## 1 Introduction
Therefore, it is critical to choose a system for measuring and quantifying cognitive load that does not hinder drivers in complex real-world driving environments and whose ability to reflect drivers' mental load is not compromised. Given the correlation between lane change accidents and drivers' visual search patterns [23], we would also like to see a method of measuring cognitive load that correlates with drivers' gaze positions. Such an approach would not only facilitate its wide application in various tests on real roads but would also allow monitoring of drivers' interactions with their forward vision, rearview mirrors, and blind spot images during lane changes in different directions. This would improve the evaluation of the relationship between drivers' cognitive load and blind spot design. An increase in cognitive load manifests itself in particular in increased distraction while driving: the higher the cognitive load, the worse the driving performance [24], accompanied by corresponding changes in the driver's visual characteristics [25; 26], which usually leads to more hazardous situations or accidents. The research by [PERSON] et al. [27] also shows that increase in cognitive load impairs driving performance, especially lateral control of the vehicle, implying an increased risk of driving accidents. Currently, there is a research gap in analyzing the effects of blind spot images on driver cognitive load, although numerous studies have investigated the degree of driver distraction. As early as 2004, [PERSON] et al. [28] used a loss of fixation analysis to qualify the distracting effect of cell phone conversations on the visual field. In recent years, more and more researchers have explored better methods and mechanisms to quantify driver distraction [29; 30]. This research has progressively addressed various scenarios, including lane changes, turning, and responses to emergencies. For example, [PERSON] et al. [31] used an embedding kernel algorithm to detect critical mismatched driver visual attention during lane changing and evaluate the safety of driver behavior during lane changing. Although many studies have investigated driver distraction during lane changing, few have analyzed the effects of blind spot images on driving safety during lane changing in intelligent vehicles. In addition, there are few studies that establish correlation models between driver cognitive load and vehicle types and blind spot location. [PERSON] et al. (2019) compared the cognitive load and mental stress of drivers in a driving simulator when the side-rear wide-angle monitor was placed in different positions and showed that positioning the monitor in the middle of the driver minimized cognitive load and facilitated information intake. Nevertheless, no relevant model was created in this study to analyze the factors influencing cognitive load, and driving simulators cannot fully replicate real driving conditions. There is, therefore, an urgent need for research to close this gap in this area. To develop a quantitative method for measuring cognitive load that minimizes driver distraction and reflects the dynamic changes in the visual interaction domain during lane changes, while using the computed cognitive load to build models for analyzing the effects of blind spot design, traffic environment, and other factors on driver cognitive load, we propose an improved AttenD algorithm capable of dynamically adjusting driving-related domains. By integrating experimental data from real vehicles that reflect authentic road traffic conditions, we build relevant models to analyze the extent of various factors affecting driver cognitive load. The main contributions of this study are as follows:  
1. We refine the AttenD algorithm to accurately capture the driver's attention during lane changes based on the improved algorithm. 2. We propose a blind spot assessment method to design blind spots, including the size and location of the blind spot, in a way that reduces the cognitive load on the driver.