Aircraft, method, and computer readable medium for controlling brake action of an aircraft

1 . An aircraft comprising:

a brake system for controlling brake action of the aircraft based on a current runway condition of a runway, the brake system comprising brakes configured to slow the aircraft during landing;

a controller device in communication with the brakes and comprising one or more processors and non-transitory computer readable medium on which executable instructions are stored, the controller device being configured to execute the executable instructions, which causes the one or more processors to be configured to:

receive data from an air traffic controller characterizing an expected runway condition of the runway and an expected brake action for the aircraft, the expected brake action defining at least an expected brake pressure to be applied by the brakes for slowing the aircraft based on the expected runway condition of the runway;

measure, via a landing data measurement and transmission component of the controller device, current landing data of the aircraft including at least a current brake action during landing and data indicating a location at which the aircraft exits the runway during landing,

wherein the data indicating the location at which the aircraft exits the runway during landing is determined via a navigation system,

wherein the current brake action includes information associated with pressure applied to the brakes measured by a pressure sensor, and

wherein the controller device is in communication with a plurality of sensors of the aircraft, including at least the pressure sensor;

transmit, by the landing data measurement and transmission component, the current landing data to a machine learning model configured to predict the current runway condition and the current brake action, the machine learning model built using a machine learning algorithm trained using historical runway condition data and historical landing data, including historical brake action data, of other aircraft landing at the runway;

compare the predicted current runway condition of the runway and the predicted current brake action of the aircraft to the data characterizing the expected runway condition of the runway and the expected brake action of the aircraft;

output at least one of the predicted current runway condition and the predicted current brake action to the air traffic controller for updating at least one of the expected runway condition and the expected brake action if the data characterizing the expected runway condition or the expected brake action does not correspond to the predicted current runway condition or the predicted current brake action; and

cause, based on the output, at least one of the aircraft or another aircraft to adjust its brake pressure to accommodate for runway conditions.

2 . The aircraft of claim 1 , wherein the current brake action comprises at least a current brake pressure applied to the brakes, and wherein the current landing data further includes a deceleration rate of the aircraft, weather data, data indicating a runway used by the aircraft, and a location at which the aircraft exits the runway during landing,

wherein the weather data is received from a weather service provider, weather radar device, or from air traffic control,

wherein data indicating the runway used by the aircraft, including a length and width of the runway, is retrieved from an on-board navigation database of the aircraft, and

wherein the aircraft further comprises:

a speedometer or accelerometer configured to measure or determine the deceleration rate of the aircraft during landing.

3 . The aircraft of claim 1 , wherein the current landing data further includes aircraft configuration parameters selected from the group consisting of: aircraft model, weight, seat configuration, number of passengers onboard, and amount of fuel onboard the aircraft.

4 . The aircraft of claim 1 , wherein the controller device is further configured to measure the current landing data using one or more sensors, of the plurality of sensors, configured to detect fluids or ice on the runway or detect contaminants on the runway; and

wherein the current landing data includes data characterizing a presence of fluids or ice on the runway or the contaminants on the runway.

5 . The aircraft of claim 1 , wherein the expected brake action and the predicted current brake action further include data characterizing: a deceleration rate of the aircraft, reverse thrust applied on the aircraft, wind effects on the aircraft during landing, and a time it takes, from touchdown, for the aircraft to reach taxi speed or landing roll.

6 . The aircraft of claim 1 , wherein the predicted current runway condition output to the air traffic controller and the expected runway condition received from the air traffic controller are characterized using a runway condition description selected from the group consisting of: “dry”, “wet”, “slippery wet”, and “contaminated”, and wherein the one or more processors are further configured to:

store the predicted current runway condition and the predicted current brake action in a cloud storage device, using Internet of Things (IoT) protocols, for analysis and made available to a next landing aircraft at the runway, flight dispatchers, or air traffic controller.

7 . The aircraft of claim 1 , wherein data indicating the runway used for data indicating the location at which the aircraft exits the runway during landing, includes at least one of a length or width of the runway.

8 . A method for controlling brake action of an aircraft based on a current runway condition of a runway on which the aircraft is landing, the aircraft comprising a brake system including brakes configured to slow the aircraft during landing, the method comprising:

executing executable instructions by a controller device in communication with the brakes and comprising one or more processors and non-transitory computer readable medium on which the executable instructions are stored, causing the one or more processors to perform steps comprising:

receiving data from an air traffic controller characterizing an expected runway condition of the runway and an expected brake action for the aircraft, the expected brake action defining at least an expected brake pressure to be applied by the brakes for slowing the aircraft based on the expected runway condition of the runway;

measuring, via a landing data measurement and transmission component of the controller device, current landing data of the aircraft including at least a current brake action during landing and data indicating a location at which the aircraft exits the runway during landing,

wherein the data indicating the location at which the aircraft exits the runway during landing is determined via a navigation system,

wherein the current brake action includes information associated with pressure applied to the brakes measured by a pressure sensor, and

wherein the controller device is in communication with a plurality of sensors of the aircraft, including at least the pressure sensor;

transmitting, by the landing data measurement and transmission component, the current landing data to a machine learning model configured to predict the current runway condition and the current brake action, the machine learning model built using a machine learning algorithm trained using historical runway condition data and historical landing data, including historical brake action data, of other aircraft landing at the runway;

comparing the predicted current runway condition of the runway and the predicted current brake action of the aircraft to the data characterizing the expected runway condition of the runway and the expected brake action of the aircraft;

outputting at least one of the predicted current runway condition and the predicted current brake action to the air traffic controller for updating at least one of the expected runway condition and the expected brake action if the data characterizing the expected runway condition or the expected brake action does not correspond to the predicted current runway condition or the predicted current brake action; and

causing, based on the output, at least one of the aircraft or another aircraft to adjust its brake pressure to accommodate for runway conditions.

9 . The method of claim 8 , wherein the current brake action comprises at least a current brake pressure applied to the brakes, and wherein the current landing data further includes a deceleration rate of the aircraft, weather data, data indicating a runway used by the aircraft, and a location at which the aircraft exits the runway during landing; and

the method further comprising:

receiving data indicating the deceleration rate of the aircraft during landing from a speedometer or accelerometer of the aircraft;

receiving the data indicating the location at which the aircraft exits the runway during landing from the navigation system;

receiving data indicating the current brake pressure applied by the brakes during landing from a pressure sensor configured to measure the current brake pressure applied by the brakes during landing;

receiving the weather data from a weather service provider, weather radar device, or from air traffic control; and

receiving data indicating the runway used by the aircraft, including a length and width of the runway, from an on-board navigation database of the aircraft.

10 . The method of claim 8 , wherein the current landing data further includes aircraft configuration parameters selected from the group consisting of: aircraft model, weight, seat configuration, number of passengers onboard, and amount of fuel onboard the aircraft.

11 . The method of claim 8 , further comprising measuring the current landing data using one or more sensors configured to detect fluids or ice on the runway or detect contaminants on the runway; and

wherein the current landing data includes data characterizing a presence of fluids or ice on the runway or the contaminants on the runway.

12 . The method of claim 8 , wherein the expected brake action and the predicted current brake action further include data characterizing: a deceleration rate of the aircraft, reverse thrust applied on the aircraft, wind effects on the aircraft during landing, and a time required, from touchdown, for the aircraft to reach taxi speed or landing roll.

13 . The method of claim 8 , wherein the predicted current runway condition output to the air traffic controller and the expected runway condition received from the air traffic controller are characterized using a runway condition description selected from the group consisting of: “dry”, “wet”, “slippery wet”, and “contaminated”, and wherein the method further comprises:

storing the predicted current runway condition and the predicted current brake action in a cloud storage device, using Internet of Things (IoT) protocols, for analysis and made available to a next landing aircraft at the runway, flight dispatchers, or air traffic controller.

14 . The method of claim 8 , wherein data indicating the runway used for data indicating the location at which the aircraft exits the runway during landing, includes at least one of a length or width of the runway.

15 . A non-transitory computer readable storage medium having executable instructions stored thereon for controlling brake action of an aircraft based on a current runway condition of a runway on which the aircraft is landing, the aircraft comprising a brake system including brakes configured to slow the aircraft during landing, the executable instructions being executable by one or more processors of a controller device in communication with the brakes, wherein upon execution of the executable instructions the one or more processors are configured to:

receive data from an air traffic controller characterizing an expected runway condition of the runway and an expected brake action for the aircraft, the expected brake action defining at least an expected brake pressure to be applied by the brakes for slowing the aircraft based on the expected runway condition of the runway;

measure, via a landing data measurement and transmission component of the controller device, current landing data of the aircraft including at least a current brake action during landing and data indicating a location at which the aircraft exits the runway during landing,

wherein the data indicating the location at which the aircraft exits the runway during landing is determined via a navigation system,

wherein the current brake action includes information associated with pressure applied to the brakes measured by a pressure sensor, and

wherein the controller device is in communication with a plurality of sensors of the aircraft, including at least the pressure sensor;

transmit, by the landing data measurement and transmission component, the current landing data to a machine learning model configured to predict the current runway condition and the current brake action, the machine learning model built using a machine learning algorithm trained using historical runway condition data and historical landing data, including historical brake action data, of other aircraft landing at the runway;

compare the predicted current runway condition of the runway and the predicted current brake action of the aircraft to the data characterizing the expected runway condition of the runway and the expected brake action of the aircraft;

output at least one of the predicted current runway condition and the predicted current brake action to the air traffic controller for updating at least one of the expected runway condition and the expected brake action if the data characterizing the expected runway condition or the expected brake action does not correspond to the predicted current runway condition or the predicted current brake action; and

cause, based on the output, at least one of the aircraft or another aircraft to adjust their brake pressure to accommodate for runway conditions.

16 . The non-transitory computer readable storage medium of claim 15 , wherein the current landing data further includes a deceleration rate of the aircraft, weather data, data indicating a runway used by the aircraft, wherein the one or more processors are further configured to:

receive data indicating the deceleration rate of the aircraft during landing from a speedometer or accelerometer of the aircraft;

receive the data indicating the location at which the aircraft exits the runway during landing from the navigation system;

receive data indicating current brake pressure applied by the brakes during landing from a pressure sensor configured to measure the current brake pressure applied by the brakes during landing;

receive the weather data from a weather service provider, weather radar device, or from air traffic control; and

receive data indicating the runway used by the aircraft, including a length and width of the runway, from an on-board navigation database of the aircraft.

17 . The non-transitory computer readable storage medium of claim 15 , wherein the current landing data further includes aircraft configuration parameters selected from the group consisting of: aircraft model, weight, seat configuration, number of passengers onboard, and amount of fuel onboard the aircraft.

18 . The non-transitory computer readable storage medium of claim 15 , wherein the one or more processors are further configured to measure the current landing data using one or more sensors configured to detect fluids or ice on the runway or detect contaminants on the runway; and

wherein the current landing data includes data characterizing a presence of fluids or ice on the runway or the contaminants on the runway.

19 . The non-transitory computer readable storage medium of claim 15 , wherein the expected brake action and the predicted current brake action further include data characterizing: a deceleration rate of the aircraft, reverse thrust applied on the aircraft, wind effects on the aircraft during landing, and a time it takes, from touchdown, for the aircraft to reach taxi speed or landing roll.

20 . The non-transitory computer readable storage medium of claim 15 , wherein the predicted current runway condition output to the air traffic controller and the expected runway condition received from the air traffic controller are characterized using a runway condition description selected from the group consisting of: “dry”, “wet”, “slippery wet”, and “contaminated”, and wherein the one or more processors are further configured to:

store the predicted current runway condition and the predicted current brake action in a cloud storage device, using Internet of Things (IoT) protocols, for further analysis and made available to a next landing aircraft at the runway, flight dispatchers, or air traffic controller.