Method and system for providing carbon offset sources. For example, the method includes determining an amount of total carbon emission of a user, receiving a desired percentage of carbon offset, determining a total number of carbon offset units corresponding to a predetermined carbon offset source, providing multiple carbon offset sources, receiving a respective number of carbon offset units corresponding to the predetermined carbon offset source for each of the multiple carbon offset sources, determining a respective cost for each of the multiple carbon offset sources, and providing a total amount of cost based upon the respective cost for each of the multiple carbon offset sources, where a total of the respective number of carbon offset units is equal to the total number of carbon offset units.

Disclosed are systems and techniques for using blockchain technology to maintain and validate a vehicle ledger. The technique includes receiving, at a master node in the system, a request to update a vehicle ledger associated with a first vehicle node comprising the system. If first criteria are met, the system updates the vehicle ledger, including: generating an updated version of the vehicle ledger using vehicle data stored in a master ledger associated with the master node, and transmitting the updated version of the vehicle ledger to the first vehicle node. If the first criteria are not met, the system forgoes updating the vehicle ledger. The vehicle data corresponds to a first vehicle associated with the first vehicle node. The master ledger is implemented using a blockchain that contains vehicle records for vehicles associated with the system. The blockchain includes a first block including vehicle data corresponding to the first vehicle.

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.

An anomaly of a moving object is detected by using a regression function according to k-nearest-neighbor crossover kernel regression. The regression function is configured to derive an estimation value for a value of a second item from a value of a first item regarding a movement of the moving object. Whether the moving object has an anomaly or not is determined, based on whether a difference between an estimation value of the second item and an actually measured value of the second item is smaller than or equal to a threshold. The estimation value of the second item is derived by the regression function from a value of the first item in the log data having values of the first item and values of the second item, and the actually measured value of the second item is also included in the second log data.

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.

An information recording device acquires outside image information corresponding to an outside image obtained by photographing the outside of a moving body from the moving body; acquires autonomous driving information indicating a state of autonomous driving control of the moving body; acquires display information for simultaneously displaying at least a part of the outside image and an autonomous driving state image indicating the state of the autonomous driving control at a time of photographing the outside image, based on the outside image information and the autonomous driving information; and records the display information in a recording medium. The autonomous driving state image includes brake information indicating whether braking of the moving body is under the autonomous driving control.

A distributed ledger operated by a group of network participants according to a set of consensus rules stores vehicle condition data and generated vehicle condition reports. The vehicle condition data may be received directly from a vehicle or an associated mobile device and the vehicle condition report may be accessed by an authorized user.

One or more driving analysis computing devices in a driving analysis system may be configured to analyze driving data, determine driving behaviors, and determine whether a collision is imminent or has occurred using vehicle-to-vehicle (V2V) communications. Determination of whether a collision has occurred may be based on X-axis, Y-axis, and Z-axis positional data from two vehicles. Driving data from multiple vehicles may be collected by vehicle sensors or other vehicle-based systems, transmitted using V2V communications, and then analyzed and compared to determine various driving behaviors by the drivers of the vehicles.

An example operation may include providing a value to a transport based on sensor data associated the transport.