Abstract: A task scheduling method includes: when a task requirement is obtained, splitting the task requirement to obtain the plurality of subtasks having a constraint relationship; performing execution condition detection on non-candidate subtasks, determining a non-candidate subtask that satisfies an execution condition as a candidate subtask, and putting the candidate subtask into a task queue; performing state detection on a server network composed of edge servers to obtain server state information and communication information; inputting the server state information, the communication information, and queue information corresponding to the task queue into an action value evaluation model to obtain the plurality of evaluated values respectively corresponding to the plurality of scheduling actions; and determining a target scheduling action from the plurality of scheduling actions by using the evaluated values, and scheduling the candidate subtask in the task queue on the basis of the target scheduling action.

Abstract: A task scheduling method includes: when a task requirement is obtained, splitting the task requirement to obtain the plurality of subtasks having a constraint relationship; performing execution condition detection on non-candidate subtasks, determining a non-candidate subtask that satisfies an execution condition as a candidate subtask, and putting the candidate subtask into a task queue; performing state detection on a server network composed of edge servers to obtain server state information and communication information; inputting the server state information, the communication information, and queue information corresponding to the task queue into an action value evaluation model to obtain the plurality of evaluated values respectively corresponding to the plurality of scheduling actions; and determining a target scheduling action from the plurality of scheduling actions by using the evaluated values, and scheduling the candidate subtask in the task queue on the basis of the target scheduling action.

Abstract: An automatic driving method includes following steps: S101: acquiring real-time traffic environment information in a travel process of an autonomous vehicle at a current moment; S102: mapping the real-time traffic environment information based on a preset mapping relationship to obtain mapped traffic environment information; S103: adjusting a target deep reinforcement learning model based on a pre-stored existing deep reinforcement learning model and the mapped traffic environment information; and S104: judging whether automatic driving is finished, and in response to the automatic driving is not finished, returning to perform the step of acquiring the real-time traffic environment information in the travel process of the autonomous vehicle at the current moment. An automatic driving system, an automatic driving device and a computer medium storing the automatic driving method are further provided.