A method for generating a lane changing decision model, a lane changing decision method for a driverless vehicle, and a device. The method for generating a lane changing decision model comprises: acquiring a vehicle lane change training sample set, wherein the training sample set comprises multiple training sample groups, each of the training sample groups comprises training samples collected at every time step during a process of a vehicle completing a lane change according to a planned lane changing trajectory, the training samples comprise a state variables and corresponding control variables set, the state variables comprise an orientation, a speed, an acceleration of a target vehicle, an orientation, a speed, and an acceleration of a vehicle in front of the target vehicle in a current lane, and an orientation, a speed, and an acceleration of a following vehicle in a target lane, and the control variables comprise the speed and an angular speed of the target vehicle; and training, by means of the training sample set, a deep reinforcement learning network-based decision model to obtain a lane changing decision model, the lane changing decision model enabling the state variables of the target vehicle to be associated with the corresponding control variables.
METHOD FOR GENERATING LANE CHANGING DECISION MODEL, LANE CHANGING DECISION METHOD FOR DRIVERLESS VEHICLE, AND DEVICE