An Improved Modeling Method for Driving Risk by Considering Driver–Vehicle–Road Factors

Quantifying the driving risks is the primary prerequisite for improving intelligent vehicles’ driving safety. However, the time-varying nature of driving risks brought on by the dynamic and complicated traffic environment makes it challenging to precisely assess them. This study presents a method for measuring driving risk and establishing a unified framework for driving risk modeling. To determine the source of driving risks, we start with traffic accidents, which are abnormal energy transfers. A unified quantitative method based on the equivalent force model is proposed and analyzed in detail to design the unified driving risk modeling framework. The modeling method of an integrated driving risk model by comprehensively considering the driver-vehicle-road multifactor is obtained. We first validate the risk quantification effectiveness through simulation experiments involving multi-vehicle interactions and further verify the feasibility of the model through three natural vehicle experiments: car-following, cut-in, and intersection conflict scenarios. The experimental findings demonstrate that the suggested approach may determine the extent and direction of driving risks with vehicle-to-vehicle technical support. The proposed method adopts a collision warning system, which can forecast driving risk, broadcast the location of the risk in advance, and provide the driver with control suggestions to ensure driving safety.