Systems and methods for automated communication with Air Traffic Control. The system comprises a processor and memory. The memory stores instructions to execute a method. The method includes receiving audio communication input from an air traffic controller (ATC). The audio communication input is then converted into text input. Next, an aircraft keyword is detected in the text input. The text input is then parsed and one or more data structures are generated from the parsed input. In some examples, the one or more data structures includes command data for controlling the aircraft. Next, the command data in the one or more data structures is verified. The one or more data structures are then transmitted to an onboard flight computer of the aircraft. Last, the one or more data structures are stored in a conversation memory.

Disclosed are an anti-collision system and method for an aircraft and an aircraft including the anti-collision system. The anti-collision system includes: a sensor data processing unit configured to process data received from a plurality of sensors to detect objects around the aircraft, and output a result about detected objects; a safeguarding box building unit configured to generate, based on an aircraft geometry database, a three-dimensional safeguarding box for the aircraft; and a risk assessment unit configured to calculate relative distances between detected objects and the aircraft, and determine whether there is a collision risk between the aircraft and an object, among the detected objects, located in the safeguarding box or to be entering into the safeguarding box, wherein the system is configured to output an alarm or a warning when there is the collision risk.

Methods and systems are provided for assisting operation of a vehicle using speech recognition and transcription. One method involves identifying an operational objective for an audio communication with respect to the vehicle, determining an expected clearance communication for the vehicle based at least in part on the operational objective, identifying a discrepancy between a transcription of the audio communication and the expected clearance communication, augmenting the transcription of the audio communication using a current operational context to reduce the discrepancy, and providing a graphical indication influenced by the augmented transcription.

A flight pushback state monitoring method based on multi-modal data fusion comprises: 1, constructing a control intention recognition rule, and recognizing a pushback intention from a control instruction sent by a controller; 2, constructing a flight intention recognition model, extracting an aircraft action from a real-time monitoring video, and capturing a flight intention; and 3, constructing an intention alignment fusion rule, and judging whether control intention information conflicts with flight intention information; by fusing the control intention and the flight intention, the method can realize the following auxiliary functions: timely judging whether the aircraft follows the pushback instruction sent by the controller, if a captain does not act according to the control instruction or acts arbitrarily without a control instruction, giving an inconsistent alarm, and a function of monitoring the flight pushback state is implemented.

Methods, devices, and systems for aircraft identification are described herein. In some examples, one or more embodiments include a computing device comprising a memory and a processor to execute instructions stored in the memory to simulate virtual light detection and ranging (Lidar) sensor data for a three-dimensional (3D) model of an aircraft type to generate a first point cloud corresponding to the 3D model of the aircraft type, generate a classification model utilizing the simulated virtual Lidar sensor data of the 3D model of the aircraft type, and identify a type and/or sub-type of an incoming aircraft at an airport by receiving, from a Lidar sensor at the airport, Lidar sensor data for the incoming aircraft, generating a second point cloud corresponding to the incoming aircraft utilizing the Lidar sensor data for the incoming aircraft, and classifying the second point cloud corresponding to the incoming aircraft using the classification model.

A method and a system for detecting wire or wire-like obstacles, which method and system are designed for an aircraft. The system for detecting wire or wire-like obstacles comprises a detection device, such as a video camera or a LIDAR device, a computer and a display device. The method includes a step of detecting at least one pylon in the surrounding environment of the aircraft via a detection device, a step of identifying a family of pylons to which each detected pylon corresponds, a step of characterizing at least one cable supported by the at least one detected pylon, and a step of determining a prohibited zone that can potentially contain each pylon and each cable and a safe zone not containing either a pylon or a cable. The prohibited zone and the safe zone may be displayed on the display device.

Apparatus and associated methods relate to ranging object(s) nearby an aircraft using triangulation. A light projector mounted at a projector location on the aircraft projects pulses of polarized light onto the scene external to the aircraft. The projected pulses of polarized light are polarized in a first polarization state. A camera mounted at a camera location on the aircraft has a shutter synchronized to the projector output pulse and receives a portion of the projected pulses of polarized light reflected by the object(s) in the scene and polarized at a second polarization state orthogonal to the first polarization state. Location(s) and/or range(s) of the object(s) is calculated, based on the projector location, the camera location, and pixel location(s) upon which the portion of light is imaged.

A system may include an aircraft including a processor. The processor may be configured to: receive a lead aircraft assignment instruction, the lead aircraft assignment instruction instructing the aircraft to follow a lead aircraft; determine whether the aircraft is receiving sufficient lead aircraft traffic data from the lead aircraft to record a four-dimensional (4D) track of the lead aircraft; upon a determination that the aircraft is receiving the sufficient lead aircraft traffic data, output an acceptance of the lead aircraft assignment instruction; receive the lead aircraft traffic data from the lead aircraft, the lead aircraft traffic data including information at least one of associated with or of the track of the lead aircraft; record the track of the lead aircraft; and output commands configured to cause (a) the aircraft to follow the recorded track, or (b) guidance content for following the recorded track of the lead aircraft to be presented.

Combined taxi signage may be generated from taxi signage for a first origination node and taxi signage for a second origination node, the first origination node and the second origination node being of a select proximity and orientation relative to an aircraft. The taxi signage for the first origination node may be generated from the first origination node and at least a first termination node stored within an airport surface routing network data, and a first turning angle determined based on a comparison between the first origination node and the at least the first termination node. The taxi signage for the second origination node may be generated from the second origination node and at least a second termination node stored within the airport surface routing network data, and a second turning angle determined based on a comparison between the second origination node and the at least the second termination node. The combined taxi signage may be included in a combined billboard displayed on the display device of the aircraft.

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.