Unified Quantitative Method of Driving Risk by Comprehensive Considering Driver-Vehicle-Road Factors

As the input of vehicle decision-making and control, risk quantification plays a vital role in improving intelligent driving safety. However, the quantification of driving risk is difficult since the dynamic and complex driver-vehicle-road traffic environment causes the time-varying and coupling of driving risk. This paper presents a unified quantitative method of driving risk and establishes an overall framework of the driving risk model. This method is based on the artificial potential field theory, vehicle kinematics, and dynamics. To determine the source of driving risk, we start from the fact that traffic accidents are abnormal energy transfers. A unified quantitative method based on the equivalent force model is proposed and analyzed in detail to design the overall framework of driving risk. The modeling method of an integrated driving risk model by comprehensive considering driver-vehicle-road factors is obtained. Moreover, we verify the feasibility of the model through three natural vehicle experiments, including a car-following scenario, a cut-in scenario, and an intersection conflict scenario. The experimental results show that the proposed driving risk assessment method can accurately identify driving risk level and direction with V2V technical support. This method applies to the 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.