## 1 Introduction  
As the number of COVID-19 infections declines, economic activities are resuming, and the traffic volume will be increased in the future. Thus, drivers may face heightened stress due to traffic congestion, which could lead to irritation. Such emotions can impair the ability to drive safely and increase the risk of causing traffic accidents. Driving a car is a constant source of stress, as it demands that drivers remain vigilant of surrounding traffic conditions in order to make appropriate decisions and precise maneuvers. A survey on Japanese drivers' behaviors indicates that the primary emotions experienced while driving include anger/irritation, fear of accidents, and impatience, as shown in Figure 1[1]. There are many factors that cause these emotions such as the behavior of other drivers with aggressive cutting in, dangerous actions of cyclists and pedestrians, and the surrounding traffic environment, such as traffic jams. Also, when other vehicles do not adhere to traffic rules or are driven aggressively, it often irritates many drivers. Controlling emotions is crucial for safe driving. Regardless of driving proficiency, the inability to manage emotions, especially under significant stress, can impair concentration. This often results in hazardous driving behaviors, potentially leading to severe accidents [2]. Substantial research is currently being conducted by industry, governmental bodies, and academic entities to develop effective systems and infrastructure for preventing car accidents. This has led to an increased emphasis on collaboration among researchers, culminating in the creation of intelligent transportation systems (ITSs). ITSs are dedicated to the application of advanced transportation technologies, merging state-of-the-art information, communication, and control technologies to craft sustainable networks that integrate people, vehicles, and roadways. Within ITSs, vehicular ad hoc networks (VANETs) provide critical roles, primarily focusing on enhancing safety, streamlining traffic flow, boosting efficiency, and facilitating the convenience of both drivers and passengers. In VANETs, vehicles, functioning as network nodes, are equipped with capabilities to relay vital data, including safety alerts and road or traffic updates, among themselves through vehicle-to-vehicle interactions and to roadside units via vehicle-to-infrastructure communication. Despite the practical deployment of VANETs incorporating various applications, existing architectures encounter a myriad of challenges. Driving anxiety is related to many factors such as transportation, clinical, cognitive psychological, social, and technical aspects. However, most of the studies about driving anxiety are based on questionnaires, and they investigate only the feelings and emotions associated with this problem, while the quantification of its detrimental effects while driving is rarely undertaken. Most of the behavioral measures are determined by accident reports and assessments of subjective experiences. They are mainly related to the extent of driving avoidance in different situations [3; 4; 5; 6; 7; 8]. Fuzzy logic (FL) significance arises from the general nature of human reasoning, which is typically approximate, particularly in the realm of common sense. FL employs linguistic variables to articulate control parameters, allowing complex issues to be expressed and understood through straightforward linguistic terms. Additionally, the use of linguistic variables enables the description of vague parameters. Fuzzy sets, representing linguistic labels, deploy membership functions to rank preferences for the potential interpretations of these labels. There are many applications of FL in different fields of science and engineering [9; 10]. This paper presents an intelligent FL-based system for deciding the driver's anxiety level (DAL). In order to investigate the effects of the considered parameters and compare the evaluation results, we implemented two models: DAL Model 1 (DALM1) and DAL Model 2 (DALM2).