With the increasing demand for road traffic safety assessment, global concerns about road safety have been rising. This is particularly evident with the widespread adoption of V2X (Vehicle-to-Everything) technology, where people are more intensively focused on how to leverage advanced technological means to effectively address challenges in traffic safety. Through the research of driving style recognition technology, accurate assessment of driving behavior and the provision of personalized safety prompts and warnings have become crucial for preventing traffic accidents. This paper proposes a risk field construction technique based on environmental data collected by in-vehicle sensors. This paper introduces a driving style recognition algorithm utilizing risk field visualization and mask learning technologies. The research results indicate that, compared to traditional classical models, the improved algorithm performs excellently in terms of accuracy, stability, and robustness, enhancing the accuracy of driving style recognition and enabling a more effective evaluation of road safety.

The dynamic characteristics of a vehicle are significantly influenced by the suspension mechanism. In this paper, the nonlinear kinestatic relations of a planar suspension mechanism are taken into account in the dynamic analysis of a vehicle. A planar suspension mechanism can be considered a 1-DOF parallel mechanism. The Jacobian is used for the kinestatic analysis of the suspension. The motions of the suspension can be represented by instantaneous screw. Based on these kinematic and static relations, the dynamic performances of a quarter-vehicle model with a planar suspension mechanism are described in terms of Lagrangian equations. Finally, as illustrated in the examples, two different kinds of road disturbances are inputted into the wheel. The dynamic responses of a quarter-vehicle model are simulated and compared with the simulation software Adams/View for the validity of the theoretical method.