## 5 Conclusions  
The objective of this study was to quantify the relationship between blind spot display and drivers' cognitive load during lane changing in intelligent vehicles. To achieve this, an improved AttenD algorithm was developed to measure the cognitive load, and various real-vehicle road-testing experiments were conducted to gather data. Subsequently, a correlation model between blind spot design and driver cognitive load was established using BLOR and CatBoost. The detailed conclusions are as follows:  
Figure 14: Traffic flow-location dependence scatter plot. Figure 15: Traffic flow-location dependence scatter plot. (1) In terms of the design of the blind spot image display's size ratio, increasing the size of the central display screen, the area designated for blind spot image display, and the proportion of the blind spot image on the central display screen can effectively assist drivers in acquiring blind spot image information, thereby reducing cognitive load during lane changes. (2) Regarding the position of the blind spot image display, the more closely the blind spot image is displayed to the driver and positioned in the upper-middle section of the central control screen, the more effectively it can reduce the driver's cognitive load. (3) Right height and traffic environment also have an impact on the cognitive load of the driver. Within a certain range, there is a strong positive correlation between the collision buffer time value and the driver's line-of-sight height. The lower the line-of-sight height, the greater the impact on the driver's cognitive load. Compared to previous research on blind spot monitoring systems or cognitive load, this paper offers the following innovations:  
(1) This study is forward-looking, focusing on the design of blind spot images on central control screens, an area that has been widely implemented in modern intelligent vehicles but lacks sufficient research. This provides a new direction for the study of human-machine interaction safety in intelligent vehicles. (2) The real-vehicle road testing method employed in this project not only produces experimental data that more accurately reflect real driving conditions but also allows for the control of variables, which enables a comparison of how the design features of blind spot images in different vehicle models impact drivers' cognitive load. This approach is superior to previous studies that evaluated driving safety solely through accident data [9] or driving simulator experiments [15]. (3) Based on data analysis, this paper establishes two models with high goodness-of-fit to analyze the factors and mechanisms affecting cognitive load. Compared to studies that only perform statistical analysis [32], this paper provides a more reliable explanation of the factors influencing cognitive load. By altering the experimental conditions, our methods can also be applied to investigate the impact of other visual or auditory information on driver cognitive load during driving, including the design of traditional rearview mirrors, radio adjustments, and the voice control functions in modern intelligent vehicles. Although this study investigated the impact of blind spot design on drivers' cognitive load, the research had some limitations. On the one hand, this study overlooked the effect of drivers' habits on cognitive load, potentially leading to individual differences in the research conclusions. On the other hand, since Type 1 vehicles allow for blind spot position adjustments in only three locations, exploring the global optimal solution for these positions poses certain challenges. In future research, we will continue to investigate the impact of blind spot position changes in other vehicle models on driver cognitive load, establish corresponding mathematical models between blind spot position parameters and time-to-collision buffer values, and develop an evaluation system for assessing the quality of blind spot image designs to assist automakers in optimizing turn signal blind spot image designs. Conceptualization, X.C., Y.L.