Abstract: A system and a method include one or more sensors configured to detect one or more forces in relation to one or more components within an internal cabin of a vehicle. An imaging device is configured to acquire one or more images of the internal cabin of the vehicle. A control unit is in communication with the one or more sensors and the imaging device. The control unit is configured to determine one or more incidents within the internal cabin based on the one or more forces detected by the one or more sensors, and the one or more images acquired by the imaging device. The system and the method can also provide recommendations regarding actions to take in response determining incident(s).

Abstract: A device includes one or more processors configured to obtain bounding box data indicating a first location history of a first bounding box associated with a first object detected in images. The one or more processors are also configured to process, using a trained model, at least the first location history to detect an event associated with flight preparation of an aircraft. The one or more processors are further configured to generate an output indicating the event.

Abstract: A system includes one or more control units configured to determine one or more fuel consumption models for an aircraft. A method includes determining, by one or more control units, one or more fuel consumption models for an aircraft. A non-transitory computer-readable storage medium comprising executable instructions that, in response to execution, cause one or more control units comprising a processor, to perform operations including determining one or more fuel consumption models for an aircraft.

Abstract: A system and a method include a control unit configured to determine one or more fuel levels for an aircraft at one or more arrival airports for one or more alternate flight plans that divert from an original flight plan. The control unit is configured to determine the one or more fuel levels based on traffic at the one or more arrival airports, and performance data for the aircraft.

Abstract: A method includes obtaining, at a device, source and destination data for one or more upcoming flights through a particular airspace. The method also includes obtaining, at the device, a map of societal impact hotspots associated with traffic through the particular airspace, and generating, based on the map of societal impact hotspots, a set of trajectories for the one or more upcoming flights through the particular airspace.

Abstract: Micro-weather risk mapping for very low-level aerial vehicles includes receiving data indicating a first geographic area; obtaining meteorologic data for a second geographic area, wherein the first geographic area is smaller than and entirely bounded by the second geographic area; determining topographic parameters associated with the first geographic area; performing a comparison of the topographic parameters associated with the first geographic area to topographic parameters associated with a plurality of predetermined micro-weather models; selecting a micro-weather model from among the plurality of predetermined micro-weather models based on the comparison; and determining, based on the meteorologic data for the second geographic area and the micro-weather model, risk data indicative of risk of particular meteorological conditions in the first geographic area.

Abstract: A method of detecting ice on a surface of an aircraft includes obtaining a first series of images captured by one or more cameras onboard the aircraft. The method also includes performing data reduction operations to generate a second series of images. The method further includes generating feature data based on the second series of images, where the feature data is indicative of changes over time of pixel data of the second series of images. The method also includes generating, based on the feature data, input data for a trained classifier and determining, using the trained classifier, whether ice is present on one or more surfaces of the aircraft.

Abstract: A system and method for conducting preflight planning for autonomous flight missions of unmanned aerial vehicles (UAVs). The system includes use of a controller to conduct quantitative risk assessments of available digital data to predict low risk flight routes based on estimated flight risk profiles. The flight risk profiles may be based upon flight safety-critical information, including real time regulatory, airspace, obstacle, and infrastructure data sets. Among other data sets, the flight risk profiles may also account for current weather, current population and traffic data, and aircraft operational data specific to the UAV involved. Each risk assessment can generate a flight risk profile dependent on proposed times of travel, from which a low risk route may be predicted for any impending autonomous aircraft flight. Such risk assessments may enhance chances of expeditious regulatory acceptance of flight plans for such predetermined flight routes.

Abstract: A method includes separating a flight plan of a vehicle into a number of portions with each portion including a particular length that is determined based on a complexity of an environment where the flight plan takes place. The complexity of the environment is based on at least one of a set of factors including at least one of a terrain of the environment, one or more obstacles, one or more no-fly zones, or one or more no-landing zones within the environment. The method also includes determining an escape route for each portion of the flight plan. The escape route includes a route to a safe landing site in response to a failure of a system onboard the vehicle. The method additionally includes generating an escape route plan for the flight plan in response to all portions of the flight plan being assigned at least one escape route.

Abstract: A system and method for conducting preflight planning for autonomous flight missions of unmanned aerial vehicles (UAVs). The system includes use of a controller to conduct quantitative risk assessments of available digital data to predict low risk flight routes based on estimated flight risk profiles. The flight risk profiles may be based upon flight safety-critical information, including real time regulatory, airspace, obstacle, and infrastructure data sets. Among other data sets, the flight risk profiles may also account for current weather, current population and traffic data, and aircraft operational data specific to the UAV involved. Each risk assessment can generate a flight risk profile dependent on proposed times of travel, from which a low risk route may be predicted for any impending autonomous aircraft flight. Such risk assessments may enhance chances of expeditious regulatory acceptance of flight plans for such predetermined flight routes.

Abstract: A method includes separating a flight plan of a vehicle into a number of portions with each portion including a particular length that is determined based on a complexity of an environment where the flight plan takes place. The complexity of the environment is based on at least one of a set of factors including at least one of a terrain of the environment, one or more obstacles, one or more no-fly zones, or one or more no-landing zones within the environment. The method also includes determining an escape route for each portion of the flight plan. The escape route includes a route to a safe landing site in response to a failure of a system onboard the vehicle. The method additionally includes generating an escape route plan for the flight plan in response to all portions of the flight plan being assigned at least one escape route.