EP4165619A1 - System und verfahren zur besseren bestimmung von wegparametern von flugzeugen - Google Patents
System und verfahren zur besseren bestimmung von wegparametern von flugzeugenInfo
- Publication number
- EP4165619A1 EP4165619A1 EP21728091.6A EP21728091A EP4165619A1 EP 4165619 A1 EP4165619 A1 EP 4165619A1 EP 21728091 A EP21728091 A EP 21728091A EP 4165619 A1 EP4165619 A1 EP 4165619A1
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- European Patent Office
- Prior art keywords
- aircraft
- trajectory
- parameter
- input parameters
- input
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- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
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- 238000000034 method Methods 0.000 title claims abstract description 61
- 238000010801 machine learning Methods 0.000 claims abstract description 29
- 238000012549 training Methods 0.000 claims abstract description 19
- 238000013459 approach Methods 0.000 claims description 39
- 238000004364 calculation method Methods 0.000 claims description 28
- 230000007613 environmental effect Effects 0.000 claims description 18
- 238000013528 artificial neural network Methods 0.000 claims description 7
- 238000004590 computer program Methods 0.000 claims description 4
- 230000003993 interaction Effects 0.000 description 10
- 238000005259 measurement Methods 0.000 description 9
- 230000008901 benefit Effects 0.000 description 4
- 238000004891 communication Methods 0.000 description 2
- 230000001934 delay Effects 0.000 description 2
- 230000033001 locomotion Effects 0.000 description 2
- 230000015654 memory Effects 0.000 description 2
- 230000006399 behavior Effects 0.000 description 1
- 238000013527 convolutional neural network Methods 0.000 description 1
- 238000003066 decision tree Methods 0.000 description 1
- 238000013135 deep learning Methods 0.000 description 1
- 230000007717 exclusion Effects 0.000 description 1
- 238000000605 extraction Methods 0.000 description 1
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- 238000007637 random forest analysis Methods 0.000 description 1
- 238000010200 validation analysis Methods 0.000 description 1
- 230000000007 visual effect Effects 0.000 description 1
Classifications
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G5/00—Traffic control systems for aircraft, e.g. air-traffic control [ATC]
- G08G5/06—Traffic control systems for aircraft, e.g. air-traffic control [ATC] for control when on the ground
- G08G5/065—Navigation or guidance aids, e.g. for taxiing or rolling
-
- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G5/00—Traffic control systems for aircraft, e.g. air-traffic control [ATC]
- G08G5/0017—Arrangements for implementing traffic-related aircraft activities, e.g. arrangements for generating, displaying, acquiring or managing traffic information
- G08G5/0026—Arrangements for implementing traffic-related aircraft activities, e.g. arrangements for generating, displaying, acquiring or managing traffic information located on the ground
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0635—Risk analysis of enterprise or organisation activities
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/40—Business processes related to the transportation industry
-
- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G5/00—Traffic control systems for aircraft, e.g. air-traffic control [ATC]
- G08G5/003—Flight plan management
- G08G5/0034—Assembly of a flight plan
-
- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G5/00—Traffic control systems for aircraft, e.g. air-traffic control [ATC]
- G08G5/0043—Traffic management of multiple aircrafts from the ground
Definitions
- the present invention relates to the determination of parameters of aircraft trajectories, for example taxiing times, or flight times.
- the present invention also relates to the use of these parameters of aircraft trajectories by operators such as air traffic controllers to regulate air traffic.
- air traffic control systems The purpose of air traffic control systems is to make the execution of flights safer, faster and more efficient. They make it possible to prevent collisions between aircraft, or dangerous situations between an aircraft and its environment (weather, terrain, etc.). They thus make it possible, by synchronizing as finely as possible the movement of aircraft, to ensure safe air traffic, but also allow aircraft to respect the scheduled flight times, and to adopt trajectories as economical as possible. possible in fuel.
- air traffic controllers receive a set of information relating to the airspace: position and predicted trajectories of aircraft, weather forecast, etc.
- the controllers can also communicate, via written messages or oral communications, with the pilots of the aircraft in order to retrieve additional information, if necessary, and give them instructions adapted to the situation, to guarantee the safety of air traffic, while by ensuring the best possible quality of service to air transport users.
- air traffic controllers can communicate to pilots the opportune moment to land or take off from an airport, or conversely instruct them to defer their approach if an airstrip is used by aircraft on time initially. planned.
- the instructions provided to the aircraft are based on predictions of the presence of aircraft in different places.
- air traffic controllers can predict in advance how long a given aircraft will be taxiing before take-off, how long take-off, cruise time, etc. This makes it possible to evaluate, for each aircraft, its time of presence in a given location, and conversely the traffic density in a given location and at a given time. This allows air traffic controllers to provide aircraft with the appropriate instructions, to ensure safety but also to optimize air traffic
- the ability of air traffic controllers to predict in advance the location of aircraft, and the duration of the various traffic phases is therefore essential, to guarantee both the safety and the efficiency of air traffic, and to manage the arrival and departure flows of aircraft from airports.
- Controllers today rely on flow management tools allowing a controller to benefit from a prediction of aircraft trajectory parameters.
- These flow management tools are today based on information resulting from a large quantity of parameters defined offline, which are tedious to introduce into the system and / or resulting from analytical calculations based on modeling which may prove to be approximate.
- Current tools would therefore require, in order to provide efficient prediction of aircraft flight parameters, to manually enter prediction results corresponding to virtually all possible situations. As this is not feasible in practice, current tools provide imprecise predictions, thus preventing air traffic controllers from benefiting from adequate support for flow management.
- the invention relates to a method implemented by a computer receiving as input a set of descriptions of aircraft trajectories, each associated with a set of input parameters, comprising, for each trajectory d an aircraft: at least one parameter of the aircraft; at least environmental parameter of the trajectory of the aircraft; said method comprising, for each trajectory: a step of forming a vector of input parameters comprising said input parameters; a step of extracting at least one parameter of the trajectory; said method comprising a step training of a supervised machine learning engine taking as input associations, for each trajectory respectively, between its vector of input parameters and less than one parameter of the trajectory.
- the supervised machine learning engine is a completely connected layer neural network.
- the at least one trajectory parameter is an exit taxiing time
- the set of entry parameters comprises at least one parameter chosen from a group comprising: a parking door identifier; a take-off runway identifier, and / or alignment point; weather information; a type of aircraft; an airline identifier; a level of ground traffic; taxiway accessibility; one hour of the day.
- the at least one trajectory parameter is an occupancy time of a landing runway
- the set of input parameters comprises at least one parameter chosen from a group comprising: an identifier of landing runway ; a parking door identifier; weather information; a type of aircraft; an airline identifier; a type of approach.
- the at least one trajectory parameter is an input taxiing time
- the set of input parameters comprises at least one parameter chosen from a group comprising: a landing runway identifier; a parking door identifier; weather information; a type of aircraft; an airline identifier; a level of ground traffic; an indication of closed taxiways; one hour of the day.
- the at least one trajectory parameter is a landing runway occupancy time
- the set of input parameters comprises at least one parameter chosen from a group comprising: a runway identifier d landing; a parking door identifier; weather information; a type of aircraft; an airline identifier; a level of ground traffic; an indication of closed taxiways; one hour of the day.
- the at least one trajectory parameter is a description of an approach trajectory
- the set of input parameters comprises at least one parameter chosen from a group comprising: an aircraft speed; a type of aircraft; an altitude at the so-called assembly point; weather information; an airline identifier; a procedure and / or type of approach; one hour of the day; a level of air traffic; flight plan data; flight data from an air traffic control system.
- the at least one trajectory parameter is an en route flight time
- the set of input parameters comprises at least one parameter chosen from a group comprising: an aircraft type, or a class aircraft speed; a flight altitude, meteorological information; an ATC sector description; a description of air restriction zones; an airline identifier of the flight plan data; flight data from an air traffic control system.
- the at least one trajectory parameter is a prediction of the trajectory of the aircraft over a time horizon
- the set of input parameters comprises at least one parameter chosen from a group comprising: a 3D position of the aircraft; an aircraft heading; information sent from the aircraft to air traffic control; flight plan data; flight data from an air traffic control system; a type of approach.
- the at least one trajectory parameter is a possibility for the aircraft to overtake a second aircraft
- the set of input parameters comprises at least one parameter chosen from a group comprising: a corridor identifier air, in which the aircraft are located; a type of the aircraft; a type of the second aircraft; an altitude of the aircraft; an altitude of the second aircraft; an aircraft speed; a speed of the second aircraft; an aircraft flight plan; a flight plan of the second aircraft.
- the subject of the invention is also a system comprising: at least one calculation unit capable of driving a supervised machine learning engine; access to at least one information storage medium storing, for each trajectory of an aircraft among a set of aircraft trajectories: a description of the trajectory; a set of input parameters associated with the trajectory comprising: at least one parameter of the aircraft; at least environmental parameter of the trajectory of the aircraft; the at least one calculation unit being configured, for each trajectory, to: form a vector of input parameters comprising the input parameters associated with the trajectory; extract at least one parameter from the trajectory; the at least one calculation unit being configured to drive a supervised machine learning engine taking as input associations, for each trajectory respectively, between its vector of input parameters and less than one parameter of the trajectory.
- the subject of the invention is also a computer program comprising program code instructions for the execution of the steps of the method according to the invention when said program is executed on a computer.
- the subject of the invention is also a method implemented by a computer receiving as input, for an aircraft trajectory, a set of input parameters comprising: at least one parameter of the aircraft; at least one environmental parameter of the trajectory of the aircraft; said method comprising: a step of forming, for the trajectory, a vector of input parameters comprising said input parameters; a step of executing a supervised learning engine to calculate, from the input vector, at least one parameter of the trajectory, said engine having been driven by a method according to the invention.
- the subject of the invention is also a computer program comprising program code instructions for the execution of the steps of the method according to the invention when said program is executed on a computer.
- a subject of the invention is also a system comprising: at least one calculation unit capable of executing a supervised machine learning engine; at least one computing unit capable of executing a supervised machine learning engine; at least one input port capable of receiving, for a trajectory of an aircraft, a set of input parameters comprising: at least one parameter of the aircraft; at least one environmental parameter of the trajectory of the aircraft; the at least one calculation unit being configured to: form, for the trajectory, a vector of input parameters comprising said input parameters; execute said supervised learning engine to calculate, from the input vector, the minus one parameter of the trajectory, said motor having been driven by a method of the invention.
- At least one calculation unit is configured to use at least one parameter of the trajectory within the framework of an air flow management application.
- FIG. 3 a calculation system allowing the training of a supervised machine learning engine for predicting at least one aircraft trajectory parameter, in a set of embodiments of the invention
- FIG. 4 a method of training a supervised machine learning engine for predicting at least one parameter of an aircraft trajectory, in a set of embodiments of the invention
- FIG. 5 a system for calculating at least one aircraft trajectory parameter using a supervised machine learning engine, in a set of embodiments of the invention
- FIG. 6 a computer-implemented method of calculating at least one parameter of an aircraft trajectory using a supervised automatic learning engine, in a set of embodiments of the invention.
- FIG. 1 represents an example of an air traffic control system, in which the invention can be implemented.
- the air traffic control system shown in FIG. 1 comprises a control tower 110, equipped with a radar 111 making it possible to locate the aircraft 120, 121 flying in a given sector.
- the control tower 110 can communicate with the aircraft, for example via a radio link, in order to give information and instructions to the aircraft, but also to receive information and requests from the aircraft.
- the control tower can receive data from external providers, such as a weather server 130.
- an air traffic controller can provide indications and instructions to the pilots of the aircraft from there. a set of data comprising the planned trajectories of aircraft in its sector, interactions with the pilots, and environmental data such as weather forecasts.
- FIG. 1 The system of Figure 1 is given by way of non-limiting example only, and the invention can be implemented in many systems for air traffic control, such as ATC or ATFM systems.
- FIG. 2 represents a set of flight phases on which the invention is able to predict trajectory parameters.
- the invention can be applied to many phases of flight.
- FIG. 2 represents an example of an aircraft trajectory 200 comprising the following phases:
- a cruise phase (in English cruise) itself composed of route phases 240, 260, and oceanic overflight 250;
- Each of these phases can be associated, in the flight plan of the aircraft, with a nominal duration. However, each of them may also experience delays, for reasons that may be related to the aircraft or to its environment.
- the taxiing phase 210 could be lengthened if the take-off runway is congested and does not allow take-off at the initially scheduled time. These various delays can also have repercussions on the later phases of the trajectory.
- the trajectory shown in FIG. 2 is provided by way of example only, and the invention could be applied to numerous other trajectories, characterized by different phases of flight.
- the invention makes it possible to predict, from a set of parameters linked to the aircraft and / or its environment, parameters of the trajectory of the aircraft, and in particular the duration of the various phases of a trajectory, in flight or ground.
- FIG. 3 represents a calculation system allowing the training of a supervised automatic learning engine for predicting at least one aircraft trajectory parameter, in a set of modes of implementation of the invention.
- System 300 is a calculation system. According to a set of embodiments of the invention, the system 300 may be a single computing device such as a computer, a server, or any other system capable of performing computer calculations. System 300 can also include a plurality of computing devices. For example, system 300 can be a server farm with multiple compute servers.
- the system 300 thus comprises at least one computing unit 310 capable of driving a supervised machine learning engine 320.
- the at least one computing unit 310 can be any type of computing unit suitable for performing computer calculations.
- the computing unit may be a processor configured with machine instructions, a microprocessor, an integrated circuit, a microcontroller, a programmable logic circuit, or any other computing unit capable of being programmed to perform computing operations.
- the supervised machine learning engine 320 can be any type of supervised machine learning engine.
- it could be a forest of decision trees (in English random forest), a network of artificial neurons, a vector-supported machine, or a deep learning engine, such as a deep neural network, a fully connected layer neural network (FCN) , or a convolutional neural network.
- FCN fully connected layer neural network
- a convolutional neural network such as a convolutional neural network.
- a supervised learning engine based on a neural network is particularly advantageous, since it allows, once the learning has been carried out, to be executed in a limited time. Running an artificial neural network, once trained, also requires a limited amount of computational resources.
- FCN completely completely connected layers
- the system 300 includes access to at least one information storage medium 330.
- the at least one information storage medium 330 can be of any type of storage suitable for storing information: hard disk, CD , DVD, magnetic tape, memory card, USB key, Flash memory, RAM.
- the information storage medium may be integrated with the system 300.
- the system 300 is a computing device such as a server
- the information storage medium may be a hard drive of the device.
- the at least one storage medium can be a set of memories distributed among the different computing devices.
- System 300 may also have access to at least one information storage medium 330 via a connection.
- the at least one information storage medium can consist of at least one hard disk accessed remotely, for example via at least one NAS server, or via a cloud computing system (from English cloud computing).
- the at least one information storage medium 330 stores a set of descriptions of aircraft trajectories 340, and, for each aircraft trajectory, a set of associated input parameters comprising: - parameters of the aircraft 341;
- trajectories of aircraft 340 can be described in different ways.
- trajectories can be expressed as 4D trajectories, with waypoints defined by latitude, longitude, and FL and time of way.
- the trajectories can also include, for each waypoint, an associated heading.
- a trajectory can also be associated with an aircraft type and / or a callsign (name of a given aircraft).
- the trajectories can include not only en-route parts, but also parts on the ground, including in particular the trajectory and the taxiing times of the aircraft.
- the parameters of the aircraft 341 can include various parameters such as the type of aircraft or the airline to which the aircraft belongs. These parameters can also include parameters related to the past trajectory or to the flight plan of the aircraft, such as:
- these parameters can include any type of parameter linked to the aircraft itself or to its planned trajectory.
- the environment parameters 342 can include many types of parameters related to the environment of the trajectory of the aircraft. These parameters may for example include parameters relating to air traffic, to the departure airport, to the arrival airport, to meteorological conditions, to a sector crossed by the aircraft. According to different embodiments of the invention, the environmental parameters 342 can for example comprise: an identifier of an aircraft parking lot; - an aircraft take-off or landing runway;
- any parameter relating to the environment of the aircraft can be used according to different embodiments of the invention.
- the concept of the aircraft environment can refer to both the physical environment, with meteorological parameters, and to parameters related to air traffic and air traffic. The present description will describe in more detail some particularly relevant parameter associations.
- the parameters can be expressed in any suitable way depending on the parameter considered.
- meteorological parameters can in particular include at least one of the following information: quantified information (temperatures, winds, pressures, etc.), for example by means of a GRIB file, descriptive text of the weather (for example example, presence of a storm, thunderstorm, etc.), SIGMET messages. More generally, any type of data providing information on the weather in the sector can be used.
- Parameters related to air traffic, or to taxiing traffic can be expressed, for example, in the form of an aircraft density.
- This density can be expressed in different ways, such as a number of aircraft in a given area, or a number of aircraft in a given volume.
- these parameters correspond to records of real situations that have occurred for the trajectories considered. They thus define, for each trajectory, the input parameters having an influence on the trajectory, and making it possible to predict certain characteristics thereof. As indicated above, these parameters include at least one parameter of the aircraft, of its past trajectory or of its flight plan, and at least one parameter of the environment of the aircraft.
- the parameters can include, according to different embodiments of the invention, any type of parameter that may have an impact on the trajectory, taxiing or flight, of the aircraft.
- the aircraft trajectories 340, aircraft parameters 341, and stored environmental parameters 342 can be obtained from different sources: sensor measurements, data collected by air traffic control services, by weather services, flight data .
- sensor measurements data collected by air traffic control services
- weather services by weather services
- flight data flight data
- One of the objectives of the invention is to enhance this data, using it as training data, in order to have more reliable predictors of trajectory parameters than the predictors of the state of the art.
- the at least one computing unit 310 is configured to cause the machine learning engine to calculate, for a given trajectory, at least one parameter of the trajectory, from the corresponding input parameters.
- the learning method implemented by at least one computing unit 310 is described in more detail with reference to Figure 4 below.
- FIG. 4 represents a method of driving a supervised automatic learning engine for predicting at least one parameter of an aircraft trajectory, in a set of embodiments of the invention .
- the method 400 takes as input a set of descriptions of aircraft trajectories such as the trajectories 340, as well as, for each trajectory, a set of input parameters comprising at least one parameter of the aircraft 341, and at least one environmental parameter 342 of the trajectory of the aircraft. These parameters can for example correspond to the parameters discussed with reference to FIG. 3, and other concrete examples of parameters according to different embodiments of the invention will be provided below.
- the method 400 comprises a subset of steps intended to associate, with each trajectory, an input parameter vector and at least one parameter of the trajectory. The steps of method 400 can be performed on all or part of the trajectories.
- the method 400 comprises a step 410 of forming a vector of input parameters comprising the input parameters associated with a given trajectory. This step makes it possible to formalize, for each trajectory, the relevant input parameters in the form of a vector that can be used as input to a machine learning engine.
- the method comprises a step 420 of extracting at least one parameter of the trajectory.
- This step consists in extracting, from a given trajectory, the parameters for which a prediction is desired.
- These parameters can for example comprise one or more parameters chosen from:
- ROT runway occupancy time
- trajectory parameters can be predicted.
- the invention is more particularly suited to the prediction of trajectory parameters having an impact on the management of air flows by air traffic controllers.
- each of the above trajectory parameters impacts the air flow management, for example by impacting the periods of availability of an airport's runways, or the ability of aircraft to overtake other slower aircraft.
- each of the parameters can be carried out in different ways depending on the parameter extracted. For example, certain parameters (go-around, possibility of an aircraft overtaking, etc.) can be recorded as events in the trajectory. They can also be deduced from recorded data, such as messages exchanged between the aircraft and the ATC service. Still others can be calculated, such as exit taxi times, by calculating the difference between take-off time and hangar exit time. Those skilled in the art will easily be able to define the most appropriate way to extract a given parameter, based on all of the available data. In general, all these data are stored in the ATM System and are easily accessible.
- the times (flight, cruising flight, taxiing %) can be obtained in the form of a duration, expressed for example in minutes, seconds ...; some parameters, such as the use of a go-around procedure, the performance of a standby procedure or the achievement of an overrun, can be obtained in binary form, indicating whether the event has taken place or not .
- Those skilled in the art will be able to easily identify the most relevant shape for each of the parameters.
- Steps 410 and 420 are repeated for each trajectory.
- the method 400 comprises a step 430 of training a supervised machine learning engine such as the motor 320, said motor taking as input the associations, for each trajectory respectively, between the vector of input parameters and at least one parameter of the trajectory corresponding to each trajectory.
- a supervised machine learning engine such as the motor 320
- the vector of input parameters serves as the vector of characteristics, and the at least one parameter of the label trajectory.
- the machine learning engine 320 can thus be trained to predict the au minus one parameter, for each trajectory, from the vector of input parameters.
- the machine learning engine 320 is able to predict, for a new path, at least one parameter thereof based on the associated input parameter vector.
- Such an engine has the advantage of requiring limited resources to predict at least one parameter of a given trajectory.
- Machine learning engines can thus predict the desired parameters of the trajectories in a limited time. This therefore makes it possible to ensure that it is possible to determine the desired parameters of the trajectories practically in real time, thus allowing use of the parameters in a real time air flow management application.
- the learning engine 320 is capable of calculating at least one parameter of a new trajectory, from a data vector of the same type as those with which it was trained, comprising in particular at least one parameter of the aircraft, and at least one environmental parameter of the trajectory of the aircraft.
- steps 410, 420 then 430 are presented in the following order: steps 410, 420 then 430.
- this order is given by way of indication, and, according to certain embodiments of the invention , some steps may be performed in different orders. For example, it is possible to perform steps 410 and 420 in the reverse order to that shown, or to perform them in parallel.
- Example 1 Drive of a motor for the calculation of path parameters linked to a departure manager (DMAN).
- DMAN departure manager
- the motor 320 can be trained to calculate parameters that can be used in a DMAN module.
- One of The main objectives of a DMAN module are to build a sequence of pre-departures of aircraft, that is to say to determine the order in which the aircraft will leave an airport.
- An essential parameter to do this is the so-called “tax-out” time, or taxiing time when exiting an aircraft, that is to say the time that the aircraft must taxi in the airport between the exit. from the hangar and take off. In practice, this time depends on a number of very different factors.
- the weather has an impact on this weather: an aircraft pilot will drive more slowly in fog or snow than in clear weather; the traffic density on the ground will also have an impact; the aircraft model may also have an impact, since aircraft do not have the same performance; the airline may also come into play, as pilots from different companies may have different practices and training.
- the engine 320 is thus capable, by comparing, within historical situations, the values of these input parameters with the output running times actually observed, of training to provide a reliable prediction of running time in output, taking into account not only the input parameters taken individually, but also the interactions between them.
- the application of the invention to the calculation of exit taxiing time is not restricted to this list of parameters: according to various embodiments of the invention, only a part of them is used. On the contrary, other parameters can be used in addition. The choice of parameters may depend on the parameters influencing the exit run time, but also on the availability of measurements for the various parameters.
- the information having an impact on visibility is particularly relevant.
- the engine 320 can be driven, either generically with data from several airports, or specifically for a given airport.
- Example 2 Drive of a motor for the calculation of trajectory parameters linked to an arrivals manager (AMAN).
- the engine 320 can be trained to calculate parameters usable in an AMAN module.
- One of the main objectives of an AMAN module is to optimize the choice of arrival runways and gates allocated to aircraft arriving at an airport, as well as to indicate arrival times at nearby airports.
- a first essential parameter for this is the ROT (Time occupied by an aircraft of a landing strip).
- a type of approach (which can for example be chosen from a visual approach, an ILS approach, etc.).
- the engine 320 is capable, by comparing, within historical situations, the values of these input parameters with the ROTs actually observed, of training to provide a reliable prediction of the ROTs, taking into account no only the input parameters taken individually, but also the interactions between them.
- a second essential parameter for optimizing the operation of an AMAN is the taxi-in time.
- the engine 320 is capable, by comparing, within historical situations, the values of these input parameters with the input run times actually observed, of training to provide a reliable prediction of the running times.
- input taxiing taking into account not only the input parameters taken individually, but also the interactions between them.
- the information having an impact on visibility is particularly relevant.
- the engine 320 can be driven, either generically with data from several airports, or specifically for a given airport.
- the drive of the engine 320 to efficiently calculate the input taxiing time and / or the ROT makes it possible to considerably improve the management of air flows within an AMAN type module.
- Example 3 Drive of a motor for the calculation of trajectories on approach.
- the engine 320 can be trained to calculate approach trajectories, used in particular in AMAN type modules. Indeed, effective prediction of approach trajectories makes it possible to calibrate the arrival of aircraft at an airport.
- the approach trajectories actually applied depend on a large number of factors. For example, the weather has an impact on these trajectories: an aircraft pilot will not use the same trajectory depending on the weather, whether it is because of different visibility, or differences in the behavior of the aircraft. aircraft; the aircraft model may also have an impact, since aircraft do not perform the same; the airline may also come into play, as pilots from different companies may have different practices and training.
- the engine 320 is thus capable, by comparing, within historical situations, the values of these input parameters with the approach trajectories actually observed, of training to provide a reliable prediction of the trajectory of 'approach, taking into account not only the input parameters taken individually, but also the interactions between them.
- the approach path can be represented in different ways. For example, it can be represented as an approach time. This can for example be represented as an approach duration, or a distribution of approach time probabilities.
- the approach trajectory can also be represented in the form of a 4D trajectory, that is to say of a series of 3D points associated with passage times, which can be supplied to an avionics calculation engine in order to build an avionics trajectory.
- the approach path can also include an indication of a specific approach situation, such as an indication that the aircraft is performing an approach procedure (in English holding pattern), a go-around procedure (in English misse approach / go around), or another special situation (temporary exclusion zone, closed airstrip, etc.).
- the learning engine 320 will thus be able to efficiently predict the occurrence of particular situations, which also allows it to better predict the approach trajectory, whatever the form used (approach time, 3D trajectory, etc. .).
- the application of the invention to the determination of approach paths is not restricted to this list of parameters: according to various embodiments of the invention, only a part of them is used. On the contrary, other parameters can be used in addition. The choice of parameters may depend on the parameters influencing the approach path, but also on the availability of measurements for the various parameters.
- the engine 320 can be driven, either generically with data from several airports, or specifically for a given airport.
- Example 4 Driving an engine for calculating flight time en route.
- the engine 320 can be trained to calculate flight times en route, used in particular in AMAN type modules. Indeed, an efficient prediction of the time spent by aircraft en route for different trajectories makes it possible to better understand their time of arrival at an airport. However, en-route flight times depend on a number of factors.
- the engine 320 is thus capable, by comparing, within historical situations, the values of these input parameters with the en-route flight times actually observed, of training to provide a reliable prediction of the time. flight en route, taking into account not only the individual input parameters, but also the interactions between them.
- weather information having an impact on the ability of an aircraft to fly over an area, or affecting the speed and / or the manner of flying over the area (wind, storms, thunderstorms %) are particularly relevant.
- the application of the invention to determining flight time en route is not restricted to this list of parameters: according to various embodiments of the invention, only a part of them is used. On the contrary, other parameters can be used in addition. The choice of parameters may depend on the parameters influencing the flight time en route, but also on the availability of measurements for the various parameters.
- the drive of the engine 320 to efficiently calculate en-route flight times makes it possible to considerably improve the management of air flows within an AMAN type module, by allowing better knowledge of the arrival times of the aircraft. .
- Example 5 Prediction of a most probable trajectory.
- the engine 320 can be trained to calculate a most probable trajectory of an aircraft. In fact, aircraft do not always exactly follow their flight plans, but it is very difficult to determine a priori what their exact trajectory will be. This prediction makes it possible in particular to adapt to the actual flow of aircraft.
- the invention makes it possible to drive the motor 320 to predict the most probable trajectories, taking as input parameters at least one parameter chosen from:
- the most probable trajectory can be represented in various forms, for example a series of 4D positions (3D positions associated with a passage time).
- the most probable trajectory will consist of a trajectory prediction over a defined time horizon, corresponding to the continuation of the trajectory of the aircraft over a given duration.
- the engine 320 is thus capable, by comparing, within historical situations, the values of these input parameters with the 3D trajectories actually followed, of training to provide a reliable prediction of the trajectory of the aircraft, taking into account not only the input parameters taken individually, but also the interactions between them.
- the engine thus trained will therefore be able to predict, in a given situation, the trajectory actually followed by the aircraft over a given time horizon, much more precisely than the methods of the state of the art.
- the application of the invention to the trajectory prediction over a time horizon is not restricted to this list of parameters: according to various embodiments of the invention, only a part of them is used. On the contrary, other parameters can be used in addition. The choice of parameters may depend on the parameters influencing the trajectory actually followed, but also on the availability of measurements for the various parameters.
- the drive of the motor 320 to efficiently calculate a trajectory over a time horizon makes it possible to considerably improve the management of air flows both within an AMAN type module and a DMAN module, in allowing a better knowledge of the trajectories actually followed by the aircraft.
- Example 6 Prediction of a possibility of overtaking.
- the engine 320 can be trained to calculate a possibility of overtaking, that is to say the ability of an aircraft to overtake an aircraft situated in front of it and flying at lower speed.
- the possibility of overtaking changes the time at which an aircraft arrives at an airport, but depends on a number of factors, such as the flight path followed, or the respective aircraft speeds. This prediction makes it possible in particular to adapt to the actual flow of aircraft.
- the invention makes it possible to train the engine 320 to predict the ability of an aircraft to overtake a second aircraft, taking as input parameters at least one parameter chosen from:
- overshoot can be represented in different ways, for example in the form of a binary value (overshoot allowed or not).
- the engine 320 is thus capable, by comparing, within historical situations, the values of these input parameters with an effective validation of the possibility of overtaking the second aircraft, of training itself to provide a reliable prediction of the possibility of overtaking a second aircraft, taking into account not only the input parameters taken individually, but also the interactions between them.
- the engine thus driven will therefore be able to predict, in a given situation, whether an aircraft is capable of overtaking a second aircraft, much more precisely than the methods of the state of the art.
- the drive of the engine 320 to efficiently calculate an overrun capacity makes it possible to considerably improve the management of air flows, in particular within an AMAN type module, by allowing better knowledge of the arrival times of the aircraft. .
- FIG. 5 represents a system for calculating at least one parameter of aircraft trajectories using a supervised machine learning engine, in a set of embodiments of the invention.
- the system 500 may for example be an ATM, ATC or ATFM system, using an air flow management application, allowing air traffic controllers to manage the flow of aircraft in a given area, for example within applications.
- AMAN AMAN, DMAN or XMAN.
- the system 500 is a calculation system. According to a set of embodiments of the invention, the system 500 may be a single computing device such as a computer, a server, or any other system capable of performing computer calculations. System 500 may also include a plurality of computing devices. For example, system 500 can be a server farm with multiple compute servers.
- the system 500 thus comprises at least one calculation unit 510 capable of executing a supervised automatic learning engine 320, similar to the supervised learning engine presented in FIG. 3.
- the supervised machine learning engine 320 was trained by a method such as method 400, and / or a system such as system 300.
- the at least one computing unit 510 can be any type of computing unit capable of performing computer calculations.
- the computing unit may be a processor configured with machine instructions, a microprocessor, an integrated circuit, a microcontroller, a programmable logic circuit, or any other computing unit capable of being programmed to perform computing operations.
- the system 500 comprises at least one input port 530 capable of receiving, for an aircraft trajectory, input parameters.
- Input parameters 541, 542 are of the same type as input parameters 341, 342.
- System 500 can therefore receive:
- the input parameters can be received in different ways. For example, flight plans and instantaneous parameters of aircraft or their trajectories can be received by radio communication with aircraft, through radar measurements, etc.
- the aircraft's environmental information can be received for example by means of measurements (radar, weather radar, etc.), by subscription to an external service (weather service, etc.), or by ATC services.
- At least one port 530 can be of different types: internet connection, radio link, etc.
- the invention is not restricted to one type of input port, and those skilled in the art will be able to adapt the reception of the input parameters to the available input channels.
- the different input parameters can be received on a single port, or several ports, of the same type or of different types.
- the at least one parameter of the aircraft 541 can be received by radio link, and the at least one environmental parameter of the trajectory of the aircraft 542 by an Internet connection.
- the at least one calculation unit 510 is configured to form, for the trajectory, a vector of input parameters comprising said input parameters.
- the at least one calculation unit 510 is also configured to calculate, from the input vector, at least one parameter of the trajectory. Many path parameters can be calculated. In particular, all of the embodiments discussed with reference to Figures 3 and 4 can be used here.
- the system 500 is thus capable of calculating at least one parameter of the trajectory of the aircraft, while benefiting from the advantages of the training of the supervised learning engine.
- the supervised learning engine 320 makes it possible to calculate the parameters of the trajectory with limited resource requirements, and a deterministic execution time. This allows the parameters to be used reactively, for example within an air flow management application, where it is important to be able to assess the impact of aircraft on each other, in real time.
- the system 500 can use it in various ways. For example, it can be displayed to at least one operator, such as an air traffic controller, through at least one screen 550. This allows the operator to use this setting for their interaction with aircraft. It can also raise an alert, if the value of the parameter causes difficulty in the management of air traffic.
- an air traffic controller such as an air traffic controller
- At least one calculation unit 510 is configured to use the at least one calculated parameter of the trajectory within the framework of an air flow management application. .
- FIG. 5 represents a single supervised learning engine 320, according to different embodiments of the invention, several different engines can be used.
- a first motor can be used to calculate trajectory parameters for an application of the “DMAN” type (exit taxiing time, take-off time, etc.), and a second motor to calculate trajectory parameters for an application.
- “AMAN” type application time of arrival, time of use of a runway for landing, time of taxiing on arrival, etc.
- FIG. 6 represents a method implemented by computer for calculating at least one parameter of an aircraft trajectory using a supervised machine learning engine, in a set of modes of implementation of the invention.
- the method 600 receives as input, for an aircraft trajectory, a set of input parameters comprising:
- the method comprises a step 610 of forming, for the trajectory, a vector of input parameters comprising said input parameters.
- the method then comprises a step 620 of executing a supervised learning engine 320 to calculate, from the input vector, at least one parameter of the trajectory, said engine having been driven by the method 400 .
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Applications Claiming Priority (2)
Application Number | Priority Date | Filing Date | Title |
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FR2006172A FR3111466A1 (fr) | 2020-06-12 | 2020-06-12 | Système et méthode pour la détermination améliorée de paramètres de trajectoire d’aéronefs |
PCT/EP2021/064635 WO2021249821A1 (fr) | 2020-06-12 | 2021-06-01 | Systeme et methode pour la determination amelioree de parametres de trajectoire d'aeronefs |
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EP4165619A1 true EP4165619A1 (de) | 2023-04-19 |
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EP21728091.6A Pending EP4165619A1 (de) | 2020-06-12 | 2021-06-01 | System und verfahren zur besseren bestimmung von wegparametern von flugzeugen |
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US (1) | US20230230490A1 (de) |
EP (1) | EP4165619A1 (de) |
AU (1) | AU2021286810A1 (de) |
FR (1) | FR3111466A1 (de) |
WO (1) | WO2021249821A1 (de) |
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EP3422130B8 (de) * | 2017-06-29 | 2023-03-22 | The Boeing Company | Verfahren und system zur autonomen steuerung eines flugzeugs |
US11164465B2 (en) * | 2017-10-27 | 2021-11-02 | International Business Machines Corporation | Real-time identification and provision of preferred flight parameters |
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US20230230490A1 (en) | 2023-07-20 |
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AU2021286810A1 (en) | 2023-02-16 |
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