Automation expectation mismatch: Incorrect prediction despite eyes on threat and hands on wheel
Human factors, 2018•journals.sagepub.com
Objective: The aim of this study was to understand how to secure driver supervision
engagement and conflict intervention performance while using highly reliable (but not
perfect) automation. Background: Securing driver engagement—by mitigating irony of
automation (ie, the better the automation, the less attention drivers will pay to traffic and the
system, and the less capable they will be to resume control) and by communicating system
limitations to avoid mental model misconceptions—is a major challenge in the human …
engagement and conflict intervention performance while using highly reliable (but not
perfect) automation. Background: Securing driver engagement—by mitigating irony of
automation (ie, the better the automation, the less attention drivers will pay to traffic and the
system, and the less capable they will be to resume control) and by communicating system
limitations to avoid mental model misconceptions—is a major challenge in the human …
Objective
The aim of this study was to understand how to secure driver supervision engagement and conflict intervention performance while using highly reliable (but not perfect) automation.
Background
Securing driver engagement—by mitigating irony of automation (i.e., the better the automation, the less attention drivers will pay to traffic and the system, and the less capable they will be to resume control) and by communicating system limitations to avoid mental model misconceptions—is a major challenge in the human factors literature.
Method
One hundred six drivers participated in three test-track experiments in which we studied driver intervention response to conflicts after driving highly reliable but supervised automation. After 30 min, a conflict occurred wherein the lead vehicle cut out of lane to reveal a conflict object in the form of either a stationary car or a garbage bag.
Results
Supervision reminders effectively maintained drivers’ eyes on path and hands on wheel. However, neither these reminders nor explicit instructions on system limitations and supervision responsibilities prevented 28% (21/76) of drivers from crashing with their eyes on the conflict object (car or bag).
Conclusion
The results uncover the important role of expectation mismatches, showing that a key component of driver engagement is cognitive (understanding the need for action), rather than purely visual (looking at the threat), or having hands on wheel.
Application
Automation needs to be designed either so that it does not rely on the driver or so that the driver unmistakably understands that it is an assistance system that needs an active driver to lead and share control.
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