Drive Risk Assessment Based on Game Theory Combinatorial Weighting—Unascertained Measure Theory

Abstract

The driving risk is assessed using the theory of unascertained measures to determine the presence of a conditional switch in the control system of a human-machine codriving vehicle. Relevant risk indicators for driving are selected, including five driver-related indicators and three vehicle-related indicators. Subsequently, each indicator’s threshold range and associated risk level are analyzed and defined. The methodologies for establishing unascertained measure and their corresponding functions for both single and multiple indicator unascertained measure are then elucidated. A game theory–based weighting method is proposed, employing ordinal relationship analysis (ORA) and entropy weighting (EW) to determine indicator weights while utilizing confidence identification criteria to ascertain risk levels. Finally, experimental analyses are conducted on the driving risk assessment model, and the simulation results demonstrated the model’s ability to distinguish between normal and risky driving. In a continuous driving simulation, the model successfully identified a peak risk period (Level V) and, following a system alert, driving behavior returned to normal risk levels within 5 min. The model demonstrated utility for control switching decisions in human-machine codriving scenarios, identifying instances where driver risk (Level IV) significantly exceeded vehicle risk (Level II), indicating a need to transfer control to the vehicle system. Consequently, the study’s findings can provide theoretical support for control switching mechanisms in human-machine codriving vehicles.