# Study on the Eye Movement Transfer Characteristics of Drivers Under Different Road Conditions
## 5 Conclusions  
In this study, we analyzed the fixation characteristics and visual transfer laws of drivers under four typical road sections (turning, straight ahead, uphill, and downhill) by eye-tracking technology, and the results revealed how drivers dynamically adjusted their visual search strategies to adapt to the complex driving tasks and road environments in the face of different road conditions; the conclusions were obtained as follows:  
In terms of fixation distribution, drivers allocate their attention to the fixation area differently in each road segment of each lane, and there is variability in the proportion of cumulative duration of fixation, the ratio of frequency of fixation, and the probability of steady state fixation for each area in the turning, straight ahead, and up and down broken road segments of the drivers. In terms of visual transfer, different road sections show obvious variability. In the turning road section, drivers' fixation transfer mainly occurs between the near front and the far front, while the transfer probability to the left and right sides is higher; in the straight road section, drivers' fixation transfer concentrates on the far front and the dashboard, and the transfer probability is higher; in the uphill road section, drivers' fixation transfer mainly concentrates on the near front and the two sides of the road, which reflects a high degree of attention to the changes in the road; whereas in the downhill road section, the driver's fixation shifts were mainly focused on the far front with the area of the sight glass, showing a concern for the ramp and rear traffic conditions. On visual smoothness, there are both commonalities and differences in different road sections. The driver's fixation behavior under all four road sections showed a high concentration of fixation on the road ahead with a total smoothness probability of up to 67% or more. At the same time, the probability of fixation smoothness for different areas varies considerably for different road sections. In conclusion, this study advances driver visual behavior optimization through innovative eye-tracking analysis, contributing to traffic safety research and applications. The empirical results of this study can provide a reference for the development of real-time feedback systems in advanced driver assistance systems. These systems can utilize the findings of this study on driver visual attention and fixation patterns to provide more targeted and context-aware alerts or assistance functions. Additionally, the conclusions drawn in this paper can provide valuable data for training AI models that predict human behavior, particularly in complex driving environments. By integrating these insights, future systems can become more adaptive, capable of dynamically adjusting to drivers' real-time needs, thereby enhancing road safety and improving the driving experience. Although this study provides valuable insights, there are some limitations that need to be considered. First, the sample size was small, with only 23 participants, and all participants were relatively young, which may limit the generalizability of the findings. Additionally, the experiment was conducted on a short stretch of road with no traffic and extremely low vehicle speeds. These conditions may have influenced how participants observed their surroundings, as the absence of traffic and slow speeds may have reduced drivers' cognitive load. Future studies should consider including a more diverse and larger sample of participants and conducting experiments under more complex and realistic driving conditions. Additionally, integrating advanced technologies such as artificial intelligence and big data analysis could further enhance the ability to predict driver behavior.