The embodiments of the present application provide a method and an apparatus for predicting an average energy consumption of an electric vehicle. The method comprises: according to a real-time voltage and a real-time current of a battery pack of an electric vehicle, determining an actual energy consumption of the electric vehicle in the traveled mileage segment corresponding to the current time, the traveled mileage segment comprising a plurality of unit mileage segments; according to the actual energy consumption of each unit mileage segment of the traveled mileage segment, determining an initial average energy consumption of the electric vehicle at the current time; acquiring a target average energy consumption and average energy consumption adjustment parameters of the electric vehicle; and according to the initial average energy consumption, the target average energy consumption and the average energy consumption adjustment parameters, determining the actual average energy consumption of the electric vehicle at the current time. The present application can accurately calculate the actual average energy consumption of the electric vehicle at the current time in real time.

Systems and methods are disclosed that relate to testing processing elements of an integrated processing system. A first system test may be performed on a first processing element of an integrated processing system. The first system test may be based at least on accessing a test node associated with the first processing element. The first system test may be accessed using a first local test controller. A second system test may be performed on a second processing element of the integrated processing system. The second system test may be based at least on accessing a second test node associated with the second processing element. The second system test may be accessed using a second local test controller.

A drive cycle controller includes a drive cycle switching unit and an output state determination unit. The drive cycle switching unit switches a drive cycle of a microcomputer, which monitors an output of a device, from a first drive cycle to a second drive cycle that is shorter than the first drive cycle if the microcomputer detects a change in an output of the device at an activation timing in the first drive cycle. The output state determination unit determines an output state of the device if the microcomputer confirms that the output has remained changed at an activation timing in the second drive cycle.

A device and method for controlling a hardware agent in a control situation having a plurality of hardware agents. The method includes ascertaining of a potential function by a first neural network; ascertaining of a control scenario for a control situation from a plurality of possible control scenarios by a second neural network; ascertaining a common action sequence for the plurality of hardware agents by seeking an optimum of the ascertained potential function over the possible common action sequences of the ascertained control scenario; and controlling at least one of the plurality of hardware agents in accordance with the ascertained common action sequence.

Systems and methods are disclosed that relate to testing processing elements of an integrated processing system. A first system test may be performed on a first processing element of an integrated processing system. The first system test may be based at least on accessing a test node associated with the first processing element. The first system test may be accessed using a first local test controller. A second system test may be performed on a second processing element of the integrated processing system. The second system test may be based at least on accessing a second test node associated with the second processing element. The second system test may be accessed using a second local test controller.

An apparatus for predicting an acceleration signal predicts an acceleration signal of an acceleration sensor installed on a vehicle for active noise control (ANC) and includes: a prediction module configured to train a predefined prediction algorithm to predict an acceleration signal after N samples compared to a point in time at which an acceleration signal is acquired by the acceleration sensor (N is a natural number), and to apply a reference acceleration signal acquired at a reference point in time to the completely trained prediction algorithm to generate a predicted acceleration signal after the N samples compared to the reference point in time; and an ANC module configured to perform ANC on the basis of the predicted acceleration signal generated by the prediction module.

Systems and methods are disclosed that relate to testing processing elements of an integrated processing system. A first system test may be performed on a first processing element of an integrated processing system. The first system test may be based at least on accessing a test node associated with the first processing element. The first system test may be accessed using a first local test controller. A second system test may be performed on a second processing element of the integrated processing system. The second system test may be based at least on accessing a second test node associated with the second processing element. The second system test may be accessed using a second local test controller.

A drive system for an agricultural vehicle is provided herein that may include a pump unit that may be connected to a power plant and configured to generate power through a flow of hydraulic fluid. A first propulsion motor may be fluidly coupled with a pump unit and configured to drive a first tractive force to a first tractive element by applying a first control signal. A sensor system may be configured to capture data indicative of a slip condition of the first tractive element. A computing system may be operably coupled with the sensor system and the first propulsion motor. The computing system may be configured to detect a slip condition of the first tractive element based on the data from the sensor system and provide a superimposed traction signal to the first tractive element to alter a displacement to the first control signal line.

A system for processing multi-modal data representing an environment to generate scene graphs of the environment is described. The system can obtain sensor data associated with a vehicle operating in the environment. In examples, the system can determine a set of features from the sensor data, including one or more objects and one or more agents present in the environment, and can generate a scene graph that represents the poses and velocities of these objects and agents relative to the environment. In some examples, based on generating the scene graph, the system can generate a knowledge graph by encoding the relationships among the identified objects and agents. In some examples, the system can generate a control signal, using attributes that represent the states of objects and agents in the knowledge graph, and provide this control signal to the vehicle in order to adjust or cause the operation of the vehicle.

The embodiments of the present application provide a method and an apparatus for predicting an average energy consumption of an electric vehicle. The method comprises: according to a real-time voltage and a real-time current of a battery pack of an electric vehicle, determining an actual energy consumption of the electric vehicle in the traveled mileage segment corresponding to the current time, the traveled mileage segment comprising a plurality of unit mileage segments; according to the actual energy consumption of each unit mileage segment of the traveled mileage segment, determining an initial average energy consumption of the electric vehicle at the current time; acquiring a target average energy consumption and average energy consumption adjustment parameters of the electric vehicle; and according to the initial average energy consumption, the target average energy consumption and the average energy consumption adjustment parameters, determining the actual average energy consumption of the electric vehicle at the current time. The present application can accurately calculate the actual average energy consumption of the electric vehicle at the current time in real time.