A method for determining a static state of an obstacle, comprising: obtaining (110; 210; 310), by an unmanned vehicle system, real-time obstacle velocities by detecting an obstacle via at least two sensors; calculating (120; 320) a belief function assignment value of each sensor for at least two status parameters respectively according to the real-time obstacle velocity corresponding to each sensor, which comprises: calculating a real-time static probability corresponding to each sensor respectively according to a mapping relation between a modulus value of velocity and a static probability and based on the real-time obstacle velocity corresponding to each sensor; and calculating the belief function assignment value of each sensor for each status parameter respectively according to the real-time static probability corresponding to each sensor and a respective belief function assignment rule corresponding to each sensor; fusing (130; 230; 340) the belief function assignment value of each sensor for each status parameter with a D-S evidence combination technology to obtain a fused belief function assignment value corresponding to each status parameter; and judging (140; 240; 350) a state of the obstacle according to the fused belief function assignment value corresponding to each status parameter; characterized in that, wherein the mapping relation between the modulus value of velocity and the static probability comprises: ƒ v = { 1, v < t e − v − t s 2, v ≥ t where, v represents the modulus value of velocity, ƒ ( v) represents the static probability, t and s each represents a preset mapping parameter; and wherein, different sensors correspond to mapping parameters of different value ranges. obtaining (110; 210; 310), by an unmanned vehicle system, real-time obstacle velocities by detecting an obstacle via at least two sensors; calculating (120; 320) a belief function assignment value of each sensor for at least two status parameters respectively according to the real-time obstacle velocity corresponding to each sensor, which comprises: calculating a real-time static probability corresponding to each sensor respectively according to a mapping relation between a modulus value of velocity and a static probability and based on the real-time obstacle velocity corresponding to each sensor; and calculating the belief function assignment value of each sensor for each status parameter respectively according to the real-time static probability corresponding to each sensor and a respective belief function assignment rule corresponding to each sensor; fusing (130; 230; 340) the belief function assignment value of each sensor for each status parameter with a D-S evidence combination technology to obtain a fused belief function assignment value corresponding to each status parameter; and judging (140; 240; 350) a state of the obstacle according to the fused belief function assignment value corresponding to each status parameter; characterized in that, wherein the mapping relation between the modulus value of velocity and the static probability comprises: ƒ v = { 1, v < t e − v − t s 2, v ≥ t where, v represents the modulus value of velocity, ƒ ( v) represents the static probability, t and s each represents a preset mapping parameter; and wherein, different sensors correspond to mapping parameters of different value ranges.