Value-based data transmission in an autonomous vehicle, comprising: acquiring sensor data from a plurality of sensors of the autonomous vehicle, the sensor data comprising a plurality of portions; determining, for each portion of the sensor data, a value based on one or more objects identified in the sensor data; determining, based on the values for the sensor data, an upload policy; and transmitting, based on the upload policy, one or more portions of the sensor data to a server.

Methods and systems are provided for a coolant system. In one example, a method may include diagnosing a condition of a pump of the coolant system based on a temperature change of coolant. The diagnostic may determine if the pump is stuck on or off.

An electrified tractor includes a vehicle body, a working machine, an electric motor, a battery, an inverter that controls input-output electric power of the battery. The electrified tractor includes a control device that controls the inverter. The control device executes a restriction process, a charging rate calculation process and a relaxation process. In the restriction process, the control device controls the inverter such that the input and output of the battery is restricted within a prescribed electric power range, when a state of the battery satisfies a restriction condition. In the charging rate calculation process, the control device calculates a charging rate of the battery when it is assumed that a work is finished in a farming field, as an estimated charging rate. In the relaxation process, the control device expands the prescribed electric power range, when the estimated charging rate is higher than a first prescribed charging rate.

A vehicle includes receiving signals from a plurality of satellites; obtaining position information based on the received signal; detecting a driving speed and yaw rate; obtaining dead reckoning information based on position information about a position of a vehicle recognized in a previous cycle and the received detection information; predicting the position information based on the obtained dead reckoning information; obtaining a value of Euclidean distance based on the position information about the position of the vehicle recognized in the previous cycle and the obtained position information; generating a first outlier filter based on the value of the Euclidean distance; obtaining a value of Mahalanobis distance based on the obtained position information and the predicted position information; generating a second outlier filter based on the value of the Mahalanobis distance; recognizing a current position of the vehicle by fusing information passing through the first outlier filter and information passing through the second outlier filter; and outputting information about the current position of the recognized vehicle as an image or a sound.

A vehicle moves through an environment (e.g., a farming, construction, mining, or forestry environment) and performs one or more actions in the environment. Portions of the environment may include moisture, such as puddles or mud patches. A control system associated with the vehicle may include a traversability model or a moisture model to help the vehicle operate in the environment with the moisture. In particular, the control system may employ the traversability model to reduce the likelihood of the vehicle attempting to traverse an untraversable portion of the environment, and the control system may employ the moisture model to reduce the likelihood of the vehicle performing an action that will damage a portion of the environment.

A system for characterizing driver behavior includes an interface and a processor. The interface is configured to receive a set of vehicle data, wherein the set of vehicle data comprises embedded image vectors that characterize vehicle data over a short time scale. The processor is configured to determine, using a long time scale model, a long time scale label based at least in part on the embedded image vectors.

A system for characterizing driver behavior includes an interface and a processor. The interface is configured to receive a set of vehicle data, wherein the set of vehicle data comprises embedded image vectors that characterize vehicle data over a short time scale. The processor is configured to determine, using a long time scale model, a long time scale label based at least in part on the embedded image vectors.

A method and a system determine a change of state of an autonomous device, such as an autonomous vehicle. A plurality of performance parameter values obtained by monitoring at least one performance parameter during the autonomous operation of the device is received. A performance quantity quantifying the quality of autonomous operation of the device, in particular the quality of driving of the autonomous vehicle, is determined based on the obtained performance parameter values and information associated with a flux of software and/or hardware related to the autonomous operation of the device. Further, a change of state value for the device is determined based on the performance quantity.

A method and system that can be used to generate or modify a schedule using data indicative of a vehicle condition reported by a vehicle is described. A server receives a first computer network transmission (CNT) comprising data indicative of the vehicle and the vehicle condition, parses the first CNT to obtain the data indicative of the vehicle and the data indicative of the vehicle condition; determines, based on the data indicative of the vehicle, a computer network address associated with a repair shop having a client disposed at the address to display content of a scheduler application, and transmits to the client disposed at the address, a second CNT comprising data to cause the client to display as part of the content of the scheduler application served by computing server an alert indicator and parsed data indicative of the vehicle and the parsed data indicative of the vehicle condition.

Systems and methods for multi-signal fault analysis are described. The system receives signal message information, over a network, from a collection device comprised of a plurality of mobile devices including a first mobile device and a second mobile device that are associated with a first user. The first signal message information includes a first maintenance message including characterization information that was received by the first mobile device from a component that includes a sensor that operates to sense a first part that is assembled into a vehicle. The second signal message information includes signal information that was received by the second mobile device including an audio signal that a microphone in the second mobile device sensed. The system analyzes the multi-signal information to diagnose a problem and communicate a message to the first user with a diagnosis of the problem.