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.

Methods, computer-readable media, software, and apparatuses include activating a telematics system to collect telematics data associated with operation of a vehicle during a first window of time, receiving, by a computing device associated with the vehicle, telematics data from the telematics system during the first window of time, identifying one or more parameters associated with operation of the vehicle based on analyzing the telematics data, determining whether the one or more parameters meets a safe driving threshold, and upon determining that the one or more parameters meets the safe driving threshold, transmitting the telematics data to a third party server or device.

Vehicles and methods of operating vehicles are disclosed herein. A vehicle includes a main frame, a work implement, and a control system. The work implement is supported by the main frame and configured to carry a payload in use of the vehicle. The control system is supported by the main frame and configured to control operation of the vehicle. The control system includes a payload measurement system configured to provide payload input indicative of a variable payload carried by the work implement in use of the vehicle and a controller coupled to the payload measurement system.

An in-vehicle sensing module for monitoring a vehicle is disclosed, which is advantageous for use in the context of a shared vehicle service, such as a car rental service, an autonomous taxi service, or a ride sharing service. The in-vehicle sensing module at least includes a controller, a cellular transceiver, and one or more integrated sensors configured to monitor a status of the vehicle. The in-vehicle sensing module utilizes appropriate algorithms, models, or thresholds to interpret sensor data and enrich the data with metadata and event detection. The in-vehicle sensing module uploads relevant sensor data, event data, or other metadata to a cloud storage backend, which is made accessible by authorized third-parties.

A driving data collection method is disclosed, including: obtaining driving scenario requirement information (S203); determining at least one sensor on a vehicle based on the driving scenario requirement information (S204); and then, sending driving data collected by the at least one sensor to a network side device (S206).

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.

Aspects of the disclosure relate to computing platforms that utilize improved machine learning techniques for dynamic device quality evaluation. A computing platform may receive driving data from a mobile device. Using the driving data, the computing platform may compute a plurality of driving metrics, which may include: a geopoint expectation rate score, a trips per day rank score, a consecutive geopoint time difference score, a global positioning system (GPS) accuracy rating score, and a distance between consecutive trips score. By applying a machine learning model to the plurality of driving metrics, the computing platform may compute a device evaluation score, indicating a quality of the driving data received from the mobile device. Based on the device evaluation score, the computing platform may set flags, which may be accessible by a driver score generation platform, causing the driver score generation platform to perform an action with regard to the mobile 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 first connected message broadcast from a first vehicle and a second connected message broadcast from a second vehicle is received, each of the first and second connected messages including proof-of-work computed from connected vehicle data regarding a third vehicle. The first and second connected messages are authenticated, responsive to a comparison of the proof-of-work for the third vehicle included in the first connected message and the proof-of-work for the third vehicle included in the second connected message. The connected vehicle data in the first connected message broadcast or second connected message broadcast is utilized for autonomous vehicle operations or driver-assistance vehicle operations, responsive to the proof-of-work being a match.

A communication adapter of a gas turbine engine of an aircraft includes a communication interface configured to wirelessly communicate with an offboard system and to communicate with an engine control of the gas turbine engine, a memory system, and processing circuitry. The processing circuitry is configured to receive an engine control dynamic data recording request from the offboard system, confirm an authentication between the communication adapter and the engine control, transfer the engine control dynamic data recording request received at the communication adapter from the offboard system to the engine control based on the authentication, and transmit an update completion confirmation of the engine control from the communication adapter to the offboard system based on a confirmation message from the engine control.