A method for operation of fleet vehicles includes collecting a set of inputs; processing the set of inputs to determine a set of actions associated with a vehicle and/or a site; and triggering the set of actions. Additionally or alternatively, the method can include aggregating any or all of the set of inputs, and/or any other suitable processes. A system for operation of fleet vehicles can include and/or interface with any or all of: a computing subsystem, a set of management subsystems, a set of user interfaces, a set of sensors, a set of fleet vehicles, a set of non-fleet vehicles, and/or any other components.

A system and method are provided for Bayesian updating of distributions of factors that affect tire wear. Information is accumulated in data storage regarding probability distributions corresponding to each of a respective plurality of tire wear factors. Vehicle data comprising movement data and location data collected in association with a vehicle is transmitted from the vehicle to a centralized (e.g., cloud) computing device or network. At least one observation corresponding to one or more of the plurality of factors is generated based on the transmitted vehicle data. A Bayesian estimation is then generated of a tire wear status at a given time for at least one tire associated with the vehicle, based at least on the at least one generated observation and the stored information regarding probability distributions. The predictions accordingly carry a measure of uncertainty, and Bayesian inference can be used to update distributions based on the observations.

A system and method are provided for predicting the non-linear progression in vehicle tire wear. The system determines an original tread depth for a tire associated with a vehicle, and further determines an initial wear rate for the tire based at least partially on the original tread depth. One or more tire conditions are measured as time-series inputs to a predictive tire wear model, for example utilizing “brush-type” tire wear models for a contact interface between a base material of the tire and road surfaces, wherein the interface is represented as a plurality of independently deformable elements, wherein a current wear rate is normalized based at least partially on said inputs to the initial wear rate for the tire. The system can then predict a tire wear status of the tire for specified future parameters, such as for example a specified future distance traveled or a time traveled.

A system and method are provided for estimating progression in vehicle tire wear. A tread depth is stored at a first (e.g., initial or unworn) stage for a given tire, along with a first set of modal frequencies for the tire. At a later (e.g., worn) stage, for example in concert with a controlled excitation of tire structural modes, a second set of corresponding modal frequencies are sensed for the tire, and a tire wear status of the tire is determined at the second stage based on a calculated frequency shift between at least one corresponding modal frequency from each of the first and second sets. In one example, an initial mass of the tire is stored, and a change in mass is calculated based on the calculated frequency shift. Alternatively, a correlation of modal frequency shift may be performed with respect to tread depth for a given tire.

The controller of the electric vehicle is configured to control the torque of the electric motor using the MT vehicle model based on the operation amount of the accelerator pedal, the operation amount of the pseudo-clutch pedal, and the shift position of the pseudo-shifter. Further, the controller is configured to execute the stall production process for changing the engine output torque used for calculation of the driving wheel torque to zero when the calculated virtual engine speed using the MT vehicle model becomes lower than the prescribed stall engine speed.

A vegetation index characteristic is assigned to an image of vegetation at a worksite. The vegetation index characteristic is indicative of how a vegetation index value varies across the corresponding image. The image is selected for predictive map generation based upon the vegetation index characteristic. The predictive map is provided to a harvester control system which generates control signals that are applied to controllable subsystems of the harvester, based upon the predictive map and the location of the harvester.

A system for displaying information is intended to be incorporated in a motor vehicle including at least a dashboard, a central console, a display screen on the central façade and a front windscreen. The display system includes a manual or automatic selection device for selecting among at least three display modes each containing information, being progressively off-centre with respect to the vehicle.

An information processing method, an electronic device, and a storage medium are provided, which are related to the fields of big data, cloud computing, automatic driving, and the like. The specific implementation includes: receiving a data acquisition task and an acquisition strategy issued by a cloud; analyzing the acquisition strategy to obtain priority information of the acquired data for the data acquisition task; and taking the priority information as an importance degree identifier corresponding to the acquired data, and performing preferential transmission after data acquisition and data screening according to the importance degree identifier.

A predictive map including predictive values of an agricultural characteristic of a worksite is obtained. An in-situ value of the agricultural characteristic is identified based on a sensor signal provided by an in-situ sensor. A corrected predictive map, including corrected predictive values of the agricultural characteristic, is generated based on the in-situ value of the agricultural characteristic. A controllable subsystem of a work machine is controlled based on the location of the work machine and the corrected predictive map.

Systems, methods and apparatuses of processing overwhelming stimuli in vehicle data recorders. For example, a data recorder can have resources, such as memory components, a controller, an inference engine, etc. The resources can be partitioned into a first subset and a second subset. Abnormal stimuli in an input stream to the recorder may cause delay for real time processing. In response, a time sliced segment of the input stream is selected and assigned to the first subset; and a remaining segment is assigned to the second subset. The first and second subsets can separately process the time sliced segment and the remaining segment in parallel and thus avoid delay in the processing of the remaining segment. An artificial neural network (ANN) can determine a width for selecting the segment processed by the first subset; and the processing result can include a preferred width used to train the ANN.