A method and system for controlling autonomous vehicles comprises a flight controller comprising at least one processor and a computer-usable medium embodying computer program code, the computer-usable medium capable of communicating with at least one processor, the computer program code comprising instructions executable by at least one processor and configured for controlling a vehicle, the flight controller further comprising: an obstacle avoidance module, a state machine, a data link module, and a control computer module.

Driver safety, vehicle safety, and environment safety may be scored based on a variety of input data concerning a driver, a vehicle, or an environment in which the vehicle drives. An overall safety score may be generated based on at least some of these three scores. These scores may be compared to thresholds to trigger or initiate actions such as providing notifications to drivers, raising or reducing vehicle insurance rates, providing coupons and promotions to drivers, or limiting vehicle speed in a manner that is personalized to the driver and/or vehicle and/or environment.

An example operation may include one or more of receiving, by a diagnostic center, malfunction information related to a transport, acquiring, by a diagnostic center, agreements on a threshold for the malfunction information from a plurality of diagnostic centers, in response to the malfunction information exceeding the threshold, storing the malfunction information on a remote storage, and deleting the malfunction information from the transport.

An architecture for monitoring at least one aircraft. The architecture comprises an avionics system configured to generate avionics data during use of the aircraft; a mobile electronic device including an analysis unit configured to convert at least one maintenance operation into operational data; and an alerter configured to display at least one item of monitoring information; and a cloud computing infrastructure. The analysis unit and the alerter are configured to implement a local operating mode and an operating mode connected to the cloud computing infrastructure.

Disclosed are systems and methods for supporting a vehicle lending services platform. Specifically, machine learning is applied to identify and accumulate data from electronic services. The machine learning is further used to identify risk associated with a potential lend of a vehicle. The identified risk is presented as part of a browser plugin graphical user interface (GUI) and/or inline with portions of a portal GUI of the vehicle lending services platform.

The disclosure relates generally to an in-vehicle feedback system, and more particularly, to an in-vehicle device with a display or graphical interface that collects driving data and provides feedback based on the driving data. The system may comprise an in-vehicle device that includes a graphical user interface and a processor and a data collection device wirelessly connected to the in-vehicle device. The in-vehicle device may be configured to receive vehicle telematics data from the data collection device and the processor may process the telematics data in real time and cause the telematics data to be displayed on the graphical user interface. The graphical user interface may include a speed display and an acceleration display.

Systems and methods for vehicle information analysis and management via a network are disclosed, including a mobile device operable to capture an image of a vehicle information sticker using an image capturing component and communicatively coupled to the network, a server operable to receive the captured image of the vehicle information sticker via the network and perform an image processing operation on the image, and a vehicle information repository database that is operable to store vehicle information associated with the vehicle information sticker in non-transitory memory after the image processing operation has been performed.

A system for detecting a leak in a duct is disclosed, including a sheath covering a duct section, a fluid concentrating device and a sensor. The sheath is attached to a boundary portion of the duct section and forms an inner space between an outer surface of the duct section and the sheath. The fluid concentrating device has an opening through the sheath connecting the inner space to an external space outside the sheath. The inner space is sealed except for the opening through the sheath. The sensor is stationed outside the opening of the fluid concentrating device and is configured to detect fluid flow from the inner space through the opening at a level corresponding to a duct leak in a pre-burst condition.

A method of optimizing rider satisfaction includes classifying, using a first neural network of a hybrid neural network, social media data sourced from a plurality of social media sources as indicative of an effect on a transportation system. The method further includes predicting, using a second neural network of the hybrid neural network, at least one aspect of rider satisfaction affected by an effect on the transportation system derived from the social media data classified as indicative of an effect on the transportation system. The method still further includes optimizing, using a third neural network of the hybrid neural network, the at least one aspect of rider satisfaction for at least one rider occupying a vehicle in the transportation system.

A diagnostic apparatus for a vehicle provided with a continuously-variable transmission including a primary pulley, a secondary pulley and a belt looped over the primary and secondary pulleys, such that each of the primary and secondary pulleys includes a hydraulic actuator to which a working fluid is to be supplied. The diagnostic apparatus is configured, in event of occurrence of slippage of the belt on at least one of the primary and secondary pulleys, to infer a factor causing the slippage of the belt, based on at least one of a hydraulic pressure of the working fluid supplied to the hydraulic actuator of the primary pulley and a hydraulic pressure of the working fluid supplied to the hydraulic actuator of the secondary pulley, upon the occurrence of the slippage of the belt.