Drivers use active gaze to monitor waypoints during automated driving

Mole, Callum; Pekkanen, Jami; Sheppard, William E. A.; Markkula, Gustav; Wilkie, Richard M. · 2021 · Crossref

DOI: 10.1038/s41598-020-80126-2

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Summary

This study investigates how driver gaze patterns change when transitioning from manual steering to automated vehicle control, addressing a gap in understanding the visual monitoring behaviors required for safe automation. While previous research indicated that gaze becomes more variable or shifts toward distant points during automation, it remained unclear whether these changes resulted from the removal of active steering control or from differences in vehicle trajectories. The authors aimed to decouple active control from perceptual stimuli to determine if the characteristic "active gaze" patterns used for trajectory control in manual driving persist when the driver is merely monitoring the system. The experiment utilized a fixed-base driving simulator with a simplified virtual track consisting of straight sections and bends. Participants drove at a constant speed of 8 m/s under three conditions: Manual control, Automated-Replay (where the vehicle followed the participant’s own recorded trajectory), and Automated-Stock (where the vehicle followed a pre-recorded, standardized trajectory). This design allowed the researchers to isolate the effect of removing steering control while keeping visual stimuli identical across Manual and Replay conditions. Gaze data were recorded and analyzed using a novel mixture modeling approach that decomposed fixations into Guiding Fixations (GFs), associated with immediate trajectory control, and other fixations, based on gaze time headway—the time required to reach the fixated point. Bayesian statistical methods were employed to estimate differences in gaze time headway and pattern consistency across conditions. The results revealed that overall gaze patterns remained highly similar across Manual and Automated conditions, with drivers continuing to track waypoints using a "move-dwell-move" sawtooth pattern. However, detailed analysis showed that drivers looked slightly further ahead during automation, with an increase in median gaze time headway of approximately 0.2 seconds compared to manual driving. This shift was consistent across both Replay and Stock automated conditions, indicating that the change was driven by the mode of control rather than specific trajectory differences. The mixture modeling confirmed that while the fundamental waypoint-tracking behavior persisted, the distribution of gaze time headway shifted rightward during automation. These findings suggest that active gaze models developed for manual driving remain applicable to automated driving scenarios. The persistence of waypoint-tracking gaze patterns implies that drivers continue to actively monitor the road ahead even when not controlling the vehicle. Consequently, deviations from these expected gaze behaviors could serve as a reliable metric for monitoring driver engagement and detecting inattention during automated driving, with potential implications for designing safer take-over request systems.

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Topics

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• gaze based attention detection
• eye movements scanning
• attention allocation
• temporal
• situational awareness
• visual

Information type

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• Empirical Findings: behavioral performance data
• Methodological Resource: tool software, measurement protocol