Autonomous driving expected functional safety hazard identification method based on the Mealy state machine

Aiming at the strong correlation between the hazards of the autonomous driving system of intelligent networked vehicles and the operating environment and control action, this paper proposes a method for identifying hazards of the expected functional safety of autonomous driving based on a mealy state machine. Firstly, the vehicle action rule base is determined by the operating environment of the vehicle and the available control action; secondly, the mapping between the mealy state machine and the vehicle operating state is established to simulate the vehicle state operation logic in the real scene; finally, by identifying the relationship between the vehicle state and the Conflicts between the operating environment and control actions identify potential hazards. In order to verify the effectiveness of the proposed method, this paper identifies 257 potential hazards through the identification of the expected functional safety hazards of the ACC system of a L2 vehicle, which is more efficient and convenient compared with the traditional STPA method.