WO2020099647A1

WO2020099647A1 - Determining location data accuracy using probe measurements

Info

Publication number: WO2020099647A1
Application number: PCT/EP2019/081511
Authority: WO
Inventors: Heiko Mund; Oleg Schmelzle
Original assignee: Tomtom Navigation B.V.
Priority date: 2018-11-15
Filing date: 2019-11-15
Publication date: 2020-05-22
Also published as: GB201818638D0

Abstract

A method is disclosed for generating an accuracy map covering a particular geographic area that represents the variation in the expected accuracy for GNSS location data at different positions within the geographic area. The accuracy map can be generated by processing accuracy values obtained from a plurality of historic probe measurements within the area. The accuracy map can then be used to estimate or predict the expected accuracy of GNSS location data at different positions within the area.

Description

DETERMINING LOCATION DATA ACCURACY

USING PROBE MEASUREMENTS

Field of the Invention

The present invention relates to the processing of probe data related to the movement of vehicles within a particular geographic area in order to determine an “accuracy map”, or profile, representing the variation within the geographic area for the expected accuracy for location data obtained from a GPS or other GNSS source

Background to the Invention

Navigation devices that include GPS (Global Positioning System) signal reception and processing functionality are well known and are widely employed as in- car or other vehicle navigation systems. In general terms, a modern navigation device may comprise a processor, memory (at least one of volatile and non-volatile, and commonly both), and map data stored within said memory. Such navigation devices are generally able to process GPS location data in order to determine a current location of the device.

The GPS location data can thus be used by the device, e.g. for providing the desired navigation functionality, or for performing other location-based services, based on the current location of the device. For example, the device may provide relevant navigation instructions (e.g.“Turn left at the next junction”), in a manner that is generally known, in order to aid a user of the device when navigating along a predetermined route. The GPS location data from a plurality of such navigation devices may also be provided to a central server, and used, for example, for building up a picture of the traffic conditions within a road network. Historic GPS location data can also be used for a range of digital mapping purposes, e.g. in order to update or build electronic maps that may then be used by such navigation devices.

As well as such navigation devices, there may be various other devices within a road network that are equipped with GPS receivers. For example, a number of vehicles within a road network may be equipped with digital cameras for obtaining a sequence of images around the road network, e.g. for providing additional information about the road network that may desirably be incorporated into advanced electronic maps. For instance, WO 201 1/095227 A1 (TomTom Germany GmbH &

Co. KG.) describes a method for digital mapping wherein local identifiers such as street names, street sign information, and the like, are extracted from image data, and then associated with a segment of an electronic map based on the location at which the image data was taken.

The accuracy of a given GPS measurement will typically depend on, e.g., the relative satellite-receiver geometry, the number of clear GPS signals that can be received, and other such (geometric) factors, such that the accuracy of GPS measurements may vary depending on time and location. A location measurement obtained from a GPS receiver can thus be associated with an accuracy value indicating the level of confidence that is associated with the location data, i.e. the likelihood that the position specified by the location data accurately reflects the actual position of the device at that time.

This information is normally provided by a GPS receiver along with the GPS location data, i.e. in the form of suitable precision data. For example, such accuracy information is often specified in terms of a so-called“dilution of precision” value, e.g., reflecting the additional multiplicative effect of navigation satellite geometry on positional measurement precision. On the other hand, there are some instances wherein location data may be provided without any associated accuracy values. For example, this may normally be the case when the location data is obtained from a digital camera equipped with a GPS device, in which case the location data is usually stored in the image metadata without any associated accuracy values.

Flowever, there are various applications where such location data is being processed where it is useful (or even essential) to know the level of accuracy associated with the location data. For example, when the location data is used for providing navigation functionality, it is important to be able to reliably map navigation instructions and guidance onto the correct location to allow a user to be effectively guided. It might therefore be desired to change the operating mode of the navigation device based on the expected reliability of the GPS measurements. For instance, in areas where the GPS measurements are known to be unreliable, the position determining module might switch into a“relative positioning” (or“dead-reckoning”) mode wherein rather than attempting to determine the absolute position of the device from the GPS data, measurements obtained from on-board odometry systems are given a higher weighting. Similarly, the nature, and frequency, of navigation instructions may be adjusted based on the reliability of the position measurements.

As another example, when the location data is processed to extract local identifiers, e.g. as described in WO 201 1/095227 A1 , knowledge of the accuracy values may be used to improve the digital mapping by giving higher weighting to extracted features for which the location is known with a higher degree of accuracy. Accordingly, the Applicants believe that there remains scope for improved methods for processing such location data.

Summary of the Invention

According to a first aspect of the present invention, there is provided a method for generating an accuracy map representing the variation within a geographic area for the expected accuracy of location data obtained from a global navigation satellite system (GNSS), the method comprising: obtaining a plurality of probe measurements from one or more device(s) within a geographic area, each of the probe measurements including GNSS location data indicative of a location within the geographic area at which the probe measurement was made and precision data indicative of an accuracy value associated with the GNSS location data for that probe measurement; determining from the obtained probe measurements expected accuracy values of GNSS location data for one or more position(s) within the geographic area; and generating from the expected accuracy values an accuracy map representing the expected accuracy of GNSS location data associated with each of the one or more position(s) within the geographic area.

Thus, according to the present invention, historic probe data that has been obtained from a given geographic area can be processed in order to determine the expected accuracy for GNSS location data at various different positions throughout that geographic area. For instance, it will be appreciated that traffic management, navigation and mapping service providers, such as TomTom International B.V., typically have access to a large archive of historic probe data (also referred to as “floating car data”) that is continuously being added to as more probe data becomes available. The probe data is normally obtained from a GPS, or other suitable GNSS, receiver associated with a probe device (e.g. carried within a vehicle) and such probe data typically contains not only measurements of the locations of the probes at different times but also precision data indicative of the accuracy of the location measurements. The present invention recognises that it is thus possible to use the historic accuracy data to build up a profile showing the expected accuracy of GNSS location data at different positions across the geographic area. For example, if the historic GNSS location data shows that a particular location (at a certain time) typically has relatively poor GNSS coverage, it can be expected that future GNSS location data obtained at that location will also have relatively low accuracy.

In this way, an accuracy“map” can be generated representing the (variation in the) expected accuracy for GNSS location data throughout the geographic area. This accuracy map can then be used to estimate accuracies for any GNSS location measurements where no precision data is available, and/or to predict the accuracy at different positions within the geographic area, e.g., at various points along a predetermined route through the geographic area along which a user is being navigated.

Such knowledge of the expected accuracy of the GNSS location data can thus be used, for example, to supplement or improve the function of a navigation device by allowing the navigation and/or positioning modes of the device to be adjusted based on the expected accuracy of the GNSS location data. As another example, the accuracy map can also be used when performing digital mapping techniques, e.g. for assigning accuracy values to data that is associated with a location but for which no accuracy data is provided, e.g. as may be the case for images taken by a digital camera. Thus, the provision of such accuracy maps may provide various advantages in a number of different applications involving the processing of GNSS location data. Prior to the present invention, the Applicants believe that no such accuracy maps were available.

Accordingly, it is believed that such accuracy maps are novel and inventive in their own right. Thus, from another aspect, there is provided an accuracy map representing the variation within a geographic area for the expected accuracy of location data obtained from a global navigation satellite system (GNSS), wherein the accuracy map includes an expected accuracy value of GNSS location data for a plurality of different positions within the geographic area. The accuracy map from this aspect is preferably generated substantially according to the methods described herein according to any of the embodiments of the first embodiment. The accuracy map may be stored in any suitable and desired fashion. However, the accuracy map is preferably stored electronically, e.g. as an electronic or digital map, as described further below.

The present invention also extends to systems for performing such methods. Accordingly, from a further aspect there is provided a system for generating an accuracy map representing the variation within a geographic area for the expected accuracy of location data obtained from a global navigation satellite system (GNSS), the system comprising a set of one or more processor(s) configured to: obtain a plurality of probe measurements from one or more device(s) within a geographic area, each of the probe measurements including GNSS location data indicative of a location within the geographic area at which the probe measurement was made and precision data indicative of an accuracy value associated with the GNSS location data for that probe measurement; determine from the obtained probe measurements expected accuracy values of GNSS location data for one or more position(s) within the geographic area; and generate from the expected accuracy values an accuracy map representing the expected accuracy of GNSS location data associated with each of the one or more position(s) within the geographic area.

It will be appreciated that these further aspects of the present invention can and preferably do include any one or more or all of the preferred and optional features of the invention described herein in respect of any embodiments of the first aspect of the invention, as appropriate. For example, even if not explicitly stated, the system may comprise means for carrying out any step or steps described in relation to the method herein in any of its aspects or embodiments, and vice versa.

In embodiments, the system and/or one or more processors and/or processing circuitry may be at least part of a server or a navigation device. The steps of the method of the present invention in any of its aspects or embodiments may therefore be carried out in part by a server and/or in part by a navigation device. For example, in embodiments, the steps of the method may be performed exclusively on a server, or some on a server and the others on a navigation device in any combination, or exclusively on a navigation device. Performance of one or more of the steps on the server may be efficient and may reduce the computational burden placed on a navigation device. Alternatively if one or more steps are performed on the navigation device, this may reduce any bandwidth required for network communication. Preferably, the invention is performed on a server. Accordingly the invention can encompass a server arranged to generate accuracy profiles in the manner described herein. In such embodiments, the generated accuracy map(s), or further data obtained from the accuracy map(s), can then be (and in some preferred embodiments is) provided to a navigation device for use thereby.

The present invention involves obtaining a plurality of probe measurements from one or more device(s) within a geographic area. Each of the probe

measurements includes GNSS location data indicative of a location within the geographic area at which the probe measurement was made. The probe

measurements also include precision data indicative of an accuracy value associated with the GNSS location data for that measurement. For instance, in a known fashion, the accuracy of the location information obtained from the GNSS location data may vary depending on time and location. The accuracy value thus provides an indication of the level of confidence associated the location indicated by the GNSS location data.

This precision data may generally be provided in any suitable format that allows the level of accuracy associated with the GNSS location data to be specified. The precision data may thus include any suitable quantifier for the level of confidence, or accuracy, associated with the GNSS location data. Preferably, the precision data is provided in the form of so-called“dilution of precision” values for the GNSS location data. For example, for each probe measurement, there may be provided a number of different dilution of precision values representing any or all of the vertical accuracy, the two-dimensional horizontal accuracy, the three-dimensional positional accuracy, the time accuracy and/or the overall geometric accuracy (including both the time and three-dimensional positional accuracy). That is, preferably, the precision data comprises a set of one or more dilution of precision value(s) for each probe measurement.

Such information is normally provided along with GNSS measurements and is thus typically already available within the archive of historic probe data. This information may be provided with the historic probe data in any desired fashion. Preferably, rather than attempting to provide a continuous range of accuracy values (e.g. as a percentage error from 0 to 100%), each probe measurement is assigned an accuracy value according to a set of accuracy classes, e.g., a“0” where no GNSS location data is available, a Ί” for relatively low accuracy measurements, a“2” for higher accuracy measurements, and so on. This may help simplify the presentation of the accuracy map so that the expected accuracy information may be provided and used in a simpler format.

The devices may thus be any devices that are capable of providing such probe data suitable for the purposes of the present invention. So, the device may be any device having a suitable GNSS receiver, such as a GPS receiver, for receiving satellite signals indicating the position of the receiver at a particular point in time, and which preferably receives updated position information at regular intervals. Such devices may include navigation devices, mobile telecommunications devices with positioning capability, position sensors, and the like.

The devices (or probes) are typically mobile devices and are preferably associated with a vehicle. For example, the devices may comprise navigation devices, which may be any of a portable navigation device mounted within a vehicle, a navigation device that is integrated with the vehicle, or navigation software running on a mobile device such as a smartphone that is carried by the vehicle’s driver.

Thus, the probe data may generally relate to the movement of a plurality of vehicles throughout the geographic area. In this case the position of the device will correspond to the position of the vehicle. (References to probe data obtained from devices associated with vehicles, may thus be understood as also including probe data obtained from a vehicle, and references to the movement of a device or devices may be replaced by a reference to the movement of a vehicle, and vice versa, even if not explicitly mentioned. The probe data may thus also be referred to as“vehicle probe data”.)

It will be appreciated that the probe data that is processed according to the present invention is preferably historical data. In this context the word“historical” should be considered to indicate data that is not directly reflective of conditions on the segment at the present time or in the recent past (perhaps within roughly the last five, ten, fifteen or thirty minutes). Historical data may for example relate to events occurring days, weeks or even years in the past.

Thus, in preferred embodiments, the step of obtaining the probe data may comprise accessing the data, i.e. the data being previously received and stored (e.g. in a historic probe data archive). For instance, in some embodiments, the probe data may be stored, e.g., in a suitable data file, and this data file can then be provided to a suitable processor for being processed to generate an accuracy map as described herein. In some arrangements, the method may comprise a step of receiving the probe data from the devices. In embodiments in which the step of obtaining the probe data involves receiving the data from the devices, the method may further comprise storing the received probe data before proceeding to carry out the other steps of the present invention. It will be appreciated that the step of receiving the probe data need not take place at the same time or place as the other step or steps of the method. Indeed, it will be appreciated that such historic probe data is readily available from a number of sources and the probe data may generally be obtained in any suitable and desired fashion.

As discussed above, the probe data may be collected from a plurality of devices, and preferably relates to the movement of those devices over time. The probe data is thus preferably also associated with temporal data, e.g. a timestamp.

In addition to the GNSS location data and the associated precision data, a probe measurement thus preferably further includes temporal data indicating the time at which that probe measurement was made. In this way it is possible to generate accuracy maps that also reflect the temporal variation in the expected accuracy of GNSS location data within a geographic area. For example, a series of accuracy maps (or a single time-dependent accuracy map) may be generated representing the expected accuracies at various different time periods, e.g. morning, afternoon, evening, or indeed any other suitable time periods as desired.

Of course, the probe data may be obtained from a combination of different devices, or a single type of device. However, the present invention is not limited to the use of positional data obtained from a particular type of device, or devices associated with a particular form of transport, e.g. vehicles, and probe data from devices associated with multiple forms of transport may equally be taken into account. Typically, any probe data indicative of the location of a device within a given geographic area may be used.

The size (and shape) of the geographic area may be set as desired, e.g. so long as there are sufficient probe measurements for that area to allow a suitable accuracy map to be generated. For example, in embodiments, accuracy maps may be generated that cover a certain real-world metropolitan area, such as a city or town. Flowever, in principle, nationwide, or even global, accuracy maps may be generated from the GNSS data (although naturally some regions will have more associated probe measurements, and therefore more densely populated accuracy maps, than other e.g. more rural locations).

The geographic area may typically contain a navigable network defined by a plurality of navigable elements. So, vehicles (and hence devices) may be substantially constrained to travelling along the navigable elements of the navigable network. For example, the navigable network may comprise a road network, wherein each navigable element represents a road or a portion of a road. In this case, a navigable element can represent a road between two adjacent intersections of the road network, or a navigable element may represent a portion of a road between two adjacent intersections of the road network. As will be appreciated, however, the navigable network is not limited to a road network, and may comprise, for example, a network of foot paths, cycle paths, rivers, and so on.

The navigable network may be represented using an electronic map. This electronic map may thus generally cover the geographic region from which the probe data is obtained, and for which the accuracy map is being generated. Where the geographic area is associated by such an electronic map, the accuracy map may be stored independently thereof. Flowever, in embodiments, the accuracy map data may be overlaid or incorporated into the electronic map representation of the navigable network, and e.g. provided together as a single map.

The electronic map (or mathematical graph, as it is sometimes known), in its simplest form, is effectively a database containing data representative of nodes, most commonly representative of road intersections, and lines between those nodes representing the roads between those intersections. In more detailed digital maps, lines may be divided into segments defined by a start node and end node. These nodes may be“real” in that they represent a road intersection at which a minimum of three lines or segments intersect, or they may be "artificial" in that they are provided as anchors for segments not being defined at one or both ends by a real node to provide, among other things, shape information for a particular stretch of road or a means of identifying the position along a road at which some characteristic of that road changes, e.g. a speed limit.

In practically all modern digital maps, nodes and segments are further defined by various attributes which are again represented by data in the database. For example, each node will typically have geographical coordinates to define its real- world position, e.g. latitude and longitude. Nodes will also typically have manoeuvre data associated therewith, which indicate whether it is possible, at an intersection, to move from one road to another; while the segments will also have associated attributes such as the maximum speed permitted, the lane size, number of lanes, whether there is a divider in-between, and so on.

According to the present invention, the probe data, however it has been obtained, is then used to generate an accuracy map covering the geographic area. For instance, each of the obtained probe measurements can be processed to determine the respective location(s) and accuracy value(s). The accuracy values can then be processed to determine an accuracy map covering the geographic area. That is, an estimate of the expected accuracy value for GNSS location data at a given position within the geographic area can be determined from the historic accuracy values obtained from a plurality of probe measurements at, or in the vicinity of, that position. This can be repeated for a plurality of positions throughout the geographic area in order to generate an accuracy map showing the expected accuracy values for GNSS location data at various different positions around the geographic area.

In embodiments, the geographic area may be divided into a number of smaller area sections, and an average expected accuracy value determined for each of those sections. For example, for a given section, an average accuracy value may be calculated from the probe measurements falling within that section. The average accuracy value calculated for that section is thus set as the expected accuracy value for all locations within that section. In this case, the size and shapes of the sections may be set as desired, e.g. depending on the desired coarseness of the accuracy map.

Flowever, more preferably, expected accuracy values are determined for a plurality of different individual positions (points) within the geographic area, and these individual values are then suitably combined in order to generate the accuracy map. The positions for which the expected accuracy values are to be determined may be selected in any suitable way in order to generate a desired accuracy map. For example, in embodiments, the positions may correspond to a number points lying along a set of grids covering the geographic area. That is, a set of rectangular grids may be defined that cover the geographic area, and expected accuracy values determined for various points along (e.g. the intersections of) these grids. This may be particularly appropriate when the electronic map is to be represented as a raster image, or an array.

However, it will be appreciated that for other representations other layouts are possible and any suitable criterion for selecting a position for which an expected accuracy value should be determined for inclusion into the accuracy map may be used. As another example, and in some preferred embodiments, the positions may correspond to points lying along a road network, e.g. corresponding to nodes, or junctions, within the road network. This may be appropriate for when the accuracy map is stored as a set of electronic map attributes that can be included into an electronic map representation of the road network.

In this case, where expected accuracy values are determined for a plurality of different individual positions (points) within the geographic area, once the positions have been selected, as desired, an expected accuracy value for each position is then calculated. The expected accuracy value for a selected position may generally be calculated in any suitable manner. However, in preferred embodiments, an expected accuracy value for a given position is determined by identifying a plurality of (e.g. all) probe measurements falling within a predetermined distance (e.g. radius) of the position, and then calculating an average (e.g. mean) of the accuracy values for each of those probe measurements. Thus, in embodiments, determining the expected accuracy value for a position within the geographic area comprises identifying a set of probe measurements falling within a predetermined distance of that position, and using the accuracy value(s) for the probe measurements within the set to determine the expected accuracy value for that position.

Preferably the average is suitably weighted. Thus, preferably, the expected accuracy value for a position within the geographic area comprises a weighted average of the accuracy value(s) for probe measurements falling within a predetermined distance of that position. The weighted average may take into account all probe measurements falling within the predetermined distance of that position. However, it is also contemplated that some filtering of the probe measurements may be performed prior to the averaging. For example, the weighted average may take into account only a subset of probe measurements falling within the predetermined distance of that position (e.g. those that are closest, or those with the highest accuracy values). The average may be weighted so that probe measurements that are further from the position for which the expected accuracy value is being determined are given a lower weighting. So, the weighting for a given probe measurement may be computed from the distance between the position and the location of that probe measurement using a suitable decay function.

The weighting may also take into account the accuracy values, e.g. so that probe measurements having higher accuracy values may be given more/less weighting, as desired. For example, it may be desired to give more weighting to less reliable measurements, as these may better reflect the overall expected accuracy value (as these may better cover a‘worst case’ type scenario). Flowever, in some cases, it may be desirable to give more weighting to more reliable measurements, e.g. if it is believed that the less accurate measurements are outliers. The weighting may further take into account the age of the probe measurements. For instance, greater weighting may be given to more recent measurements so that the accuracy map can be reliably updated.

As mentioned above, the probe measurements preferably also include temporal data, e.g. in the form of a timestamp, indicating the time at which the probe measurement was made. Preferably, the accuracy maps also reflect the temporal variation in the expected accuracy of GNSS location data within the geographic area. Thus, in embodiments, the accuracy values may be binned based on their respective time periods and the accuracy values within each‘bin’ (time period) can then be processed together to determine an accuracy map for that time period. For example, a series of accuracy maps may be generated each being associated with a different time period, e.g. morning, afternoon, evening, or indeed any other suitable time periods as desired. Thus, in embodiments, the method further comprises binning the probe measurements according to the time period at which they were obtained, and generating accuracy maps for a plurality of different time periods. Alternatively, for each position, rather than determining a single expected accuracy value, a time- dependent expected accuracy‘profile’ can be determined, the profile consisting of a set of accuracy values associated with different time periods. Accordingly, in embodiments, a set of one or more time-dependent accuracy map(s) can be generated.

Once it has been generated, an accuracy map can then be provided, e.g. for output, for use as desired. The generated accuracy map(s) may be stored and represented in any suitable way, e.g. depending on which positions expected accuracy values were calculated for. For example, in embodiments, an accuracy map may be stored as a raster image, or as an array of accuracy values. As another example, an accuracy map may be represented using a set of contour lines delimiting areas of the same expected accuracy. As yet another example, the accuracy map could be stored using batched dynamic adaptive meshes. In embodiments, where the geographic area is represented using an electronic map, the accuracy values could be added into the electronic map as map attributes.

It will be appreciated that once the accuracy map has been generated, an application needs only the accuracy map to be able to estimate the accuracy for any GNSS location measurements within the area covered by the accuracy map. That is, the accuracy map can generally be provided and stored separately from the original probe data.

The accuracy map can be used in various ways. For example, broadly speaking, the accuracy map allows for an estimation of the accuracy value for GNSS location data at any position covered by the accuracy map. The accuracy map can thus be used to estimate an accuracy value for GNSS measurements for which there is no accuracy information available, and/or to identify regions within the geographic area where measurements are expected to become more inaccurate.

Knowledge of such accuracy information may be useful for a number of applications. For example, when processing digital photos with GPS positions to extract local information, e.g. for use within a method like that described in WO 201 1/095227 A1 , the accuracy values are not normally stored in the image metadata. The accuracy values obtained from an accuracy map can thus be used to assign a confidence level for any identifiers extracted from such image data, and e.g. to restrict the method to images with expected high location accuracy.

The accuracy map can also be provided to a navigation device. For instance, the accuracy map allows for the identification (and prediction) of regions having low reliability, e.g. due to poor reception. This information can thus be used to predict parts of a route that is being navigated with low accuracy. This information can then be used in term by the navigation device in a number of ways. For example, the positioning module of the navigation device may switch into a different operating mode when entering an area that is predicted to have low reception, e.g. to give a higher weighting to odometer measurements compared to GNSS measurements.

The accuracy values can also be used to tailor the navigation instructions based on the expected reliability of the position data, e.g. to provide instructions further in advance in areas where the positioning data is expected to be more unreliable. So, in embodiments, the accuracy profile may be used by navigation devices in order to predict the accuracy at various points along a predetermined route, e.g. to allow the positioning mode and/or navigation experience to be adapted based on the expected accuracy of the measurements.

It will be appreciated that the means for carrying out any of the steps described in relation to the method or apparatus may comprise a set of one or more processor(s) and/or suitable processing circuitry. The present invention is therefore preferably a computer implemented invention, and any of the steps described in relation to any of the aspects or embodiments of the invention may be carried out under the control of a set of one or more processor(s) and/or suitable processing circuitry. The processing circuitry may generally be implemented either in hardware or software, as desired.

For instance, and without limitation, the means or processing circuitry for carrying out any of the steps described in relation to the method or system may comprise one or more suitable processor or processors, controller or controllers, functional units, circuitry, processing logic, microprocessor arrangements, etc., that are operable to perform the various steps or functions, etc., such as appropriately dedicated hardware elements (processing circuitry) and/or programmable hardware elements (processing circuitry) that can be programmed to operate in the desired manner. Thus, it will be appreciated that the methods in accordance with the present invention may be implemented at least partially using software.

Accordingly, it will be seen that, when viewed from further aspects and in further embodiments, the present invention extends to a computer program product comprising computer readable instructions adapted to carry out any or all of the method described herein when executed on suitable data processing means. The invention also extends to a computer software carrier comprising such software.

Such a software carrier could be a physical (or non-transitory) storage medium or could be a signal such as an electronic signal over wires, an optical signal or a radio signal such as to a satellite or the like.

The present invention in accordance with any of its further aspects or embodiments may include any of the features described in reference to other aspects or embodiments of the invention to the extent it is not mutually inconsistent therewith.

Various features of embodiments of the invention will be described in further detail below. Figures

Various aspects of the teachings of the present invention, and arrangements embodying those teachings, will hereafter be described by way of illustrative example with reference to the accompanying drawings, in which:

Figure 1 is a schematic illustration of a Global Positioning System (GPS) that is usable by a navigation device to generate the probe measurements that may be processed according to embodiments of the present invention; Figure 2 is a schematic illustration of electronic components of a navigation device;

Figure 3 is a schematic diagram of a communications system including a navigation device and a server;

Figure 4 shows an example of typical probe data for a particular road network that may be processed according to embodiments of the present invention;

Figure 5 illustrates an example of how an expected accuracy value may be determined for a particular position within the road network;

Figures 6A, 6B and 6C illustrate some examples of decay functions that may be used when determining an expected accuracy value; and

Figure 7 is a flowchart illustrating a method of generating an accuracy profile according to an embodiment of the present invention.

Description

Figure 1 illustrates an example view of a typical Global Positioning System (GPS), usable by navigation devices. Such systems are generally well-known and are used for a variety of purposes. In general, GPS is a satellite-radio based navigation system capable of determining continuous position, velocity, time, and in some instances direction information for an unlimited number of users. Formerly known as NAVSTAR, the GPS incorporates a plurality of satellites which orbit the earth in extremely precise orbits. Based on these precise orbits, GPS satellites can relay their location to any number of receiving units.

The GPS system is implemented when a device, specially equipped to receive GPS data, begins scanning radio frequencies for GPS satellite signals. Upon receiving a radio signal from a GPS satellite, the device determines the precise location of that satellite via one of a plurality of different conventional methods. The device will continue scanning, in most instances, for signals until it has acquired at least three different satellite signals (noting that position is not normally, but can be determined, with only two signals using other triangulation techniques).

Implementing geometric triangulation, the receiver utilizes the three known positions to determine its own two-dimensional position relative to the satellites. This can be done in a known manner. Additionally, acquiring a fourth satellite signal will allow the receiving device to calculate its three dimensional position by the same geometrical calculation in a known manner. The position and velocity data can be updated in real time on a continuous basis by an unlimited number of users.

As shown in Figure 1 , the GPS system is denoted generally by reference numeral 100. A plurality of satellites 120 are in orbit about the earth 124. The orbit of each satellite 120 is not necessarily synchronous with the orbits of other satellites 120 and, in fact, is likely asynchronous. A GPS receiver 140 is shown receiving spread spectrum GPS satellite signals 160 from the various satellites 120.

The spread spectrum signals 160, continuously transmitted from each satellite 120, utilize a highly accurate frequency standard accomplished with an extremely accurate atomic clock. Each satellite 120, as part of its data signal transmission 160, transmits a data stream indicative of that particular satellite 120. It is appreciated by those skilled in the relevant art that the GPS receiver device 140 generally acquires spread spectrum GPS satellite signals 160 from at least three satellites 120 for the GPS receiver device 140 to calculate its two-dimensional position by triangulation. Acquisition of an additional signal, resulting in signals 160 from a total of four satellites 120, permits the GPS receiver device 140 to calculate its three-dimensional position in a known manner.

Figure 2 is an illustrative representation of electronic components of a navigation device 200 according to an embodiment of the present invention, in block component format. It should be noted that the block diagram of the navigation device 200 is not inclusive of all components of the navigation device, but is only representative of many example components.

The navigation device 200 is located within a housing (not shown). The housing includes a processor 210 connected to an input device 220 and a display screen 240. The input device 220 can include a keyboard device, voice input device, touch panel and/or any other known input device utilised to input information; and the display screen 240 can include any type of display screen such as an LCD display, for example. In one arrangement the input device 220 and display screen 240 are integrated into an integrated input and display device, including a touchpad or touchscreen input so that a user need only touch a portion of the display screen 240 to select one of a plurality of display choices or to activate one of a plurality of virtual buttons.

The navigation device may include an output device 260, for example an audible output device (e.g. a loudspeaker). As output device 260 can produce audible information for a user of the navigation device 200, it is should equally be understood that input device 240 can include a microphone and software for receiving input voice commands as well. The navigation device may also include an accelerometer 290.

In the navigation device 200, processor 210 is operatively connected to and set to receive input information from input device 220 via a connection 225, and operatively connected to at least one of display screen 240 and output device 260, via output connections 245, to output information thereto. Further, the processor 210 is operably coupled to a memory resource 230 via connection 235 and is further adapted to receive/send information from/to input/output (I/O) ports 270 via connection 275, wherein the I/O port 270 is connectible to an I/O device 280 external to the navigation device 200. The memory resource 230 comprises, for example, a volatile memory, such as a Random Access Memory (RAM) and a non-volatile memory, for example a digital memory, such as a flash memory. The external I/O device 280 may include, but is not limited to an external listening device such as an earpiece for example. The connection to I/O device 280 can further be a wired or wireless connection to any other external device such as a car stereo unit for hands free operation and/or for voice activated operation for example, for connection to an ear piece or head phones, and/or for connection to a mobile phone for example, wherein the mobile phone connection may be used to establish a data connection between the navigation device 200 and the internet or any other network for example, and/or to establish a connection to a server via the internet or some other network for example.

Figure 2 further illustrates an operative connection between the processor 210 and an antenna/receiver 250 via connection 255, wherein the antenna/receiver 250 can be a GPS antenna/receiver for example. It will be understood that the antenna and receiver designated by reference numeral 250 are combined schematically for illustration, but that the antenna and receiver may be separately located components, and that the antenna may be a GPS patch antenna or helical antenna for example.

Further, it will be understood by one of ordinary skill in the art that the electronic components shown in Figure 2 are powered by power sources (not shown) in a conventional manner. As will be understood by one of ordinary skill in the art, different configurations of the components shown in Figure 2 are considered to be within the scope of the present application. For example, the components shown in Figure 2 may be in communication with one another via wired and/or wireless connections and the like.

Referring now to Figure 3, the navigation device 200 may establish a "mobile" or telecommunications network connection with a server 302 via a mobile device (not shown) (such as a mobile phone, PDA, and/or any device with mobile phone technology) establishing a digital connection (such as a digital connection via known Bluetooth technology for example). Thereafter, through its network service provider, the mobile device can establish a network connection (through the internet for example) with a server 302. As such, a "mobile" network connection is established between the navigation device 200 (which can be, and often times is mobile as it travels alone and/or in a vehicle) and the server 302 to provide a "real-time" or at least very "up to date" gateway for information.

The establishing of the network connection between the mobile device (via a service provider) and another device such as the server 302, using an internet (such as the World Wide Web) for example, can be done in a known manner. This can include use of TCP/IP layered protocol for example. The mobile device can utilize any number of communication standards such as CDMA, GSM, WAN, etc.

As such, an internet connection may be utilised which is achieved via data connection, via a mobile phone or mobile phone technology within the navigation device 200 for example. For this connection, an internet connection between the server 302 and the navigation device 200 is established. This can be done, for example, through a mobile phone or other mobile device and a GPRS (General Packet Radio Service) connection (GPRS connection is a high-speed data connection for mobile devices provided by telecom operators; GPRS is a method to connect to the internet).

The navigation device 200 can further complete a data connection with the mobile device, and eventually with the internet and server 302, via existing Bluetooth technology for example, in a known manner, wherein the data protocol can utilize any number of standards, such as the GSRM, the Data Protocol Standard for the GSM standard, for example.

The navigation device 200 may include its own mobile phone technology within the navigation device 200 itself (including an antenna for example, or optionally using the internal antenna of the navigation device 200). The mobile phone technology within the navigation device 200 can include internal components as specified above, and/or can include an insertable card (e.g. Subscriber Identity Module or SIM card), complete with necessary mobile phone technology and/or an antenna for example. As such, mobile phone technology within the navigation device 200 can similarly establish a network connection between the navigation device 200 and the server 302, via the internet for example, in a manner similar to that of any mobile device.

For GPRS phone settings, a Bluetooth enabled navigation device may be used to correctly work with the ever changing spectrum of mobile phone models, manufacturers, etc., model/manufacturer specific settings may be stored on the navigation device 200 for example. The data stored for this information can be updated. In Figure 3 the navigation device 200 is depicted as being in communication with the server 302 via a generic communications channel 318 that can be implemented by any of a number of different arrangements. The server 302 and a navigation device 200 can communicate when a connection via communications channel 318 is established between the server 302 and the navigation device 200 (noting that such a connection can be a data connection via mobile device, a direct connection via personal computer via the internet, etc.).

The server 302 includes, in addition to other components which may not be illustrated, a processor 304 operatively connected to a memory 306 and further operatively connected, via a wired or wireless connection 314, to a mass data storage device 312. The processor 304 is further operatively connected to transmitter 308 and receiver 310, to transmit and send information to and from navigation device 200 via communications channel 318. The signals sent and received may include data, communication, and/or other propagated signals. The transmitter 308 and receiver 310 may be selected or designed according to the communications requirement and communication technology used in the

communication design for the navigation system 200. Further, it should be noted that the functions of transmitter 308 and receiver 310 may be combined into a signal transceiver.

Server 302 is further connected to (or includes) a mass storage device 312, noting that the mass storage device 312 may be coupled to the server 302 via communication link 314. The mass storage device 312 contains a store of navigation data and map information, and can again be a separate device from the server 302 or can be incorporated into the server 302.

The navigation device 200 is adapted to communicate with the server 302 through communications channel 318, and includes processor, memory, etc. as previously described with regard to Figure 2, as well as transmitter 320 and receiver 322 to send and receive signals and/or data through the communications channel 318, noting that these devices can further be used to communicate with devices other than server 302. Further, the transmitter 320 and receiver 322 are selected or designed according to communication requirements and communication technology used in the communication design for the navigation device 200 and the functions of the transmitter 320 and receiver 322 may be combined into a single transceiver.

Software stored in server memory 306 provides instructions for the processor 304 and allows the server 302 to provide services to the navigation device 200. One service provided by the server 302 involves processing requests from the navigation device 200 and transmitting navigation data from the mass data storage 312 to the navigation device 200. Another service provided by the server 302 includes processing the navigation data using various algorithms for a desired application and sending the results of these calculations to the navigation device 200.

The communication channel 318 generically represents the propagating medium or path that connects the navigation device 200 and the server 302. Both the server 302 and navigation device 200 include a transmitter for transmitting data through the communication channel and a receiver for receiving data that has been transmitted through the communication channel.

The communication channel 318 is not limited to a particular communication technology. Additionally, the communication channel 318 is not limited to a single communication technology; that is, the channel 318 may include several

communication links that use a variety of technology. For example, the

communication channel 318 can be adapted to provide a path for electrical, optical, and/or electromagnetic communications, etc. As such, the communication channel 318 includes, but is not limited to, one or a combination of the following: electric circuits, electrical conductors such as wires and coaxial cables, fibre optic cables, converters, radio-frequency (RF) waves, the atmosphere, empty space, etc.

Furthermore, the communication channel 318 can include intermediate devices such as routers, repeaters, buffers, transmitters, and receivers, for example.

In one illustrative arrangement, the communication channel 318 includes telephone and computer networks. Furthermore, the communication channel 318 may be capable of accommodating wireless communication such as radio frequency, microwave frequency, infrared communication, etc. Additionally, the communication channel 318 can accommodate satellite communication.

The communication signals transmitted through the communication channel 318 include, but are not limited to, signals as may be required or desired for given communication technology. For example, the signals may be adapted to be used in cellular communication technology such as Time Division Multiple Access (TDMA), Frequency Division Multiple Access (FDMA), Code Division Multiple Access (CDMA), Global System for Mobile Communications (GSM), etc. Both digital and analogue signals can be transmitted through the communication channel 318. These signals may be modulated, encrypted and/or compressed signals as may be desirable for the communication technology. The server 302 includes a remote server accessible by the navigation device 200 via a wireless channel. The server 302 may include a network server located on a local area network (LAN), wide area network (WAN), virtual private network (VPN), etc. The server 302 may include a personal computer such as a desktop or laptop computer, and the communication channel 318 may be a cable connected between the personal computer and the navigation device 200. Alternatively, a personal computer may be connected between the navigation device 200 and the server 302 to establish an internet connection between the server 302 and the navigation device 200. Alternatively, a mobile telephone or other handheld device may establish a wireless connection to the internet, for connecting the navigation device 200 to the server 302 via the internet.

The navigation device 200 may be provided with information from the server 302 via information downloads which may be periodically updated automatically or upon a user connecting navigation device 200 to the server 302 and/or may be more dynamic upon a more constant or frequent connection being made between the server 302 and navigation device 200 via a wireless mobile connection device and TCP/IP connection for example. For many dynamic calculations, the processor 304 in the server 302 may be used to handle the bulk of the processing needs, however, processor 210 of navigation device 200 can also handle much processing and calculation, oftentimes independent of a connection to a server 302.

As indicated above in Figure 2, a navigation device 200 includes a processor 210, an input device 220, and a display screen 240. The input device 220 and display screen 240 are integrated into an integrated input and display device to enable both input of information (via direct input, menu selection, etc.) and display of information through a touch panel screen, for example. Such a screen may be a touch input LCD screen, for example, as is well known to those of ordinary skill in the art. Further, the navigation device 200 can also include any additional input device 220 and/or any additional output device 241 , such as audio input/output devices for example.

The navigation device 200 is typically mounted within a vehicle. Thus, as the navigation device 200 moves around a road network, it can report back to server 302 its location data, as obtained from the GPS antenna/receiver 250. For any given road network there will typically be a plurality of such devices reporting back their GPS location data, such that the server is provided with a large archive of probe data (also called floating car data) which is continuously being updated as more probe data becomes available. Figure 4 shows an example of typical probe data that may be obtained from a particular road network.

It will be appreciated that the accuracy of a GPS position measurement generally depends on both time and location. The probe data contains not only the positions of probes (e.g. vehicles) at different times but also the accuracy values associated with the GPS position measurement. For instance, for each position measurement, a GPS receiver usually delivers different Dilution of Precision (DOP) values, including:

• VDOP - Vertical Dilution of Precision, vertical accuracy, height;

• FIDOP - Horizontal Dilution of Precision, horizontal accuracy, 2D-coordinates;

• PDOP - Positional Dilution of Precision, Position accuracy, 3D-coordinates;

• TDOP - Time Dilution of Precision, time accuracy; and

• GDOP - Geometric Dilution of Precision, Overall-accuracy, 3D-coordinates and time,

This precision data may be stored in various formats. For example, the DOP values may be specified using a simplified classification in four different accuracy classes (0 - 3). Flowever, in embodiments, any suitable quantifiers for the accuracy values of the GPS position measurements may be used.

According to the present invention, this historic GPS probe data is used to generate an accuracy map that can then be used to estimate the accuracy of GPS data for positions where no accuracy data is (yet) available. The accuracy map thus represents the variation across the geographic area covered by the map in the expected accuracy for GPS location data. The accuracy map may be represented in a number of different ways. For example, an accuracy map may be represented as any of raster images or arrays of accuracy values; contour lines of lines with the same accuracy; BDAM - Batched Dynamic Adaptive Meshes; or attributes on a digital road map.

According to an embodiment, the first step for generating an accuracy map is to select all points for which a mean (expected) accuracy value should be computed. In most cases the points for which an expected accuracy value is computed form one or more rectangular grids. This is particularly the case where the map is to be represented as a raster image, or an array, in which case the points should lie on well-defined grids. Flowever for the other representations are also other layouts possible. For instance, if the accuracy map is to be represented as attributes on a digital road map the points should lie on the road network. In that case, the positions may be selected to correspond to the nodes and shape points of the road network.

After the selection of the points, a mean accuracy value is computed for each selected point. In order to compute a mean accuracy value for each selected point c, all GPS probe points inside a buffer with radius r around the point c are selected, as shown in Figure 5. In order to compute the mean accuracy value one can use the weighted arithmetic mean, but it will be appreciated that any other suitable average is usable. The weight may be computed by the distance between the buffer centre c and the GPS point using a suitable decay function. In general, any suitable monotonically non-increasing decay function may be used. Some examples are shown in Figures 6A, 6B and 6C.

In order to compute the mean accuracy value one can use the weighted arithmetic mean, but any other mean is usable. The weight is computed by the distance between the buffer centre c and the GPS point using the decay function.

This can then be repeated for a plurality of different points within the geographic area. After the computation of the mean accuracy values the calculated accuracy values are stored in a map in any suitable manner, e.g. as described above.

In addition to this, it is also possible to consider the timestamps for the GPS positions in order to generate a time-dependent accuracy map. For example, a given time period (e.g. a day) may be divided into several time slots (or‘bins’), and the GPS measurements may be binned accordingly. A separate accuracy map can then be generated for each time slot. In this case, a time-dependent decay function may be used to additionally weight the accuracy values according to the time.

Alternatively, all of the accuracy values can be stored in a single map. In this case, each accuracy value may contain a time dependent accuracy profile instead of a single accuracy value.

Figure 7 is a flowchart illustrating a method of generating an accuracy profile according to an embodiment of the present invention. As shown, in a first step (step 701 ), historic probe data is obtained for a particular geographic area. One or more point(s) within the geographic area are then selected (step 702), and an expected accuracy value calculated for each of the selected points (step 703). An accuracy profile, or map, is then built from the computed expected accuracy values (step 704).

The accuracy map thus allows estimating the accuracy values for GPS positions where no accuracy values available. For instance, from the stored accuracy map it is easy to extract the expected accuracy value for any given position that is covered by the map. This information can then be used, e.g., to estimate the accuracy for GPS positions where no accuracy values available.

For example, sometimes GPS position data is available without any accuracy values. This may be the case where the data is obtained from a digital camera equipped with a GPS device wherein the position data is usually stored in the image metadata without any accuracy values. Flowever accuracy values may be very useful for the utilisation of such position data. For example, in WO 201 1/095227 A1 , a method of extracting local identifiers from images and associating extracted local identifiers with positional data is described. The generated accuracy maps or accuracy profiles can be used to estimate the accuracy of GPS positions where no accuracy values available, and can then be used to restrict the methods disclosed in WO 201 1/095227 A1 to images with high accuracy, or to compute a confidence level for the extracted identifiers.

Furthermore the generated accuracy map may also be stored on a navigation device. In that case, the navigation unit is able to predict parts of the route with a low accuracy. This can be used by the positioning or guidance module of the navigation device. For example, when entering such a low accuracy area the navigation device may then change the positioning mode. Also, the guidance module may consider low accuracy areas and adapt the driving instructions accordingly.

The accuracy map also allows for areas with reception problems to be identified and visualised.

It will be appreciated that whilst various aspects and embodiments of the present invention have heretofore been described, the scope of the present invention is not limited to the particular arrangements set out herein and instead extends to encompass all arrangements, and modifications and alterations thereto, which fall within the scope of the appended claims.

Whilst some embodiments described in the foregoing detailed description refer to GPS, it should be noted that the navigation device may utilise any kind of position sensing technology as an alternative to (or indeed in addition to) GPS. For example the navigation device may utilise using other global navigation satellite systems such as the European Galileo system. Equally, it is not limited to satellite based but could readily function using ground based beacons or any other kind of system that enables the device to determine its geographic location.

It will also be appreciated that embodiments of the invention can be implemented as a computer program product for use with a computer system, the computer program product being, for example, a series of computer instructions stored on a tangible data recording medium, such as a diskette, CD-ROM, ROM, or fixed disk, or embodied in a computer data signal, the signal being transmitted over a tangible medium or a wireless medium, for example, microwave or infrared. The series of computer instructions can constitute all or part of the functionality described above, and can also be stored in any memory device, volatile or non-volatile, such as semiconductor, magnetic, optical or other memory device.

It will also be well understood by persons of ordinary skill in the art that whilst embodiments described herein implement certain functionality by means of software, that functionality could equally be implemented solely in hardware (for example by means of one or more ASICs (application specific integrated circuit)) or indeed by a mix of hardware and software. As such, the scope of the present invention should not be interpreted as being limited only to being implemented in software.

It will thus be understood that the present invention has been described above purely by way of example, and modifications of detail can be made within the scope of the invention. Each feature disclosed in the description, and (where appropriate) the claims and drawings may be provided independently or in any appropriate combination. Lastly, it should also be noted that whilst the

accompanying claims set out particular combinations of features described herein, the scope of the present invention is not limited to the particular combinations hereafter claimed, but instead extends to encompass any combination of features or embodiments herein disclosed irrespective of whether or not that particular combination has been specifically enumerated in the accompanying claims at this time.

Thus, although the present invention has been described with reference to various embodiments, it will be understood by those skilled in the art that various changes in form and detail may be made without departing from the scope of the invention as set forth in the accompanying claims. It should also be noted that whilst the accompanying claims set out particular combinations of features described herein, the scope of the present invention is not limited to the particular combinations hereafter claimed, but instead extends to encompass any combination of features or embodiments herein disclosed irrespective of whether or not that particular combination has been specially enumerated in the accompanying claims at this time.

Claims

Claims;

1. A method for generating an accuracy map representing the variation within a geographic area for the expected accuracy of location data obtained from a global navigation satellite system (GNSS), the method comprising:

obtaining a plurality of probe measurements from one or more device(s) within a geographic area, each of the probe measurements including GNSS location data indicative of a location within the geographic area at which the probe measurement was made and one or more dilution of precision value(s) for each probe measurement indicative of an accuracy value associated with the GNSS location data for that probe measurement;

determining from the obtained probe measurements expected accuracy values of GNSS location data for one or more position(s) within the geographic area; and

generating from the expected accuracy values an accuracy map representing the expected accuracy of GNSS location data associated with each of the one or more position(s) within the geographic area.

2. A method for generating an accuracy map representing the variation within a geographic area for the expected accuracy of location data obtained from a global navigation satellite system (GNSS), the method comprising:

obtaining a plurality of probe measurements from one or more device(s) within a geographic area, each of the probe measurements including GNSS location data indicative of a location within the geographic area at which the probe measurement was made and precision data indicative of an accuracy value associated with the GNSS location data for that probe measurement;

3. The method of claim 1 or 2, wherein determining the expected accuracy value for a position within the geographic area comprises identifying a set of probe measurements falling within a predetermined distance of that position, and using the accuracy value(s) for the probe measurements within the set to determine the expected accuracy value for that position.

4. The method of claim 1 , 2 or 3, wherein the expected accuracy value for a position within the geographic area comprises a weighted average of the accuracy value(s) for probe measurements falling within a predetermined distance of that position.

5. The method of any preceding claim, wherein each of the probe

measurements further includes temporal data indicating the time at which the probe measurement was made, and wherein the method comprises generating a set of one or more time-dependent accuracy map(s).

6. The method of any preceding claim, comprising providing the accuracy map for output as a set of contour lines representing contours of constant accuracy.

7. The method of any preceding claim, comprising providing the accuracy map for output as a raster image and/or an array of expected accuracy values.

8. The method of any preceding claim, comprising providing the accuracy map for output as a set of map attributes for inclusion within an electronic map representing the geographic area.

9. The method of any preceding claim, wherein the precision data comprises one or more dilution of precision value(s) for each probe measurement.

10. An accuracy map representing the variation within a geographic area for the expected accuracy of location data obtained from a global navigation satellite system (GNSS), wherein the accuracy map includes an expected accuracy value of GNSS location data for a plurality of different positions within the geographic area.

11 . A system for generating an accuracy map representing the variation within a geographic area for the expected accuracy of location data obtained from a global navigation satellite system (GNSS), the system comprising a set of one or more processor(s) configured to:

obtain a plurality of probe measurements from one or more device(s) within a geographic area, each of the probe measurements including GNSS location data indicative of a location within the geographic area at which the probe measurement was made and one or more dilution of precision value(s) for each probe

measurement indicative of an accuracy value associated with the GNSS location data for that probe measurement;

determine from the obtained probe measurements expected accuracy values of GNSS location data for one or more position(s) within the geographic area; and generate from the expected accuracy values an accuracy map representing the expected accuracy of GNSS location data associated with each of the one or more position(s) within the geographic area.

12. A system for generating an accuracy map representing the variation within a geographic area for the expected accuracy of location data obtained from a global navigation satellite system (GNSS), the system comprising a set of one or more processor(s) configured to:

obtain a plurality of probe measurements from one or more device(s) within a geographic area, each of the probe measurements including GNSS location data indicative of a location within the geographic area at which the probe measurement was made and precision data indicative of an accuracy value associated with the GNSS location data for that probe measurement;

13. The system of claim 1 1 or 12, wherein the one or more processor(s) determine the expected accuracy value for a position within the geographic area by identifying a set of probe measurements falling within a predetermined radius of that position, and using the accuracy value(s) for the probe measurements within the set to determine the expected accuracy value for that position.

14. The system of claim 12 or 13, wherein the expected accuracy value for a position within the geographic area comprises a weighted average of the accuracy value(s) for probe measurements falling within a predetermined radius of that position.

15. The system of claims 12 to 14, wherein each of the probe measurements further includes temporal data indicating the time at which the probe measurement was made, and wherein the system is configured to generate a set of one or more time-dependent accuracy map(s).

16. The system of any of claims 10 to 13, wherein the system is configured to provide the accuracy map for output as: (i) a set of contour lines representing contours of constant accuracy; (ii) a raster image and/or an array of expected accuracy values; and/or (iii) a set of map attributes for inclusion within an electronic map representing the geographic area.

17. A computer program comprising software code that when executing on a data processor performs the method of any one of claims 1 to 9.