A supervision method for a flight state of an unmanned aerial vehicle includes respectively establishing communication connections with the unmanned aerial vehicle and a supervision server, receiving identity information about the unmanned aerial vehicle and flight information about the unmanned aerial vehicle sent by the unmanned aerial vehicle, automatically sending the identity information about the unmanned aerial vehicle and the flight information to the supervision server in an on-line mode, receiving at least one of a flight restriction instruction or warning information sent by the supervision server, and forwarding the flight restriction instruction to the unmanned aerial vehicle, so that the unmanned aerial vehicle executes the flight restriction instruction, thereby restricting flight behaviour of the unmanned aerial vehicle in an on-line flight mode via the flight restriction instruction.

A method is provided for simplifying a takeover of control of a motor vehicle by a vehicle-external operator. In the method, images of the surroundings of the vehicle are captured from the vehicle and semantically segmented. Errors in a corresponding segmentation model are predicted on the basis of at least one such image each. If a corresponding error prediction triggering a request for the takeover of control is made, an image-based visualization is automatically generated in which exactly one region corresponding to the error prediction is visually highlighted. The request and the visualization are then sent to the vehicle-external operator.

A method is provided for simplifying a takeover of control of a motor vehicle by a vehicle-external operator. In the method, images of the surroundings of the vehicle are captured from the vehicle and semantically segmented. Errors in a corresponding segmentation model are predicted on the basis of at least one such image each. If a corresponding error prediction triggering a request for the takeover of control is made, an image-based visualization is automatically generated in which exactly one region corresponding to the error prediction is visually highlighted. The request and the visualization are then sent to the vehicle-external operator.

A multi-machine cooperation method, a scheduling device, and a multi-machine cooperation system are described. The multi-machine cooperation method includes: determining, by a first autonomous robot when detecting an abnormal condition during operation, whether the abnormal condition can be independently processed; and when the abnormal condition cannot be independently processed, sending, by the first autonomous robot, an assistance request to another device in an Internet of Things in which the first autonomous robot is located. In the specification, a multi-machine cooperation operation between autonomous robots or between an autonomous robot and another device can be implemented.

A multi-machine cooperation method, a scheduling device, and a multi-machine cooperation system are described. The multi-machine cooperation method includes: determining, by a first autonomous robot when detecting an abnormal condition during operation, whether the abnormal condition can be independently processed; and when the abnormal condition cannot be independently processed, sending, by the first autonomous robot, an assistance request to another device in an Internet of Things in which the first autonomous robot is located. In the specification, a multi-machine cooperation operation between autonomous robots or between an autonomous robot and another device can be implemented.

A method for automatically controlling a vehicle based on a driving specification received from an external infrastructure. In a normal mode the vehicle is controlled along a trajectory specified thereby and in a test mode the vehicle is controlled along a test trajectory that deviates from the trajectory specified by the driving specification, and/or is controlled using test parameters. This allows the reliable operation of the infrastructure to be verified.

A method for automatically controlling a vehicle based on a driving specification received from an external infrastructure. In a normal mode the vehicle is controlled along a trajectory specified thereby and in a test mode the vehicle is controlled along a test trajectory that deviates from the trajectory specified by the driving specification, and/or is controlled using test parameters. This allows the reliable operation of the infrastructure to be verified.

A method is provided that includes: obtaining a first state information of the remote driving system, where the remote driving system is configured to perform remote control on a vehicle communicatively connected with the remote driving system; determining whether an abnormality occurs in the remote driving system based on the first state information; determining abnormality information of the remote driving system in response to the occurrence of the abnormality in the remote driving system; and adjusting a state of the remote control based on the abnormality information.

A method is provided that includes: obtaining a first state information of the remote driving system, where the remote driving system is configured to perform remote control on a vehicle communicatively connected with the remote driving system; determining whether an abnormality occurs in the remote driving system based on the first state information; determining abnormality information of the remote driving system in response to the occurrence of the abnormality in the remote driving system; and adjusting a state of the remote control based on the abnormality information.

An autonomous driver system for an agricultural vehicle assembly includes a sensor interface configured for coupling with one or more of vehicle sensors or implement sensors and a function interface configured for coupling with one or more of vehicle actuators or implement actuators. An autonomous driving controller is in communication with the sensor and function interfaces. The autonomous driving controller is configured to autonomously implement a planned agricultural operation with the agricultural vehicle and the agricultural implement. The controller is configured to identify and remedy one or more operation disturbances outside of the planned agricultural operation including identifying the one or more operation disturbances with one or more of the vehicle or implement sensors and selecting one or more remedial actions for the one or more operation disturbances. The controller is configured to implement the one or more remedial actions with one or more of the vehicle or implement actuators.