A work vehicle includes: a chassis; a first axle carried by the chassis; a pair of first wheels rotatably coupled to the first axle; a first weight sensor associated with the first axle and configured to output a first weight signal; a second axle carried by the chassis; a pair of second wheels rotatably coupled to the second axle; a second weight sensor associated with the second axle and configured to output a second weight signal; and a controller operatively coupled to the first and second weight sensors. The controller is configured to: receive the first and second weight signals; determine a weight distribution of the work vehicle based on the received first and second weight signals; analyze the determined weight distribution to determine at least one recommended operating parameter; and output a recommendation signal based on the at least one recommended operating parameter.

This application relates generally to sensor systems and, more particularly, relates to systems and methods for management of smart wheel sensors that collect actionable sensor data from a rotatable component of a vehicle's wheel. In certain embodiments, a system includes a vehicle body; a rotatable component configured to rotate relative to the vehicle body; an energy harvesting component disposed along a circumference of the rotatable component, wherein the energy harvesting component is configured to generate electric power based on a force to the rotatable component; a sensor configured to produce sensor data by using the electric power while disposed on the rotatable component; and at least one processor disposed within the vehicle body, the at least one processor configured to perform an action within the vehicle body based on a parameter value meeting a threshold value, wherein the parameter value is based on the sensor data.

Systems and methods for sensing a tire parameter from a rotating wheel are disclosed. In some embodiments, a system includes: a rotatable component configured to rotate; a piezoelectric transducer disposed along a circumference of the rotatable component, where the piezoelectric transducer is configured to generate an offload voltage based on a mechanical deformation of the piezoelectric transducer; and at least one processor in communication with the piezoelectric transducer, the at least one processor configured to determine a temperature value based on the offload voltage.

Aspects of the disclosure relate to computing platforms that utilize improved techniques for dynamic event verification. A computing platform may receive first source data comprising driving data associated with a vehicle over a time period. Based on the first source data, the computing device may determine that the vehicle experienced an event, resulting in an event output. In response to determining the event output, the computing device may generate a request for second source data associated with the vehicle over the time period. The computing device may receive, from a sensor device, the second source data. Based on a comparison of the first source data to the second source data, the computing platform may determine an event comparison output. The computing platform may determine that the event comparison output exceeds a predetermined comparison threshold, and may send an indication of an event in response.

The present disclosure provides a method in a data processing system that includes at least one processor and at least one memory. The at least one memory includes instructions executed by the at least one processor to implement a bounded-error estimator system. The method includes receiving information about a plurality of vehicle states of a vehicle from at least one sensor, determining that the information is missing data about at least one vehicle state of the plurality of vehicle states, and determining an estimated vehicle state associated with a final vehicle state. Determining the estimated vehicle state includes calculating a plurality of augmented states for each of the vehicle states included in the plurality of vehicle states and calculating the estimated vehicle state based on the plurality of augmented states. The estimated vehicle state is provided to a vehicle control system of the vehicle.

A system may comprise a device. The device may be configured to receive, from one or more sensor devices of the machine, sensor data associated with wear of one or more components of an undercarriage of the machine; and predict, using a machine learning model and the sensor data, an amount wear of the one or more components based on a wear rate of the one or more components. The machine learning model is trained, using training data, to predict the wear rate of the one or more components. The training data includes two or more of: historical sensor data, historical inspection data, or simulation data, of a simulation model, from one or more third devices. The device may perform an action based on the amount of wear.

Methods and systems for autonomous and semi-autonomous vehicle control, routing, and automatic feature adjustment are disclosed. Sensors associated with autonomous operation features may be utilized to search an area for missing persons, stolen vehicles, or similar persons or items of interest. Sensor data associated with the features may be automatically collected and analyzed to passively search for missing persons or vehicles without vehicle operator involvement. Search criteria may be determined by a remote server and communicated to a plurality of vehicles within a search area. In response to which, sensor data may be collected and analyzed by the vehicles. When sensor data generated by a vehicle matches the search criteria, the vehicle may communicate the information to the remote server.

Systems, methods, and storage media for generating a predicted discharge profile of a vehicle battery pack are disclosed. A method includes receiving, by a processing device, data pertaining to cells within a battery pack installed in each vehicle of a fleet of vehicles operating under a plurality of conditions, the data received from at least one of each vehicle in the fleet of vehicles, providing, by the processing device, the data to a machine learning server, directing, by the processing device, the machine learning server to generate a predictive model, the predictive model based on machine learning of the data, generating, by the processing device, the predicted discharge profile of the vehicle battery pack from the predictive model, and providing the discharge profile to an external device.

Implementations described and claimed herein provide systems and methods for risk assessment. In one implementation, a telematics-centric driving risk value is generated for a specific individual by determining one or more demographic segments corresponding to the specific individual and calculating one or more risk factor values associated with the one or more demographic segments using telematics data. A telematics-weighted personalized risk value is generated by: determining one or more telematics metrics from the telematics data; calculating a telematics persona risk value based on the one or more telematics metrics; calculating a behavioral persona risk value based on one or more behavioral metrics; calculating a household persona risk value based on one or more household metrics; and calculating a finance persona risk value based on one or more finance metrics. A telematics-centric risk prediction value is generated based on the telematics-centric driving risk value and the telematics-weighted personalized risk value.

A task managing system for testing and configuring one or more vehicles includes a plurality of task execution controllers. Each of the plurality of the task execution controllers defines a set of communication nodes configured to wirelessly communicate with a set of vehicles of a plurality of vehicles. The task execution controller includes a processor configured to execute instructions stored in a nontransitory computer-readable medium to operate as a task application module configured to execute a task order on a selected vehicle from the set of vehicles by way of a selected communication node from the set of communication nodes. The task order defines one or more software-based tasks to be performed on the selected vehicle