Rockwell Collins, Inc.

B/e Aerospace, Inc.

Subhashish Chakravarty

Matthew Michael Lorch

Jaclyn A Hoke

Ross D Anderson

Steven E Koenck

Angela N Dunlay

Bernard Dion

Adam Mottram

Diane D McClatchy

Amrit Chatterjee

Matthew T Higgins

Yogesh Selvaraj

Rajdeep Sawant

Lennon Ignatius Dhanaraj

Victor Vishal Rana

Bikramjit Karmakar

Hemant Kumar

A system and method for automated video generation for training machine and deep learning algorithms in aviation environments generates photorealistic digital human characters and an aviation environment according to a desired scenario, including fixtures, free objects, lighting and physics configurations, and camera views. Character actions in the desired scenario are mapped to pose sequences which may be manually generated or transferred from image sequences of human activities, including both main character activities specified by the scenario, characters responding to the main characters, and background character actions. A video automation pipeline animates character actions performed by the digital human characters into video datasets with annotation files incorporating detailed pixel and depth information for each frame. Video datasets may include variant video datasets differentiated from the primary dataset by changes in domain variants (e.g., character attributes, environmental attributes) while preserving the portrayal of the desired scenario.

System and method for automated video generation for training of machine learning algorithms in aviation environments

A pilot, commander, or user within an aircraft may ask a query via voice. The query may be converted into text and used to search a knowledge graph. The result from the query may be provided to the user such that the user may obtain improved situational awareness during a mission or otherwise while flying. A line-replaceable unit of the aircraft may execute an architecture for searching the knowledge graph. The knowledge graph may include a schema based, at least in part, on a mission requirement of an aircraft and one or more aircraft systems of the aircraft. Thus, the user may ask for the effect of various sensors or components of the aircraft systems as it relates to the mission.

Voice recognition of situational awareness

A system and method for sensor quality detection continuously monitors a context (vehicle state, proximal environmental factors, surrounding environmental conditions) surrounding a vehicle as well as actual sensor output. The system compares actual sensor output with expected sensor output to determine if the sensor has become degraded or if the degradation is due to the surrounding context. Once the system determines the context-based or actual degradation, it determines if the sensor is able to complete an assigned tasking with the degradation within the specific mission-based requirements of the sensor. Should the degraded sensor be unable to complete the task, the system transfers the tasking to a second vehicle equipped with an operational sensor.

Sensor quality detection

A system and method for automation of flight functions across an aircraft cockpit provides an open systems architecture to meet next generation mission capabilities for increased aircrew effectiveness. A mission reasoner (MR) receives inputs from a vehicle health system, mission phase analysis, and external assets (wingmen) to build a decision network based on a knowledge database providing a decision aid to automate flight functions across the cockpit. The MR reduces crew workload by providing an automated decision aid and increased situational awareness of own ship aircraft and cooperating nearby vehicle health status relating to a phase of mission success. The MR provides predictive decisions and alternative actions to complete one or more desired mission objectives and determines predictive maintenance and future failures to optimize condition-based maintenance and reduce cost.

Mission reasoner system and method

A method includes displaying content including an environment, a user entity, and a synthetic entity. The user entity is controllable by a user with respect to the environment and the synthetic entity. The environment and the synthetic entity operate according to a pre-scripted scheme. The method further includes receiving user information. The user information is at least one of a user input and a current physiological state of the user. The method further includes modifying the pre-scripted scheme based on the user information and prior user information. The prior user information comprises at least one of a prior user input and a prior physiological state of the prior user received in response to an interaction of the prior user with the pre-scripted scheme.

Intelligent adaptive entities

A system and method are provided for tracking access. At least one passive wireless identification device is provided at a personnel access point. Communication is established between a mobile device and the at least one passive wireless identification device when the mobile device passes the personnel access point. Occurrences of the established communication between the mobile device and the at least one passive wireless identification device are separately communicated to a remote data collection device. The occurrences of the established communication between the mobile device and the at least one passive wireless identification device are recorded in the remote data collection device. Data based on the recorded occurrences of the established communication between the mobile device and the at least one passive wireless identification device is output from the remote data collection device.

Tracking personnel access to aircraft on the ground

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Marion, IA, United States of America

Subhashish Chakravarty