Evaluation of Visual Risk Perception of Automated Driving Tasks by Analyzing Gaze Pattern Dispersion.

Automated driving essentially transforms the role of human drivers from active decision-makers to passive supervisors. The change in gaze patterns elicited by such a transformation may influence vehicle control during manual takeover, thereby altering the visual risk perception. However, the specific impact of automated driving on visual risk perception remains unclear. Therefore, this study aims to evaluate the visual risk perception of automated driving tasks by analyzing the gaze pattern dispersion, which reflects the coverage of visual attention distribution. Ten participants perform manual and automated driving tasks. Each driving task includes acceleration, maintaining constant speeds, and deceleration phases. The constant speeds were set to 40, 60, and 80 km/h, and the deceleration rates were set to -2.5, -5.0, and -7.5 m/s2. The probability density estimation method is proposed to calculate gaze density regions that reflect the gaze patterns dispersion. The results indicate that automated driving causes more dispersed gaze patterns in the initial acceleration and lower speed phases. However, gaze patterns are not significantly dispersed in the highest speed and all deceleration phases during automated driving. Furthermore, gaze patterns are highly constrained by the increasing deceleration rates during manual driving. Conversely, automated driving does not constrain gaze patterns among the three deceleration rates. The dispersed gaze patterns during automated driving suggest that the visual risk perception may be decreased, potentially resulting in the weakening of takeover control. Therefore, it is necessary to judiciously design the takeover control of automated driving to ensure driving safety during initial acceleration and lower speeds.