A system for managing a set of fleet vehicles includes a central command unit having a processor and tangible, non-transitory memory on which instructions are recorded. The fleet vehicles have one or more respective electronic components and respective vehicle controllers adapted to perform self-diagnosis of their respective electronic components. The central command unit is adapted to receive notification of the self-diagnosis and classify the respective electronic components as working ones and non-working ones. The central command unit is adapted to perform component matching, including polling inventory across the fleet vehicles. Working ones of the respective electronic components are selectively redistributed for reuse across the fleet vehicles. In some embodiments, at least one of the fleet vehicles is designated as a master vehicle and the central command unit is stored in the master vehicle. In other embodiments, the central command unit is stored in a cloud computing service.

Described herein are systems, methods, and non-transitory computer readable media for evaluating vibrational characteristics of a vehicle such as an autonomous vehicle and vibrational characteristics of a road surface in conjunction with one another to identify anomalous vehicle vibrational characteristics that may be indicative of potential damage to the structure of a vehicle. A baseline vibrational signature may be generated for a road segment based on sensor data received from multiple vehicles traversing the road segment. A vibrational signature may also be generated for a particular vehicle being evaluated based on real-time sensor data. If the vibrational signature for the vehicle deviates from the baseline vibrational signature for the road segment by more than a threshold amount, the vehicle may be deemed to be exhibiting anomalous vibrational characteristics indicative of potential vehicular damage. One or more actions in response thereto may then be taken such as automatically halting the vehicle.

A controller for a hybrid vehicle performs charging control when a shift range of the hybrid vehicle is a first range, and does not perform the charging control when the shift range of the hybrid vehicle is a second range, the charging control being control of charging a power storage device with electric power generated by a generator driven by an engine. The controller records diagnosis information when an SOC of the power storage device is equal to or lower than a first threshold value and the shift range of the hybrid vehicle is the first range, and does not record the diagnosis information when the SOC of the power storage device is equal to or lower than the first threshold value and the shift range of the hybrid vehicle is the second range.

The present disclosure relates to a contextual service device and method of use to identify an operator's needs, such as in the event a vehicle crash, vehicle breakdown, theft, and/or vehicle related quarry in real time. In various aspects, the device is self-sufficient and may be installed and activated in a single step. Furthermore, the device is not reliant upon external systems of components of the vehicle to operate and may be updated over the air to improve cash detection accuracy.

Method and system for evaluating vehicle insurance claims. For example, the method includes collecting pre-accident images of a vehicle before one or more accidents, collecting telematics data of the vehicle during the one or more accidents, determining predicted post-accident images of the vehicle based upon the pre-accident images and the telematics data after the one or more accidents, receiving submitted post-accident images for vehicle insurance claims associated with the vehicle, comparing the predicted post-accident images with the submitted post-accident images, and determining whether frauds have been committed in the vehicle insurance claims based upon the comparison of the predicted post-accident images with the submitted post-accident images.

A method for detecting time rollovers is disclosed. The method may include receiving time data including week data and second data and processing the time data to generate a first date. The method may include generating, based on the first date and an offset value, a second date and obtaining, when the second date is prior to a baseline date, a network date. The method may include assigning the network date as the baseline date and processing the network date and the first date to determine an updated offset value. The method may include storing the updated offset value as the offset value and determining, based on the network date, a system date.

In some embodiments, a method comprises: identifying past trips, each of the past trips having a past trip characteristic similar to a particular trip characteristic of a particular trip; obtaining a vehicle identifier used during each of the past trips; obtaining status of one or more onboard services provided during each of the past trips; generating interface data representing: a past trip header, the past trip characteristic for each of the past trips, the vehicle identifier used during each of the past trips, an icon for each of the past trips having a feature indicating the status of one or more onboard services provided during the past trip, and an additional icon indicating status of onboard services provided during the particular trip; transmitting the interface data to cause a display device to generate/display a viewer.

The present disclosure provides a device associated with a vehicle, the device including a set of sensors configured to generate a set of data associated with the vehicle; and one or more controllers configured to analyze the set of data to determine if an accident involving the vehicle has occurred, responsive to a determination that an accident involving the vehicle has occurred, determine an accident-severity score associated with the vehicle involved in the accident, the accident-severity score being based on a set of accident-severity scoring rules and at least a portion of the set of data, and transmit an output signal representative of the determined accident-severity score.

The present disclosure is directed to improving safety of drivers that drive vehicles that belong to a fleet of vehicles. This may include identifying individual drivers that should receive a training or coaching session. Scores that associated vehicle type, vehicle weight class, time of day, number of stops/starts, and other factors could be evaluated to identify drivers that may have been assigned a highest risk factor. Company telematic data as well as public data that identify types of roadways, roadway conditions, experience levels, and other factors may be combined with numbers of accidents or speeding tickets when an analysis is performed. Methods described herein may be implemented according to a machine learning model based on a processor executing instructions out of a memory. This may include deep learning or the neural network technology.

A data processing apparatus includes a communicator, an acquisition unit, and an output controller. The communicator is configured to receive request data from a server. The request data contains a content of a request for collection of traveling state data of a vehicle. The acquisition unit is configured to acquire the traveling state data of the vehicle on the basis of the request data received by the communicator. The output controller is configured to cause an output device to output an acquisition status of the traveling state data acquired by the acquiring unit.