###### Abstract  
Human-driven vehicles (HDVs) will share the road with connected autonomous vehicles (CAVs) in the near future. Accordingly, the investigation of the interactive behavior of HDV drivers toward CAVs is becoming critical. In this study, a questionnaire survey was first conducted. The heterogenous clusters of HDV drivers were revealed through the latent profile analysis based on the collected dataset, with the focus on their trust and familiarity with CAVs, their attitudes towards sharing the road with CAVs, and their risk perception and perceived behavior control when they faced the CAVs. Subsequently, the correlation between the respective latent cluster and several socio-demographic factors was understood based on the multinomial logistic regression model, and the choice behavior of each cluster in different interactive driving scenarios was revealed. Three vital findings were reported. (1) Three profile clusters of HDV drivers (i.e., negative individuals, neutral individuals, and positive individuals) were revealed. (2) The drivers of a low/middle income and with a long driving experience were more likely to be negative individuals, whereas the CAV experience can make drivers feel positive towards CAVs. (3) Negative individuals might give up on changing lanes when a CAV platoon driving was noticed in the target lanes; in addition, they might raise more rigorous requirements for vehicle spacing in the lane-changing process when finding CAVs driving in the target lanes. To be specific, negative and neutral individuals preferred driving in front of the CAV platoons. The findings can provide references for developing effective management measures or CAV control strategies for transportation systems. c 2024 14 1768 2024 2  
congestion of a mixed traffic flow [3; 4; 5; 6; 7]. However, human factors are generally disregarded in most existing model simulation studies. Different from the stable and accurate driving strategies of CAVs, the driving behavior of humans can be dependent on numerous dynamics factors (e.g., socio-economic factors, psychological factors, personality traits, driving skills, and driving desires [8]). It is obvious that human factors will play an essential role in governing the safety and dynamics of mixed traffic. In both simulation research and real-world road environments, a significant number of traffic crashes persist due to interactions between HDV Drivers and CAVs [9; 10]. In addition, several empirical studies have suggested that HDV drivers are likely to change their driving behavior in a negative manner when interacting with CAVs [11; 12]. Thus, the investigation of the interactive behavior of HDV drivers to CAVs takes on critical significance [13; 14]. Since real-world data lack detail for in-depth analysis, there are still uncertainties about the above-mentioned interactions. Thus, the fundamental questions to be answered are what factors account for or influence HDV drivers' subjective feelings when interacting with CAVs and how CAVs will affect the behavior and performance of HDV drivers. To address the above-described questions, the aim of this study is to better understand HDV drivers' psychological characteristics when facing CAVs and the decision-making tendencies of drivers with different psychological characteristics in various HDV-CAV interaction scenarios. This is achieved by using a two-step method consisting of latent profile analysis and the multinomial logistic regression model, based on the data from a designed questionnaire survey in China. Particularly, the main contributions of this study lie in the following:  
1. The way heterogenous HDV drivers cluster in accordance with their psychological characteristics when they faced CAVs were revealed, and the variables affecting individual classification were analyzed;
2. The behavior choice of each HDV driver cluster was identified in a wide variety of interactive driving scenarios;
3.