Visual Behavior During Takeovers in Human–Machine Co-Driving on Grassland Highways: Effects of Multi-Risk Scenarios, Adverse Weather and Driver Gender

Grassland highways present a distinctive application context for human–machine co-driving (HMCODR), yet the evidence on takeover behavior in such environments remains limited. This study employed a driving simulator experiment to investigate how takeover scenarios, weather conditions, and driver gender influence eye-movement behavior during automated-driving takeovers on a representative Inner Mongolia grassland highway. 36 student participants completed a 2 (gender) × 3 (scenario: stationary vehicle ahead, ramp vehicle merging, and livestock intruding into lane) × 3 (weather: clear, rain–snow, and sandstorm) mixed experimental design. The mean fixation duration (MFD), fixation rate (FR), mean saccade duration (MSD), saccade amplitude (SA), and relative change in pupil area (RCPA) were recorded using a wearable eye tracker, and a mixed-design ANOVA was conducted based on these five metrics. The scenario and weather had significant effects on all eye-movement measurements (p < 0.05), with interactions observed for some indices. The ramp vehicle merging scenario elicited denser visual scanning, as reflected by a shorter MFD and a higher FR. In the livestock intruding into lane scenario, drivers exhibited a broader visual search, characterized by a longer MSD and a larger SA, along with greater pupil-area fluctuations (a higher RCPA); these effects tended to be more pronounced under rain–snow and sandstorm conditions. Across most of the condition combinations, the female drivers tended to show a higher FR, a shorter MFD, and a higher RCPA, whereas male drivers tended to show a slightly larger MSD and SA. Among all the eye-movement indices, RCPA showed a relatively more pronounced gender difference, with female drivers exhibiting values approximately 16–32% higher than male drivers. These findings extend the takeover research to grassland highway contexts and suggest that takeover assistance should place greater emphasis on hazard-relevant cues and timely gaze guidance under complex scenarios and adverse weather conditions, while the observed differences in the visual-response patterns may also inform more personalized prompting strategies to better support safety in high-risk takeover situations.