Abstract: A system and method for integrity monitoring generates primary control output via a primary module. The primary control output is configured to be used to control an autonomous vehicle (AV). The system and method receive situational data comprising AV data and environmental data. The system and method generate, using a monitor module configured to monitor the AV, a set of fallback actions based on at least the situational data. The system and method generate, using the monitor module, a fallback status based on at least the set of fallback actions, where the fallback status is configured to correspond to a determination of whether to override the primary control output.

Abstract: A method for testing platforms (e.g., live, virtual, and/or constructive platforms associated with autonomous aircraft systems and their component subsystems) in a live/virtual/constructive (LVC) environment. In embodiments, the method includes determining, via a testbed engine, the development state of a platform component under test. The method includes retrieving a test to be executed, the test including test conditions to be applied to the component. The method includes determining whether the component is enabled to respond to the test conditions. The method includes, if the component is enabled to respond to the test conditions, executing the test while monitoring the component to detect a first output response and a second output response. The method includes identifying, via the testbed engine, at least one change in the development state of the component by comparing the first and second output responses.

Abstract: The method and corresponding system for autonomous operation may include implementing a safe system controller for autonomous vehicles to receive a set of event data for an event encountered during operation of a vehicle from a status engine of the vehicle; analyze the received set of event data; determine a vehicle system state based on the analyzed set of event data; receive a set of automation operational parameters from an automation engine of the vehicle; receive a set of autonomy operational parameters from an autonomy engine of the vehicle; determine a response to the event from the set of automation operational parameters and the set of autonomy operational parameters based on the determined vehicle system state; and provide the determined response to the automation engine and the autonomy engine to adjust an operational parameter of the vehicle.

Abstract: A method for testing platforms (e.g., live, virtual, and/or constructive platforms associated with autonomous aircraft systems and their component subsystems) in a live/virtual/constructive (LVC) environment. In embodiments, the method includes determining, via a testbed engine, the development state of a platform component under test. The method includes retrieving a test to be executed, the test including test conditions to be applied to the component. The method includes determining whether the component is enabled to respond to the test conditions. The method includes, if the component is enabled to respond to the test conditions, executing the test while monitoring the component to detect a first output response and a second output response. The method includes identifying, via the testbed engine, at least one change in the development state of the component by comparing the first and second output responses.

Abstract: A system includes a machine learning engine configured to receive training data including a plurality of input conditions associated with a state space and a plurality of response maneuvers associated with the state space and train a learning system using the training data and a reward function including a plurality of terms associated with a plurality of end state spaces, each term in the plurality of terms defines an end reward value for each end state space. A value function and policy are generated. The value function comprising a plurality of values, wherein each response maneuvers in the plurality of response maneuvers is associated with a value in the plurality of values related to transitioning from the state space to each end state space, the policy indicative of connections between the state spaces, plurality of values, and the respective end reward value for the plurality of end state spaces.

Abstract: A system for manufacturing, testing, integrating, and operating of live or virtual platforms and components thereof uses a simulation engine and/or a testbed engine in a live-virtual-constructive (LVC) environment. The system can be used in a pure simulated environment or for testing of individual subsystems on a live platform (e.g., a live aircraft) with remaining subsystems in simulation, to incremental integration of all subsystems onto live aircraft.

Abstract: Machine learning, evaluating, and reinforced learning within systems or apparatuses enables autonomy to a complexity level beyond automation. Inferences are made using machine learning based on observations, images, or video feed of operator input. The inferences are evaluated or classified and maneuvers are performed based on the evaluating or the classification. The performed maneuvers may be further evaluated for scoring or weighting. The reinforcement learning may perform updates based on the scoring, weighting, and a maximizing reward function such that the machine learning is constantly improving.

Abstract: Safe practical autonomy is ensured by encapsulating an unreliable or untrusted machine learning algorithm within a control-based algorithm. A safety envelope is utilized to ensure that the machine learning algorithm does not output control signals that are beyond safe thresholds or limits. Secure practical autonomy is ensured by verification using digital certificates or cryptographic signatures. The verification may be for individual partitions of an autonomous system or apparatus. The partitions include trusted and untrusted partitions. Trusted partitions are verified for security, while untrusted partitions are verified for safety and security.

Abstract: A system includes a machine learning engine. The machine learning engine is configured to receive training data including a plurality of first input conditions and a plurality of first response maneuvers associated with the first input conditions. The machine learning engine is configured to train a learning system using the training data to generate a second response maneuver based on a second input condition.

Abstract: A system, device, and method for operating an aircraft autonomously are disclosed. The widget creating system may include a plurality of aircraft systems and autonomous pilot processing unit (APPU). The APPU may be configured to acquire first model data representative of either a combined strategic/operational behavior model or an operational behavior model; acquire second model data representative of an execution behavior model; acquire third model data representative of a strategic behavior model when a combined strategic/operational behavior model is not employed; acquire input data representative of at least one aircraft system parameter of each aircraft system of the plurality of aircraft systems, where the input data is acquired through the execution behavior model; monitor each aircraft system of the plurality of aircraft systems for an expected operation; and generate output data representative of at least one command provided to at least one aircraft system of the plurality of aircraft systems.