Patent Description:
Certain exterior aircraft lights, such as landing lights, taxi lights, and runway turnoff lights, need to be turned on or activated for both takeoff and landing of the aircraft and regardless of the time of day or external visibility in accordance with FAA guidelines. Currently, these exterior aircraft lights are manually controlled by the pilot. Both takeoff and landing of an aircraft are crucial times in the operation of the aircraft, and again operation of the noted exterior aircraft lights currently requires manual intervention by the pilot.

Landing lights, taxi lights, and runway turnoff lights for an aircraft commonly produce high beam/high intensity light output. As such, operation of these exterior aircraft lights causes higher fuel consumption. Fuel for at least some aircraft while in flight is a non-renewable resource (e.g., aircraft that are not configured to be refueled during flight). In addition, landing lights, taxi lights, and runway turnoff lights are commonly operated at a constant light intensity, which also has a cost in relation to power/fuel consumption. Lighting control systems are disclosed in <CIT> and <CIT>.

What may be characterized as an exterior aircraft lighting (or light) control system is presented herein, and defined by the claims. Both the configuration of such an exterior aircraft light control system and the operational characteristics/operation of such an exterior aircraft light control system are within the scope of this Summary.

Automated control of at least one exterior aircraft light may include acquiring altitude data on an associated aircraft. This altitude data may be assessed/monitored to identify for an existence of an what may be characterized as an "altitude condition. " This altitude condition (e.g., stored in memory, such as a predetermined altitude value) may exist when the altitude data is one of less than or no greater than a predetermined altitude. Such an assessment of the altitude data may be executed by a controller, and including without any required input or intervention by aircraft personnel. When an altitude condition is identified, at least one exterior light of the aircraft may be automatically activated, such as through the noted controller.

Automated control of at least one exterior aircraft light may entail capturing or acquiring a first image (e.g., a real-time image that is outside of the aircraft) with a first camera that is at least partially disposed on an exterior of the aircraft or that is otherwise able to acquire images exteriorly of the aircraft. The first camera may be of any appropriate size, shape, configuration, and/or type, and at least part of the first camera may be disposed at any appropriate location on the exterior of the aircraft. This first image from the first camera may be transmitted to a trained image classification model, which may process the image (or portion thereof) to determine a first visibility classification for the captured image. Operation of at least one exterior aircraft light may be controlled at least in part based on this determined first visibility classification. The automation of activating at least one exterior aircraft light and the automation of controlling the subsequent operation of at least one exterior aircraft light (e.g., the intensity of light output from a given exterior aircraft light) may be used individually or in combination.

Automated control in accordance with the foregoing may be used in relation to any appropriate exterior aircraft light or combination of exterior aircraft lights (including simultaneous control of multiple exterior aircraft lights), such as landing lights, taxi lights, and runway turnoff lights. Landing lights, taxi lights, and runway turnoff lights enhance visibility for the pilot during operation of the aircraft, and automating one or more aspects relating to these particular lights during taxiing of the aircraft, during take-off of the aircraft, and during landing of the aircraft allows the pilot/crew to focus on operation of the aircraft. Notwithstanding the foregoing, the aircraft may be configured to allow aircraft personnel (e.g., the pilot) to manually activate at least one exterior aircraft light and including to manually activate landing lights, taxi lights, and runway turnoff lights on a simultaneous basis.

The noted trained image classification model may be of a configuration that is selected from the group consisting of a machine learning configuration or a deep learning configuration (i.e., a given trained image classification module will be one of a machine learning configuration or a deep learning configuration). A support vector machine, a recurrent neural network, or a convolutional neural network each may be used for the trained image classification model.

An understanding of the present disclosure may be further facilitated by referring to the following detailed description and claims in connection with the following drawings. Reference to "in accordance with various embodiments" in this Brief Description of the Drawings also applies to the corresponding discussion in the Detailed Description.

An aircraft is illustrated in <FIG> and is identified by reference numeral <NUM>. The aircraft <NUM> includes a fuselage <NUM>, a pair of wings <NUM>, a pair of engines <NUM> for each wing <NUM>, a vertical stabilizer <NUM> at an aft end section of the aircraft <NUM>, and a cockpit <NUM> at forward end section of the aircraft <NUM>. A landscape camera <NUM> and a taxi aid camera <NUM> may be disposed aft of the forward landing gear of the aircraft <NUM> (or at any appropriate location) and may be mounted to/co-located on the fuselage <NUM> at any appropriate location. Each of the cameras <NUM>, <NUM> may be of any appropriate size, shape, configuration, and/or type. The aircraft <NUM> also includes landing lights <NUM>, taxi lights <NUM>, and runway turnoff lights <NUM>. These lights <NUM>, <NUM>, and <NUM> may be of any appropriate configuration and disposed at any appropriate location or combination of locations on the exterior of the aircraft <NUM>.

A block diagram of the aircraft <NUM> is presented in <FIG>, although it is applicable to an aircraft of any appropriate configuration. Both the landscape camera <NUM> and the taxi aid camera <NUM> are operatively connected with a trained image classification model <NUM>. Generally, the aircraft <NUM> may utilize one or more cameras configured to acquire images exteriorly of the aircraft <NUM> for purposes of the exterior aircraft lighting control protocols addressed herein. Images that are captured/acquired by the cameras <NUM>, <NUM> are provided to the trained image classification model <NUM> and as will be discussed in more detail below. The trained image classification model <NUM> may be operatively interconnected with a data structure <NUM> of any appropriate configuration (e.g., a database; such an operative interconnection being optional). This data structure <NUM> may be stored in any appropriate memory. The trained image classification model <NUM> is operatively interconnected with at least an automated controller <NUM>. One or more of the data structure <NUM> and the automated controller <NUM> may be separate and discrete from the trained image classification model <NUM>.

The aircraft <NUM> includes the noted automated controller <NUM> as well as a manual controller <NUM>. Both the automated controller <NUM> and the manual controller <NUM> are operatively interconnected with and control operation of each of the landing lights <NUM>, the taxi lights <NUM>, and the runway turnoff lights <NUM>. The automated controller <NUM> requires no manual intervention or input by any aircraft personnel (including no required input from the pilot). The manual controller <NUM> may be used by aircraft personnel (including the pilot) to manually control at least one aspect of the operation of the landing lights <NUM>, taxi lights <NUM>, and runway turnoff lights <NUM> (e.g., activating or turning on the lights <NUM>, <NUM>, <NUM>). If the manual controller <NUM> is used to activate the landing lights <NUM>, taxi lights <NUM>, and runway turnoff lights <NUM>, the aircraft <NUM> may be configured to operate these lights <NUM>, <NUM>, <NUM> at a predetermined intensity (e.g., for maximum output).

An altimeter <NUM> is also operatively interconnected with the automated controller <NUM>, namely to provide altitude data to the automated controller <NUM>. Image data from one or more of the cameras <NUM>, <NUM>, along with altitude data from the altimeter <NUM>, may be used by the aircraft <NUM> to automatically control operation of each of the landing lights <NUM>, taxi lights <NUM>, and runway turnoff lights <NUM>. Therefore, both altitude data (altimeter <NUM>) and image data (cameras <NUM>, <NUM>) may be used by the automated controller <NUM>.

The trained image classification model <NUM> may be of a machine learning configuration (e.g., a support vector machine or SVM), or may be of a deep learning structure or configuration (e.g., a recurrent neural network or RNN; a feed-forward neural network; a convolutional neural network or CNN). Generally, the trained image classification model <NUM> is trained on images to control operation of the landing lights <NUM>, the taxi lights <NUM>, and the runway turnoff lights <NUM> based upon a visibility classification of these images that is determined by the trained image classification model <NUM>. Representative images <NUM>, <NUM> that may be used to train the trained image classification model <NUM> (or that may be classified by the model <NUM> after being trained) are illustrated in <FIG> and are identified by reference numerals <NUM>, <NUM>, respectively. A large number of images will typically be used to train/define the trained image classification model <NUM>, and each of these images will be labeled based upon the associated visibility/visibility condition (commonly referred to as "supervised learning").

A block diagram of the trained image classification model <NUM> is presented in <FIG>. The trained image classification model <NUM> includes one or more processors <NUM> (or a processing unit/system), memory <NUM>, and one or more classification algorithms <NUM>. The trained image classification model <NUM> may utilize any appropriate processing arrangement/architecture. The classification algorithm(s) <NUM> may be stored in the memory <NUM> and is/are used by the trained image classification model <NUM> to classify images for use in controlling operation of the landing lights <NUM>, taxi lights <NUM>, and runway turn off lights <NUM> (e.g., for outputting a visibility classification for a given image for use by the automated controller <NUM> to control operation of the landing lights <NUM>, taxi lights <NUM>, and/or runway turnoff lights <NUM>).

Visibility classification of images provided by one or more of the cameras <NUM>, <NUM> is undertaken by the trained image classification model <NUM> to output (to the automated controller <NUM>) a visibility classification for an image received from one or more of the cameras <NUM>, <NUM>. In this regard and referring to <FIG>, the data structure <NUM> (<FIG>) may include any appropriate number of visibility classifications <NUM>. Each visibility classification <NUM> has a corresponding exterior aircraft light control parameter(s) <NUM> (or more generally a control signal) in the data structure <NUM>. A given exterior aircraft light control parameter(s) <NUM> will provide a desired light intensity output from the landing lights <NUM>, the taxi lights <NUM>, and the runway turnoff lights <NUM>. As such, once the trained image classification model <NUM> determines a visibility classification for a given image from the landscape camera <NUM> or the taxi aid camera <NUM>, and outputs the determined visibility classification to the automated controller <NUM>, this in turn identifies the control parameter(s) <NUM> that will be used to control operation of the landing lights <NUM>, the taxi lights <NUM>, and the runway turnoff lights <NUM> (e.g., an intensity of light output from the corresponding light <NUM>, <NUM>, <NUM>).

A determined visibility classification may be output from the trained image classification model <NUM> to the automated controller <NUM>. The automated controller <NUM> may use this determined visibility classification from the trained image classification model <NUM> to control operation of the landing lights <NUM>, the taxi lights <NUM>, and the runway turnoff lights <NUM> (e.g., an intensity of light output from the corresponding lights <NUM>, <NUM>, <NUM>). For instance, the automated controller <NUM> may use the data structure <NUM> to determine the exterior light control parameter(s) <NUM> that correspond with the determined visibility classification from the trained image classification model <NUM>, and then the automated controller <NUM> may use the corresponding exterior light control parameter(s) <NUM> to control operation of the landing lights <NUM>, the taxi lights <NUM>, and the runway turnoff lights <NUM> (e.g., an intensity of light output from the corresponding lights <NUM>, <NUM>, <NUM>).

As noted above, the trained image classification model <NUM> may be in the form of a support vector machine or SVM. A SVM is an algorithm in machine learning which may be used for visibility classification in accordance with this disclosure. Generally, a SVM may be used to separate data into two halves, depending on how the SVM learns from the data on which the SVM is trained. A schematic is presented in <FIG> regarding a SVM. A plurality of extracted features 146a are disposed on one side of a plane <NUM>, while a different plurality of different extracted features 146b are disposed on the other side of the plane <NUM>. The extracted features 146a, 146b may be local features (e.g., geometric) or global features (e.g., topographical or statistical). The extracted features 146a, 146b may include/be represented by any appropriate data in the image data, such as RGB pixel data, gray scale data, or the like from an image on which the SVM was trained. In any case, the SVM will assess image data provided by one or more of the cameras <NUM>, <NUM> (e.g., extracted features from the image data) and will determine if the extracted features from the image data correspond with the extracted features 146a on one side of the plane <NUM> (one visibility classification), or with the features 146b on the other side of the plane <NUM> (a different visibility classification). The SVM may be of a multi-class classification SVM for purposes of the trained image classification model <NUM> (e.g., the trained image classification model <NUM> may use any appropriate number of comparative sets of extracted features, each of which would be at least generally in accord with <FIG> (e.g., one feature set on one side of a plane and corresponding with one visibility classification, and another feature set on the opposite side of this plane and corresponding with a different visibility classification)).

Any appropriate feature extraction algorithm may be used in conjunction with a SVM for purposes of the trained image classification model <NUM>, for instance statistical features, features that are invariant to global deformation, and features that represent global and local properties of characters and that have high tolerances to distortions and style variations.

A CNN is another option for the trained image classification model <NUM> as previously noted. A CNN generally utilizes three types of layers - convolutional, pooling, and a fully-connected layer. In the convolutional layer, a filter is applied to the input (i.e., an image from one of the cameras <NUM>, <NUM> of the aircraft <NUM>) to create a feature map that summarizes the various features that were detected in the image. The output from the convolutional layer is transmitted to the pooling layer for reduction of the size of the feature map. The convolutional and pooling processes may be repeated, as required. In any case, the resulting feature map is transmitted to the fully-connected layer for comparison of probability of features existing in conjunction with others to identify the corresponding visibility classification. That is and with regard to the trained image classification model <NUM> being in the form of a CNN, an image from the cameras <NUM> and/or <NUM> is the input to the CNN. This data is processed in different layers of the CNN to classify them into different categories in the last layer of the CNN (e.g., for determining a visibility classification). Each layer of the neural network (e.g., hidden layers) perform different functions to work on all pixels of the input image (e.g., feature extraction, flattening of the image, and the like).

A RNN is yet another option for the trained image classification model <NUM> as previously noted. RNNs have one or more recurrent or cyclic connections. Generally, the cyclic connection in a RNN (for the case of the trained image classification model <NUM>) records temporal relations or dependencies regarding the image provided by one of the cameras <NUM>, <NUM> of the aircraft <NUM> and that is used to determine the corresponding visibility classification. That is for the trained image classification model <NUM> being in the form of a RNN, the information cycles through a loop. When the RNN makes a decision (determines a visibility classification in this case), the RNN considers the current input (an image from the cameras <NUM> and/or <NUM>) as well as what the RNN has learned from prior inputs (e.g., images from the cameras <NUM> and/or <NUM>) that were previously provided to and processed by the RNN.

A block diagram of the automated controller <NUM> is presented in <FIG>. The automated controller <NUM> includes one or more processors <NUM> (or processing unit/system), memory <NUM>, and one or more input/out devices <NUM> (e.g., for receiving altitude data from the altimeter <NUM>; for receiving a visibility classification output from the trained image classification model <NUM>; for outputting a control signal to each of the landing lights <NUM>, taxi lights <NUM>, and runway turnoff lights <NUM>). The automated controller <NUM> may utilize any appropriate processing arrangement/architecture. As noted, altitude data may be used to automatically activate/turn on the landing lights <NUM>, taxi lights <NUM>, and runway turnoff lights <NUM>. In this regard, an altitude condition <NUM> (e.g., <FIG>) may be stored in the memory <NUM> for use by the automated controller <NUM>.

<FIG> illustrates an exterior aircraft lighting (or light) control protocol that may be utilized by the aircraft <NUM> and that is identified by reference numeral <NUM>. Altitude data <NUM> from the altimeter <NUM> may be provided (<NUM>) to the automated controller <NUM>. In the event that the altitude data <NUM> is determined by the automated controller <NUM> to be less than or no more than a predetermined value of any appropriate magnitude, the automated controller <NUM> will automatically turn on the landing lights <NUM>, the taxi lights <NUM>, and the runway turnoff lights <NUM> (<NUM>) - no intervention by any personnel of the aircraft <NUM> is required. As such, the protocol <NUM> may be configured such that the landing lights <NUM>, the taxi lights <NUM>, and the runway turnoff lights <NUM> will be automatically turned on (and remain on) while the aircraft <NUM> is on the ground and other than in a stationary/parked position, during takeoff, during landing of the aircraft <NUM>, or any combination thereof. Once the aircraft <NUM> takes off and then reaches the noted predetermined altitude, the automated controller <NUM> will automatically turn off the landing lights <NUM>, the taxi lights <NUM>, and the runway turnoff lights <NUM> (<NUM>) - again, no intervention by any personnel of the aircraft <NUM> is required.

Real-time images/image data <NUM> captured/acquired by the landscape camera <NUM> or the taxi aid camera <NUM> (<NUM>) are transmitted to the trained image classification model <NUM> (<NUM>) in the case of the exterior aircraft lighting control protocol <NUM> of <FIG>. The trained image classification model <NUM> determines the visibility classification of this image/image data <NUM> (<NUM>). Once this visibility classification has been determined (<NUM>) by the trained image classification model <NUM> and output to the automated controller <NUM>, the corresponding exterior light control parameter(s) <NUM> (or more generally a control signal) are sent by the automated controller <NUM> to the landing lights <NUM>, the taxi lights <NUM>, and the runway turnoff lights <NUM> (<NUM>) such that these lights <NUM>, <NUM>, <NUM> provide a corresponding intensity of light output <NUM>.

An exterior aircraft lighting (or light) control protocol is illustrated in <FIG> and is identified by reference numeral <NUM>. The altitude data <NUM> provided by the altimeter <NUM> (<FIG>) is assessed to determine if it satisfies an altitude condition <NUM> (<NUM>). Again, this determination may be undertaken by/utilizing the automated controller <NUM>. This altitude condition <NUM> may be a predetermined value of any appropriate magnitude, and again may be stored in memory <NUM> of the automated controller <NUM> (<FIG>). The altitude condition <NUM> may exist when the current altitude of the aircraft <NUM> (via the altitude data <NUM>) is less than or no more than a predetermined altitude or value (e.g., while the aircraft <NUM> is on the ground; during takeoff; during landing). If the altitude data <NUM> satisfies the altitude condition <NUM> (<NUM>), a number of actions may be automatically initiated (i.e., without any intervention or action by any personnel of the aircraft <NUM>). One such action is that at least one of the landing lights <NUM>, the taxi lights <NUM>, and the runway turnoff lights <NUM> may be activated (<NUM>) and this may be done through the automated controller <NUM> (the landing lights <NUM>, taxi lights <NUM>, and runway turnoff lights <NUM> may be simultaneously activated (<NUM>)). Another such action is that a real-time image <NUM> is captured/acquired (<NUM>) by at least one of the cameras <NUM>, <NUM> (e.g., an image that is exterior of the aircraft <NUM>).

The captured image <NUM> is transmitted to the trained image classification model <NUM> (<NUM>). The visibility classification for the captured image <NUM> is determined using the trained image classification model <NUM> (<NUM>). The visibility classification determined by the trained image classification model <NUM> may be output or provided to the automated controller <NUM>. Operation of at least one of the exterior aircraft lights <NUM>, <NUM>, <NUM> is thereafter controlled per the determined visibility classification (<NUM>), for instance by the associated exterior light control parameter(s) <NUM> (e.g., via the automated controller <NUM> using this determined visibility classification to acquire the relevant exterior light control parameter(s) <NUM>, for instance using the data structure <NUM> of <FIG>). This "control" may be of the intensity of the light that is output from the controlled exterior aircraft lights <NUM>, <NUM>, and/or <NUM>. The current altitude data <NUM> from the altimeter <NUM> may continue to be assessed for satisfaction of the altitude condition <NUM> (<NUM>). If the altitude condition <NUM> is determined to no longer exist (e.g., the aircraft <NUM> is now above a predetermined altitude), activated ones of the exterior aircraft lights <NUM>, <NUM>, <NUM> may be deactivated (<NUM>) and including simultaneously (e.g., using the automated controller <NUM>).

The exterior aircraft light control protocol <NUM> may be configured to determine a visibility classification for a captured image <NUM> from each of the landscape camera <NUM> and the taxi aid camera <NUM> (e.g., captured images <NUM> from the cameras <NUM>, <NUM> that have a common time stamp). In the event that the trained image classification model <NUM> determines a different visibility classification for these two different captured images <NUM>, the protocol <NUM> may be configured to utilize the determined visibility classification that provides a higher intensity light output for activated ones (<NUM>) of the exterior aircraft lights <NUM>, <NUM>, <NUM> (e.g., if only the exterior aircraft light <NUM> has been activated, only its light output will be controlled in the noted manner; if only the exterior aircraft lights <NUM>, <NUM> have been activated, only their respective light outputs will be controlled in the noted manner).

<FIG> presents an exterior aircraft lighting (or light) control protocol <NUM> that addresses training of the image classification model <NUM> and subsequent use thereof, and including at least generally in accordance with the foregoing. A database is created that includes various images from different times of the day (e.g., daytime; nighttime) and/or of different visibility/lighting conditions (e.g., cloudy, foggy, sunny), and each of these images is categorized, assigned, or labeled with a visibility classification <NUM> (<NUM>). Image processing may be used to identify the most prominent features in each of the images and that will be used to identify the corresponding visibility classification <NUM> (<NUM>). This may entail using a feature extraction algorithm (<NUM>, <NUM>). A support vector machine (SVM) or a recurrent neural network (RNN) that uses supervised learning may be created to categorize/classify images (<NUM>). That is, the image classification model <NUM> is created and trained (<NUM>, <NUM>, <NUM>). After training, an image may be captured by one or more exterior cameras on the aircraft <NUM>, for instance the landscape camera <NUM> and/or the taxi aid camera <NUM> (<NUM>). The trained image classification model <NUM> is then used to categorize this captured image using image processing to interpret the visibility, for instance to determine the corresponding visibility classification (<NUM>). The exterior light control parameter(s) <NUM> associated with the determined visibility classification may then be used to control the operation of the exterior aircraft lights <NUM>, <NUM>, <NUM> in accordance with the foregoing (<NUM>).

The protocol <NUM> of Figure <NUM> also illustrates that the trained image classification model <NUM> may be of a machine learning configuration or of a deep learning configuration. In each case, a labeled data set is created (<NUM>). This data set includes various images from different times of the day (e.g., daytime; nighttime) and/or of different visibility/lighting conditions (e.g., cloudy, foggy, sunny), and each of these images is categorized, assigned, or labeled with a visibility classification <NUM> (<NUM>). In the case where the image classification model <NUM> is of a deep learning configuration (e.g., CNN, RNN), feature extraction is not required to train the image classification model (<NUM>). In contrast and for the case where the image classification model <NUM> is of a machine learning configuration and as addressed above, feature extraction (<NUM>) is used for each of the images that are included in a labeled dataset (<NUM>) (stated another way, features are extracted for the labeled images (<NUM>)) and thereafter the image classification model <NUM> is trained using only the extracted features from each of the images (<NUM>).

An image may be captured by one or exterior cameras on the aircraft <NUM>, for instance the landscape camera <NUM> and/or the taxi aid camera <NUM> (<NUM>). The trained image classification model <NUM> is then used to categorize this captured image according to the training to interpret the visibility, for instance to determine the corresponding visibility classification (<NUM>). The exterior light control parameter(s) associated with the determined visibility classification may then be used to control the operation of the exterior aircraft lights <NUM>, <NUM>, <NUM> (<NUM>), for instance using the corresponding exterior light control parameter(s) <NUM> in accordance with the foregoing.

In various embodiments and including in accordance with the foregoing, memory is configured to store information used in the control of exterior aircraft lights <NUM>, <NUM>, <NUM>. In various embodiments, memory comprises a computer-readable storage medium, which, in various embodiments, includes a non-transitory storage medium. In various embodiments, the term "non-transitory" indicates that the memory is not embodied in a carrier wave or a propagated signal. In various embodiments, the non-transitory storage medium stores data that, over time, changes (e.g., such as in a random access memory (RAM) or a cache memory). In various embodiments, memory comprises a temporary memory. In various embodiments, memory comprises a volatile memory. In various embodiments, the volatile memory includes one or more of RAM, dynamic RAM (DRAM), static RAM (SRAM), and/or other forms of volatile memories.

In various embodiments, memory is configured to store computer program instructions for execution by a processor (e.g., processor <NUM>; processor <NUM>) in relation to the control of exterior aircraft lights <NUM>, <NUM>, <NUM>. In various embodiments, applications and/or software utilize(s) memory in order to temporarily store information used during program execution. In various embodiments, memory includes one or more computer-readable storage media. In various embodiments, memory is configured to store larger amounts of information than volatile memory. In various embodiments, memory is configured for longer-term storage of information. In various embodiments, memory includes non-volatile storage elements, such as, for example, electrically programmable memories (EPROM), electrically erasable and programmable (EEPROM) memories, flash memories, floppy discs, magnetic hard discs, optical discs, and/or other forms of memories.

In various embodiments, a processor used in relation to the control of exterior aircraft lights <NUM>, <NUM>, <NUM> (e.g., processor <NUM>; processor <NUM>) is configured to implement functionality and/or process instructions. In various embodiments, such processor is configured to process computer instructions stored in memory. In various embodiments, such a processor includes one or more of a microprocessor, a controller, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other equivalent discrete or integrated logic circuitry. System program instructions and/or processor instructions may be loaded onto memory that is used in relation to the control of exterior aircraft lights <NUM>, <NUM>, <NUM>. The system program instructions and/or processor instructions may, in response to execution by operator, cause the relevant processor to perform various operations used in relation to the control of exterior aircraft lights <NUM>, <NUM>, <NUM>.

The term "non-transitory" is to be understood to remove only propagating transitory signals per se from the claim scope and does not relinquish rights to all standard computer-readable media that are not only propagating transitory signals per se.

Any feature of any other various aspects addressed in this disclosure that is intended to be limited to a "singular" context or the like will be clearly set forth herein by terms such as "only," "single," "limited to," or the like. Merely introducing a feature in accordance with commonly accepted antecedent basis practice does not limit the corresponding feature to the singular. Moreover, any failure to use phrases such as "at least one" also does not limit the corresponding feature to the singular. Use of the phrase "at least substantially," "at least generally," or the like in relation to a particular feature encompasses the corresponding characteristic and insubstantial variations thereof (e.g., indicating that a surface is at least substantially or at least generally flat encompasses the surface actually being flat and insubstantial variations thereof). Finally, a reference of a feature in conjunction with the phrase "in one embodiment" does not limit the use of the feature to a single embodiment.

The foregoing description has been presented for purposes of illustration and description. Furthermore, the description is not intended to limit the invention to the form disclosed herein. Consequently, variations and modifications commensurate with the above teachings, and skill and knowledge of the relevant art, are within the scope of the present invention as defined by the claims.

Claim 1:
A method of controlling an exterior aircraft light, comprising:
acquiring altitude data of an aircraft (<NUM>);
identifying a first altitude condition, said first altitude condition being that said altitude data is one of less than or no greater than a predetermined altitude, wherein said identifying is executed by a controller (<NUM>);
activating a first exterior aircraft light (<NUM>, <NUM>, <NUM>) in response to an existence of said first condition being identified, wherein said activating is executed by said controller;
capturing a first image exteriorly of said aircraft;
transmitting said first image to a trained image classification model (<NUM>);
whereby a database is created that includes various images from different times of the day and/or of different visibility/lighting conditions, and each of these images is categorized, assigned, or labelled with a visibility classification;
determining a first visibility classification for said first image using said trained image classification model; and
controlling a magnitude of a light output from said first exterior aircraft light based on said first visibility classification.