## 1 Introduction
To sum up, the three-dimensional morphology of ruts has a non-negligible impact on the distribution of pavement water depth, but the current pavement water identification methods cannot be directly used to identify diseased pavement water areas; the close link between pavement conditions and vehicle safety has not yet been systematically incorporated into models for predicting the risk of hydroplaning. For the safety analysis of water-filled pavement, [PERSON] (2008) [8; 9; 10; 11] and [PERSON] (2010) [12] established the tire-water flow pavement model and CFD simulation, and found that the tire inflation pressure, tread pattern, and load have a significant effect on the critical hydroplaning speed. And through simulation analysis, a critical hydroplaning speed prediction model considering the above mentioned various influencing factors was obtained. [PERSON] (2015) [13; 14] quantitatively analyzed the influence of water film thickness and vehicle speed on vehicle side-turning angle on water-filled roads and proposed a safety evaluation method, considering driving safety under water-filled rutting conditions. According to the Euler coupling algorithm, [PERSON] (2016) [15] simulated and analyzed the influence of the spacing, width, and depth of the transverse grooves on the hydroplaning speed of the tire. [PERSON] (2017) [16] took the vehicle dynamics analysis software ADAMS 2016 as the simulation platform, and proposed to use tire parameters, vehicle parameters, and road coefficient parameters to construct a coupled vehicle-tire-road model. [PERSON] (2018) [17] built a set of rainy and sunny car driving stability evaluation systems based on the full-car model, and used the braking distance and lateral distance to conduct stability research for straight roads and curves in rainy days. [PERSON] (2019) [18] detected and reconstructed the rutting section based on three-dimensional laser technology, calculated and obtained the water distribution of the section, and used the CarSim vehicle model to analyze the lateral stability. [PERSON] (2022) [19] analyzed the effect of water film thickness on the tire adhesion coefficient based on a three-phase coupled model. However, the study did not consider the effect of rutting geometric features on water film thickness. [PERSON] et al. (2022) [20] constructed an adhesion coefficient whole vehicle model. However, the study did not address the dynamic calculation of water film thickness and its effect on the adhesion coefficient. In order to address the safety issue of slippery road surfaces, some researchers have also carried out studies on the calculation and dynamic correction of water film thickness, e.g., [PERSON] et al. (2023) [21] proposed a water film thickness prediction method based on the correction of measured data, which improved the calculation accuracy of the model by optimizing the Manning's coefficient. However, this study mainly focused on airport runways and did not consider the influence of rutting geometric features on water film thickness and traffic safety. Scholars at home and abroad have conducted research on road surface water film, road surface runoff, adhesion between tires and road surfaces under water-filled conditions, the impact of water-filled roads on vehicles, and evaluation indicators of water-filled ruts, and have established a series of mathematical models and simulation models. Despite these advancements, current studies still have notable shortcomings, such as a lack of integration between rut-induced ponding and vehicle safety assessment. Many studies tend to treat rutting and ponding in pavements as isolated phenomena rather than considering the interactions between rutting three-dimensional morphology, water film thickness, and vehicle behavior. In addition, real-time risk prediction is inadequate. Existing waterlogging identification methods are unable to dynamically predict hazardous areas under different rainfall conditions, which limits their practical application in real-world driving environments.