Operating conditions corresponding to a harvesting operation being performed by a mobile harvesting machine are detected along with a priority of a first performance pillar metric relative to a second performance pillar metric. An operating characteristic of the mobile harvesting machine is detected and a performance pillar metric value is identified for the first performance pillar metric based on the detected operating characteristic. A performance limitation corresponding to the first performance pillar metric is identified based on the detected operating conditions and an aggressiveness setting is detected that is indicative of an operating settings change threshold. It is then determined whether a settings change is to be performed based on the first performance pillar metric value, the priority of the first performance pillar metric, the first performance limitation and the settings change threshold and if the settings change is to be performed, a settings change actuator is controlled to execute the settings change.

A transport-monitoring module for monitoring a transport of a transport unit of a passenger-transportation device is arranged on the transport unit. The transport-monitoring module has a sensor and localization devices. During the transport, through an interface unit, a connection with a data memory of the device is created. The interface unit is configured so that transport-monitoring data that are to be stored, which are based on sensor data of the sensor and localization data of the localization devices, are permanently stored in the data memory. In this way, the transport-monitoring module is simplified, so that it is no longer an item that is worth stealing. In addition, a reliable access to the stored transport-monitoring data after the end of the transport is secured.

A monitoring and maintenance system that utilizes imperial and theoretical data to compare parts, vehicles, users, regions, wear intensity indexes over time and tracking information to provide a sophisticated data collection system for heavy-duty equipment or rental equipment. This tracking is designed to better the specifications, designs, training, preventative maintenance, and replacement wear understanding of fleet management.

A door assist system for a vehicle is disclosed. The system comprises an actuator configured to control a position of the door about a hinge assembly. An interference sensor forms a portion of a door opening seal and is configured to detect an obstruction in a plurality of detection regions. The system further comprises a controller configured to control the actuator in response to a detection signal from the interference sensor.

A method and system for driver monitoring by fusing contextual data with event data to determine context as cause of event is provided. The method includes detecting an event from event data received from one or more inertial sensors associated with a vehicle of a driver or with the driver. The method also includes determining a context from an audio or video feed received from one or more devices associated with the vehicle or the driver. Further, the method includes fusing the event with the context to determine the context as a cause of the event. In addition, the method includes assigning a score to a driver performance metric based at least on the context and the event.

A system aggregates data related to a non-conformance system in an aircraft and allocates resources to repairing the non-conformance condition. The system includes a service bus that receives and distributes the non-conformance condition and an optimizer tool that generates repair alternatives. The optimizer tool allows dynamic reconfiguration of rules to activate and deactivate or change a weight of individual rules in a rule set.

A system and method of detecting damage to a component may include a first sensor and a processor. The method may include the steps of receiving, by the processor, a first data for the component from a first sensor, aligning, by the processor, the first data with a reference model, determining, by the processor, a feature dissimilarity between the first data and the reference model, classifying, by the processor, the feature dissimilarity, and determining, by the processor, a probability that the feature dissimilarity indicates damage to the component.

A vehicle information service implemented on one or more computers of a service provider network implements a first application programmatic interface (API) that allows a client to define inclusion parameters and a sample size for a fleet of vehicles from which vehicle data is to be collected. The vehicle information service also implements a second API that notifies the client when the requested vehicle data has been collected from the vehicle fleet. Additionally, the vehicle information service provides the client access to the collected vehicle data. The vehicle information service manages the collection of the vehicle data from the client defined vehicle fleet without requiring further client involvement and notifies the client when the collection of the vehicle data is complete.

A method, a device, and a system for processing information of a vehicle are disclosed. In the embodiments, information about a connected vehicle is acquired, and a display image of a virtual vehicle is generated; driving information of the connected vehicle is acquired; whether the vehicle is currently in a driving state is determined according to the driving information; and when the connected vehicle is in the driving state, an orientation of a head of the virtual vehicle is adjusted to keep consistency with an orientation of a head of the connected vehicle; and when the connected vehicle is in a non-driving state, an orientation of a head of the virtual vehicle is adjusted towards a location of the connected vehicle. The present disclosure can feed back a vehicle condition in real time, dynamically and truly, and position a vehicle more accurately.

Methods and systems for determining risk associated with operation of autonomous vehicles using autonomous communication are provided. According to certain aspects, autonomous operation features associated with a vehicle may be determined, including features associated with autonomous communication between vehicles or with infrastructure. This information may be used to determine risk levels for a plurality of features, which may be based upon test data regarding the features or actual loss data. Expected use levels and autonomous communication levels may further be determined and used with the risk levels to determine a total risk level associated with operation of the vehicle. The autonomous communication levels may indicate the types of communications, the levels of communication with other vehicles or infrastructure, or the frequency of autonomous communication. The total risk level may be used to determine or adjust aspects of an insurance policy associated with the vehicle.