Collision sensing using historical vehicle data

Abstract

The present invention is entitled "Collision Awareness Using Historical Vehicle Data." The present disclosure relates to methods, computer program products, and systems for providing ground vehicle tracking data, including indications of potential collision zones, to an airport map display system onboard an aircraft. In one example, a method includes identifying historical navigation route data, airport guidance features, and a predicted path for a first vehicle. The method also includes determining a predicted position along the predicted path and determining a predicted position of a second vehicle, and comparing vehicle envelopes of the two vehicles to determine a predicted collision zone for the vehicle.

Description

[0001] This application claims priority to Indian provisional application No. 202011006508, filed on February 14, 2020, the entire contents of which are hereby incorporated by reference.

[0002] The present disclosure relates to collision sensing for vehicles. Background Art

[0003] As commercial air traffic has continued to grow over the years, airports have become increasingly busy. Consequently, collision avoidance systems have been implemented that use various sensors, imaging devices, radars, and other hardware components mounted on aircraft to help prevent potential collisions between one aircraft and another. These hardware components increase the weight, maintenance complexity, and generally the overall cost of such vehicles.

[0004] Increasingly, air traffic also involves very large passenger aircraft with very long wingspans, which can sometimes reduce wingtip clearance margins when the aircraft are moving on the airport ground. Furthermore, multiple aircraft in an area may be powered off at any given time or otherwise not transmitting tracking beacons that could be used to reduce the likelihood of a collision with another vehicle. Aircraft may be powered off and towed by a trailer, in which case aircraft ground collisions or collisions between aircraft and other vehicles are even more likely to occur. Furthermore, in areas of an airport where aircraft or aircraft trailers navigate unmarked routes, such as in airport apron areas or hangar bays, wingtip collisions may occur at an even higher rate due to the seemingly unlimited routes that an aircraft or trailer can take to reach its intended destination. Summary of the Invention

[0005] The present disclosure relates to methods, systems, and computer program products for predicting a potential collision zone for a vehicle at an expected time using historical vehicle data, clearance information for one or more vehicles, and / or airport guidance features. In some examples, a vehicle may transmit its current position to a user interface (UI) device (e.g., an electronic flight bag (EFB)) or to a remote data server (e.g., a cloud-based data server). The remote data server or EFB may use one or more of historical navigation route data, clearance information for one or more vehicles, and / or airport guidance features to predict a potential collision zone and provide an indication of the potential collision zone to a user. The historical navigation route data may be based on transponder position data and stored in a database of historical vehicle data. Additionally, airport guidance features may include data stored in a database that provides information about the location of guidance markers, such as guide lines painted on the ground, guidance signs, architectural features, and other information that provides guidance to vehicles throughout a particular airport location. A collision awareness system may use the historical navigation route data and airport guidance features to predict a route and predict the vehicle's position along the route to determine the potential collision zone. In some cases, the collision awareness system may provide potential collision zone data for display on an EFB, such as on an Airport Moving Map Display (AMMD) application executing on the EFB.

[0006] In some examples, a ground vehicle tracking system can be used to determine airport ground object transit data using, for example, multilateration sensors or other airport system sensors. This data can be used to confirm or validate predictions of potential collision zones predicted by a collision awareness system using one or more of historical navigation route data, clearance information for one or more vehicles, and / or airport guidance features. Some transiting ground objects or types of transiting ground objects may or may not actively transmit messages or signals that can be received by certain types of multilateration sensors or other airport system sensors, or may not respond to certain types of interrogation signals transmitted by multilateration sensors or other airport system sensors, such as when airport system sensors utilize cooperative monitoring with objects other than aircraft that are not typically configured to cooperate. In some examples, a transiting aircraft may be towed by an aircraft tractor (e.g., a trailer transporting other vehicles). In such cases, the towed aircraft may be powered off during this time, preventing the aircraft from transmitting signals that can be used to track the vehicle's position. Furthermore, the vehicle may be located in an area of the airport that provides limited guidance to the vehicle via airport guidance features. For example, the apron area of an airport may not include painted guidance features on the ground that can be referenced in the airport guidance database. Therefore, complex maneuvers and high traffic areas at various airport locations increase the possibility of potential vehicle collisions (e.g., wingtip collisions, etc.).

[0007] According to various techniques of the present disclosure, a collision awareness system may utilize one or more of historical navigation route data and / or airport guidance features to predict potential collision zones between vehicles. In addition, the collision awareness system may utilize vehicle clearance information, such as clearance information from air traffic controllers (ATC), to predict potential collision zones between vehicles passing over the ground, where at least one vehicle is moving, whether or not being towed. In some examples, the collision awareness system may be executed on a remote server that collects data (such as the location of the vehicles), updates a database, and predicts collision zones. In another example, the collision awareness system may be executed at least in part on an EFB or other user interface device.

[0008] In some examples, the collision awareness system can receive clearance information in text or voice form, process the clearance information, and determine navigation information for the vehicle, or predict the vehicle's current location based on the clearance information. For example, if a vehicle receives clearance information to a specific gate in an apron area, but then loses power to the vehicle's avionics system, the collision awareness system can determine how much time has passed since the vehicle received the clearance information, how long the vehicle will historically take to reach the destination point or another target marker on a path toward the destination point, and predict the vehicle's location at any specific point in time. In any case, the collision awareness system can use historical navigation route data and airport guidance features to predict the vehicle's location, predict the vehicle's trajectory, and predict the trajectories of other vehicles to determine whether overlap between the envelopes of two or more vehicles indicates a potential collision at an anticipated or future time.

[0009] In this way, a collision awareness system can be implemented without requiring additional hardware to be installed on an aircraft, and can provide reliable indications of predicted collision zones within an airport by utilizing specialized computing systems to overlay data, such as airport ground data overlaid with historical navigation route data. Furthermore, the collision awareness system can utilize machine learning models to provide such predictions, trained on specific data inputs, allowing for continuous modeling and updating of predicted routes as vehicles traverse the routes. For example, the collision awareness system can predict a route for a first vehicle, but when the first vehicle begins traveling along the predicted route, an updated predicted route for the first vehicle can be determined, for example, based on data received from the vehicle (e.g., speed information, position information, etc.). This allows the collision awareness system to provide dynamic predictions in flight as objects move throughout the airport and as historical navigation route data evolves with changing conditions. Furthermore, the collision awareness system can predict collision zones based on aircraft and airport characteristics, incorporating both general and specific information from a variety of vehicle types and airport locations.

[0010] In one example, a method includes obtaining, by processing circuitry of a ground collision awareness system, historical navigation route data for one or more reference vehicles, the historical navigation route data being based on transponder position data. The method also includes identifying, by the processing circuitry, a plurality of airport guidance features for a particular airport location, the airport guidance features including guidance marker information. The method also includes determining, by the processing circuitry, a predicted path for a first vehicle, the predicted path comprising a first portion and a second portion, the first portion of the predicted path being predicted using the guidance marker information, and the second portion of the predicted path being predicted using the historical navigation route data. The method also includes determining, by the processing circuitry, a predicted position of the first vehicle along the predicted path at an expected time. The method also includes determining, by the processing circuitry, a predicted position of a second vehicle relative to approximately the same expected time. The method also includes performing, by the processing circuitry, a comparison of a first vehicle envelope of the first vehicle with a second vehicle envelope of the second vehicle at the predicted position. The method also includes identifying, by the processing circuitry, an overlap between the first vehicle envelope and the second vehicle envelope. The method also includes determining, by the processing circuitry, a predicted collision zone for the first vehicle and the second vehicle at the expected time based at least in part on the overlap between the first vehicle envelope and the second vehicle envelope.

[0011] In another example, a ground collision awareness system comprising a processor and a memory is disclosed. The memory is configured to store: historical navigation route data for one or more reference vehicles, wherein the historical navigation route data is based on transponder position data; and a plurality of airport guidance features for one or more airport locations, wherein the airport guidance features include guidance marker information. The processor of the ground collision awareness system is configured to determine a predicted path for a first vehicle, the predicted path comprising a first portion and a second portion, the first portion of the predicted path being predicted using the guidance marker information and the second portion of the predicted path being predicted using the historical navigation route data; determine a predicted position of the first vehicle along the predicted path at an expected time; determine a predicted position of the second vehicle relative to approximately the same expected time; perform a comparison of a first vehicle envelope of the first vehicle with a second vehicle envelope of the second vehicle at the predicted position; identify an overlap of the first vehicle envelope and the second vehicle envelope; and determine a predicted collision zone for the first vehicle and the second vehicle at the expected time based at least in part on the overlap of the first vehicle envelope and the second vehicle envelope.

[0012] In another example, a non-transitory computer-readable storage medium having instructions stored thereon is disclosed. The instructions, when executed, cause one or more processors to: obtain historical navigation route data for one or more reference vehicles, the historical navigation route data being based on transponder position data; identify a plurality of airport guidance features for a particular airport location, the airport guidance features comprising guidance marker information; determine a predicted path for a first vehicle, the predicted path comprising a first portion and a second portion, the first portion of the predicted path being predicted using the guidance marker information and the second portion of the predicted path being predicted using the historical navigation route data; determine a predicted position of the first vehicle along the predicted path at an expected time; determine a predicted position of a second vehicle relative to approximately the same expected time; perform a comparison of a first vehicle envelope of the first vehicle with a second vehicle envelope of the second vehicle at the predicted position; identify an overlap of the first vehicle envelope and the second vehicle envelope; and determine a predicted collision zone for the first vehicle and the second vehicle at the expected time based at least in part on the overlap of the first vehicle envelope and the second vehicle envelope.

[0013] The present disclosure also relates to an article comprising a computer-readable storage medium. The computer-readable storage medium includes computer-readable instructions that can be executed by a processor. The instructions cause the processor to perform any part of the technology described herein. The instructions can be, for example, software instructions, such as those used to define software or computer programs. The computer-readable medium can be a computer-readable storage medium, such as a storage device (e.g., a disk drive or an optical drive), a memory (e.g., a flash memory, a read-only memory (ROM), or a random access memory (RAM)), or any other type of volatile or non-volatile memory or storage element that stores instructions (e.g., in the form of a computer program or other executable file) to cause the processor to perform the technology described herein. The computer-readable medium can be a non-transitory storage medium.

[0014] The details of one or more examples are set forth in the accompanying drawings and the description below. Other features, objects, and advantages will be apparent from the description and drawings, and from the claims. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 is a conceptual block diagram depicting an exemplary collision sensing system that interacts with various components to determine a predicted collision zone for a vehicle, according to aspects of the present disclosure.

[0016] Figure 2 is a conceptual block diagram of an exemplary computing system having an exemplary computer-executable collision sensing system according to aspects of the present disclosure.

[0017] Figure 3A flow chart depicts an exemplary process for identifying predicted collision zones that a collision awareness system according to aspects of the present disclosure may implement using one or more of historical navigation route data and / or airport guidance features.

[0018] Figure 4 Depicted are exemplary techniques that a collision awareness system according to aspects of the present disclosure may implement for aligning historical navigation routes with airport guidance features.

[0019] Figures 5A to 5C A conceptual diagram depicts a portion of an airport with various aircraft and ground vehicles on airport runways, taxiways, and other airport surfaces, and an exemplary collision awareness system for predicting positions of the various aircraft and ground vehicles on the airport surfaces, in accordance with aspects of the present disclosure.

[0020] Figure 6 is a diagram of an exemplary graphical output display of an airport ground map that may be implemented by a two-dimensional airport moving map display (2D AMMD) application that may be implemented on an electronic flight bag (EFB), such as a flight crew member's tablet computer in the cockpit of a particular aircraft on the airport ground, in accordance with aspects of the present disclosure.

[0021] Figure 7 is a conceptual block diagram depicting an example airport network system with example ground sensors in accordance with aspects of the present disclosure. DETAILED DESCRIPTION

[0022] Various examples are generally described below, relating to methods, computer program products, and electronic systems that can provide collision awareness data (including indications of potential collision zones) from a collision awareness system. The collision awareness system can provide such data to an application onboard an aircraft (e.g., an Airport Moving Map Display (AMMD) application) via a wireless network. For example, the collision awareness system can provide such indications of potential collision zones on an electronic flight bag (EFB), which can be implemented on a tablet computer or similar user interface device. Flight crew members can view and use the AMMD, enhanced with information from the collision awareness system, while pilots are controlling an aircraft on the airport ground, such as during taxiing, parking, etc. In some examples, a tug operator can view and use the AMMD, enhanced with information from the collision awareness system, while towing an aircraft to a destination according to an ATC clearance. The collision awareness system can determine potential collision zones with one or more other ground vehicles (e.g., other aircraft or ground vehicles) and transmit warnings of potential collision zones to the EFB. Thus, implementations of the present disclosure can provide better situational awareness for controlling the ground movement of aircraft on airport taxiways, including in limited visibility weather conditions, without requiring any new hardware to be installed in the aircraft itself (and therefore without requiring certification of the new hardware by the relevant aviation authorities), and without requiring the coordinated participation of other aircraft. Implementations of the present disclosure can not only reduce the likelihood of an aircraft colliding with another aircraft or ground vehicle, but can also provide airports with additional benefits such as smoother taxiing and fewer interruptions or delays due to confusion or lack of situational awareness of ground traffic.

[0023] Figure 1 is a conceptual block diagram depicting exemplary components of a collision awareness system environment 102. The collision awareness system may be implemented in a system including Figure 1 1. The exemplary collision awareness system environment 102 includes various exemplary components of the collision awareness system environment 102. In the illustrated example, the collision awareness system environment 102 includes various components, including ground and / or air vehicles 111, traffic controllers 114, one or more data servers 132, various databases or data repositories 105, and a user interface device 104. Thus, the collision awareness system may be implemented as software installed on one or more of the components of the collision awareness system environment 102.

[0024] While vehicles 112A-112N may sometimes be referred to as aircraft having various configurations, the technology of this disclosure is not limited thereto, and vehicles 112A-112N may include other vehicles, such as helicopters, hybrid tiltrotor aircraft, urban air vehicles, jets, quadcopters, hovercraft, space shuttles, unmanned aerial vehicles (UAVs), flying robots, etc. Furthermore, while vehicles 113A-113N may sometimes be referred to as tug trucks, the technology of this disclosure is not limited thereto, and vehicles 113A-113N may include other vehicles, such as unmanned ground vehicles, transit ground vehicles, unmanned tug trucks (e.g., remotely operated vehicles), luggage cart vehicles having multiple vehicles attached via linkages, fuel trucks, airport shuttles, container loaders, belt loaders, catering vehicles, emergency vehicles, snow plows, or ground maintenance equipment, etc. In some examples, vehicle 111 may receive direct communications from traffic controller 114, such as via radio or cellular communications. For example, traffic controller 114 may transmit permission information directly to one of aircraft 112 or to tug truck 113 indicating a destination port for parking the aircraft.

[0025] In some examples, user interface devices 104A-104N may include a variety of user interface devices. For example, user interface device 104 may include a tablet, laptop, phone, EFB, augmented reality headset or virtual reality headset, or other types of user interface devices. User interface device 104 may be configured to receive ground vehicle movement data indicating a potential collision zone from a collision awareness system. According to exemplary aspects of the present disclosure, such as Figures 5A to 5C In those aspects of the present invention, the user interface device 104 may also be configured to generate (eg, render) and present an AMMD showing an indication of a passing ground vehicle and a potential collision zone.

[0026] Network 130 may include any number of different types of network connections, including satellite connections and Wi-Fi. TM For example, network 130 may include the use of geostationary satellites 105A, low earth orbit satellites 105B, global navigation satellite systems 105C, cellular base station transceivers 160 (e.g., for 3G, 4G, LTE, and / or 5G cellular network access), and / or Wi-Fi. TMThe network established by the access point. In turn, the geostationary satellite 105A and the low earth orbit satellite 105B can communicate with a gateway that provides access to the network 130 for one or more devices implementing the collision awareness system. The cellular base station transceiver can have a connection that provides access to the network 130. In addition, the global navigation satellite system can communicate directly with the vehicle 111, for example to triangulate (or otherwise calculate) the current position of the vehicle 111. These various satellite, cellular and Wi-Fi network connections can be managed by different third-party entities, referred to herein as "operators." In some examples, the network 130 may include a wired system. For example, the network 130 may include an Ethernet system, such as the present disclosure. Figure 7 In some examples, network 130 may include a multilateration local area network (LAN), such as the Figure 7 The multilateration system LAN shown.

[0027] In some examples, any of the devices in the collision awareness system environment 102 that implement one or more techniques of the collision awareness system can be configured to communicate with any of the various components via the network 130. In other cases, a single component of the collision awareness system environment 102 can be configured to implement all of the techniques of the collision awareness system. For example, the collision awareness system can include a system resident on the vehicle 111, the data server 132, the traffic controller 114, or the user interface devices 104A / 104N. In some examples, the collision awareness system can operate as part of a software package installed on one or more computing devices. For example, the traffic controller 114 can operate software that implements one or more of the various techniques of the disclosed collision awareness system. For example, a software version of the collision awareness system can be installed on the computing device of the traffic controller 114. Similarly, the disclosed collision awareness system can be included in the user interface device 104 or one or more data servers 132. For example, the data server 132 can include a cloud-based data server that implements the disclosed collision awareness system.

[0028] In some examples, one or more data servers 132 may be configured to receive input data (e.g., vehicle position data, airport guidance features, clearance information, etc.) from network 130 according to one or more techniques of this disclosure, determine a predicted collision zone, and may output the predicted collision zone data to a server. Figure 1104, traffic controller 114, or vehicle 111. In some examples, data server 132 may include data repository 105. In other examples, some or all of data repository 105 may be embodied as a separate device that interacts with other components of collision awareness system environment 102, either directly or via network 130. For example, where a collision awareness system is implemented at least in part on one or more of data servers 132, data repository 105 may interact with data server 132, either directly or via network 130.

[0029] In some examples, the database may include historical vehicle data 106, airport guidance data 108, and in some cases, clearance data 110. Although shown as a single data repository 105, the database shown as part of the data repository 105 may be embodied as a separate object. In some examples, the database included in the vehicle or external to the vehicle may be or include a key-value data repository, such as an object-based database or dictionary. In non-limiting examples, the database may include any data structure (and / or combination of multiple data structures) for storing and / or organizing data, including but not limited to a relational database (e.g., an Oracle database, a MySQL database, etc.), a non-relational database (e.g., a NoSQL database, etc.), an in-memory database, a spreadsheet, such as a comma-separated value (CSV) file, an extensible markup language (XML) file, a TeXT (TXT) file, a flat file, a spreadsheet file, and / or any other widely used or specialized format for data storage. For example, historical navigation route data may be laid out as separate structured XML fragments.

[0030] Databases are typically stored in one or more data repositories. Therefore, each database mentioned herein (e.g., in the description herein and / or in the drawings of this application) should be understood to be stored in one or more data repositories. In various examples, outgoing requests and / or incoming responses can be transmitted in any suitable format. For example, XML, JSON and / or any other suitable format can be used for API requests and responses or other. As described herein, data transmission refers to transmitting data from one of the vehicle 111, traffic controller 114, data server 132 or user interface device 104 via the network 130, and receiving data at the user interface device 104, data server 132, traffic controller 114 or vehicle 111 via the network 130. Data transmission can follow various formats, such as database format, file, XML, HTML, RDF, JSON, system-specific file format, data object format or any other format, and can be encrypted or have data of any available type.

[0031] In some examples, the historical vehicle data 106 may store historical navigation route data, vehicle data (eg, maintenance logs, safety zone envelope data, etc.) An exemplary visual depiction of some historical navigation route data may be as shown in Table 1 below.

[0032] Table 1

[0033] speed latitude longitude Epoch time real time 10 41.97267 -87.89229 1496021663 1:34:23 11 41.97268 -87.89236 1496021664 1:34:24 10 41.97266 -87.8924 1496021665 1:34:25 9 41.97268 -87.89246 1496021666 1:34:26 13 41.9727 -87.89256 1496021667 1:34:27 8 41.97268 -87.89257 1496021668 1:34:28 10 41.97268 -87.89264 1496021669 1:34:29 9 41.97266 -87.89269 1496021670 1:34:30 11 41.97267 -87.89278 1496021671 1:34:31 11 41.97265 -87.89284 1496021672 1:34:32

[0034] The historical navigation route data in a data set may correspond to data obtained for a specific one of vehicles 111. For example, Table 1 above may include data related to a specific one of vehicles 111 (such as vehicle 112A or vehicle 113A). Furthermore, historical navigation route data may be relative to a specific location, such as a specific airport location. Thus, the historical navigation route data may include additional data entries for "Vehicle ID" and "Airport Identifier." In any case, the above table is merely an example of some of the historical navigation route data that database 105 may manage and store over time. The navigation route data may be based on data received directly from each aircraft, such as from transponder data, or may include tracking data obtained in other ways, such as from external sensors. In some examples, the data entry related to "Speed," as shown, may relate to ground speed. Historical vehicle data 106 may store speed in any suitable units, such as knots per hour, meters per second, etc. Historical vehicle data 106 may also store acceleration data determined from the speed data or received directly from one of the vehicles 111 or external sensors.

[0035] In some examples, airport guidance data 108 may include a map or other data representation of the airport surface, including guidance features configured to guide vehicles through specific airport locations. The surface may be, for example, a taxiway, runway, gate area, apron, hangar bay, or other traffic lane or surface at an airport. For the purposes of this disclosure, the term "airport" applies equally to an aviation base, an aircraft landing field, or any other type of permanent or temporary airport. In some examples, airport guidance data 108 may include multiple databases specific to a specific airport or multiple nearby airports. For example, airport guidance data 108 may include an airport-specific database of fixed ground objects, including real-time or recent imaging or detection, or a combination of both, to provide fixed ground object information and airport guidance features, such as the coordinates of guidance lines fixed to the airport surface. In any case, data server 132 or other components of collision awareness system environment 102 may be configured to access one or more of airport guidance data 108. For example, the data server 132 may identify a specific airport location, such as a specific airport in a specific city, and may access airport guidance data 108 specific to the identified airport location. In some examples, the airport guidance data 108 for multiple airports may be included in a single data repository 105, rather than in separate data repositories 105 as is the case in some examples.

[0036] In some examples, data repository 105 may also include permission data 110. In some examples, permission data 110 may be included as a separate data repository 105. For example, permission data 110 may reside in a data repository stored on a computing system of a traffic controller 114. For example, a traffic controller 114 for a particular airport may include permission data 110. Traffic controller 114 may also include other data contained in data repository 105. Permission data 110 may include textual or audible permission information generated by traffic controller 114 and / or vehicle 111, as well as communications between traffic controller 114 and receiving vehicle 111.

[0037] In some examples, traffic controller 114 may transmit taxiway or runway clearance information to one of vehicles 111 in text format or as a voice message. In some examples, one of vehicles 111 may retrieve taxiway or runway clearance information from traffic controller 114. For example, one of vehicles 111 may perform a database query for clearance data 110 or otherwise request clearance data 110 from traffic controller 114 or a data repository 105 storing clearance data 110. In some examples, a text or voice message may be transmitted directly from traffic controller 114 (e.g., live communication or from clearance database 110) to one of vehicles 111. One of vehicles 111 may then transmit the clearance information to one or more external systems (e.g., a cloud system) via an aircraft data gateway communication unit (ADG). For example, vehicle 111 or traffic controller 114 may transmit the clearance information to a device implementing a collision awareness system.

[0038] In various scenarios, the traffic controller 114 may send a copy of the clearance message (text or voice message) to the data server 132 (e.g., a cloud system) via a secure communication protocol. Once the data is available on the data server 132, the data server 132 may convert any voice-related taxiway or runway clearance information into text and store the clearance information in a predefined location in the data repository 105.

[0039] The collision awareness system implemented on one or more components of the collision awareness system environment 102 can utilize data from the data repository 105 to determine a predicted collision zone for the vehicle. Thus, when the aircraft 112 is taxiing, taking off, landing, or stationary on the airport grounds, the collision awareness system can help mitigate or reduce collisions between the vehicle 111 (including the body, wingtips, or other portions of the vehicle 111) and other aircraft, ground vehicles, or other transiting or moving objects on the airport grounds (collectively, "transiting ground objects"). For purposes of this disclosure, a "transiting ground object" may refer to any aircraft, ground vehicle, or other object on the airport grounds, including objects that are permanently fixed in place and that can be monitored by the collision awareness system.

[0040] Figure 2 is a conceptual block diagram of an exemplary computing system 138 having an exemplary computer-executable collision sensing system 140. Figure 1 As described above, the collision sensing system 140 may be embodied in any number of different devices, such as those described in connection with FIG. Figure 1One or more of the components of the collision awareness system environment 102 described herein. For simplicity, the computing system 138 implementing the collision awareness system 140 may be described as executing the various techniques of the present disclosure across one or more data servers 132, such as on a cloud server. However, it should be understood that the computing system 138 may be implemented on a traffic controller 114, a user interface device 104, a vehicle 111, or other network device designed to provide vehicle collision awareness. That is, the collision awareness system 140 may be executed on any one or more of the processing circuits 142 of the computing devices corresponding to the traffic controller 114, the user interface device 104, the vehicle 111, or other network devices, and combinations thereof. Furthermore, where one or more of databases 106, 108, or 110 are implemented as a storage device separate from storage device 146, the collision sensing system 140 may execute based on data from storage device 146 and / or data repository 105, the storage device being included in any one or more of the processing circuits 142 of a computing device corresponding to a traffic controller 114, user interface device 104, vehicle 111, or other network device. In some examples, storage device 146 may include one or more of databases 106, 108, or 110.

[0041] Thus, computing system 138 may implement collision sensing system 140 via processing circuitry 142, communication circuitry 144, and / or storage device 146. In some examples, computing system 138 may include a display device 150. For example, where computing system 138 is embodied in one of user interface device 104, vehicle 111, or traffic controller 114, computing system 138 may include display device 150 integral to the particular device. In some examples, display device 150 may include any display device, such as a liquid crystal display (LCD) or light-emitting diode (LED) display, or other type of screen, that processing circuitry 142 may utilize to present information related to the predicted collision zone. In some examples, display device 150 may not be included in computing system 138. For example, computing system 138 may be one of data servers 132 configured to perform the various techniques of this disclosure and transmit collision zone data to another device, such as one of user interface devices 104, for display.

[0042] In an example including a display device 150, the display device 150 can be configured to graphically present collision zone information on a ground navigation application implemented by the aircraft system. Furthermore, the display device 150 can be configured to graphically present position / velocity information of one or more transiting ground objects on the ground navigation application implemented by the aircraft system. For example, the display device can generate graphical display format data based on the position and velocity information configured to be compatible with the graphical output of an AMMD application, allowing the AMMD application to overlay, superimpose, or otherwise integrate the graphical display format data with existing AMMD graphical display output. The display device 150 generates an output including or in the form of graphical display format data, allowing the output to be easily configured to be received and graphically presented by an AMMD application executed on an EFB (e.g., on a tablet) in the cockpit of an aircraft moving on the airport grounds, as described further below. In this way, the collision awareness system 140 can provide an immediately usable output, including an alert or warning, via the display device 150 to notify the pilot or other flight crew of the potential danger of an impending collision, allowing the pilot or flight crew to take appropriate action.

[0043] In some examples, display device 150, including one or more display processors, may be combined in an integrated collision avoidance logic and display processing subsystem into a single processor, electronic system and / or device, or software system with an integrated implementation of collision awareness system 140. For example, user interface device 104 may include collision awareness system 140 and display device 150 as a single device, such as an EFB.

[0044] In some examples, processing circuitry 142 may include fixed-function circuitry and / or programmable processing circuitry. Processing circuitry 142 may include any 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 equivalent discrete or analog logic circuitry. In some examples, processing circuitry 142 may include multiple components, such as any combination of one or more microprocessors, one or more controllers, one or more DSPs, one or more ASICs, one or more FPGAs, and other discrete or integrated logic circuitry. Functionality attributed herein to processing circuitry 142 may be embodied in software, firmware, hardware, or any combination thereof.

[0045] In some examples, communication circuitry 144 may include a wireless network interface card (WNIC) or other type of communication module. In some examples, communication circuitry 144 may have an Internet Protocol (IP) port coupled to an Ethernet connection or coupled to an output port so that communication circuitry 144 receives output from processing circuitry 142. Communication circuitry 144 may be configured to connect to a Wi-Fi TM In some examples, the communication circuit 144 may be separate from the collision sensing system 140. For example, the collision sensing system 140 may include the processing circuit 142 of the computing system 138, while the communication circuit may be included as a separate computing system.

[0046] In some examples, collision sensing system 140 may include one or more storage devices 146. In some examples, storage device 146 may include one or more of data repositories 105 and may be similarly configured to store data. For example, storage device 146 may include any data structure (and / or combination of multiple data structures) for storing and / or organizing data, including but not limited to a relational database (e.g., an Oracle database, a MySQL database, etc.), a non-relational database (e.g., a NoSQL database, etc.), an in-memory database, a spreadsheet, such as a comma-separated value (CSV) file, an extensible markup language (XML) file, a TeXT (TXT) file, a flat file, a spreadsheet file, and / or any other widely used or specialized format for data storage.

[0047] In some examples, storage device 146 may include executable instructions that, when executed, cause processing circuitry 142 to perform various techniques of the present disclosure. Furthermore, storage device 146 may include a machine learning (ML) model 148. In some examples, ML model 148 may be included on a separate storage device. For example, ML model 148 may be stored in data repository 105 on data server 132. In such examples, processing circuitry 142 may execute ML model 148 via network connection 130.

[0048] In some examples, according to certain examples of the present disclosure, a trained ML model can be used to process and predict paths, vehicle positions, or collision zones where the ML model is considered advantageous (e.g., predictive modeling, inference detection, context matching, natural language processing, etc.). Examples of ML models that can be used with aspects of the present disclosure include classifiers and non-classifying ML models, artificial neural networks ("NN"), linear regression models, logistic regression models, decision trees, support vector machines ("SVM"), naive or non-naive Bayesian networks, K-nearest neighbor ("KNN") models, k-means models, clustering models, random forest models, or any combination thereof. These models can be trained based on data stored in the data repository 105. For example, for illustrative purposes only, certain aspects of the present disclosure will be described using predicted paths generated from training an ML model on data from the data repository 105.

[0049] The collision perception system 140 may access or incorporate an ML system or pattern recognition system. Based on a large training dataset of past movements of aircraft, ground vehicles, and other objects, such as statistically sampled over time at a particular airport or a representative set of airports, the ML model 148 may incorporate knowledge of the predictable future movements of aircraft, ground vehicles, or other objects based on statistical training of one or more ML models 148 or pattern recognition systems. For example, such an ML system or pattern recognition system may also incorporate statistical training of observed movements of aircraft, ground vehicles, and other objects passing through the airport grounds, where the observed movements are related to various conditions, such as traffic levels, weather and visibility conditions, and time of day. The one or more initially trained ML models 148 may be further refined using the large amount of data on the movements of aircraft, ground vehicles, and other objects passing through the airport grounds, compared to the movements predicted by the one or more ML models 148. Furthermore, the collision awareness system 140 of the computing system 138 may implement an expert rule system that may combine knowledge of general airline gate assignments, specific gate assignments for a particular aircraft or a given flight, and data regarding assigned taxi routes between gate areas and runways, which the ML model 148 may use to predict a route. For example, in accordance with the techniques of this disclosure, the processing circuitry 142 may deploy an ML model 148 that is trained on historical navigation route data from the historical vehicle data 106 and on general airline gate assignments to predict a route for one of the vehicles 111, including worst-case and best-case scenario routes that may be combined to determine a single predicted route.

[0050] In some examples, collision awareness system 140 can be enabled and implemented in existing airport systems, vehicles 111, and / or user interface devices 104 with minimal hardware or software changes. Furthermore, in some examples, collision awareness system 140 can be configured to provide reliable false alarm mitigation and the ability to use various data inputs, such as from Automatic Dependent Surveillance-Broadcast (ADS-B) sources, and provide coverage for any type of vehicle 111 that could potentially collide with a fixed structure or another of vehicles 111.

[0051] Figure 3 A flow chart depicts an exemplary process 300 that the collision sensing system 140 may implement to provide collision zone predictions according to exemplary aspects of the present disclosure. In some examples, the process 300 may include optional features. In this example, the process 300 includes obtaining (e.g., by the processing circuitry 142 of the collision sensing system 140) historical navigation route data (302) for one or more vehicles 111. For example, the processing circuitry 142 may identify the historical navigation route data from the historical vehicle data 106. The historical navigation route data may be based at least in part on transponder position data from the vehicle 111. For example, the vehicle 111 may transmit navigation route data via the network 130, which may be stored over time in the historical vehicle data repository 106 as historical navigation route data.

[0052] Process 300 also includes identifying (e.g., by processing circuitry 142) a plurality of airport guidance features for a particular airport location (304). For example, processing circuitry 142 may identify airport guidance features for a particular airport from airport guidance data 108. In some examples, the airport guidance features may include guidance marking information, such as guidance signs and guidance lines for the airport location. For example, the guidance lines may include coordinates of line markings affixed to the ground of the airport (e.g., by being painted on the ground).

[0053] Process 300 also includes determining (e.g., by processing circuitry 142) a predicted path for the first vehicle (306). In some examples, the predicted path may include a first portion of the predicted path and a second portion of the predicted path. For example, the first portion of the predicted path may include an area of the airport having guidance features, such as on a taxiway or runway, while the second portion of the predicted path may include an area of the airport without defined guidance features or an area where guidance features are not available from the database. In such an example, processing circuitry 142 may use airport guidance features (such as specific guidance markers or guidance sign information) to predict the first portion of the predicted path and may use historical navigation route data from data repository 106 to predict the second portion of the predicted path. Furthermore, processing circuitry 142 may use historical navigation route data from data repository 106 to predict the first portion of the predicted path. For example, processing circuitry 142 may receive information about speed information of vehicle 111 (e.g., the current speed of the aircraft) from a particular one of vehicles 111. In such an example, the processing circuit 142 may align historical waypoints (e.g., as described with reference to Table 1), speed, and time parameters with the ground guidance lines included in the airport guidance data 108. The historical navigation route data may indicate the average time it takes for a vehicle to travel from one location along the guidance line to another location along the guidance line based on various factors (such as time of day, vehicle size and weight, traffic flow information, etc.). Thus, the processing circuit 142 may estimate various forward positions along the guidance line based on the historical navigation route data. Figure 4 An illustrative example of processing circuitry 142 using historical navigation route data aligned along (eg, mapped to) an exemplary airport guidance feature is described.

[0054] In some examples, the predicted path for the first vehicle is based on a combination of initial predicted paths for the first vehicle, the initial predicted paths including a likelihood that the first vehicle will travel the first initial predicted path and a likelihood that the first vehicle will travel the second initial predicted path. For example, the processing circuit 142 may combine or average predicted paths representing multiple best-case historical path segments and worst-case historical path segments to generate the predicted path. The predicted path may be specific to a particular one of the vehicles 111 and may include a path connecting points of the particular one of the vehicles 111 from a first time (e.g., A1(t1)) to one or more other expected times (e.g., A1(t1+n), where n represents an integer to be added to t representing the first time).

[0055] In such an example, processing circuitry 142 may determine at least two initial predicted paths for the first vehicle. Processing circuitry 142 may also identify a likelihood that the first vehicle will travel any of the initial predicted paths. For example, the likelihood may include an indication of a best-case path, meaning the predicted path is most likely to occur; an indication of a worst-case path, meaning the predicted path is least likely to occur; or other paths with likelihoods falling between the best-case path and the worse-case path. In any case, processing circuitry 142 may determine the predicted path for the first vehicle based on a combination of the at least two initial predicted paths for the first vehicle. This combination may be based on processing circuitry 142 deploying an ML model capable of determining the initial predicted paths, combining the predicted paths, or both to determine a combined predicted path. In some cases, the combination may be based on the likelihood information, such that paths that are more likely to occur under the circumstances are given more weight, and paths that are less likely to occur under the same circumstances are given less weight. For example, processing circuitry 142 may determine a weighted average of the initial predicted paths, or deploy one of ML models 148 to determine a weighted average of the initial predicted paths, or some other combination.

[0056] In some examples, processing circuitry 142 may classify the predicted path data and transmit it to data repository 106. Processing circuitry 142 may classify the predicted path based on aircraft type so that the predicted path can be referenced for future use by similarly positioned vehicles 111.

[0057] In some examples, processing circuitry 142 may retrieve certain historical data from historical vehicle data 106 to identify a predicted path based on the specific vehicle type and / or current state information of the vehicle. For example, the specific vehicle may be an aircraft carrier executing the disclosed techniques on an EFB located in the cockpit of the aircraft carrier. In such an example, processing circuitry 142 of the EFB may select the best available historical data to determine the predicted path. In some examples, the selected best available historical data may be dynamic from location to location, used to identify the predicted path using aircraft carrier parameters. In some examples, an ML model may be used to select the best available path based on the current and historical data.

[0058] In some examples, processing circuitry 142 may identify permission information from a traffic controller that defines one or more destination markers for first vehicle 111. For example, processing circuitry 142 may query a database for the permission information. The permission information may include the destination location as one destination marker for the first vehicle, but may also include multiple destination markers along a path to the destination location, such that the vehicle will follow a path along the destination markers to reach the destination location.

[0059] In some examples, processing circuitry 142 may identify one or more target aiming features from a plurality of airport guidance features based at least in part on the clearance information. In such examples, the one or more target aiming features may be configured to provide guidance through a specific portion of the airport toward one or more target markers. For example, the target aiming features may include airport guidance features that direct a vehicle 111 toward or towards a target, such as by providing an arrow (whether virtual or real) that guides the vehicle 111 toward the target. Thus, processing circuitry 142 may use the one or more target aiming features to identify a first portion of the predicted path. For example, the first portion may include the airport guidance features, such that historical navigation data and the airport guidance features may be used in conjunction with each other to determine a predicted path for the vehicle 111 through the first portion of the predicted path. In such examples, processing circuitry 142 may use the clearance information and historical navigation route data to identify a second portion of the predicted path, the second portion of the predicted path including the first vehicle's destination location, as defined by the clearance information, as one of the one or more target markers. The second portion of the predicted path may pass through an area of the airport's apron that does not include a guidance feature, and therefore, the historical navigation data may be used to predict this portion of the path. It should be noted that in some examples, such as when the vehicle is exiting the apron or gate area toward a runway, the first portion and the second portion may be switched. That is, in some examples, the second portion of the predicted path may include the airport apron area, or may include a taxiway with ground guidance markings, depending on the intended direction of travel of the vehicle 111 (e.g., toward a gate, toward a runway, toward a hangar bay, or somewhere in between, etc.).

[0060] Process 300 also includes determining (e.g., by processing circuitry 142) a predicted position of the first vehicle along the predicted path at the expected time (308). In some examples, processing circuitry 142 may implement a regression algorithm to predict the instantaneous accurate position using previous position, velocity, and heading information (e.g., A1(t+1) to A1(t+2), where time t is in seconds). In some scenarios, a regression model is used to minimize position bias errors in historical data. In some examples, data points (such as those shown in Table 1 above) may be used to calculate the cumulative distance of a particular vehicle 111. For example, processing circuitry 142 may calculate the cumulative distance from A1(t1) to A1(t+n). In such an example, assuming that all intermediate points are locally linear, processing circuitry 142 may determine all intermediate path points for the path segment from time 't' to 't+n'. In some examples, processing circuitry 142 may utilize a function such as a great circle distance formula, a great circle earth model, or an equivalent projection system formula to determine the position information based on the calculated distance and direction. Processing circuit 142 may determine the directionality of one of vehicles 111 from the predicted position along the predicted path. Figures 5A to 5CAs shown, A1(t1) may be determined based on a function using the current position of the specific vehicle 111 and the accumulated distance according to the following equation [1] or [2].

[0061] In such an example, processing circuitry 142 may determine movement information for the first vehicle at its current location. In some examples, the movement information may include speed information for the first vehicle. For example, processing circuitry 142 may receive sensor data or transponder data from one of vehicles 111 indicating the speed at which particular vehicle 111 is traveling. Thus, processing circuitry 142 may use the movement information and historical navigation route data to identify a predicted location for the first vehicle. For example, processing circuitry 142 may determine how far particular vehicle 111 will travel along the predicted path based on the speed at which particular vehicle 111 is traveling at its current location along the predicted path.

[0062] Process 300 also includes determining (e.g., by processing circuitry 142) a predicted position of a second vehicle relative to approximately the same expected time (310). For example, processing circuitry 142 may determine a predicted position of the first of vehicles 111 at a time 15 seconds in the future, and may therefore determine a predicted position of the other of vehicles 111 at a time 15 seconds in the future. In essence, processing circuitry 142 may determine a predicted position for each vehicle at any number of expected times, and processing circuitry 142 will likely find a future time or time range (e.g., 14-15 seconds in the future) that indicates when a vehicle collision is likely to occur. In some examples, the expected time corresponding to the predicted position of the second vehicle may be the same as the expected time corresponding to the predicted position of the first vehicle. For example, the predicted positions of both vehicles may correspond to an expected time 15 seconds in the future. However, in some cases, the predicted positions may not correspond to exactly the same expected time. For example, due to the size of the vehicles, the predicted position at T=14 seconds and the predicted position at T=15 seconds may indicate that the vehicle envelopes overlap at either 14 seconds or 15 seconds. In another example, the predicted positions may be determined at different intervals. For example, the predicted position of a first vehicle may be determined on a second-by-second basis, while the predicted position of a second vehicle may be determined on a half-second or every-second basis. In such cases, processing circuitry 142 may perform interpolation techniques to predict collision zones at approximately the same time (e.g., within a half-second or several seconds of each other), but possibly not exactly the same time.

[0063] Process 300 also includes performing a comparison of the vehicle envelopes of the first vehicle and the second vehicle at the predicted position (e.g., by processing circuitry 142) (312). For example, processing circuitry 142 may retrieve the vehicle envelope data from historical vehicle data 106. The vehicle envelope data may include a safety zone envelope for a single vehicle 111 or multiple vehicles 111, such as in the case where aircraft 112A is towed by vehicle tug 113A.

[0064] Process 300 also includes identifying (e.g., by processing circuit 142) an overlap of vehicle envelopes (314). In some cases, processing circuit 142 may predict the position of one vehicle 112A to be turning toward stationary vehicle 112B. Processing circuit 142 may determine that by turning, the safety zone envelope of vehicle 112A will overlap with stationary vehicle 112B and, therefore, may identify a predicted collision zone. That is, process 300 may end (316) after determining (e.g., by processing circuit 142) a predicted collision zone for the first and second vehicles at an expected time based, at least in part, on the overlap of the vehicle envelopes. In the exemplary process described above, one or both of the vehicles may be powered off, such that the avionics equipment is not continuously operating to provide updates regarding the position of vehicle 111. Thus, processing circuit 142 uses the techniques and data described in process 300 to predict the movement of vehicle 111, where an accurate position may not be available for vehicle 111 based on sensor or transponder data.

[0065] In some examples, the processing circuitry 142 executing process 300 may include processing circuitry 142 of one or more remote data servers 132. In such examples, processing circuitry 142 of one or more remote data servers 132 may receive the current location, or an indication of the current location, of first vehicle 111. Processing circuitry 142 may execute all or part of process 300 to determine a predicted collision zone. In some examples, processing circuitry 142 of the remote server may transmit the predicted collision zone from the remote server to first vehicle 111, such as to an EFB or other user interface device 104 corresponding to vehicle 111.

[0066] Figure 4Depicted are exemplary techniques that collision awareness system 140 may implement for aligning a historical navigation route with airport guidance features when determining a predicted location of vehicle 111. In some examples, processing circuitry 142 may align the historical navigation route so that it coincides with airport guidance features (such as ground guidance lines). In an example, processing circuitry 142 may predict the path of first vehicle 111 from temporary source point 404A to temporary destination point 404B (e.g., a target marker). Source and destination points 404 may be located in an area of an airport having airport guidance features. For example, ground guidance line 406 may be located between source and destination points 404. In some examples, processing circuitry 142 may determine source and destination points 404 based on various predicted points along the predicted path, wherein the predicted path may be updated as vehicle 111 approaches each predicted point along the predicted path. In some examples, source and destination points 404 may be based on vehicle clearance data received from traffic controller 114. In some examples, processing circuitry 142 may utilize a combination of clearance data and historical navigation route data to determine points 404A and 404B configured to guide vehicle 111 to a final destination point that may deviate from an airport area having ground guidance lines, such as ground guidance line 406.

[0067] like Figure 4 As shown, processing circuit 142 may determine historical navigation route data 410 between points 404A and 404B. Processing circuit 142 may determine historical navigation route data 410 from airport guidance data 108. In some examples, Figure 4 The historical navigation route data 410 can be a combination (e.g., an average) of multiple predicted paths combined into a single predicted path 410, which is composed of various predicted points along the predicted path 410 (e.g., based on a weighted average based on the likelihood of each predicted path).

[0068] Processing circuitry 142 may align historical data points 410 along the predicted path between points 404A and 404B to determine an aligned predicted path 412. Aligned predicted path 412 may be aligned along ground guidance features, such as ground guidance lines 406. According to various techniques of the present disclosure, processing circuitry 142 may use aligned predicted path 412 to determine predicted points (e.g., target markers) along aligned predicted path 412 at expected times in order to determine collision zones. It should be understood that target markers refer to points on the ground that the vehicle may target as it advances along the path in order to navigate to a final target marker or final destination. For example, the target markers may change over time as processing circuitry 142 updates the predicted path. Processing circuitry 142 may further update the predicted position along the updated predicted path over time, for example, based on changes in vehicle speed.

[0069] Figures 5A to 5C A conceptual diagram depicting a portion of an airport 500 with various aircraft and ground vehicles on runways, taxiways, and other airfield surfaces is depicted. Figures 5A to 5C The various aircraft and ground vehicles shown include aircraft 112 and ground vehicle 113 (for simplicity, Figures 5A to 5C As above with respect to Figure 1 As discussed, collision perception system 140 may be configured to determine a predicted path and position of vehicle 111, the actual position and velocity of vehicle 111, determine an alert envelope, and predict a collision zone for vehicle 111. Collision perception system 140 may then output position and velocity information for one or more passing ground objects, as well as an indication of a potential collision zone, to network 130 so that these outputs from collision perception system 140 may be received by an EFB on at least one of vehicles 111 among the passing ground objects on the ground of airport 500.

[0070] Figure 5A A simplified example of two vehicles A1 and A2 is shown, which may be aircraft of vehicles 112A-112N, but for simplicity will be referred to as vehicles A1 and A2 when illustrating the progression of time using the tX designator. Figures 5A to 5C In the example of , t0-tX indicates the time in seconds. For example, t0 indicates the initial starting point at time 0, and t5 indicates the predicted position after 5 seconds. Figure 5A In FIG, vehicle A1 has received permission to park at a specific destination location. Processing circuit 142 may predict path 502 for vehicle A1 according to various techniques of the present disclosure. For example, processing circuit 142 may deploy an ML model to determine a combined predicted path based on best-case and worst-case path predictions as informed by historical navigation route data at airport 500 or other airports. Processing circuit 142 may determine the predicted position of vehicle A1 along predicted path 502 at any time interval. Although 5A to Figure 5C The example of shows a 5 second interval, but the technology of the present disclosure is not limited thereto, and any time interval including a variable time interval may be used. Figure 5A In the example of FIG, four predicted positions of vehicle A1 are predicted at 5 seconds, 10 seconds, 15 seconds, and 30 seconds. Processing circuit 142 may use historical navigation route data aligned with airport guidance features, if available, to determine the predicted positions, or in some examples, may use historical navigation route data without airport guidance features when airport guidance features are unavailable, such as in areas of airport 500 where guidance lines do not exist (e.g., apron areas, gate areas, etc.).

[0071] The processing circuitry 142 may further predict a predicted path and location for vehicle A2. In some examples, vehicle A2 may be an aircraft that receives clearance information from the traffic controller 114 indicating one or more target landmarks for vehicle A2. For example, the processing circuitry 142 may use the predicted current location of a second vehicle to identify the predicted path for the second vehicle. In such examples, the processing circuitry 142 may predict the current location of the second vehicle based on historical navigation route data. For example, the processing circuitry 142 may deploy an ML model trained on historical navigation route data, airport guidance features, and / or clearance information to determine the predicted path. The ML model may identify patterns in the historical navigation route data, airport guidance features, and / or clearance information that indicate a predicted path that the vehicle may take toward a target landmark or target destination location.

[0072] Thus, processing circuitry 142 may predict a location of vehicle A2 along predicted route 503, the predicted location including at least one time (e.g., t10, t15, t30) that is consistent with the predicted location relative to vehicle A1. In some examples, processing circuitry 142 may determine the predicted location of the second vehicle using the predicted current location of the second vehicle and one or more of: historical navigation route data, a plurality of airport guidance features, or clearance information for the second vehicle. Figure 5A In the example of , processing circuitry 142 may determine that an overlap of the safety zone envelopes for vehicles A1 and A2 will occur at an expected time of 15 seconds in the future unless some changes are made to the system, such as a deceleration or acceleration of one or the other vehicle.

[0073] As information becomes available, processing circuit 142 may perform another prediction at various intervals using the new information (such as velocity or acceleration data for vehicles A1 and A2). In any case, processing circuit 142 may identify the predicted collision zone as the overlap region 508. Although the safety zone envelope is Figures 5A to 5C , but the safety zone envelope may be any shape and may be specific to the shape of a particular vehicle 111. For example, where vehicle 112A (e.g., A1) is a particular aircraft having a particular size and shape, the safety zone envelope for vehicle 112A may be similar to the size and shape of vehicle 112A such that a detected overlap will indicate the location on the vehicle where the overlap is predicted to occur. Figure 5A In the example of , the processing circuit 142 may determine that the overlap of the envelopes 508 indicates that the nose of vehicle A2 is predicted to collide with the left wing of vehicle A1 at the expected time t15.

[0074] If there is any collision zone in front of the aircraft, the processing circuit 142 generates a visual or text notification for display on the user interface device 104. For example, the following equation [1] is used to calculate the cumulative distance along the track from time 't' to time 't+n', where 't' is in seconds and 'n' is a positive integer value.

[0075]

[0076] Where 'u' refers to rate or velocity, 't' refers to change in time, and 'a' refers to acceleration (change in velocity).

[0077] like Figure 3 As shown, historical performance / tracking data has speed and velocity information. Acceleration is calculated using the speed difference at each position. If the speed is constant (i.e., 'a' = 0), then equation (1) (above) simplifies to equation [2] (below):

[0078] distance = u*t[2]

[0079] exist Figure 5B In the example of , vehicle A3 represents an aircraft trailer pulling an aircraft. Processing circuit 142 may predict the path of vehicle A3 and determine that vehicles A2 and A3 are not predicted to collide because at 5 seconds, vehicle A1 is predicted to pass the intersection of the predicted paths of vehicles A1 and A3. Figure 5B In the example of FIG5 , processing circuitry 142 may use historical navigational route data, such as to predict a first portion of a predicted path for vehicle A3 in an apron area of airport 500. Processing circuitry 142 may use historical navigational route data and airport guidance features, such as to predict a second portion of a predicted path for vehicle A3 in an area of airport 500 that includes guidance features that vehicle 111 is expected to follow to reach a predefined destination.

[0080] Figure 5C The example is similar, except that vehicle A4 is predicted to be near the path of A3. Figure 5C In the example of , vehicle A4 may be a parked vehicle with its avionics system turned off. Therefore, the processing circuit 142 may use the historical navigation route data to determine the predicted position of vehicle A4 and determine the predicted path of vehicle A4. In this example, vehicle A4 does not have a predicted path in the foreseeable future based on the clearance information, historical navigation route data, and / or other aircraft information available to the processing circuit 142 (such as the flight time associated with vehicle A4, etc.). In such an example, the processing circuit 142 may determine whether vehicle A3 will have sufficient clearance to follow the predicted path without pinching the parked vehicle A4. In some examples, the processing circuit 142 may determine a predicted collision zone between vehicles A3 and A4 and provide a notification for display on one of the user interface devices 104.

[0081] Figure 6 FIG6 is a diagram of an exemplary graphical output display 610 of an airport ground map that may be implemented by a two-dimensional airport moving map display (2D AMMD) application that may be implemented on an EFB. For example, the 2D AMMD application may display a map of an airport ground map on one of the user interface devices 104, such as a particular aircraft (e.g., Figure 1 In one embodiment, the 2D AMMD application may be implemented on an EFB tablet in a cockpit of aircraft 112 in the aircraft 112. In other examples, the 2D AMMD application may be implemented on another device other than one of the user interface devices 104. In other examples, a graphical output display similar to graphical output display 610 may be implemented by a 3D AMMD application that may be implemented on an EFB or other application package that executes on a tablet or other type of computing and display device.

[0082] The AMMD application graphical output display 610 (or "AMMD 610") includes representations (e.g., graphical icons) of transiting ground vehicles that may be received and / or decoded by a transiting ground object overlay module of the AMMD application executing on the user interface device 104 providing the AMMD 610. Figure 6 In the example display shown, the AMMD 610 thus includes a graphical icon of the aircraft 612 (e.g., in a cockpit where the user interface device 104 is being used, the graphical icon may correspond to Figure 1 Vehicle 111 and Figures 5A to 5C one of the vehicles A1-A4 in the FIG); graphical icons of other mobile vehicles 614, 616, and 618 (eg, corresponding to other vehicles 111); and graphical icons of ground vehicles 622, 624 (eg, corresponding to ground vehicle 113).

[0083] AMMD 610 also includes representations of airport guidance features, such as ground guidance markings 642 and 644. AMMD 610 may also include representations of apron areas 652, 654, and 656 adjacent to taxiways 634, 636, and 638, as well as airport terminal sections 662, 664, and 666. AMMD 610 may include indications of potential collision zones provided by collision awareness system 140 based on predicted collision zones determined and transmitted by collision awareness system 140, such as warning graphic 670 and textual warning notification 672 between ownship icon 612 and aircraft icon 614. In one example, ownship 612 may have a predicted route from between ground guidance markings 642 to apron area 656. In such a case, a first portion of the predicted route may include a portion of taxiway 634, and a second portion of the predicted route may include a portion of apron area 656. In any case, the first portion may correspond to an area of the particular airport location that includes airport guidance features, such as ground guidance markings, and the second portion may correspond to an area of the particular airport location that does not include airport guidance features, such as an apron area.

[0084] In some examples, vehicle 614 and / or vehicle 612 may not be physically present at the location shown on AMMD 610. That is, AMMD 610 may display the predicted location of vehicle 614 and / or vehicle 612 at or near predicted collision zone 670. AMMD 610 may also display a predicted route over time along with the current location and one or more predicted locations. In another example, AMMD 610 may display one or more predicted locations of second vehicle 614, allowing the user to view the predicted route and predicted location that contribute to the predicted collision zone for own aircraft 612 and second vehicle 614. The user may toggle various aspects of the displayed information on and off, such as turning the predicted location on and off. In one example, the predicted location and / or predicted route may be displayed as a hologram or other blurred depiction of the vehicle's moving or stationary position to indicate to the user that the location or route is not the actual route, but rather represents a predicted route that is subject to change over time based on the prediction from collision awareness system 140.

[0085] In this example, the collision sensing system 140 can be connected to the wireless network via an extended range wireless network (via a wireless router such as Figure 7The wireless router 710 (e.g., the wireless router 710) is connected to an aircraft system of a specific aircraft 612 (e.g., an EFB application running on the user interface device 104), where the specific aircraft may be among the passing ground objects being monitored by the collision awareness system 140. In some examples, the collision awareness system 140 may establish a secure wireless communication channel with the EFB application running on the user interface device 104 or with another aircraft system on the specific aircraft via an extended range wireless network, and then transmit its information, including the position and velocity information of one or more passing ground objects, via the secure wireless communication channel.

[0086] In various examples, simply by upgrading the software of the EFB application implementing the examples of the present disclosure, the EFB application executing on the user interface device 104 can therefore receive all information transmitted by the collision awareness system 140 and receive all the benefits of the collision awareness system 140. Thus, in various examples, the pilot or flight crew can obtain the benefits of the present disclosure without requiring any new hardware (because the EFB application of the present disclosure can be executed on the EFB tablet or other EFB that the flight crew already has), without requiring any hardware or software changes to the installed equipment of the aircraft itself, and therefore without requiring the certification process for any newly installed aircraft systems. The pilot, flight crew, or aircraft operator can also enjoy the benefits of specific implementations of the present disclosure without having to rely on new hardware or software from the original equipment manufacturer (OEM) of the installed hardware or software systems in the aircraft.

[0087] Figure 7 is a conceptual block diagram depicting an exemplary airport network system with exemplary ground sensors that may be used in conjunction with the collision awareness system 140. Figure 7 In the illustrated example, the exemplary airport network system includes a collision awareness system 140 connected to a wireless router 710 via communication circuitry 144. Collision awareness system 140 can be communicatively connected to various types of airport ground sensors, including surface movement radar (SMR) transceivers 720, multilateration sensors 722, multilateration reference transmitters 724, and / or to additional types of airport ground sensors, via a multilateration system LAN 770 or redundant airport system Ethernet local area networks (LANs) 716A and 716B ("airport system LANs 716"). Multilateration sensors 722 can collect data regarding the movement of ground vehicles 111 and provide the data to collision awareness system 140 via network 130 (e.g., airport system Ethernet LAN 716, etc.), so that collision awareness system 140 can use such data to confirm various predictions based on non-sensor data.

[0088] In some examples, processing circuitry 142 may receive ground sensor data from a first vehicle and / or a second vehicle in vehicles 111. Processing circuitry 142 may receive ground sensor data collected as described in various techniques in U.S. Patent Publication No. 2016 / 0196754, filed January 6, 2015, to Lawrence J. Surace, the entire contents of which are hereby incorporated by reference. For example, SMR transceiver 720 is connected to at least one SMR antenna 726. SMR transceiver 720 and SMR antenna 726 may be configured to detect, monitor, and collect data from various airport surfaces and to detect transiting ground objects on the airport surface, including aircraft, ground vehicles, and any other mobile or transient objects on the surface (or "transiting ground objects"). In such examples, processing circuitry 142 may use data from one or more SMR transceivers 720, multilateration sensors 722, or other airport ground sensors and combine the data from these multiple airport ground sensors to generate position and velocity information for one or more transiting ground objects on the airport surface. Processing circuitry 142 may use position and velocity information for one or more transiting ground objects on the airport surface to determine a predicted position along a predicted path by extrapolating the position using the current position, velocity information, and predicted changes in speed or position as informed by historical navigational route data.

[0089] In any case, processing circuitry 142 can then determine the current location of the first vehicle and / or the second vehicle from the ground sensor data. In some cases, processing circuitry 142 can determine the current location of one vehicle while the other vehicle is parked and out of range of the ground sensor. In any case, processing circuitry 142 can use historical navigation route data to predict the current location of the other vehicle. In such an example, processing circuitry can use the current locations of the first vehicle and the second vehicle 111 to identify both the predicted location of the first vehicle and the predicted location of the second vehicle.

[0090] Multilateration sensors 722 can be configured to detect, monitor, and collect data from various airport surfaces in a manner that can supplement the detection of SMR transceiver 720 and detect transiting ground objects on the airport surface. Exemplary multilateration sensor data collection techniques are described in U.S. Patent Publication No. 2016 / 0196754. For example, multilateration sensors 722 can be implemented as omnidirectional antenna sensors positioned at various remote locations around the airport.

[0091] In some examples, collision awareness system 140 may be connected to any one or more of a variety of other types of sensors configured to detect transiting ground objects on the airport surface. For example, processing circuitry 142 of collision awareness system 140 may be communicatively connected to and configured to receive data from one or more microwave sensors, optical imaging sensors, ultrasonic sensors, lidar transceivers, infrared sensors, and / or magnetic sensors. In some examples, collision awareness system 140 may incorporate features and / or components of an airport surface monitoring system, such as an Advanced Surface Movement Guidance and Control System (A-SMGCS) or an Airport Surface Detection Equipment-X (ASDE-X) system. For example, collision awareness system 140 may incorporate one or more of SMR transceiver 720 and SMR antenna 726, multilateration sensor 722, and / or other airport surface sensors.

[0092] In some examples, collision awareness system 140 may combine or integrate signals or sensor inputs from a combination of ground mobile radar, multilateration sensors, and satellites. One or more types of airport ground sensors may be configured to generate signals indicating the position of passing ground objects within a selected accuracy (such as five meters), for example, enabling processing circuitry 142 to generate position and velocity information for passing ground objects with a similar level of accuracy. Processing circuitry 142 may also be communicatively connected, at least sometimes (at all times or only at certain times), to sensors located outside the vicinity of the airport, such as imaging sensors hosted on satellites, airships, or drones with imaging and communication capabilities.

[0093] Processing circuitry 142 may be configured to use data from SMR transceiver 720 and / or multilateration sensor 722 and multilateration reference transmitter 724 to assess or determine the position and velocity of transiting ground objects on the airport surface, and to generate position and velocity information for one or more transiting ground objects on the airport surface based at least in part on data from SMR transceiver 720 and / or from multilateration sensor 722 and / or from one or more other airport surface sensors. In some examples, processing circuitry 142 may generate the position and velocity of one or more airport surface vehicles or other ground support equipment (such as a tanker, pushback tractor, airport shuttle, container loader, belt loader, baggage cart, catering vehicle, emergency vehicle, snowplow, or ground maintenance equipment) at one or more times.

[0094] In some examples, multilateration sensor 722 may perform active cooperative interrogation of moving aircraft on the airport grounds. For example, multilateration sensor 722 may transmit an interrogation signal via the 1030 / 1090 megahertz (MHz) Traffic Collision Avoidance System (TCAS) surveillance band. In some examples, multilateration sensor 722 may include an Automatic Dependent Surveillance-Broadcast (ADS-B) transceiver (e.g., a Mode S ADS-B transceiver) configured to receive ADS-B messages from aircraft on the airport grounds. Various aircraft moving on the airport grounds (at least those that have their ADS-B systems active while on the ground) may automatically transmit ADS-B messages that may be received by multilateration sensor 722. Multilateration sensor 722 using ADS-B may receive the ADS-B messages and transmit them to processing circuitry 142, potentially along with additional data (such as the time of receipt), thereby facilitating processing circuitry 142 to determine and generate position and velocity information for the responding aircraft.

[0095] In some examples, processing circuitry 142 of collision awareness system 140 may be configured to output position and velocity information generated for the transiting ground objects to communication circuitry 144, and therefore to extended-range wireless router 710, for transmission via a wireless local area network. For example, processing circuitry 142 of collision awareness system 140 may output position and velocity information for one or more transiting ground objects at a selected Ethernet connection or output port connected to communication circuitry 144, e.g., via a WNIC, to an IP address.

[0096] The extended-range wireless network established by wireless router 710 (and potentially additional wireless routers in communicative connection with communication circuitry 144) can extend its range throughout the entire airport and include all taxiways, runways, gate areas, apron areas, hangar bays, and other traffic lanes within its range. Thus, the extended-range wireless network provided by wireless router 710 can include all aircraft on the airport ground within range and can potentially provide wireless connectivity in the cockpits of all aircraft, including wireless connectivity to the EFBs of the pilots or crew of the various aircraft. In some examples, extended-range wireless router 710 can be combined with collision awareness system 140 in a single unit or component.

[0097] Thus, in various examples, in a collision sensing system 140 comprising processing circuitry 142 and communication circuitry 144, processing circuitry 142 is configured to receive data from one or more airport ground sensors (e.g., one or both of SMR transceiver 720 and multilateration sensor 722) configured to detect transiting ground objects on the airport ground. Processing circuitry 142 of collision sensing system 140 may be further configured to generate position and velocity information for one or more transiting ground objects on the airport ground based, at least in part, on the data from the one or more airport ground sensors. Communication circuitry 144 of collision sensing system 140 may be configured to receive the position and velocity information for the one or more transiting road surface objects from processing circuitry 142 and output the position and velocity information for the one or more transiting road surface objects to wireless router 710 for transmission via a wireless local area network.

[0098] Any of a variety of processing devices (such as the collision sensing system 140, other components that interact with the collision sensing system 140 and / or implement one or more techniques of the collision sensing system 140, or other central processing units, ASICs, graphics processing units, computing devices, or any other type of processing device) may perform process 300 or portions or aspects thereof. The collision sensing system 140 and / or other components that interact with the collision sensing system 140 and / or implement one or more techniques of the collision sensing system 140, as disclosed above, may be implemented in any of various types of circuit elements. For example, the processor of the collision sensing system 140 or other components that interact with the collision sensing system 140 and / or implement one or more techniques of the collision sensing system 140 may be implemented as one or more ASICs, as magnetic non-volatile random access memory (RAM) or other types of memory, mixed-signal integrated circuits, central processing units (CPUs), field programmable gate arrays (FPGAs), microcontrollers, programmable logic controllers (PLCs), systems on a chip (SoCs), sub-portions of any of the foregoing, interconnected or distributed combinations of any of the foregoing, or any other type of component or components that can be configured to use airport guidance features, historical data, and / or clearance information to predict collision zones at expected times and perform other functions according to any of the examples disclosed herein.

[0099] The functions performed by the electronic devices associated with the device systems described herein may be implemented at least in part by hardware, software, firmware, or any combination thereof. For example, various aspects of these techniques may be implemented within one or more processors (including one or more microprocessors, DSPs, ASICs, FPGAs, or any other equivalent integrated or discrete logic circuits, as well as any combination of such components), embodied in electronic devices included in components of system 140 or other systems described herein. The terms "processor," "processing device," or "processing circuitry" may generally refer to any of the aforementioned logic circuits, alone or in combination with other logic circuits, or any other equivalent circuitry.

[0100] Such hardware, software, and firmware may be implemented within the same device or within separate devices to support the various operations and functions described herein. In addition, any of the units, modules, or components described may be implemented together or separately as discrete but interoperable logical devices. Describing different features as modules or units is intended to highlight different functional aspects and does not necessarily mean that such modules or units must be implemented by separate hardware or software components. On the contrary, the functions associated with one or more modules or units may be performed by separate hardware or software components, or integrated in common or separate hardware or software components. For example, although the collision sensing system 140 is Figure 1 105, but the collision awareness system 140 can be executed on one or more of the data servers 132, the vehicles 111, the traffic controller 114, the user interface device 104, the data repository 105, or any combination thereof. In one example, the collision awareness system 140 can be implemented simultaneously on multiple devices, such as the data server 132 and the vehicle 111.

[0101] When implemented in software, the functionality attributed to the devices and systems described herein may be embodied as instructions on a computer-readable medium such as random access memory (RAM), read-only memory (ROM), non-volatile random access memory (NVRAM), electrically erasable programmable read-only memory (EEPROM), flash memory, magnetic data storage media, optical data storage media, etc. The instructions may be executed to support one or more aspects of the functionality described in this disclosure. The computer-readable medium may be non-transitory.

[0102] Various aspects of the disclosure have been described. These and other aspects are within the scope of the following claims.

Claims

1. A ground collision sensing system, comprising: a memory configured to store: historical navigation route data for one or more reference vehicles, wherein the historical navigation route data is based on the transponder position data, and a plurality of airport guidance features for one or more airport locations at an airport, wherein the airport guidance features include information regarding the location of guidance markers, wherein the guidance markers include guide lines painted on the airport ground, guidance signs in the airport, architectural features, and other information that provides guidance to the vehicle throughout a particular airport location; as well as a processor in communication with the memory, wherein the processor is configured to: determining a predicted path for a first vehicle, the predicted path comprising a first portion and a second portion, the first portion of the predicted path being predicted using the information regarding the location of the guidance marker, and the second portion of the predicted path being predicted using the historical navigation route data; determining a predicted position of the first vehicle along the predicted path at an expected time; determining a predicted position of a second vehicle relative to approximately the same expected time; performing a comparison of a first vehicle envelope of the first vehicle and a second vehicle envelope of the second vehicle at the predicted position; identifying an overlap between the first vehicle envelope and the second vehicle envelope; and A predicted collision zone of the first vehicle and the second vehicle at the expected time is determined based at least in part on the overlap of the first vehicle envelope and the second vehicle envelope.

2. The system of claim 1 , wherein to determine the predicted path of the first vehicle, the processor is further configured to: determining a first initial predicted path for the first vehicle using the information regarding the location of the guidance marker and the historical navigation route data; identifying a first likelihood that the first vehicle will travel the first initial predicted path using the historical navigation route data; determining a second initial predicted path for the first vehicle using the information regarding the location of the guidance marker and the historical navigation route data; identifying a second likelihood that the first vehicle will travel the second initial predicted path using the historical navigation route data; and The predicted path of the first vehicle is determined based on a combination of the first initial predicted path of the first vehicle and the second initial predicted path of the first vehicle, the combination being based at least in part on the first likelihood and the second likelihood.

3. The system of claim 1 , wherein the processor is further configured to: identifying permission information from a traffic controller defining one or more temporary destination points for the first vehicle; identifying one or more temporary destination targeting features from the plurality of airfield guidance features based at least in part on the clearance information, the one or more temporary destination targeting features configured to provide guidance through a specific portion of a specific airfield location toward the one or more temporary destination points; identifying the first portion of the predicted path using the one or more temporary destination targeting features; and The second portion of the predicted path is identified using the permission information and the historical navigation route data, the second portion of the predicted path including the destination location of the first vehicle defined by the permission information as a temporary destination point among the one or more temporary destination points.

4. The system of claim 1 , wherein the processor is further configured to: receiving ground sensor data of the first vehicle or the second vehicle; determining a current position of the first vehicle or the second vehicle from the ground sensor data; and The predicted position of the first vehicle or the predicted position of the second vehicle is identified using the current position of the first vehicle or the second vehicle.

5. The system of claim 1 , wherein the processor is further configured to: Determining movement information of the first vehicle at a current position, the movement information including speed information of the first vehicle; and The predicted position of the first vehicle is identified using the movement information and the historical navigation route data.

6. The system of claim 1 , wherein the processor is further configured to: receiving a current position of the first vehicle; and The predicted collision zone is transmitted from a remote server to the first vehicle using a wireless network.

7. The system of claim 1 , wherein the processor is further configured to: identifying a predicted path for the second vehicle using a predicted current position of the second vehicle, the predicted current position of the second vehicle being based on the historical navigation route data; and The predicted position of the second vehicle is determined using the predicted current position of the second vehicle and one or more of: the historical navigation route data, the plurality of airport guidance features, or clearance information for the second vehicle.

8. The system of claim 1 , wherein the processor is further configured to: The predicted collision zone is transmitted to a device corresponding to at least one of: the first vehicle, the second vehicle, a third vehicle configured to transport the first vehicle or the second vehicle, or a remote server.

9. The system of claim 1, wherein the first portion comprises a taxiway including ground guidance markings, and wherein the second portion comprises an airport apron area.

10. A method for predicting a vehicle collision zone, the method comprising: obtaining, by a processing circuit of a ground collision sensing system, historical navigation route data of one or more reference vehicles, the historical navigation route data being based on the transponder position data; identifying, by the processing circuitry, a plurality of airport guidance features for one or more airport locations at an airport, the airport guidance features including information regarding locations of guidance markers, wherein the guidance markers include guidance lines painted on the airport ground surface, guidance signs within the airport, architectural features, and other information that provides guidance to the vehicle throughout the particular airport location; determining, by the processing circuitry, a predicted path for a first vehicle, the predicted path comprising a first portion and a second portion, the first portion of the predicted path being predicted using the information regarding the location of the guidance marker, and the second portion of the predicted path being predicted using the historical navigation route data; determining, by the processing circuit, a predicted position of the first vehicle along the predicted path at an expected time; determining, by the processing circuitry, a predicted position of a second vehicle relative to approximately the same expected time; performing, by the processing circuit, a comparison of a first vehicle envelope of the first vehicle and a second vehicle envelope of the second vehicle at the predicted position; identifying, by the processing circuit, an overlap between the first vehicle envelope and the second vehicle envelope; as well as A predicted collision zone of the first vehicle and the second vehicle at the expected time is determined by the processing circuit based at least in part on the overlap of the first vehicle envelope and the second vehicle envelope.

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