DDRAM: Dynamic Driving Risk Assessment Method Based on Predictable Multidimensional Kinematic Features

As autonomous driving technologies continue to evolve, the demand for precise and prompt risk assessment in complex traffic scenarios has become increasingly critical to guarantee driving safety. Traditional driving risk field models typically trigger warnings only when vehicles enter each other’s risk zones, thereby failing to predict potential future spatial conflicts. This limitation significantly compromises the timeliness and reliability of warnings, particularly in multiagent scenarios such as intersections. To mitigate this limitation, we accordingly propose a dynamic driving risk assessment method (DDRAM) based on predictable multidimensional kinematic features. Based on the distributed digital twin information system architecture, a “query-centric” multimodal trajectory prediction algorithm driven by agent interaction is designed to achieve high-precision predictions of the upcoming motion states of traffic participants. By deconstructing the predicted kinematic features, we construct a multisource fusion model to dynamically assess collision risks. Furthermore, we design a quantifiable risk warning matrix to characterize potential risks with greater accuracy. This method significantly enhances the early warning capabilities in complex traffic scenarios. Experimental results demonstrate that DDRAM significantly increases the average precollision warning time (PCWT) by 76% and reduces the average precollision warning error (PCWE) by 72% compared with existing approaches.