Exploring User Acceptance of Autonomous Vehicles: Impact of Driver and Vehicle Styles

Autonomous vehicles (AVs) provide an effective solution for enhancing traffic safety. In the last few years, there have been significant efforts and progress in the development of AVs. However, the public acceptance has not fully kept up with technological advancements. Public acceptance can restrict the growth of AVs. This study focuses on investigating the acceptance and takeover behavior of drivers when interacting with AVs of different styles in various scenarios. Manual and autonomous driving experiments were designed based on the driving simulation platform. To avoid subjective bias, principal component analysis (PCA) and the Gaussian mixture model (GMM) were used to classify driving styles. A total of 34 young participants (male-dominated) were recruited for this study. And they were classified into three driving styles (aggressive, moderate, and conservative). And AV styles were designed into three corresponding categories according to the different driving behavior characteristics. This study reveals that drivers generally prefer driving scenarios with lower risk levels. When drivers perceive safety, they are more likely to adopt more efficient AVs. Additionally, drivers tend to accept AVs that align better with their driving styles. However, it is not found that more aggressive or conservative AVs have a significant impact on their acceptance. Takeover behavior has been identified as a significant mediator of acceptance, with the potential to influence drivers' perceptions and attitudes. There is a marked decline in acceptance when takeover behavior happens. The results show that regulating takeover behavior is essential for the development of AVs that promote greater acceptance. And this study contributes theoretical support to the development of adaptive AVs.