## 1 Introduction  
The use of autonomous technologies in in the automotive industry is an inevitable trend, as automated driving systems (ADS) can reduce traffic congestion, increase fuel efficiency, and improve road safety [1, 2, 3]. A prerequisite for realizing these benefits of technology advances and reaching the full potential of automation is that users must trust and accept ADS. But many users may harbor distrust due to their preconceived notions [4, 5], which may subsequently lead to the disuse of part or all of the functions of the automation [6]. On the contrary, over-trust in ADS could lead to excessive reliance on this technology, and further, to its misuse [7], which is suspected to be a major cause of various accidents involving automated vehicles [8]. To prevent such situations, drivers must be aware of the capabilities and limitations of the ADS they use and adjust their trust level to the real automation capability [9]. According to the Society of Automotive Engineers (SAE), under certain conditions, highly automated driving systems are responsible for all driving maneuvers on the road [10], and users of these systems will delegate operational, tactical, or strategic tasks to the driving system and will be encouraged to engage in non-driving related tasks (NDRT) [11, 12]. In this case, users in the vehicle may have little information about the road ahead, prompting an insufficient awareness of potential hazards, which can exacerbate their feelings of in security. Therefore, building user trust in the vehicle system is particularly important in highly automated driving systems. The industry and academia have been testing methods that have the potential to improve awareness and levels of trust in ADS, such as intelligent agents exhibiting human traits [13] and system uncertainty information [14]. One promising approach is the use of automation transparency, which can fill in gaps in people's understanding of ADS's behavior and also increase situational trust in ADS by communicating the agent's explanations, upcoming strategies, or decision-making rationale [15; 16; 17; 18]. [PERSON] and [PERSON] defined automation transparency as \"the degree of shared intent and shared awareness between a human and a machine\" [19], indicating that transparent automation should communicate its capability and performance, its decision-making logic and rationale, and its intent and future states to the human agent. The majority of research concerning achieving transparency involved the visualization of additional meta-information, or information qualifiers via user interfaces [20; 21; 22; 23], which has been found to be an intuitive way to convey large amounts of information without increasing the perceived workload [17; 20; 24]. In order to clarify transparency requirements to understand the intelligent agent's task parameters, logic, and predicted outcomes, [PERSON] et al. developed the Situation Awareness-Based Agent Transparency (SAT) model [25], based on the theory of situation awareness (SA) [26], which mirrors the three levels of perception, comprehension, and projection (see Figure 1). In the SAT model, the basic information about the agent's current state and goals, intentions, and proposed actions are provided at the first level. The agent's reasoning process and the constraints/affordances when planning those actions are provided at the second level. Information regarding the agent's projections of the future states (such as predicted consequences and likelihood of success/failure) are provided at the third level. Previous studies have already confirmed the effects of SAT on operator trust. For example, [PERSON] et al. indicated that with increasing levels of agent transparency, operator performance and reliance on key parts of the interface increased [27]. [PERSON] et al. found that operator performance and trust in the agent increased as a function of the agent transparency level [20]. [PERSON] et al.