## 1 Introduction  
According to the World Health Organization's (WHO) statistical report on the risks to human life, road traffic accidents are now the eighth most common cause of death each year, with 19 million people per year dying in road accidents worldwide [1]. An analysis of the U.S. Highway Administration's traffic accident report shows that 90% of accidents are due to human factors [2], particularly high-risk events such as lane changes made by emergency vehicles. Statistics of accident data from 2010 to 2017 showed that serious accidents caused by emergency vehicles changing lanes accounted for up to 17% of the total number of accidents [3]. Studies have been conducted to analyze the causes of traffic accidents when changing lanes, and it was concluded that about 20% of accidents that occur while changing lanes are due to the driver having limited visibility [4]. The main reason for the high number of such accidents is the driver's failure or inability to adequately inspect their blind spot in a timely manner [5], and rearview mirrors alone cannot holographically perceive the risk [6]. It is true that many drivers perceive information about the traffic environment in their blind spot by looking around the vehicle to ensure a safe lane change. However, this practice distracts the driver's attention and increases the risk while driving [7]. In addition,the duration of the lane change is very limited, and too many perceptual cues will increase the driver's cognitive load and increase the likelihood of unsafe driving behavior [8]. To raise awareness of the risks associated with driving, car manufacturers have implemented blind spot monitoring (BSM) systems that alert drivers to potential hazards when they are changing lanes. Studies have demonstrated that vehicles equipped with BSM systems are 14% less likely to be involved in lane-changing accidents compared to those without BSM systems [9]. With advancements in HMI interfaces, intelligent vehicles now display blind spot images when the turn signal is activated, aiding drivers in recognizing potential hazards in their blind spots during lane changes or turns, thereby mitigating accidents due to reduced visibility. However, the development of blind spot imaging technology remains rudimentary, lacking standardized design guidelines, resulting in considerable variability among car brands. User experience data indicate that the inadequate design of blind spot systems in current smart cars increases drivers' cognitive load and exacerbates distracted driving. Therefore, it is crucial to explore the mechanisms by which blind spot design influences drivers' cognitive load to provide a foundation for the advancement of intelligent vehicle technologies. A major challenge in this research is to measure and quantify the cognitive load of drivers without disturbing them while they are driving on real roads. There are various methods for measuring cognitive load, which have been discussed in detail in previous studies [10]. Among the methods commonly used to assess cognitive load, subjective and physiological measures predominate. Subjective measures usually rely on questionnaire responses that reflect drivers' perceptions to measure the extent of their cognitive load (e.g., NASA Task Load Index [11] and Subjective Workload Assessment Technique [12] scales). In contrast, physiological measures assess cognitive load by measuring physiological indicators such as respiratory rate [13; 14], pupil diameter [15; 16], cardiovascular activity [14; 17; 18], and brain activity [19; 20; 21]. Although subjective measurements are simple and easy to perform, the accuracy of their results is often compromised due to individual differences in the interpretation and perception of rating scales. Physiological measures provide greater accuracy in representing cognitive load but are intrusive, making them impractical for in-vehicle road testing in this study as the equipment required to record electroencephalogram and electrocardiogram signals would interfere with driving.