A camera with a field of view toward an external environment of an aircraft is disposed within an aircraft door such that a ground surface is within the field of view of the camera during taxiing of the aircraft. A display device is disposed within an interior of the aircraft. A processor is operatively coupled to the camera and to the display device. The processor analyzes image data captured by the camera for docking guidance by identifying, within the captured image data, a region on the ground surface corresponding to an alignment fiducial indicating a parking location for the aircraft, determining, based on the region of the captured image data corresponding to the alignment fiducial indicating the parking location, a relative location of the aircraft with respect to the alignment fiducial, and outputting an indication of the relative location of the aircraft to the alignment fiducial.

A system for an aircraft includes an optical sensor, at least one aircraft sensor, and a controller. The optical sensor is configured to emit a laser outside the aircraft, and the at least one aircraft sensor is configured to sense at least one aircraft condition. The controller is configured to determine a first operational state of the aircraft based upon the at least one aircraft condition and determine a second operational state of the aircraft based on the at least one aircraft condition, and operate the optical sensor to emit the laser at a first intensity during the first operational state and a second intensity during the second operational state, wherein the second intensity is greater than the first intensity.

An airport ground collision alerting system and a method of operating an airport ground collision alerting system. The airport ground collision alerting system comprises an automatic dependent surveillance-broadcast, ADS-B, receiver; a first database coupled to the ADS-B receiver and configured for look-up of aircraft type data based on aircraft ID data received via the ADS-B receiver and for storing aircraft position and heading data received via the ADS-B receiver; a second database configured to store digital map data for one or more airports; a processor unit coupled to the first and second databases and an application programming interface, API, configured to couple the airport ground collision alerting system to a display device.

Autoland systems and processes for landing an aircraft without pilot intervention are described. In implementations, the autoland system includes a memory operable to store one or more modules and at least one processor coupled to the memory. The processor is operable to execute the one or more modules to identify a plurality of potential destinations for an aircraft. The processor can also calculate a merit for each potential destination identified, select a destination based upon the merit; receive terrain data and/or obstacle data, the including terrain characteristic(s) and/or obstacle characteristic(s); and create a route from a current position of the aircraft to an approach fix associated with the destination, the route accounting for the terrain characteristic(s) and/or obstacle characteristic(s). The processor can also cause the aircraft to traverse the route, and cause the aircraft to land at the destination without requiring pilot intervention.

An aerial vehicle is provided. The aerial vehicle can include a plurality of sensors mounted thereon, an avionics system configured to operate at least a portion of the aerial vehicle, and a machine vision controller in operative communication with the avionics system and the plurality of sensors. The machine vision controller is configured to perform a method. The method includes obtaining sensor data from at least one sensor of the plurality of sensors, determining performance data from the avionic system or an additional sensor of the plurality of sensors, processing the sensor data based on the performance data to compensate for movement of the unmanned aerial vehicle, identifying at least one geographic indicator based on processing the sensor data, and determining a geographic location of the aerial vehicle based on the at least one geographic indicator.

A gateway retrieval alert device is configured for use in an aircraft ground support system. The gateway retrieval alert device is connected to an intercom gateway, either through a direct wireless connection or through a direct connection to a headset of a tug operator, the headset in turn being wirelessly connected to the intercom gateway. The integrity of the wireless link to the intercom gateway is monitored, and when the integrity of the link falls below a set threshold, an alert is issued. The alert is issued externally to the communication network between the ground crew and pilot.

A gateway retrieval alert device is configured for use in an aircraft ground support system. The gateway retrieval alert device is connected to an intercom gateway, either through a direct wireless connection or through a direct connection to a headset of a tug operator, the headset in turn being wirelessly connected to the intercom gateway. The integrity of the wireless link to the intercom gateway is monitored, and when the integrity of the link falls below a set threshold, an alert is issued. The alert is issued externally to the communication network between the ground crew and pilot.

A computer-implemented method is provided for training a supervised machine learning engine able to predict characteristics of aircraft trajectories from parameters of an aircraft, and environment parameters of the aircraft trajectory. A system able to train the supervised machine learning engine, a system for using the engine, and a computer-implemented method for using the engine are provided. The methods and systems provided are particularly useful for air traffic flow management applications.

The present invention relates to an autonomous, multi-use, subterranean aircraft pull-through system that connects with an aircraft arriving at the ramp pick-up point and transits through the terminal building, in a unidirectional movement, where the aircraft is serviced, then disconnects from the aircraft at the ramp release point. The autonomous, subterranean aircraft pull-through system receives multiple aircraft in a row, with rows adjacent to each other, where aircraft are nose to tail, and side by side, occupying the smallest footprint in the industry. The autonomous, subterranean aircraft pull-through system is remotely controlled and operates autonomously in a subterranean manner to assist in servicing the aircraft. The system helps to speed up the aircraft handling component of airside operations, improve safety, reduce emissions, and the cost factors borne by both airports and airlines. The autonomous, subterranean aircraft pull-through system design is versatile in handling all code A-F (ICAO) aircraft.

A system includes a memory system configured to store a plurality of instructions to provide aircraft communication visualization and a processing system. The processing system configured to execute the instructions to result in retrieving a plurality of status data associated with aircraft communication at a target location from a database, determining a plurality of geographic location information for a plurality of aircraft parking locations at the target location, accessing a plurality of imagery data associated with a view at the target location, and generating an aircraft communication visualization map for the target location. The aircraft communication visualization map includes aircraft communication information overlaid upon an image based on the imagery data and the aircraft parking locations at the target location.