Abstract: Solutions are provided for auto-labeling sensor data for machine learning (ML). An example includes: determining a platform's own position; recording, from a sensor aboard the platform, sensor data comprising a sensor image; receiving position data for at least one intruder object (e.g., a nearby airborne object); based at least on the position data for the intruder object and the platform's position, determining a relative position and a relative velocity of the intruder object; based at least on the relative position and a relative velocity of the intruder object and a field of view of the sensor, determining an expected position of the intruder object in the sensor image; labeling the sensor image, wherein the labeling comprises annotating the sensor image with a region of interest and an object identification; and training an artificial intelligence (AI) model using the labeled sensor image.

Abstract: Illustrative embodiments provide a method and system for communicating air traffic control information. An audio signal comprising voice activity is received. Air traffic control information in the voice activity is identified using an artificial intelligence algorithm. A text transcript of the air traffic control information is generated and displayed on a confirmation display. Voice activity in the audio signal may be detected by identifying portions of the audio signal that comprise speech based on a comparison between the power spectrum of the audio signal and the power spectrum of noise and forming speech segments comprising the portions of the audio signal that comprise speech.

Abstract: A method, apparatus and computer program product are provided to generate a model of one or more objects relative to a vehicle. In the context of a method, radar information is received in the form of in-phase quadrature (IQ) data and the IQ data is converted to one or more first range-doppler maps. The method further includes evaluating the one or more first range-doppler maps with a machine learning model to generate the model that captures the detection of the one or more objects relative to the vehicle. A corresponding apparatus and computer program product are also provided.

Abstract: Solutions are provided for auto-labeling sensor data for machine learning (ML). An example includes: determining a platform's own position; recording, from a sensor aboard the platform, sensor data comprising a sensor image; receiving position data for at least one intruder object (e.g., a nearby airborne object); based at least on the position data for the intruder object and the platform's position, determining a relative position and a relative velocity of the intruder object; based at least on the relative position and a relative velocity of the intruder object and a field of view of the sensor, determining an expected position of the intruder object in the sensor image; labeling the sensor image, wherein the labeling comprises annotating the sensor image with a region of interest and an object identification; and training an artificial intelligence (AI) model using the labeled sensor image.

Abstract: Illustrative embodiments provide a method and system for communicating air traffic control information. An audio signal comprising voice activity is received. Air traffic control information in the voice activity is identified using an artificial intelligence algorithm. A text transcript of the air traffic control information is generated and displayed on a confirmation display. Voice activity in the audio signal may be detected by identifying portions of the audio signal that comprise speech based on a comparison between the power spectrum of the audio signal and the power spectrum of noise and forming speech segments comprising the portions of the audio signal that comprise speech.