# Modeling Driver's Real-Time Confidence in Autonomous Vehicles
## 1 Introduction
### The Reasons for Distrusting Auto Driving  
Those who have experienced an autonomous driving system will understand the sense of frustration it can bring to drivers during its operation. Current autonomous driving systems have significant deficiencies in both their intelligence or human-like nature, as well as the human-computer experience during their operation [14]. Some imperfect autonomous driving systems do not issue any warning signals in scenarios where there is no potential for a direct collision, such as when there is pedestrian activity on the side road or through a traffic-light-free intersection. This is because the perception system determines that there is no direct interaction threat, and the vehicle decides not to slow down as a normal driver would. This can result in a high driver takeover rate. Additionally, current autonomous vehicles lack the ability to inform the driver of the reasons for their decisions [15]. Only the results of the decisions are presented on the interface, which limits the experience of human-computer interaction. This leads to drivers lacking confidence in the autonomous driving system in certain situations and choosing to take over the control. This paper argues that the problem of human-machine exchange can be solved through testing and optimization. However, the driver's lack of confidence due to the dangerous behavior of the autonomous driving system is a relatively difficult problem to define. Some operations that do not seem to be problematic from the perspective of the vehicle, such as changing lanes in a safe situation, can still affect the driver's confidence if they are performed multiple times in a short period. Additionally, when the autonomous driving system's control is unstable, it can not only lead to dangerous consequences but can also interfere with the driver's judgment.  
According to the mentioned influence of driver confidence on autonomous driving systems, we believe that it is necessary to conduct a survey on the current level of confidence in driving autonomous vehicles and to summarize the main factors influencing confidence.  
Based on the relationship between vehicle driving and those influencing factors, some numerical formulas should be developed to form a virtual driver's real-time confidence model. The confidence model is expected to provide a reference for the occupant state during unmanned testing of AVs, identify unreasonable boundary scenarios, and assist in optimizing the system for non-safety-related issues, thereby enabling the widespread use of autonomous driving.  
In this paper, we conducted a social survey on the factors that significantly affect driving confidence when using auto-driving systems, summarized several important reasons, and investigated the degree of people's willingness to take over. The results of the survey were analyzed in detail, and the VC model is constructed based on three aspects: external vehicle interference, ego vehicle actions, and environment complexity. The validity of the virtual confidence model was finally verified through the volunteers' marking process. The paper is structured as follows: Section 2 presents the results of the survey on the main factors that affect confidence when using autonomous driving systems. In Section 3, we demonstrate the VC model and provide detailed calculations of its components. Section 4 analyzes the test results of the VC model. The future of the VC model is discussed in Section 5, and Section 6 concludes the paper.