Eye Tracking to Evaluate the Effects of Interruptions and Workload in a Complex Task.

OBJECTIVE: To use eye tracking to understand the effects of interruptions in different workload conditions as part of a monitoring and change detection task. BACKGROUND: Interruptions are detrimental to performance in complex, multitasking domains. There is a need for better display design techniques that help users overcome interruptions regardless of their workload level. This requires understanding a user's attentional state immediately after an interruption in order to determine what type of display adjustments are most suitable. METHOD: An emergency dispatching simulator was developed with a visual primary task and auditory interruptive task. Two levels of workload were induced by varying the number of emergency vehicles to monitor for changes and the rate of changes to monitor. Eye tracking, performance, and subjective measures (NASA-Task Load Index) were collected and analyzed for 41 participants. RESULTS: As expected, high workload interacted with interruptions to further degrade primary task performance and alter participants' attention allocation immediately after the interruption. Participants in the high workload condition had more narrowed, slower scan patterns immediately after the interruption as compared to before the interruption, as evidenced by lower scanpath length per second and mean saccade amplitude. However, this change was not observed in low workload. CONCLUSION: High workload modulates the effects of interruptions on performance and eye movements. Users in the high workload condition struggle to quickly scan the display in the seconds following an interruption. APPLICATION: The results can provide insight into the type of display adjustments needed right after an interruption in a high-workload environment.

In-vehicle displays to support driver anticipation of traffic conflicts in automated vehicles.

OBJECTIVE: This paper investigates the effectiveness of in-vehicle displays in supporting drivers' anticipation of traffic conflicts in automated vehicles (AVs). BACKGROUND: Providing takeover requests (TORs) along with information on automation capability (AC) has been found effective in supporting AV drivers' reactions to traffic conflicts. However, it is unclear what type of information can support drivers in anticipating traffic conflicts, so they can intervene (pre-event action) or prepare to intervene (pre-event preparation) proactively to avert them. METHOD: In a driving simulator study with 24 experienced and 24 novice drivers, we evaluated the effectiveness of two in-vehicle displays in supporting anticipatory driving in AVs with adaptive cruise control and lane keeping assistance: TORAC (TOR + AC information) and STTORAC displays (surrounding traffic (ST) information + TOR + AC information). Both displays were evaluated against a baseline display that only showed whether the automation was engaged. RESULTS: Compared to the baseline display, STTORAC led to more anticipatory driving behaviors (pre-event action or pre-event preparation) while TORAC led to less, along with decreased attention to environmental cues that indicated an upcoming event. STTORAC led to the highest level of driving safety, as indicated by minimum gap time for scenarios that required driver intervention, followed by TORAC, and then the baseline display. CONCLUSIONS: Providing surrounding traffic information to drivers of AVs, in addition to TORs and automation capability information, can support their anticipation of potential traffic conflicts. Without the surrounding traffic information, drivers can over-rely on displays that provide TORs and automation capability information.