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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Neutral and inherently immobile polysaccharides are induced to migrate in an electric field through interactions with a detergent added to the electrophoretic electrolyte buffer. Before analysis the polysaccharides are converted to fluorescent derivatives to enable detection, but choice of a tag can also be utilized for modulation of the electrophoretic mobility. Three cases are discussed and exemplified, namely detergent-solute, detergent-solute+tag, and detergent-tag interactions. Anionic as well as cationic surfactants were exploited along with different derivatization reagents. Depending on the approach chosen, different kinds of information about sample composition and distribution(s) can be obtained, including degree of substitution, distribution of molecular weight (obtained in free solution without sieving media) and polymer conformation. A shift in polymer conformation upon a change in solvent composition can be monitored.

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