Adaptive automation: automatically (dis)engaging automation during visually distracted driving
Background Automated driving is often proposed as a solution to human errors. However, fully automated driving has not yet reached the point where it can be implemented in real traffic. This study focused on adaptively allocating steering control either to the driver or to an automated pilot based o...
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doaj-772581d8d8d548d280de6ea9f6eddee92020-11-25T00:39:10ZengPeerJ Inc.PeerJ Computer Science2376-59922018-10-014e16610.7717/peerj-cs.166Adaptive automation: automatically (dis)engaging automation during visually distracted drivingChristopher D.D. Cabrall0Nico M. Janssen1Joost C.F. de Winter2Cognitive Robotics Department, Delft University of Technology, The NetherlandsBioMechanical Engineering Department, Delft University of Technology, The NetherlandsBioMechanical Engineering Department, Delft University of Technology, The NetherlandsBackground Automated driving is often proposed as a solution to human errors. However, fully automated driving has not yet reached the point where it can be implemented in real traffic. This study focused on adaptively allocating steering control either to the driver or to an automated pilot based on momentary driver distraction measured from an eye tracker. Methods Participants (N = 31) steered a simulated vehicle with a fixed speed, and at specific moments were required to perform a visual secondary task (i.e., changing a CD). Three conditions were tested: (1) Manual driving (Manual), in which participants steered themselves. (2) An automated backup (Backup) condition, consisting of manual steering except during periods of visual distraction, where the driver was backed up by automated steering. (3) A forced manual drive (Forced) condition, consisting of automated steering except during periods of visual distraction, where the driver was forced into manual steering. In all three conditions, the speed of the vehicle was automatically kept at 70 km/h throughout the drive. Results The Backup condition showed a decrease in mean and maximum absolute lateral error compared to the Manual condition. The Backup condition also showed the lowest self-reported workload ratings and yielded a higher acceptance rating than the Forced condition. The Forced condition showed a higher maximum absolute lateral error than the Backup condition. Discussion In conclusion, the Backup condition was well accepted, and significantly improved performance when compared to the Manual and Forced conditions. Future research could use a higher level of simulator fidelity and a higher-quality eye-tracker.https://peerj.com/articles/cs-166.pdfAutomated drivingAdaptive automationEye trackingDriver distractionDriving simulatorDual task |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Christopher D.D. Cabrall Nico M. Janssen Joost C.F. de Winter |
spellingShingle |
Christopher D.D. Cabrall Nico M. Janssen Joost C.F. de Winter Adaptive automation: automatically (dis)engaging automation during visually distracted driving PeerJ Computer Science Automated driving Adaptive automation Eye tracking Driver distraction Driving simulator Dual task |
author_facet |
Christopher D.D. Cabrall Nico M. Janssen Joost C.F. de Winter |
author_sort |
Christopher D.D. Cabrall |
title |
Adaptive automation: automatically (dis)engaging automation during visually distracted driving |
title_short |
Adaptive automation: automatically (dis)engaging automation during visually distracted driving |
title_full |
Adaptive automation: automatically (dis)engaging automation during visually distracted driving |
title_fullStr |
Adaptive automation: automatically (dis)engaging automation during visually distracted driving |
title_full_unstemmed |
Adaptive automation: automatically (dis)engaging automation during visually distracted driving |
title_sort |
adaptive automation: automatically (dis)engaging automation during visually distracted driving |
publisher |
PeerJ Inc. |
series |
PeerJ Computer Science |
issn |
2376-5992 |
publishDate |
2018-10-01 |
description |
Background Automated driving is often proposed as a solution to human errors. However, fully automated driving has not yet reached the point where it can be implemented in real traffic. This study focused on adaptively allocating steering control either to the driver or to an automated pilot based on momentary driver distraction measured from an eye tracker. Methods Participants (N = 31) steered a simulated vehicle with a fixed speed, and at specific moments were required to perform a visual secondary task (i.e., changing a CD). Three conditions were tested: (1) Manual driving (Manual), in which participants steered themselves. (2) An automated backup (Backup) condition, consisting of manual steering except during periods of visual distraction, where the driver was backed up by automated steering. (3) A forced manual drive (Forced) condition, consisting of automated steering except during periods of visual distraction, where the driver was forced into manual steering. In all three conditions, the speed of the vehicle was automatically kept at 70 km/h throughout the drive. Results The Backup condition showed a decrease in mean and maximum absolute lateral error compared to the Manual condition. The Backup condition also showed the lowest self-reported workload ratings and yielded a higher acceptance rating than the Forced condition. The Forced condition showed a higher maximum absolute lateral error than the Backup condition. Discussion In conclusion, the Backup condition was well accepted, and significantly improved performance when compared to the Manual and Forced conditions. Future research could use a higher level of simulator fidelity and a higher-quality eye-tracker. |
topic |
Automated driving Adaptive automation Eye tracking Driver distraction Driving simulator Dual task |
url |
https://peerj.com/articles/cs-166.pdf |
work_keys_str_mv |
AT christopherddcabrall adaptiveautomationautomaticallydisengagingautomationduringvisuallydistracteddriving AT nicomjanssen adaptiveautomationautomaticallydisengagingautomationduringvisuallydistracteddriving AT joostcfdewinter adaptiveautomationautomaticallydisengagingautomationduringvisuallydistracteddriving |
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