A vehicle control method, comprising: obtaining sensor information of different modalities of different onboard devices of a vehicle, and performing modality transformation on the sensor information to generate short-cycle message information of a unified modality; sending the short-cycle message information to a first data model at a remote end; generating long-cycle message information by a second data model configured at the vehicle based on the sensor information with the different modalities, the short-cycle message information having better real-time performance than the long-cycle message information, and the long-cycle message information being used to trigger the first data model to generate vehicle control information; sending the long-cycle message information to the first data model at the remote end; receiving the vehicle control information returned by the first data model at the remote end, the vehicle control information being a response of the first data model at the remote end to the long-cycle message information and being generated based on the short-cycle message information and the long-cycle message information; and controlling the vehicle based on the vehicle control information, wherein the first data model is a deep learning network located at a far-end server with complex information processing capability and high-level logical analysis capability, which can understand and analyze the tasks included in the long-cycle message information as an input and autonomously determine and generate rich and mutually supportive data and materials associated with the vehicle control information based on the long-cycle message information. obtaining sensor information of different modalities of different onboard devices of a vehicle, and performing modality transformation on the sensor information to generate short-cycle message information of a unified modality; sending the short-cycle message information to a first data model at a remote end; generating long-cycle message information by a second data model configured at the vehicle based on the sensor information with the different modalities, the short-cycle message information having better real-time performance than the long-cycle message information, and the long-cycle message information being used to trigger the first data model to generate vehicle control information; sending the long-cycle message information to the first data model at the remote end; receiving the vehicle control information returned by the first data model at the remote end, the vehicle control information being a response of the first data model at the remote end to the long-cycle message information and being generated based on the short-cycle message information and the long-cycle message information; and controlling the vehicle based on the vehicle control information, wherein the first data model is a deep learning network located at a far-end server with complex information processing capability and high-level logical analysis capability, which can understand and analyze the tasks included in the long-cycle message information as an input and autonomously determine and generate rich and mutually supportive data and materials associated with the vehicle control information based on the long-cycle message information.