Risk Level Estimating and Modeling of Complex Scenarios for Intelligent Vehicles Based on Graph Classification

Accurate estimation of the risk level of driving scenarios is the basis to ensure the safe driving of vehicles, and it is also an important embodiment of vehicle intelligence. Aiming at the complex driving scene where multiple traffic participants coexist, in this paper, a scene risk level estimation method was proposed based on graph classification to complete the modeling of the scene and effectively evaluate the risk level of the current scene. The real vehicle experiment results show that the operation feature data of adopted driver can well represent the driver's understanding to the risk level of the scene, and the graphical representation model can effectively explain the various dynamic traffic participants and their interaction in the scene. The proposed method can more accurately evaluate the risk level of complex driving scenarios, and promote the development of intelligent vehicle safety driving systems in complex environments.