Dynamic traffic scenarios present significant challenges for autonomous driving technology. Under single-vehicle intelligence constraints, autonomous vehicles can only perceive instantaneous information from surrounding vehicles, while passengers desire comprehensive knowledge about the vehicle’s operational state within a broader context. The limitation in perceiving beyond-line-of-sight information leads to potential decision-making delays for autonomous vehicles. This paper addresses the state assessment of autonomous vehicles within traffic flow by introducing “Overtaking Frequency” and developing a dynamic time-window-based overtaking frequency function. We first propose an overtaking frequency definition formula to evaluate the autonomous vehicle’s state. Subsequently, we conduct an in-depth analysis of factors influencing overtaking frequency and incorporate dynamic time-window theory to adapt the overtaking frequency function to various traffic flow characteristics. Furthermore, through quantitative simulation experiments, we investigate the correlation between time window length and multiple parameters, including the velocity difference between adjacent lane vehicles and the ego vehicle, as well as the ego vehicle’s velocity. The relationship between these parameters and time window length is fitted using second-order Fourier functions and exponential functions, ultimately optimizing the overtaking frequency function.