarios. 22 professional truck drivers participated in the truck driving simulator experiment and everyone was instructed to drive under three task conditions during highly automated driving: Driver monitoring condition (drivers were instructed to monitor the surroundings), Driver not-monitoring condition (drivers were provided with a hand-held tablet and were asked to use this), and Eyes-closed condition (drivers were not allowed to open their eyes). The total take-over response time was divided into the perception response time and the movement response time by manual video annotation. Results showed significantly longer total take-over times with high variability in both Driver not-monitoring and Eyes-closed conditions compared to the Driver monitoring condition. Hand movement response time was found to be the dominant component of the total take-over time, being influenced by the motoric manoeuvres to resume physical readiness before taking over control (e.g., putting away the hand-held tablet, or adjusting seating position). These results suggest the importance of a personalized driver readiness predictor as an input parameter for a safe and comfortable transition of control. © 2019 Elsevier Ltd


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https://api.elsevier.com/content/article/PII:S1369847818301347?httpAccept=text/plain,
http://dx.doi.org/10.1016/j.trf.2019.04.006 under the license https://www.elsevier.com/tdm/userlicense/1.0/
https://www.narcis.nl/publication/RecordID/oai%3Aris.utwente.nl%3Apublications%2F8b680724-caf9-47bc-bb87-69dd8a5f45a2,
https://repository.tudelft.nl/view/tno/uuid:5ea3532f-cb71-4fde-98f8-e96f1fec6617,
https://research.utwente.nl/en/publications/transitions-to-manual-control-from-highly-automated-driving-in-no,
http://www.sciencedirect.com/science/article/pii/S1369847818301347,
https://academic.microsoft.com/#/detail/2946388607
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Published on 01/01/2019

Volume 2019, 2019
DOI: 10.1016/j.trf.2019.04.006
Licence: Other

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