Systems, methods and apparatuses to control power usage of a data storage device. For example, the data storage device has a temperature sensor configured to measure the temperature of the data storage device are provided. A controller of the data storage device determines a set of operating parameters that identify an operating condition of the data storage device. An inference engine of the data storage device determines, using an artificial neural network in the data storage device and based on the set of operating parameters, an operation schedule for a period of time of processing input and output of the data storage device. The operation schedule is configured to optimize a performance of the data storage device in the period of time without the temperature of the data storage device going above a threshold.

A maintenance system is provided for an environment conditioning element of an environmental control system (ECS) of a vehicle. The system includes a data collection module configured to receive geographical areas of travel for the vehicle over respective periods of time. The data collection module is configured to determine a pollution value and a time value for each of the geographic areas of travel. The system further includes a pollution count module coupled to the data collection module and receiving the pollution values and the time values. The pollution count module is configured to determine a pollution count for the environment conditioning element based on the pollution values and the time values. The system further includes a reporting module coupled to the pollution count module and receiving the pollution count. The reporting module is configured to generate a report for a user that includes the pollution count.

Systems and methods for crash determination in accordance with embodiments of the invention are disclosed. In one embodiment, a vehicle telematics device includes a processor and a memory storing a crash determination application, wherein the processor, on reading the crash determination application, is directed to obtain sensor data from at least one sensor installed in a vehicle, calculate peak resultant data based on the sensor data, where the peak resultant data describes the acceleration of the vehicle over a first time period, generate crash score data based on the peak resultant data and a set of crash curve data for the vehicle, where the crash score data describes the likelihood that the vehicle was involved in a crash based on the characteristics of the vehicle and the sensor data, and provide the obtained sensor data when the crash score data exceeds a crash threshold to a remote server system.

Systems and methods for route management are disclosed. A system for use with a communication network includes an electronic logging device, a global positioning device, and a server. The server communicates via a communication network and to access vehicle data and global positioning data via the communication network. The server includes a processor(s) and a non-transitory computer-readable medium storing instructions thereon, that when executed by the one or more processors, cause the processor(s) to: analyze the vehicle data to identify start-and-stop events of the vehicle; analyze each of the start-and-stop events with respect to criteria to identify which of the start-and-stop events is associated with an actual visit event; identify the locations and the times from the global positioning data that correspond to the actual visit events; and correlate the locations and the times of the vehicle with the respective ones of the actual visit events.

Described herein are systems and methods for applying machine learning to telematics data to generate a unique vehicle fingerprint by periodically receiving telematics data generated at a plurality of sensors of a vehicle; standardizing the telematics data; aggregating the standardized telematics data; applying a trained machine learning model to embed the aggregated telematics data into a low-dimensional state; and generating a unique vehicle fingerprint, the vehicle fingerprint comprising a static component, a dynamic component, or both a static component and a dynamic component; including iterative repetition to update the dynamic component of the vehicle fingerprint.

Systems and methods for crash determination in accordance with embodiments of the invention are disclosed. In one embodiment, a vehicle telematics device includes a processor and a memory storing a crash determination application, wherein the processor, on reading the crash determination application, is directed to obtain sensor data from at least one sensor installed in a vehicle, calculate peak resultant data based on the sensor data, where the peak resultant data describes the acceleration of the vehicle over a first time period, generate crash score data based on the peak resultant data and a set of crash curve data for the vehicle, where the crash score data describes the likelihood that the vehicle was involved in a crash based on the characteristics of the vehicle and the sensor data, and provide the obtained sensor data when the crash score data exceeds a crash threshold to a remote server system.

The invention provides a control unit and a control system for a vehicle capable of reducing a workload of deleting a record of information of an unintended event detected by a certain control unit by preventing the other control unit from recording the information of the event. Control units (50a to 50d) that are mounted on the vehicle and connected in a mutually communicable manner each include an event processing section (55). The event processing section (55) executes one or both of processing to switch whether to notify a recording request of the information of the event and processing to switch necessity/unnecessity of recording of the information of the event.

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.

A vehicle control device including a microcontroller, a wifi/bluethoot device, a voltage stabilizer circuit and a voltage protector circuit, a battery and a battery charge control circuit, an analogue-digital bus can adapter circuit of transceiver type, an optical-digital signal adapter, a radiofrequency (rf) module, an external antenna adapter circuit and a non-volatile data storage device (rom) that allows the generation of an internal and an external wireless network at the same time and connection with other devices, to make control, warning, management and storage of driver's data and his driving style, his load and related units in real-time, and the interrelation with other vehicles that also have the device, for communication.