To the degree that vigilance is required of automated vehicle drivers, performance errors and associated safety risks are likely to occur as a function of time on task. Vigilance should be a focal safety concern in the development of vehicle automation.

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Though not often mentioned, the price point of many eye tracking systems may be a factor limiting their adoption in research. Recently, several inexpensive eye trackers have appeared on the market, but to date little systematic research has been conducted to validate these systems. The present experiment attempted to address this gap by evaluating and comparing five different eye trackers, the Eye Tribe Tracker, Tobii EyeX, Seeing Machines faceLAB, Smart Eye Pro, and Smart Eye Aurora for their gaze tracking accuracy and precision. Results suggest that all evaluated trackers maintained acceptable accuracy and precision, but lower cost systems frequently also experienced high rates of data loss, suggesting that researchers adopting low cost systems such as those evaluated here should be judicious in their research usage.

Objective: The current study investigated driver vigilance in partially automated vehicles to determine whether increased task demands reduce a driver’s ability to monitor for automation failures and whether the vigilance decrement associated with hazard detections is due to driver overload. Background: Drivers of partially automated vehicles are expected to monitor for signs of automation failure. Previous research has shown that a driver’s ability to perform this duty declines over time. One possible explanation for this vigilance decrement is that the extreme demands of vigilance causes overload and leads to depletion of limited attentional resources required for vigilance. Method: Participants completed a 40-min drive in a simulated partially automated vehicle and were tasked with monitoring for hazards that represented potential automation failures. Two factors were manipulated to test the impact of monitoring demands on performance: Spatial uncertainty and event rate. Results: As predicted, hazard detection performance was poorer when monitoring demands were increased, and performance declined as a function of time on task. Subjective reports also indicated high workload and task-induced stress. Conclusion: Drivers of partially automated vehicles are impaired by the vigilance decrement and elevated task demands, meaning that safe operation becomes less likely when the demands associated with monitoring automation increase and as a drive extends in duration. This study also supports the notion that vigilance performance in partially automated vehicles is likely due to driver overload. Application: Developers of automation technologies should consider countermeasures that attenuate a driver’s cognitive load when tasked with monitoring automation.