Patent Description:
Maps have been used for centuries for providing route geometry and geographical information. Conventional paper maps including static images of roadways and geographic features from a snapshot in history have given way to digital maps presented on computers and mobile devices. These digital maps can be updated and revised such that users have the most-current maps available to them each time they view a map hosted by a mapping service server.

Methods of establishing the location of a device for navigation purposes has been an age-old problem with solutions ranging from compasses to following stars in the sky. More recently, satellite-based locationing means have provided a relatively accurate mechanism for establishing the location of a device capable of communicating with the navigational satellites. However, the accuracy of satellite-based locationing and navigation remains a challenge, particularly as precise locationing becomes necessary for applications such as autonomous vehicle control.

Patent application (Pub. No. <CIT>) discloses a technique for preventing decrease in positioning precision due to reflected waves from a navigation satellite that is blocked by an obstacle in a positioning technique using a GNSS. A navigation signal processing device includes a data obtaining unit <NUM>, an elevation angle mask setting unit <NUM>, and a point cloud position data obtaining unit <NUM>. The data obtaining unit <NUM> performs positioning based on navigation signals from navigation satellites. The elevation angle mask setting unit <NUM> sets a condition for restricting utilization of the navigation signals from a specific navigation satellite. The point cloud position data obtaining unit <NUM> obtains three-dimensional point cloud position data of the surroundings of an antenna that receives the navigation signals. The elevation angle mask setting unit <NUM> sets the condition based on the three-dimensional point cloud position data. <NPL>) discloses a precision positioning technique that can be applied to vehicles or mobile robots in urban environments using a single frequency GNSS receiver and also a technique to realize code multipath mitigation that uses an omnidirectional IR (infrared) camera to exclude invisible satellites.

The dependent claims define exemplar embodiments of the invention. A method, apparatus, and computer program product are provided in accordance with an example embodiment for establishing a location of a device based on a global navigation satellite system, and more particularly, to filtering the satellites used for establishing the location of the device based on an established likelihood of accuracy of the satellites.

The above summary is provided merely for purposes of summarizing some example embodiments to provide a basic understanding of some aspects of the invention.

Having thus described example embodiments of the invention in general terms, reference will now be made to the accompanying drawings, which are not necessarily drawn to scale, and wherein:.

Some embodiments of the present invention will now be described more fully hereinafter with reference to the accompanying drawings, in which some, but not all, embodiments of the invention are shown. As used herein, the terms "data," "content," "information," and similar terms may be used interchangeably to refer to data capable of being transmitted, received and/or stored in accordance with embodiments of the present invention.

As defined herein, a "non-transitory computer readable medium," which refers to a physical medium (e.g., volatile or non-volatile memory device), can be differentiated from a "transitory computer-readable medium," which refers to an electromagnetic signal. In at least one example embodiment, a non-transitory computer readable medium is a tangible non-transitory computer readable medium.

A method, apparatus, and computer program product are provided herein in accordance with an example embodiment for establishing a location of a device based on a global navigation satellite system, and more particularly, to filtering the satellites used for establishing the location of the device based on an established likelihood of accuracy of the satellites. <FIG> illustrates a communication diagram of an example embodiment of a system for implementing example embodiments described herein. The illustrated embodiment of <FIG> includes a map data service provider system <NUM>, a processing server <NUM> in data communication with a user equipment (UE) <NUM> and/or a geographic map database, e.g., map database <NUM> through a network <NUM>, and one or more mobile devices <NUM>. The mobile device <NUM> may be associated, coupled, or otherwise integrated with a vehicle, such as an advanced driver assistance system (ADAS), for example. Additional, different, or fewer components may be provided. For example, many mobile devices <NUM> may connect with the network <NUM>. The map data service provider system <NUM> may include computer systems and a network of a system operator. The processing server <NUM> may include the map database <NUM>, such as a remote map server. The network may be wired, wireless, or any combination of wired and wireless communication networks, such as cellular, Wi-Fi, internet, local area networks, or the like.

The user equipment <NUM> may include a mobile computing device such as a laptop computer, tablet computer, mobile phone, smart phone, navigation unit, personal data assistant, watch, camera, or the like. Additionally or alternatively, the user equipment <NUM> may be a fixed computing device, such as a personal computer, computer workstation, kiosk, office terminal computer or system, or the like. Processing server <NUM> may be one or more fixed or mobile computing devices. The user equipment <NUM> may be configured to access the map database <NUM> via the processing server <NUM> through, for example, a mapping application, such that the user equipment may provide navigational assistance to a user among other services provided through access to the map data service provider <NUM>.

The map database <NUM> may include node data, road segment data or link data, point of interest (POI) data, or the like. The map database <NUM> may also include cartographic data, routing data, and/or maneuvering data. According to some example embodiments, the road segment data records may be links or segments representing roads, streets, or paths, as may be used in calculating a route or recorded route information for determination of one or more personalized routes. The node data may be end points corresponding to the respective links or segments of road segment data. The road link data and the node data may represent a road network, such as used by vehicles, cars, trucks, buses, motorcycles, and/or other entities. Optionally, the map database <NUM> may contain path segment and node data records or other data that may represent pedestrian paths or areas in addition to or instead of the vehicle road record data, for example. The road/link segments and nodes can be associated with attributes, such as geographic coordinates, street names, address ranges, speed limits, turn restrictions at intersections, and other navigation related attributes, as well as POIs, such as fueling stations, hotels, restaurants, museums, stadiums, offices, auto repair shops, buildings, stores, parks, etc. The map database <NUM> can include data about the POIs and their respective locations in the POI records. The map database <NUM> may include data about places, such as cities, towns, or other communities, and other geographic features such as bodies of water, mountain ranges, etc. Such place or feature data can be part of the POI data or can be associated with POIs or POI data records (such as a data point used for displaying or representing a position of a city). In addition, the map database <NUM> can include event data (e.g., traffic incidents, construction activities, scheduled events, unscheduled events, etc.) associated with the POI data records or other records of the map database <NUM>.

The map database <NUM> may be maintained by a content provider e.g., a map data service provider in association with a services platform. By way of example, the map data service provider can collect geographic data to generate and enhance the map database <NUM>. There can be different ways used by the map data service provider to collect data. These ways can include obtaining data from other sources, such as municipalities or respective geographic authorities. In addition, the map data service provider can employ field personnel to travel by vehicle along roads throughout the geographic region to observe features and/or record information about them, for example. Also, remote sensing, such as aerial or satellite photography, can be used to generate map geometries directly or through machine learning as described herein.

The map database <NUM> may be a master map database stored in a format that facilitates updating, maintenance, and development. For example, the master map database or data in the master map database can be in an Oracle spatial format or other spatial format, such as for development or production purposes.

For example, geographic data may be compiled (such as into a platform specification format (PSF) format) to organize and/or configure the data for performing navigation-related functions and/or services, such as route calculation, route guidance, map display, speed calculation, distance and travel time functions, and other functions, by a navigation device, such as by user equipment <NUM>, for example. The navigation-related functions can correspond to vehicle navigation, pedestrian navigation, or other types of navigation. While example embodiments described herein generally relate to vehicular travel along roads, example embodiments may be implemented for pedestrian travel along walkways, bicycle travel along bike paths, boat travel along maritime navigational routes, etc. The compilation to produce the end user databases can be performed by a party or entity separate from the map data service provider. For example, a customer of the map data service provider, such as a navigation device developer or other end user device developer, can perform compilation on a received map database in a delivery format to produce one or more compiled navigation databases.

As mentioned above, the server side map database <NUM> may be a master geographic database, but in alternate embodiments, a client side map database <NUM> may represent a compiled navigation database that may be used in or with end user devices (e.g., user equipment <NUM>) to provide navigation and/or map-related functions. For example, the map database <NUM> may be used with the end user device <NUM> to provide an end user with navigation features. In such a case, the map database <NUM> can be downloaded or stored on the end user device (user equipment <NUM>) which can access the map database <NUM> through a wireless or wired connection, such as via a processing server <NUM> and/or the network <NUM>, for example.

In one embodiment, the end user device or user equipment <NUM> can be an in-vehicle navigation system, such as an ADAS, a personal navigation device (PND), a portable navigation device, a cellular telephone, a smart phone, a personal digital assistant (PDA), a watch, a camera, a computer, and/or other device that can perform navigation-related functions, such as digital routing and map display. An end user can use the user equipment <NUM> for navigation and map functions such as guidance and map display, for example, and for determination of one or more personalized routes or route segments based on one or more calculated and recorded routes, according to some example embodiments.

The processing server <NUM> may receive probe data from a mobile device <NUM>. The mobile device <NUM> may include one or more detectors or sensors as a positioning system built or embedded into or within the interior of the mobile device <NUM>. Alternatively, the mobile device <NUM> uses communications signals for position determination. The mobile device <NUM> may receive location data from a positioning system, such as a global positioning system (GPS), cellular tower location methods, access point communication fingerprinting, or the like. The server <NUM> may receive sensor data configured to describe a position of a mobile device, or a controller of the mobile device <NUM> may receive the sensor data from the positioning system of the mobile device <NUM>. The mobile device <NUM> may also include a system for tracking mobile device movement, such as rotation, velocity, or acceleration. Movement information may also be determined using the positioning system. The mobile device <NUM> may use the detectors and sensors to provide data indicating a location of a vehicle. This vehicle data, also referred to herein as "probe data", may be collected by any device capable of determining the necessary information, and providing the necessary information to a remote entity. The mobile device <NUM> is one example of a device that can function as a probe to collect probe data of a vehicle.

More specifically, probe data (e.g., collected by mobile device <NUM>) is representative of the location of a vehicle at a respective point in time and may be collected while a vehicle is traveling along a route. While probe data is described herein as being vehicle probe data, example embodiments may be implemented with pedestrian probe data, marine vehicle probe data, or non-motorized vehicle probe data (e.g., from bicycles, skate boards, horseback, etc.). According to the example embodiment described below with the probe data being from motorized vehicles traveling along roadways, the probe data may include, without limitation, location data, (e.g. a latitudinal and/or longitudinal position, and/or height, GPS coordinates, proximity readings associated with a radio frequency identification (RFID) tag, or the like), rate of travel, (e.g. speed), direction of travel, (e.g. heading, cardinal direction, or the like), device identifier, (e.g. vehicle identifier, user identifier, or the like), a time stamp associated with the data collection, or the like. The mobile device <NUM>, may be any device capable of collecting the aforementioned probe data. Some examples of the mobile device <NUM> may include specialized vehicle mapping equipment, navigational systems, mobile devices, such as phones or personal data assistants, or the like.

An example embodiment of a processing server <NUM> may be embodied in an apparatus as illustrated in <FIG>. The apparatus, such as that shown in <FIG>, may be specifically configured in accordance with an example embodiment of the present invention for establishing a location of a device based on a global navigation satellite system, and more particularly, to filtering the satellites used for establishing the location of the device based on an established likelihood of accuracy of the satellites. The apparatus may include or otherwise be in communication with a processor <NUM>, a memory device <NUM>, a communication interface <NUM>, and a user interface <NUM>. In some embodiments, the processor (and/or co-processors or any other processing circuitry assisting or otherwise associated with the processor) may be in communication with the memory device via a bus for passing information among components of the apparatus. The memory device may be non-transitory and may include, for example, one or more volatile and/or non-volatile memories. In other words, for example, the memory device may be an electronic storage device (for example, a computer readable storage medium) comprising gates configured to store data (for example, bits) that may be retrievable by a machine (for example, a computing device like the processor <NUM>). The memory device may be configured to store information, data, content, applications, instructions, or the like, for enabling the apparatus to carry out various functions in accordance with an example embodiment of the present invention. For example, the memory device could be configured to buffer input data for processing by the processor. Additionally or alternatively, the memory device could be configured to store instructions for execution by the processor.

As noted above, the apparatus <NUM> may be embodied by processing server <NUM>. However, in some embodiments, the apparatus may be embodied as a chip or chip set. In other words, the apparatus may comprise one or more physical packages (for example, chips) including materials, components and/or wires on a structural assembly (for example, a baseboard). The structural assembly may provide physical strength, conservation of size, and/or limitation of electrical interaction for component circuitry included thereon. The apparatus may therefore, in some cases, be configured to implement an example embodiment of the present invention on a single "system on a chip. " As such, in some cases, a chip or chipset may constitute a means for performing one or more operations for providing the functionalities described herein.

The processor <NUM> may be embodied in a number of different ways. For example, the processor may be embodied as one or more of various hardware processing means such as a coprocessor, a microprocessor, a controller, a digital signal processor (DSP), a processing element with or without an accompanying DSP, or various other processing circuitry including integrated circuits such as, for example, an ASIC (application specific integrated circuit), an FPGA (field programmable gate array), a microcontroller unit (MCU), a hardware accelerator, a special-purpose computer chip, or the like. As such, in some embodiments, the processor may include one or more processing cores configured to perform independently. A multi-core processor may enable multiprocessing within a single physical package. Additionally or alternatively, the processor may include one or more processors configured in tandem via the bus to enable independent execution of instructions, pipelining and/or multithreading.

In an example embodiment, the processor <NUM> may be configured to execute instructions stored in the memory device <NUM> or otherwise accessible to the processor. Alternatively or additionally, the processor may be configured to execute hard coded functionality. As such, whether configured by hardware or software methods, or by a combination thereof, the processor may represent an entity (for example, physically embodied in circuitry) capable of performing operations according to an embodiment of the present invention while configured accordingly. Thus, for example, when the processor is embodied as an ASIC, FPGA or the like, the processor may be specifically configured hardware for conducting the operations described herein. Alternatively, as another example, when the processor is embodied as an executor of software instructions, the instructions may specifically configure the processor to perform the algorithms and/or operations described herein when the instructions are executed. However, in some cases, the processor may be a processor specific device (for example, a mobile terminal or a fixed computing device) configured to employ an embodiment of the present invention by further configuration of the processor by instructions for performing the algorithms and/or operations described herein. The processor may include, among other things, a clock, an arithmetic logic unit (ALU) and logic gates configured to support operation of the processor.

The apparatus <NUM> of an example embodiment may also include a communication interface <NUM> that may be any means such as a device or circuitry embodied in either hardware or a combination of hardware and software that is configured to receive and/or transmit data to/from a communications device in communication with the apparatus, such as to facilitate communications with one or more user equipment <NUM> or the like. In this regard, the communication interface may include, for example, an antenna (or multiple antennae) and supporting hardware and/or software for enabling communications with a wireless communication network. Additionally or alternatively, the communication interface may include the circuitry for interacting with the antenna(s) to cause transmission of signals via the antenna(s) or to handle receipt of signals received via the antenna(s). In some environments, the communication interface may alternatively or also support wired communication. As such, for example, the communication interface may include a communication modem and/or other hardware and/or software for supporting communication via cable, digital subscriber line (DSL), universal serial bus (USB) or other mechanisms.

The apparatus <NUM> may also include a user interface <NUM> that may, in turn be in communication with the processor <NUM> to provide output to the user and, in some embodiments, to receive an indication of a user input. As such, the user interface may include a display and, in some embodiments, may also include a keyboard, a mouse, a joystick, a touch screen, touch areas, soft keys, one or more microphones, a plurality of speakers, or other input/output mechanisms. In one embodiment, the processor may comprise user interface circuitry configured to control at least some functions of one or more user interface elements such as a display and, in some embodiments, a plurality of speakers, a ringer, one or more microphones and/or the like. The processor and/or user interface circuitry comprising the processor may be configured to control one or more functions of one or more user interface elements through computer program instructions (for example, software and/or firmware) stored on a memory accessible to the processor (for example, memory device <NUM>, and/or the like).

Example embodiments of the present invention provide a mechanism for establishing a location of a device (e.g., user equipment <NUM>) based on a global navigation satellite system, and more particularly, to filtering the satellites used for establishing the location of the device within a map (e.g., stored in map database <NUM>) based on an established likelihood of accuracy of the satellites relative to the device. The use of satellites for navigational purposes relies upon geo-spatial positioning of an earth-bound device based on the signals received from a plurality of geo-synchronous satellites that are within signal range of the device. Global Navigation Satellite System (GNSS) generically encompasses a variety of satellite-based navigational systems, such as the Global Positioning System (GPS), Globalnaya Navigazionnaya Sputnikovaya Sistems (GLONASS). Galileo satellite navigation system, BeiDou navigation satellite system, and other regional systems. Each of these systems determines the location of an object or device based on satellite signals received from a plurality of satellites. An advantage to a plurality of satellites used in these systems is accuracy, redundancy, and availability.

While GNSS-based location establishment may be relatively accurate when a device has a clear line-of-sight with the GNSS satellites from which signals are received, establishing an accurate location when satellites do not have a clear line-of-sight may be difficult and the reliability and accuracy may be compromised. Further, even when one of a plurality of satellites used in establishing the location of a device does not have a clear line-of-sight the accuracy of the established location may be compromised and considered unreliable or not reliable enough for certain applications such as autonomous driving of a vehicle.

<FIG> illustrates an example of challenges faced by GNSS location systems. As shown, a device <NUM>, which may be apparatus <NUM> such as a vehicle with a navigation system or a mobile computing device, for example, may be situated within an urban setting referred to herein as an "urban canyon" between tall buildings <NUM> and <NUM>. While <FIG> illustrates a simplified example substantially in two dimensions, it is understood that real world applications exist in three dimensions and <FIG> is simplified for ease of understanding. Within an urban canyon as shown, the device <NUM> has line-of-sight to only two satellites: <NUM> and <NUM>. The device <NUM> does not have line-of-sight with satellites <NUM> and <NUM>, though the signals from each of these satellites may be reflected by buildings as shown and still received by the device <NUM>. Conventional GNSS systems will receive signals and use the received signals to establish a location. However, as illustrated, the signals from satellite <NUM> and <NUM> are indirect and lead to errors in location estimation. As such, the estimation of a location of device <NUM> incorporating the signals from satellites <NUM> and <NUM> may be inaccurate to a substantial degree, such as on the order of feet or yards away from the actual location.

Embodiments described herein relate to identification of visible satellites or those with a line-of-sight to the device for which location is sought. When operating in a dense urban environment, among dense vegetation such as a forest or among trees, or within certain geological formations (e.g., canyons), buildings, vegetation, or geological features may block direct line-of-sight communication between the GNSS transponder of a device and one or more satellites, as illustrated in <FIG>. A fixed-horizon filtering approach may be used to discard satellites located below a threshold of the visible horizon. However, the fixed filtering approach then presumes satellites above the horizon can contribute information during the position determination process. In the fixed-horizon filtering approach, occlusions toward the horizon that is atop the ground, such as buildings in urban environments, vegetation, or geological features, are not accounted for. This results in multipath reflections and signal attenuation resulting in inaccurate computing of GNSS locations.

Provided herein is a method of generating an adaptive, above-ground satellite horizon to filter out occluded satellites above the ground, referred to herein as a "learned-elevation mask". The learned-elevation mask can be used to augment the fixed horizon satellite filter. The learned-elevation mask may be computed on-the-fly using data from a camera equipped with horizon detection and semantic segmentation capabilities. Optionally, a digital elevation map of the environment with unfiltered GNSS location may be used for the learned-elevation mask. Offline implementation can use a cached-based system of learned-elevation masks that are computed from crowd-sourced data and applied to a current or future GNSS position.

A learned-elevation mask provides a mechanism to filter out satellites that are established to not have direct line-of-sight to a device. Such a learned-elevation mask may be able to filter out satellites <NUM> and <NUM> from <FIG> while using satellites <NUM> and <NUM> to establish a more accurate GNSS position. There are several ways in which a learned-elevation mask may be established. These methods may be used exclusive from one another or in combination to potentially improve or enhance the learned-elevation mask.

<FIG> illustrates an example embodiment in which a static elevation mask is used to filter satellites determined or estimated to be below the horizon. Device <NUM> which includes a GNSS antenna and receiver filters signals from satellites <NUM> and <NUM> below the static elevation mask <NUM> as being below the horizon, while satellites <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, and <NUM> are not filtered by a static elevation mask. <FIG> illustrates a similar arrangement of satellites and device <NUM>, including buildings <NUM> in the environment of the device <NUM>. A learned-elevation mask, as described herein, may go beyond the static elevation mask <NUM> of the horizon and include geographic or structural features, such as buildings <NUM>, around which the learned-elevation mask <NUM> may exist. In such a scenario, any signals received from satellites <NUM>, <NUM>, <NUM> may be blocked or filtered out, even if received via reflection within the urban canyon. While this reduces the number of satellites contributing to the location determination of device <NUM>, the remaining satellites are established as line-of-sight satellites such that they can be relied upon, and the accuracy of the location may be more definitively established.

A learned-elevation mask may be generated, at least in part, from sensors of a device. For example, an apparatus <NUM> may include a vehicle that includes an advanced driver assist system that features navigation and route guidance. The navigation may establish a position of the vehicle using GNSS to accurately position the vehicle on a map and to provide at least some degree of autonomous vehicle control or driver assistance (e.g., route guidance). Such a vehicle may also be equipped with sensors that may generally be used to facilitate autonomous vehicle control. Sensors such as image sensors or LiDAR (light distancing and ranging) sensors may be used by the vehicle to understand the surroundings or environment of the vehicle. Optionally, such sensors may be included in a device explicitly for the purpose of establishing a learned-elevation mask as described herein. The image sensors and/or LiDAR sensors may determine a type of environment of the vehicle, and may be able to provide a level of understanding of the environment of a vehicle to a system, such as the map data service provider <NUM> or to a processor <NUM> of the apparatus <NUM> itself. This understanding of the environment of the device may be used to build a learned-elevation mask.

For example, if a device determines it is surrounded by buildings, the device may establish that a learned-elevation mask is appropriate to improve the accuracy of the GNSS location estimation. Optionally, an approximate location within a geographic area may indicate to a device that a learned-elevation mask is appropriate. For example, if a location estimate or approximation indicates that a device is within Manhattan in New York City, a learned-elevation mask may be necessary to accurately establish a location of the device since the ubiquity of tall buildings block direct line-of-sight with GNSS satellites in many circumstances.

An elevation mask may be learned based on an understanding of the surroundings or environment of a device. Within a city, such as within New York City and specifically Manhattan, the buildings may be mapped in three dimensions. This three-dimensional mapping of buildings may allow a learned-elevation mask to be readily generated based on the understanding of the buildings around the device.

Learned-elevation masks once established through sensors of an apparatus such as a vehicle, may be stored in a memory, such as memory <NUM> of the apparatus <NUM> or in map database <NUM> of the map data service provider <NUM> to benefit other apparatuses traveling along a similar path. These may be cached learned-elevation masks such that vehicles or apparatuses, even those lacking sufficient sensors to generate their own learned-elevation masks may use cached elevation masks to filter out GNSS satellites that are more likely to be of diminished accuracy through a lack of line-of-sight.

<FIG> illustrates a block diagram of the processing of data for using a learned-elevation mask to improve the accuracy of location determination according to an example embodiment of the present disclosure. As shown, sensor data is received at <NUM> from sensors of an apparatus, such as a vehicle with a sensor package that may facilitate autonomous vehicle control. The sensor data may be from image sensors or LiDAR sensors, for example. The sensor data is processed using semantic segmentation to estimate building, vegetation, or geological formation locations within the environment of the apparatus. The data may be processed locally on the apparatus, such as by processor <NUM> of apparatus <NUM>, or may be processed on a server, such as by map data service provider <NUM> using processing server <NUM>. A fixed satellite horizon or static elevation mask may be retrieved at <NUM>, which may be retrieved from local memory (e.g., memory <NUM>) or from a server (e.g., from map database <NUM>). Using the fixed satellite horizon and the estimated building, vegetation, or geological formation locations, a learned satellite mask may be generated at <NUM>, such as the learned-elevation mask of <FIG>. Cached learned elevation masks <NUM> may be used to inform the generated learned-elevation mask such that the accuracy of a learned-elevation mask may be generated and cached. Not all apparatuses will have the sensor capabilities to sense their environment to inform a learned-elevation mask, in which case the processing may be limited to the fixed elevation mask of <NUM> combined with the cached learned-elevation masks <NUM> to establish an elevation mask to be used by the apparatus.

Embodiments described herein serve to improve the accuracy with which location is determined, particularly using GNSS location systems. For purposes of verifying and/or updating road map geometry, it may be desirable to identify a position of a particular apparatus or probe. Such a position may also be used to support various navigation operations, routing functionality, assisted-driving technology, and/or the like.

Establishing an accurate location for an apparatus may be critical in some circumstances, such as in navigation or route guidance, or in autonomous vehicle control. Establishing an accurate location may allow a vehicle associated with the apparatus to map-match the location to a road segment in order to accurately place the vehicle on a road within a network of roads. In densely populated areas with a relatively high concentration of roadways within a confined space, location accuracy may be critical in order to establish the proper route guidance for a user. Further, establishing an accurate location through map matching may enable an autonomous vehicle to retrieve map data relating to the road segment on which the vehicle associated with the apparatus is traveling. Accurate map matching to a road segment may be instrumental for some autonomous vehicle control. For example, a highway or limited access freeway may have a speed limit of <NUM> miles per hour, while a service road adjacent to the highway may have a speed limit of <NUM> miles per hour due to a commercial or residential nature of the service road. Mis-identifying a road segment as freeway or adjacent service road may indicate that the road along which a vehicle is traveling has a substantially different speed limit than actually present. As such, accurate location identification may be critical for a variety of reasons. According to an example embodiment, upon establishing an accurate location using methods described herein, a location of the apparatus may be map-matched and a vehicle associated with the apparatus controlled according to rules set for the map-matched road segment.

<FIG> illustrates a flowchart depicting a method according to example embodiments of the present invention. It will be understood that each block of the flowcharts and combination of blocks in the flowcharts may be implemented by various means, such as hardware, firmware, processor, circuitry, and/or other communication devices associated with execution of software including one or more computer program instructions. For example, one or more of the procedures described above may be embodied by computer program instructions. In this regard, the computer program instructions which embody the procedures described above may be stored by a memory device <NUM> of an apparatus employing an embodiment of the present invention and executed by a processor <NUM> of the apparatus. As will be appreciated, any such computer program instructions may be loaded onto a computer or other programmable apparatus (for example, hardware) to produce a machine, such that the resulting computer or other programmable apparatus implements the functions specified in the flowchart blocks. These computer program instructions may also be stored in a computer-readable memory that may direct a computer or other programmable apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture the execution of which implements the function specified in the flowchart blocks. The computer program instructions may also be loaded onto a computer or other programmable apparatus to cause a series of operations to be performed on the computer or other programmable apparatus to produce a computer-implemented process such that the instructions that execute on the computer or other programmable apparatus provide operations for implementing the functions specified in the flowchart blocks.

It will also be understood that one or more blocks of the flowcharts, and combinations of blocks in the flowcharts, can be implemented by special purpose hardware-based computer systems that perform the specified functions, or combinations of special purpose hardware and computer instructions.

<FIG> illustrates a flowchart of a method for establishing a location of a device based on a global navigation satellite system, and more particularly, to filtering the satellites used for establishing the location of the device based on an established likelihood of accuracy of the satellites. According to the flowchart of <FIG>, sensor data relating to an environment of an apparatus is received at <NUM>. Object locations are estimated within the environment based on the sensor data at <NUM>. This identifies any objects and their respective locations proximate the sensor. A static elevation mask may be received at <NUM> which may identify the horizon below which GNSS satellite signals are discounted or not considered when establishing a position since they cannot reasonably be presumed to have a line-of-sight to the apparatus. At <NUM>, a learned-elevation mask is generated based, at least in part, on the static elevation mask and the estimated object location(s) within the environment. This learned-elevation mask goes beyond the horizon mask to discount GNSS satellite signals from satellites that are estimated to not have a direct line-of-sight to the apparatus. Signals are received from a plurality of GNSS satellites at <NUM>, while signals from the GNSS satellites that are estimated to not have a line-of-sight to the apparatus are filtered out at <NUM>. A location of the apparatus is established at <NUM> using only signals from GNSS satellites having a line-of-sight to the apparatus. At <NUM>, route guidance or autonomous vehicle control is provided based on the established location of the apparatus.

In an example embodiment, an apparatus for performing the method of FIG. <NUM> above may comprise a processor (e.g., the processor <NUM>) configured to perform some or each of the operations (<NUM>-<NUM>) described above. The processor may, for example, be configured to perform the operations (<NUM>-<NUM>) by performing hardware implemented logical functions, executing stored instructions, or executing algorithms for performing each of the operations. Alternatively, the apparatus may comprise means for performing each of the operations described above. In this regard, according to an example embodiment, examples of means for performing operations <NUM>-<NUM> may comprise, for example, the processor <NUM> and/or a device or circuit for executing instructions or executing an algorithm for processing information as described above.

Claim 1:
An apparatus (<NUM>) to facilitate autonomous or semi-autonomous control of a vehicle comprising at least one processor and at least one non-transitory memory including computer program code instructions stored thereon, the computer program code instructions configured to, when executed, cause the apparatus to at least:
receive sensor data (<NUM>) of an environment of the apparatus (<NUM>, <NUM>, <NUM>);
retrieve a static elevation mask (<NUM>, <NUM>);
receive signals from a plurality of Global Navigation Satellite System "GNSS" satellites (<NUM>, <NUM>, <NUM>, <NUM>), characterised in that the sensor data is obtained by a sensor and processed using semantic segmentation to estimate object location, with respect to the sensor, of at least one object within the environment (<NUM>), and the apparatus is further characterised in that the computer program code instructions are further configured to, when executed, cause the apparatus to:
retrieve at least one previously generated learned-elevation mask (<NUM>) for a current GNSS location;
generate a learned-elevation mask (<NUM>, <NUM>) based, at least in part, on the static elevation mask, the at least one previously generated learned-elevation mask and the at least one estimated object location within the environment;
filter the signals from the plurality of GNSS satellites based on the learned-elevation mask (<NUM>) to eliminate from consideration a subset of satellites (<NUM>, <NUM>, <NUM>, <NUM>, <NUM>) established as not having a line-of-sight with the apparatus;
establish a location of the apparatus from remaining satellites established as having a line-of-sight with the apparatus; and
provide for at least one of route guidance or autonomous vehicle control based on the established location of the apparatus.