Patent Description:
Therefore, there is a need for an approach for automatically selecting the most appropriate sensor system for high-definition map feature accuracy and reliability specifications.

According to one aspect of the invention, is provided according to claim <NUM>.

According to another aspect of the invention, an apparatus is provided according to claim <NUM>.

According to another aspect of the invention, a non-transitory computer-readable storage medium is provided according to claim <NUM>.

According to another embodiment, an apparatus comprises means for selecting at least one survey point that has a known physical location. The apparatus also comprises means for initiating a plurality of passes to capture a plurality of images of the at least one survey point using a sensor system. For each pass of the plurality of passes, the apparatus further comprises means for calculating an estimated location of the at least one survey point based on the plurality of images and calculating error data based on the estimated location and the known physical location. The apparatus also comprises means for generating an error curve with respect to a number of the plurality of passes based on the error data for said each pass. The apparatus further comprises means for providing an output indicating a target number of passes to meet an error specification based on the error curve.

In addition, for various example aspects, the following is applicable: a method comprising facilitating a processing of and/or processing (<NUM>) data and/or (<NUM>) information and/or (<NUM>) at least one signal, the (<NUM>) data and/or (<NUM>) information and/or (<NUM>) at least one signal based, at least in part, on (or derived at least in part from) any one or any combination of methods (or processes) disclosed in this application.

For various example aspects, the following is also applicable: a method comprising facilitating access to at least one interface configured to allow access to at least one service, the at least one service configured to perform any one or any combination of network or service provider methods (or processes) disclosed in this application.

For various example aspects, the following is also applicable: a method comprising facilitating creating and/or facilitating modifying (<NUM>) at least one device user interface element and/or (<NUM>) at least one device user interface functionality, the (<NUM>) at least one device user interface element and/or (<NUM>) at least one device user interface functionality based, at least in part, on data and/or information resulting from one or any combination of methods or processes disclosed in this application as relevant to any embodiment of the invention, and/or at least one signal resulting from one or any combination of methods (or processes) disclosed in this application as relevant to any embodiment of the invention.

For various example aspects, the following is also applicable: a method comprising creating and/or modifying (<NUM>) at least one device user interface element and/or (<NUM>) at least one device user interface functionality, the (<NUM>) at least one device user interface element and/or (<NUM>) at least one device user interface functionality based at least in part on data and/or information resulting from one or any combination of methods (or processes) disclosed in this application as relevant to any embodiment of the invention, and/or at least one signal resulting from one or any combination of methods (or processes) disclosed in this application as relevant to any embodiment of the invention.

In various example aspects, the methods (or processes) can be accomplished on the service provider side or on the mobile device side or in any shared way between service provider and mobile device with actions being performed on both sides.

For various example embodiments, the following is applicable: An apparatus comprising means for performing a method of the claims.

Still other aspects, features, and advantages of the invention are readily apparent from the following detailed description, simply by illustrating a number of particular embodiments and implementations, including the best mode contemplated for carrying out the invention. The invention is also capable of other and different embodiments, and its several details can be modified in various obvious respects, all without departing from the spirit and scope of the invention. Accordingly, the drawings and description are to be regarded as illustrative in nature, and not as restrictive.

Examples of a method, apparatus, and computer program for selecting a sensor system based on its ability to meet high-definition map feature accuracy and reliability specifications are disclosed. In the following description, for the purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of the embodiments of the invention. It is apparent, however, to one skilled in the art that the embodiments of the invention may be practiced without these specific details or with an equivalent arrangement. In other instances, well-known structures and devices are shown in block diagram form in order to avoid unnecessarily obscuring the embodiments of the invention.

<FIG> is a diagram of a system capable of automatically selecting the most appropriate sensor system for high-definition map feature accuracy and reliability specifications, according to one embodiment. As indicated above, automated driving is quickly becoming a reality following advances in machine learning, computer vision, and compute power. The ability to perceive the world with an accurate semantic understanding can enable one or more vehicles 101a-101n (also collectively referred to as vehicles <NUM>) (e.g., autonomous, high assisted driving (HAD), or semi-autonomous vehicles) to obey driving rules and avoid collisions without direct human intervention. As these perceptual abilities have improved, so too has the need for highly accurate and up-to-date digital maps. Path planning, for instance, requires knowledge of what to expect beyond a vehicle <NUM>'s perceptual horizon, and driving in complicated urban environments with many occluding objects requires a knowledge of what cannot be detected by one or more vehicle sensors 103a-103n (also collectively referred to as vehicle sensors <NUM>)(e.g., a camera sensor, Light Detection and Ranging (LiDAR), etc.).

In response, map service providers (e.g., operating a mapping platform <NUM>) are creating extremely accurate and up-to-date high-resolution maps. High-definition digital maps in the form of models of the environment are needed for a wide range of automated applications including transportation, guidance (e.g., farming and/or harvesting), and search and rescue. Learning and automating the map creation and update process has therefore been a major research focus in the robotics and artificial intelligence (AI) community for years.

Different sources of raw data (e.g., image data) can be exploited to make such high-definition digital maps. For example, top-down sources, like satellite, aerial, and drone images, can be used to precisely determine the location of roads and other features on the Earth (e.g., map features such as lane lines, road boundaries, etc.). These images help create maps at a much larger scale but are more limited to surface features.

Ground sources like vehicles <NUM> and/or robots fitted with vehicle sensors <NUM> (e.g., GPS, inertial measure unit (IMU), LiDAR, radio detection and ranging (Radar), etc.) may also be exploited to make high definition digital maps. Processing ground sources generally requires more effort and resources to do a larger scale city level collection; however, ground sources can detect map features that are on the Earth's surface as well as traffic lights, signs, etc., which may not be visible from top-down images or sources. Moreover, the advent of less expensive systems (e.g., vehicle sensors <NUM>) deployed on vehicles <NUM> at scale, has enabled crowd-sourced mapping and change detection to become increasingly feasible. However, the positional quality of the map features derived from such data is heavily dependent on the sensor system used for the data collection and the reliability on the number of such observations/passes. For high-definition map use (e.g., with centimeter level accuracy), correct sensor pose data is essential. In addition, crowd-sourced map and change detection data can result in tremendous volumes of data of often uncertain quality that can require considerable compute resources, time, and expense to effectively process. Accordingly, map service providers face significant technical challenges to automatically select the most appropriate sensor system before the corresponding data is utilized in connection with modern autonomous applications (e.g., autonomous driving).

To address these problems, the system <NUM> of <FIG> introduces an automatic way to select a sensor system based on its ability to infer the position and the number of observations required to achieve a certain amount of accuracy and reliability of semantic road features (e.g., lane markings, signs, etc.). In one embodiment, the most appropriate sensor system is the sensor system <NUM> that has the highest accuracy and reliability relative to the other available sensor systems <NUM>. In other words, the most appropriate sensor system <NUM> has the minimalist error tolerance and spread around the error relative to the other available sensor systems <NUM>. In one embodiment, the system <NUM> can automatically select the most appropriate sensor system <NUM> based on respective accuracy and/or reliability percentages as well as the kind of situations where the determined accuracy and reliability specifications are not met by a particular sensor system <NUM>.

In one embodiment, the system <NUM> obtains a set of survey points <NUM> (e.g., ground control points) among diverse geographical areas that involve, for instance, tree canopies (e.g., area 201a), open sky areas (e.g., area 201b), urban canyons (e.g., area 201c), natural canyons, etc., as depicted in the example map of <FIG>. In one instance, the system <NUM> can obtain each survey point <NUM> from one or more vendor archives (e.g., a geographic database <NUM>) via the communication network <NUM>. For instance, to facilitate and/or monitor the accuracy of digital map data stored in the geographic database <NUM>, one or more content providers 113a-<NUM> (e.g., map service providers) can designate survey points <NUM> that have precise known location data associated with them (e.g., in the form of <Latitude, Longitude, Elevation>). These points play a vital role in being able to measure the quality of different sensor systems <NUM>.

In one embodiment, the system <NUM> determines the relevant accuracy and reliability standards - error tolerance and the spread around the error (e.g., standard deviation) based on the intended automated application (e.g., autonomous driving) for which the selection of a sensor system <NUM> is being made. For example, the system <NUM> may require a higher degree of accuracy and reliability for ensuring safe automated driving functions, particularly in highly populated areas (e.g., area 201c) or at high speeds and/or the system <NUM> may require a relatively lower degree of accuracy and reliability for ensuring safe automated guidance functions (e.g., autonomous harvesting), particularly among vast acres of farmland (e.g., area 201b).

In one embodiment, the system <NUM> selects the sensor systems <NUM> (e.g., camera sensors, LiDAR sensors, Radar, infrared sensors, thermal sensors, and the like) for which the quality of capture poses needs to be estimated. In other words, the system <NUM> selects among the one or more sensor systems <NUM> for which the system <NUM> has access to the corresponding data (e.g., stored in or accessible via the geographic database <NUM>). By way of example, the capture pose may include data on sensor position (e.g., location when the corresponding images were captured), sensor pose information (e.g., pointing direction), technical parameters (e.g., field of view, focal length, camera lens, etc.).

In one embodiment, for each sensor system (e.g., camera, LiDAR, etc.), the system <NUM> prompts one or more vehicles <NUM> (e.g., autonomous vehicles) with the respective sensor system <NUM> to drive around, within, etc., one or more areas (e.g., areas 201a, 201b, 201c) to capture each survey point <NUM>. In one embodiment, the system <NUM> can also capture a survey point <NUM> using one or more user equipment (UE) 115a-115n (also collectively referred to herein as UEs <NUM>) associated with a vehicle <NUM> (e.g., an embedded navigation system), a user or a passenger of a vehicle <NUM> (e.g., a mobile device, a smartphone, etc.), or a combination thereof using sensing systems like cameras and running perception algorithms on the acquired data (e.g., by executing one or more applications 117a-117n). In one instance, the vehicles <NUM>, the vehicle sensors <NUM>, the UE <NUM>, and the applications 117a-117n (also collectively referred to herein as applications <NUM>) all have connectivity to the mapping platform <NUM> via the communication network <NUM>.

In one embodiment, the system <NUM> collects all the captures (e.g., camera images) taken within a certain radius and marks the pixel position of the survey points <NUM> in each capture if the survey points <NUM> are visible (e.g., using the computer vision system <NUM>). For example, the system <NUM> may obtain the captures from the geographic database <NUM>. In one instance, for each capture that has a marked point, the system <NUM> generates a ray from the camera center to the pixel position in the case of an image sensor <NUM> and determines the distance of the ray from the 3D position of the survey point <NUM>. In one embodiment, this distance is recorded as an error associated with the capture observation. In the case of LiDAR/Radar sensors, the system <NUM>, for instance, identifies the point position of the survey point <NUM> in the cloud and measures the distance from it from the surveyed position to determine the error associated with the captured observation.

In one instance, the system <NUM> prompts the one or more vehicles <NUM> to make multiple passes to capture a survey point <NUM> multiple times and potentially under multiple conditions. Preferably, the one or more vehicles <NUM> capture each survey point <NUM> under different conditions, including multiple and varying speeds to obtain variation in the quality of the GPS signal and GPS/position cumulative drift.

In one embodiment, the system <NUM> plots the error associated with each capture observation on a curve and determines the mean/standard of deviation with respect to the number of passes. The system <NUM>, in one instance, then correlates the number of passes on the curve with the specified error tolerance and spread (e.g., based on the autonomous application of interest). In one embodiment, the system <NUM> counts a survey point <NUM> as void if no number of passes can satisfy the required accuracy/reliability specifications.

In one embodiment, for each sensor system <NUM> (e.g., camera, IMU, LiDAR, Radar, etc.), the system <NUM> generates a histogram of passes needed to satisfy the relevant accuracy and reliability specifications using all the survey points <NUM>. In one instance, the system <NUM> determines the mean number of passes and variation across the survey points <NUM> as well as the number of survey points <NUM> for which the specifications were not met. In one embodiment, the system <NUM> then compares the quality of the sensor systems <NUM> based on the mean number of passes and variation across survey points <NUM> to select or choose the most appropriate sensor system <NUM> based on the calculated statistics.

<FIG> is an example of a map including diverse geographical areas and known survey points, according to one embodiment. In this example, the map <NUM> includes diverse geographical areas including, for instance, tree canopies (201a), open sky areas (201b), and urban canyons (201c) and each of the areas 201a-201c includes at least one survey point <NUM> (e.g., 107a, 107b, and 107c) with a known quality/accuracy (e.g., a physically verified location). Consequently, one or more vehicles <NUM> can drive around the respective areas to obtain one or more captures (e.g., camera images) of the respective survey points <NUM> in diverse geographical conditions. It is contemplated that the diverse geographic areas contribute to variations in the quality of the captures which the system <NUM> can consider when comparing the quality of the sensor system <NUM>.

<FIG> is a diagram of the components of the mapping platform <NUM>, according to one embodiment. By way of example, the mapping platform <NUM> includes one or more components for automatically selecting the most appropriate sensor system <NUM> for high-definition map feature accuracy and reliability specifications, according to the various embodiments described herein. It is contemplated that the functions of these components may be combined or performed by other components of equivalent functionality. In one embodiment, the mapping platform <NUM> includes a data collection module <NUM>, a communication module <NUM>, a data processing module <NUM>, a graphing module <NUM>, a user interface (UI) module <NUM>, a training module <NUM>, a computer vision system <NUM>, and a machine learning system <NUM>, all with connectivity to the geographic database <NUM>. The above presented modules and components of the mapping platform <NUM> can be implemented in hardware, firmware, software, or a combination thereof. Though depicted as a separate entity in <FIG>, it is contemplated that the mapping platform <NUM> may be implemented as a module of any other component of the system <NUM>. In another embodiment, the mapping platform <NUM> and/or the modules <NUM>-<NUM> may be implemented as a cloud-based service, local service, native application, or combination thereof. The functions of the mapping platform <NUM> and/or the modules <NUM>-<NUM> are discussed with respect to <FIG> and <FIG>.

<FIG> is a flowchart of a process for automatically selecting the most appropriate sensor system for high-definition map feature accuracy and reliability specifications, according to one embodiment. In various embodiments, the mapping platform <NUM>, the computer vision system <NUM>, the machine learning system <NUM>, and/or any of the modules <NUM>-<NUM> may perform one or more portions of the process <NUM> and may be implemented in, for instance, a chip set including a processor and a memory as shown in <FIG>. As such, the mapping platform <NUM>, the computer vision system <NUM>, the machine learning system <NUM>, and/or the modules <NUM>-<NUM> can provide means for accomplishing various parts of the process <NUM>, as well as means for accomplishing embodiments of other processes described herein in conjunction with other components of the system <NUM>. Although the process <NUM> is illustrated and described as a sequence of steps, its contemplated that various embodiments of the process <NUM> may be performed in any order or combination and need not include all the illustrated steps.

In step <NUM>, the data collection module <NUM> selects at least one survey point that has a known physical location. In one embodiment, the known physical locations of the one or more survey points <NUM> may be determined by survey techniques, queried from digital map data (e.g., stored in or accessed via the geographic database <NUM>), and/or any other equivalent technique. In one instance, the known location is based on precise location data (e.g., in the form of <Latitude, Longitude, Elevation>). Examples of survey points with known physical locations, include, but are not limited to, ground control points which have identifiable physical features whose locations have been precisely surveyed. In one embodiment, a survey point <NUM> may refer to any feature that is identifiable by a vehicle sensor <NUM> (e.g., a camera sensor, LiDAR, Radar, etc.), a UE <NUM>, or a combination thereof such as physical feature on the ground and/or common road furniture (e.g., ground paint, signs, poles, traffic lights, etc.). In other words, it is contemplated that survey points <NUM> refer to a broader category of features than just ground control points.

In step <NUM>, the communication module <NUM> initiates a plurality of passes (e.g., by a vehicle <NUM>) to capture a plurality of images of the at least one survey point <NUM> using a sensor system <NUM>. By way of example, the communication module <NUM> may initiate the plurality of passes by transmitting one or more commands or prompts to one or more vehicles <NUM>, one or more drivers of the vehicles <NUM>, or a combination thereof via a UE <NUM>, an application <NUM>, or a combination thereof.

In one embodiment, the plurality of images including the at least one survey point <NUM> are captured by one or more vehicles <NUM> (e.g., autonomous vehicles) including one or more vehicle sensors <NUM> (e.g., GPS, IMU, camera, LiDAR, Radar, etc.) while the one or more vehicles <NUM> drive or travel in the geographic area including the survey points <NUM>. In one instance, the plurality of images may also be captured by one or more UE <NUM> (e.g., a mobile device) associated with a vehicle <NUM> and/or a driver or passenger of the vehicle <NUM>. In one embodiment, the communication module <NUM> transmits the one or more commands or prompts in such a way that the plurality of passes are performed under one or more different temporal and/or contextual conditions. For example, the different conditions may include passes by the one or more vehicles <NUM> at multiple and varying vehicle speeds, at different times of the day and/or night, during different weather conditions (e.g., sunny, rainy, foggy, snowing, etc.), or a combination thereof.

In step <NUM>, for each pass of the plurality of passes, the data processing module <NUM> calculates an estimated location of the at least one survey point <NUM> based on the plurality of images and calculates error data based on the estimated location and the known physical location. By way of example, each pass of a survey point <NUM> by a vehicle <NUM> (e.g., an autonomous vehicle) using one or more vehicle sensors <NUM> (e.g., camera, LiDAR, etc.) in a geographic area (e.g., areas 201a, 201b, 201c) will likely generate a number of captures (e.g., camera images) of a survey point <NUM>. In one embodiment, if the data processing module <NUM> knows the location of the vehicle sensor <NUM> that generated the capture, the data processing module <NUM> can use one or more mathematical principles, for example, to estimate the location of the captured survey point <NUM>. In one instance, the data processing module <NUM> can also calculate the amount of the error associated with the estimated location and the vehicle sensor <NUM>.

In one embodiment, wherein the sensor system <NUM> includes a camera system, the data processing module <NUM> can determine the estimated location of each survey point <NUM> based on a ray generated from a location of the camera system <NUM> used to capture each image through a pixel location on an image plane of the plurality of images. In one instance, the data processing module <NUM> can determine the location of the camera system <NUM> from image metadata or other data associated with each image (e.g., stored in the geographic database <NUM>). In one embodiment, the data processing module <NUM> determines the location of the camera system <NUM> within in a common coordinate system (e.g., a global or a real-world coordinate system indicating <Latitude, Longitude, Elevation>).

In one embodiment, the data processing module <NUM> can use the camera pose data and/or camera technical specifications (e.g., focal length, camera lens, aperture, exposure, etc.), for instance, to locate a physical location of an image plane for each capture (e.g., camera image) within the common coordinate system. The image plane refers to the apparent location in three-dimensional space of the image, thereby enabling the data processing module <NUM> to translate each pixel location (including feature-labeled or detected pixel locations) in an image of each survey point <NUM> (if visible) into the common coordinate system. In one instance, the data processing module <NUM> projects a ray (e.g., a line or line segment) from the physical location of the camera system <NUM> location through the image plane at the marked or labeled pixel location corresponding to the survey point to calculate the estimated location of the captured survey point <NUM>. By way of example, a labeled pixel is a pixel annotated or marked by a labeler (e.g., a human labeler) as corresponding to a feature of interest (e.g., a survey point <NUM>), and a detected pixel is a pixel determined by a computer system (e.g., the computer vision system <NUM> using machine learning) to be classified as corresponding to a feature of interest.

In one embodiment, the plurality of images can be labeled with one or more survey points <NUM> that are identifiable in the images. Labeling, for instance, refers to identifying pixels or groups of pixels in the images that correspond to the captured survey points <NUM>, typically but not necessarily by a human. In addition or alternatively, the pixels corresponding to a survey point <NUM> in an image can be detected by automated machine processes. For example, the data collection module <NUM> can detect any map feature that is visible in ground-level imagery (or imagery from any perspectives or views of interest). The data collection module <NUM> can use, for instance, the computer vision system <NUM> in combination with the machine learning system <NUM> (e.g., a neural network or equivalent) to recognize the pixels of images that correspond to the visible survey points <NUM>. For example, the known survey points <NUM> can include but are not limited to intersection-related features, which are generally visible in both top-down and ground-level images. While any kind of visible features can be used according to the embodiments described herein, intersection-related features (e.g., curvilinear geometry intersection features) are particularly suited for automated identification (e.g., via the computer vision system <NUM>) because they exhibit the following properties: (<NUM>) have a consistent definition; (<NUM>) are uniquely identifiable; (<NUM>) have spatial sparsity; and/or (<NUM>) are generalizable across different geographic areas (e.g., areas 201a-201c).

In one embodiment, the data processing module <NUM> calculates the distance between the projected ray and the corresponding survey point <NUM> wherein the distance represents the error between the estimated location and the known physical location of the survey point <NUM>. In one instance, the data processing module <NUM> can map the true location of a known survey point <NUM>, using the known physical location of the survey point in the common coordinate system (e.g., the real-world location given by <Latitude, Longitude, Elevation> or equivalent). The data processing module <NUM> can then compute the minimum perpendicular distance between the true location of the survey point <NUM> and the corresponding ray. In other words, the minimum perpendicular distance represents the calculated error or error data between the estimated location and the actual location for each pass.

In one embodiment, wherein the sensor system <NUM> includes a LiDAR system or a Radar system, the data processing module <NUM> can similarly determine the estimated location of each survey point <NUM>, except in these instances, the point position is within a point cloud generated by the LiDAR or Radar system <NUM> rather than on an image plane as described above.

In step <NUM>, the graphing module <NUM> generates an error curve with respect to a number of the plurality of passes based on the error data for said each pass. By way of example, each pass of a survey point <NUM> by a vehicle <NUM> (e.g., with a camera sensor <NUM>) may generate multiple images of the survey point <NUM> (e.g., image A, image B, and image C). The images A-C may be taken by one or more vehicles <NUM> at the same location under the same or similar conditions, at different locations under different conditions, or a combination thereof. As described above, the data processing module <NUM> can calculate an estimated location of the survey point (e.g., estimated survey point location A, estimated survey point location B, and estimated survey point location C) for each image of each pass (e.g., passes <NUM>-<NUM>) as well as the error associated with each estimate and each pass (e.g., errors 1A, 1B, 1C, 2A, 2B, 2C, and 3A, 3B, and 3C). In one embodiment, the graphing module <NUM> can plot the various error values on a curve wherein the x-axis represents, for instance, the number of passes by a survey point <NUM> and the y-axis represents, for instance, the degree of error between the estimated location and the actual location. In one instance, it is contemplated that under uniform conditions, the curve plotted by the graphing module <NUM> would represent a downward slope left to right such that the degree of error would decrease as a vehicle <NUM> made more passes of the survey point <NUM>. In one embodiment, the error data includes a mean, a standard deviation, of a combination thereof of an error between the estimated location and the known location for said each pass.

In step <NUM>, the UI module <NUM> provides an output indicating a target number of passes to meet an error specification based on the error curve. By way of example, the output may be a visual representation in an application <NUM> of the target passes for each sensor system <NUM>. In one embodiment, the target number of passes is selected based on a target error tolerance, a target error spread, or a combination thereof. By way of example, the target error tolerance or spread may be based on the autonomous application and/or context under consideration. For example, the digital maps used for autonomous driving in a busy urban center may require a much higher tolerance than the digital maps used for autonomous driving in the countryside. Likewise, the digital maps used for autonomous driving may require a much higher tolerance than the digital maps for autonomous guidance (e.g., farming and/or harvesting).

In one embodiment, the graphing module <NUM> can generate a histogram of the number of the plurality of passes that meets the error specification using all the survey points (e.g., in a geographic area). In other words, the graphing module <NUM> can visually represent the frequency of passes (e.g., along the x-axis) relative to the error specification (e.g., along the y-axis). In one embodiment, the UI module <NUM> can output the histogram (e.g., via an application <NUM>) so that a user can quickly assess the accuracy or quality of each sensor system <NUM> and then select the most appropriate sensor system <NUM> for the high-definition map feature accuracy and reliability specifications. In one instance, the graphing module <NUM> can also generate a histogram of the mean number of the plurality of passes across one or more of the at least one survey point <NUM>.

In one embodiment, the UI module <NUM> in connection with the data processing module <NUM> can provide an output indicating a number of the survey points for which the error specification is not met. In other words, the data processing module <NUM> can identify the accuracy and reliability percentages of the various sensor systems <NUM> as well as the kinds of situations where accuracy and reliability specifications are not met. For example, the crowd-sourced mapping data from a camera sensor system <NUM> in inclement weather conditions at high speed may not satisfy the accuracy and reliability specifications irrespective of the number of passes made by the camera sensor system <NUM>.

In one embodiment, the data processing module <NUM> interacts with the training module <NUM> and the machine learning system <NUM> to automatically compare or select the sensor system <NUM> in relation to another sensor system <NUM> based on the error curve. In one embodiment, the training module <NUM> can continuously provide and/or update a machine learning model (e.g., a support vector machine (SVM), a neural network, decision tree, etc.) of the machine learning system <NUM> during training using, for instance, supervised deep convolution networks or equivalents. In other words, the training module <NUM> trains a machine learning model using the various inputs to enable the machine learning system <NUM> to automatically compare the quality of sensor systems <NUM> based on accuracy and reliability specifications. Generally, a machine learning model (e.g., a neural network) is trained to manipulate an input feature set to make a prediction about the feature set or the phenomenon/observation that the feature set represents. In one embodiment, the training features for the machine learning model include the applicable accuracy and reliability standards, number of passes made in relation to one or more survey points <NUM>, conditions during which the passes were made, sensor systems <NUM> used in the capturing of the survey points <NUM>, survey points <NUM> for which the error specification was not met, and the corresponding error curves. In one embodiment, the machine learning system <NUM> can consequently predict the accuracy and reliability percentages for the various sensor systems <NUM> and the kinds of situations where accuracy and reliability specifications will not be met.

<FIG> is a diagram illustrating an example of rays projected to estimate the location of a captured survey point and to determine the error associated with the capture observation, according to one embodiment. As shown in the example of <FIG>, images <NUM> and <NUM> are processed to estimate the location of the captured survey point <NUM> having a known physical location (e.g., in the form of <Latitude, Longitude, Elevation>).

In one embodiment, the images <NUM> and <NUM> are derived from passes of the survey point <NUM> by one or more vehicles <NUM> (e.g., an autonomous vehicle) with one or more vehicle sensors <NUM> (e.g., camera, LiDAR, Radar, etc.). In this example, the images <NUM> and <NUM> are captured using a camera sensor <NUM> and represent all the captures of the survey point <NUM> within a certain radius. In one instance, the pixel positions <NUM> and <NUM> corresponding to the survey point <NUM> are marked (e.g., by a human) with respect to the images <NUM> and <NUM>, respectively. In one instance, the mapping platform <NUM> uses the pose data and/or camera parameters of the camera system <NUM> to determine the physical location of the image planes <NUM> and <NUM> (e.g., corresponding to images <NUM> and <NUM>, respectively), which represent the location and orientation of the images <NUM> and <NUM> with respect to the coordinate system <NUM>.

In one embodiment, for each labeled or detected pixel location <NUM> and <NUM> of the images <NUM> and <NUM>, the mapping platform <NUM> generates rays <NUM> and <NUM> originating from the center of the camera sensor <NUM> through each of the labeled or detected pixel locations <NUM> and <NUM>. To determine the accuracy of the estimated survey point locations, the mapping platform <NUM> can iteratively evaluate the closeness between rays <NUM> and <NUM> and the known location of the survey point <NUM>. In one instance, the mapping platform <NUM> determines the closeness value by computing line segments <NUM> and <NUM> between the known location of the survey point <NUM> and the rays <NUM> and <NUM>, respectively. As shown, the line segments <NUM> and <NUM> are drawn orthogonal to the known location of the survey point <NUM>. In one embodiment, this orthogonality helps ensure that the line segments <NUM> and <NUM> are the shortest or minimum distance between the rays <NUM> and <NUM> and the known location of the survey point <NUM>.

In one embodiment, the minimum perpendicular distances <NUM> and <NUM> between the known location of the survey point <NUM> and the rays <NUM> and <NUM> represent the error associated with the capture observation. In one instance, the minimum perpendicular distances can be aggregated using different measures of central tendency (e.g., mean, median, mode, and so forth). In another embodiment, a weighting scheme based on an inverse distance of the survey point <NUM> to the center of each camera sensor <NUM> could also be used since the confidence in observing a physical point by a camera <NUM> changes inversely as a function of the distance from the capture.

As described above, the mapping platform <NUM> can provide an aggregation of error data indicating the quality of the data of the sensor system <NUM> relative to the number of passes. In one embodiment, the mapping platform <NUM> can calculate the deviation of the aggregate error data to provide an output (e.g., a histogram) associated with the quality of the sensor system <NUM>. In one embodiment, the mapping platform <NUM> can count as void if no number of passes can satisfy an error threshold. In one instance, the mapping platform <NUM> can access the error threshold stored in or accessible via the geographic database <NUM>.

<FIG> are diagrams of example use interfaces for automatically selecting the most appropriate sensor system based on high-definition map feature accuracy and reliability specifications, according to one embodiment. In this example, a UI <NUM> is generated for a UE <NUM> (e.g., an embedded navigation system) associated with an autonomous vehicle <NUM> that can enable a user (e.g., a passenger) to access one or more applications <NUM> (e.g., a navigation application, an analytics application, etc.) while traveling through a large metropolitan city (e.g., San Francisco).

Referring to <FIG>, in one embodiment, the system <NUM> can notify or alert the user via the UI <NUM> that a severe storm warning has been issued that will likely affect traffic, path planning, estimated time of arrival, etc. By way of example, the system <NUM> may receive the notification from a services platform <NUM> (e.g., an OEM platform) including one or more services 125a-125n (also collectively referred as services <NUM>) (e.g., a weather service), a content provider <NUM>, or a combination thereof. As previously described above, safe and effective path planning, particularly for autonomous vehicles, requires knowledge of what to expect beyond a vehicle's perceptual horizon and driving in complicated urban environments with many occluding requires a knowledge of what cannot be seen.

In one instance, the system <NUM> can generate the UI <NUM> such that it includes an input <NUM> to enable a user to update the route guidance or path planning based on the most accurate and/or up-to-date mapping data (e.g., crowd-sourced data based on current weather conditions). However, as described above, it is important for the user to know the accuracy and reliability of such data before the data is utilized (e.g., in a navigation application <NUM>) to make changes to one or more map features. Specifically, the positional quality of the map features derived from sensor data is heavily dependent on the sensor system <NUM> (e.g., GPS, IMU, camera, LiDAR, Radar, etc.) used for the data collection and the reliability on the number of such observations/passes. By way of example, the user can interact with the input <NUM> via one or more physical interactions (e.g., a touch, a tap, a gesture, typing, etc.), one or more voice commands (e.g., "yes," "update route guidance," etc.), or a combination thereof.

In one embodiment, the system <NUM> can generate the UI <NUM> such that it includes a series of inputs (e.g., inputs <NUM> and <NUM>) that enable a user input various contextual characteristics or values so that the system <NUM> can filter among the various types of available sensor system data (e.g., via the communication network <NUM>) to reduce the computational resources and time needed for comparing vast amounts of data, as depicted in <FIG>. In this example, the input <NUM> enables the user to select among various relevant autonomous activities (e.g., driving, guidance, transport, search and rescue, etc.) and the input <NUM> enables the user to select among various diverse geographic areas (e.g., tree canopy, open sky, urban canyon, natural canyon, etc.). In this example, the user has selected "driving" and "urban canyon. " By way of example, in one embodiment, the inputs <NUM> and <NUM> have the same or similar functionality as the inputs <NUM> in terms of a user's ability to input information. In one instance, it is contemplated, that the system <NUM> can automatically detect the applicable autonomous application and/or geographic area when the safety of the autonomous vehicle <NUM>, passengers, and/or other persons or vehicles in the area reaches a certain threshold.

Referring to <FIG>, in one embodiment, the system <NUM> can generate the UI <NUM> such that it includes one or more outputs (e.g., charts <NUM> and <NUM>) indicating a target number of passes to satisfy an error specification (e.g., accuracy and reliability standards) based on the error curve of that sensor system <NUM> as described in the embodiments above. In this example, the chart <NUM> represents the target number of passes for a camera sensor system <NUM> as shown by the error curve <NUM> and the chart <NUM> represents the target number of passes for a LiDAR sensor system <NUM> as shown by the error curve <NUM>. Specifically, the y-axis of each chart <NUM> and <NUM> represents the amount of error associated with the capture observation and the x-axis represents the number of passes. With respect to autonomous driving as indicated by the user in <FIG>, the system <NUM> can determine that the number of passes corresponding to an error value equal to or less than one (<NUM>) (as shown by the dotted line <NUM>) is required to meet the accuracy and reliability standards or specifications. In this example, four (<NUM>) passes are required with respect to the camera sensor system <NUM> and two (<NUM>) passes are required with respect the LiDAR sensor system <NUM>.

In one embodiment, the system <NUM> can also generate the outputs <NUM> and <NUM> so that they include visual representations of the number of passes corresponding to the available crowd-sourced data so that the system <NUM> and/or the user can quickly compare and/or select the most appropriate sensor system <NUM> based on the calculated statistics. For example, in this instance, the available crowd-sourced camera sensor data includes four pass data (as depicted by the columns <NUM>) whereas the available crowd-sourced LiDAR sensor data only includes one pass data (as depicted by the column <NUM>). Thus, while LiDAR sensor data is generally considered more accurate relative to camera sensor data, in this instance, the crowd-sourced camera sensor data meets the accuracy and reliability requirements whereas the crowd-sourced LiDAR data does not (i.e., the camera sensor system is the most appropriate sensor system <NUM> in this context). In one embodiment, the system <NUM> can generate the UI <NUM> such that it presents the automatically selected sensor system <NUM> as a recommendation, or a prompt based on the system <NUM>'s comparison of the quality of sensor systems <NUM>. In one instance, the system <NUM> can also generate the UI <NUM> such that it includes an input <NUM> to enable the user to accept or reject the recommendation of the system <NUM>. By way of example, in the case of a severe storm as described above where the conditions may negatively impact the accuracy of the captured observations, the user may want to reject the recommendation of the system <NUM> and to accept the LiDAR sensor data or to determine whether other any other sensor data is available (e.g., Radar, etc.). Again, it is contemplated that in certain circumstances, where the safety of the autonomous vehicle <NUM>, the passenger, or persons or vehicles nearby reaches a certain threshold, the system <NUM> may automatically select the most appropriate sensor system and corresponding data to update the guidance and/or path planning to ensure the safety of the autonomous vehicle <NUM>, the passenger, and/or any nearby persons or vehicles.

Returning to <FIG>, in one embodiment, the mapping platform <NUM> performs the process for automatically selecting the most appropriate sensor system based on high-definition map feature accuracy and reliability specifications as discussed with respect to the various embodiments described herein. For example, with respect to autonomous driving, transportation, guidance, search and rescue, and/or other similar applications, the mapping platform <NUM> can compare the quality of capture pose data based on the number of passes required to satisfy set error tolerance and spread requirements.

In one embodiment, the machine learning system <NUM> of the mapping platform <NUM> includes a neural network or other machine learning system to compare or select sensor systems <NUM> (e.g., camera, LiDAR, Radar, etc.) for high-definition map feature accuracy and reliability specifications and/or identify kinds of situations where such specifications are not met. For example, when the inputs to the machine learning model are histograms of the passes needed to satisfy the accuracy and reliability standards, the output can include one or more recommended sensor systems <NUM> and corresponding map data for changing and/or updating high definition digital maps. In one embodiment, the neural network of the machine learning system <NUM> is a traditional convolutional neural network which consists of multiple layers of collections of one or more neurons (which are configured to process a portion of an input data).

In one instance, the machine learning system <NUM> and/or the computer vision system <NUM> also have connectivity or access over the communication network <NUM> to the geographic database <NUM> which can store the accuracy and reliability standards, error curves, mean/standard of deviation with the numbers of passes, etc. for each sensor system <NUM>.

In one embodiment, the mapping platform <NUM> has connectivity over the communication network <NUM> to the services platform <NUM> (e.g., an OEM platform) that provides the services <NUM>. By way of example, the services <NUM> may be third party services and include mapping services, navigation services, travel planning services, notification services, social networking services, content (e.g., audio, video, images, etc.) provisioning services, application services, storage services, contextual information determination services, location-based services, information based services (e.g., weather, news, etc.), etc. In one embodiment, the services <NUM> use the output of the mapping platform <NUM> (e.g., recommended sensor system <NUM>) to provide up-to-date services <NUM> such as navigation, mapping, other location-based services, etc..

In one embodiment, the mapping platform <NUM> may be a platform with multiple interconnected components. The mapping platform <NUM> may include multiple servers, intelligent networking devices, computing devices, components and corresponding software. In addition, it is noted that the mapping platform <NUM> may be a separate entity of the system <NUM>, a part of the one or more services <NUM>, a part of the services platform <NUM>, or included within a UE <NUM> and/or a vehicle <NUM>.

In one embodiment, the content providers <NUM> may provide content or data (e.g., geographic data, parametric representations of mapped features, global or a real-world coordinate system data indicating <Latitude, Longitude, Elevation> for one or more survey points, <NUM> etc.) to the vehicles <NUM>, the mapping platform <NUM>, geographic database <NUM>, the UEs <NUM>, the applications <NUM>, the services platform <NUM>, and/or the services <NUM>. The content provided may be any type of content, such as map content, survey data, textual content, audio content, video content, image content, etc. In one embodiment, the content providers <NUM> may provide content that may aid in the detecting and locating of survey points <NUM>, road furniture (e.g., ground paint, signs, poles, traffic lights, etc.), and/or other relevant features. In one embodiment, the content providers <NUM> may also store content associated with the vehicles <NUM>, the mapping platform <NUM>, the geographic database <NUM>, the UEs <NUM>, the computer vision system <NUM>, the machine learning system <NUM>, the services platform <NUM>, and/or the services <NUM>. In another embodiment, the content providers <NUM> may manage access to a central repository of data, and offer a consistent, standard interface to data, such as a repository of the geographic database <NUM>.

In one embodiment, a UE <NUM> (e.g., a mobile device) and/or a vehicle <NUM> (e.g., an autonomous vehicle) may execute a software application <NUM> to capture image data or other observation data of one or more survey points <NUM> for automatically selecting the most appropriate sensor system <NUM> according to the embodiments described herein. By way of example, the applications <NUM> may also be any type of application that is executable on a UE <NUM> and/or a vehicle <NUM>, such as autonomous driving applications, mapping applications, analytical applications (e.g., visually graphing and/or comparing), location-based service applications, navigation applications, content provisioning services, camera/imaging applications, media player applications, social networking applications, calendar applications, and the like. In one embodiment, the applications <NUM> may act as a client for the mapping platform <NUM> and perform one or more functions associated with automatically selecting a sensor system <NUM> alone or in combination with the machine learning system <NUM>.

By way of example, the UEs <NUM> are any type of embedded system, mobile terminal, fixed terminal, or portable terminal including a built-in navigation system, a personal navigation device, mobile handset, station, unit, device, multimedia computer, multimedia tablet, Internet node, communicator, desktop computer, laptop computer, notebook computer, netbook computer, tablet computer, personal communication system (PCS) device, personal digital assistants (PDAs), audio/video player, digital camera/camcorder, positioning device, fitness device, television receiver, radio broadcast receiver, electronic book device, game device, or any combination thereof, including the accessories and peripherals of these devices, or any combination thereof. It is also contemplated that a UE <NUM> can support any type of interface to the user (such as "wearable" circuitry, etc.). In one embodiment, a UE <NUM> may be associated with a vehicle <NUM> (e.g., a mobile device) or be a component part of the vehicle <NUM> (e.g., an embedded navigation system).

In one embodiment, the vehicles <NUM> are configured with various sensors <NUM> for generating or collecting images or representations of one or more survey points <NUM> (e.g., for processing by the mapping platform <NUM>), related geographic data, etc. Although the vehicles <NUM> are depicted as automobiles, it is contemplated that the vehicles <NUM> may be any type of vehicle capable of including one or more sensors <NUM> (e.g., a car, a truck, a motorcycle, a bike, a scooter, etc.). In one embodiment, the sensed data represents sensor data associated with a geographic location or coordinates at which the sensor data was collected. By way of example, the vehicle sensors <NUM> may include GPS for gathering location data, IMU data (e.g., for understanding the motion of a vehicle <NUM> during a capture), LIDAR, Radar, a network detection sensor for detecting wireless signals or receivers for different short-range communications (e.g., Bluetooth, Wi-Fi, Li-Fi, near field communication (NFC) etc.), temporal information sensors, a camera/imaging sensor for gathering image data (e.g., the camera sensors may automatically capture survey points or ground control point imagery, etc. for analysis), an audio recorder for gathering audio data, velocity sensors mounted on steering wheels of the vehicles, switch sensors for determining whether one or more vehicle switches are engaged, and the like. In one embodiment, the GPS sensors <NUM> may be used to determine GPS/position cumulative drift of a vehicle <NUM>.

Other examples of sensors of the UEs <NUM> and/or the vehicles <NUM> (e.g., sensors <NUM>) may include light sensors, orientation sensors augmented with height sensors and acceleration sensor (e.g., an accelerometer can measure acceleration and can be used to determine orientation of the vehicle), tilt sensors to detect the degree of incline or decline of a vehicle <NUM> along a path of travel, moisture sensors, pressure sensors, etc. In a further example embodiment, sensors about the perimeter of a vehicle <NUM> may detect the relative distance of a vehicle <NUM> from a lane or roadway, the presence of other vehicles, pedestrians, traffic lights, potholes and any other objects, or a combination thereof. In one scenario, the sensors may detect weather data, traffic information, or a combination thereof. In one embodiment, a UE <NUM> and/or a vehicle <NUM> may include GPS or other satellite-based receivers to obtain geographic coordinates from one or more satellites <NUM> for determining current location and time. Further, the location of a vehicle <NUM>, a vehicle sensor <NUM>, and/or a UE <NUM> can be determined by visual odometry, triangulation systems such as A-GPS, Cell of Origin, or other location extrapolation technologies. In yet another embodiment, one or more sensors can determine the status of various control elements of a vehicle <NUM>, such as activation of wipers, use of a brake pedal, use of an acceleration pedal, angle of the steering wheel, activation of hazard lights, activation of head lights, etc..

In one embodiment, the communication network <NUM> of the system <NUM> includes one or more networks such as a data network, a wireless network, a telephony network, or any combination thereof. It is contemplated that the data network may be any local area network (LAN), metropolitan area network (MAN), wide area network (WAN), a public data network (e.g., the Internet), short range wireless network, or any other suitable packet-switched network, such as a commercially owned, proprietary packet-switched network, e.g., a proprietary cable or fiber-optic network, and the like, or any combination thereof. In addition, the wireless network may be, for example, a cellular network and may employ various technologies including enhanced data rates for global evolution (EDGE), general packet radio service (GPRS), global system for mobile communications (GSM), Internet protocol multimedia subsystem (IMS), universal mobile telecommunications system (UMTS), etc., as well as any other suitable wireless medium, e.g., worldwide interoperability for microwave access (WiMAX), Long Term Evolution (LTE) networks, code division multiple access (CDMA), wideband code division multiple access (WCDMA), wireless fidelity (Wi-Fi), wireless LAN (WLAN), Bluetooth®, Internet Protocol (IP) data casting, satellite, mobile ad-hoc network (MANET), and the like, or any combination thereof.

By way of example, the vehicles <NUM>, vehicle sensors <NUM>, mapping platform <NUM>, content providers <NUM>, UEs <NUM>, applications <NUM>, computer vision system <NUM>, machine learning system <NUM>, services <NUM>, services platform <NUM>, and/or satellites <NUM> communicate with each other and other components of the system <NUM> using well known, new or still developing protocols. In this context, a protocol includes a set of rules defining how the network nodes within the communication network <NUM> interact with each other based on information sent over the communication links. The protocols are effective at different layers of operation within each node, from generating and receiving physical signals of various types, to selecting a link for transferring those signals, to the format of information indicated by those signals, to identifying which software application executing on a computer system sends or receives the information. The conceptually different layers of protocols for exchanging information over a network are described in the Open Systems Interconnection (OSI) Reference Model.

<FIG> is a diagram of a geographic database, according to one embodiment. In one embodiment, the geographic database <NUM> includes geographic data <NUM> used for (or configured to be compiled to be used for) mapping and/or navigation-related services, such as for video odometry based on the captured features (e.g., survey points <NUM>). In one embodiment, the geographic database <NUM> includes high resolution or high definition (HD) mapping data that provide centimeter-level or better accuracy of map features. For example, the geographic database <NUM> can be based on LiDAR or equivalent technology to collect billions of 3D points and model road surfaces and other map features down to the number lanes and their widths. In one embodiment, the HD mapping data (e.g., HD data records <NUM>) capture and store details such as the slope and curvature of the road, lane markings, roadside objects such as signposts, including what the signage denotes. By way of example, the HD mapping data enable highly automated vehicles (e.g., vehicles <NUM>) to precisely localize themselves on the road.

In one embodiment, geographic features (e.g., two-dimensional or three-dimensional features) are represented using polygons (e.g., two-dimensional features) or polygon extrusions (e.g., three-dimensional features). For example, the edges of the polygons correspond to the boundaries or edges of the respective geographic feature. In the case of a building, a two-dimensional polygon can be used to represent a footprint of the building, and a three-dimensional polygon extrusion can be used to represent the three-dimensional surfaces of the building. It is contemplated that although various embodiments are discussed with respect to two-dimensional polygons, it is contemplated that the embodiments are also applicable to three-dimensional polygon extrusions. Accordingly, the terms polygons and polygon extrusions as used herein can be used interchangeably.

In one embodiment, the following terminology applies to the representation of geographic features in the geographic database <NUM>.

"Node" - A point that terminates a link.

"Line segment" - A straight line connecting two points.

"Link" (or "edge") - A contiguous, non-branching string of one or more line segments terminating in a node at each end.

"Shape point" - A point along a link between two nodes (e.g., used to alter a shape of the link without defining new nodes).

"Oriented link" - A link that has a starting node (referred to as the "reference node") and an ending node (referred to as the "non reference node").

"Simple polygon" - An interior area of an outer boundary formed by a string of oriented links that begins and ends in one node. In one embodiment, a simple polygon does not cross itself.

"Polygon" - An area bounded by an outer boundary and none or at least one interior boundary (e.g., a hole or island). In one embodiment, a polygon is constructed from one outer simple polygon and none or at least one inner simple polygon. A polygon is simple if it just consists of one simple polygon, or complex if it has at least one inner simple polygon.

In one embodiment, the geographic database <NUM> follows certain conventions. For example, links do not cross themselves and do not cross each other except at a node. Also, there are no duplicated shape points, nodes, or links. Two links that connect each other have a common node. In the geographic database <NUM>, overlapping geographic features are represented by overlapping polygons. When polygons overlap, the boundary of one polygon crosses the boundary of the other polygon. In the geographic database <NUM>, the location at which the boundary of one polygon intersects they boundary of another polygon is represented by a node. In one embodiment, a node may be used to represent other locations along the boundary of a polygon than a location at which the boundary of the polygon intersects the boundary of another polygon. In one embodiment, a shape point is not used to represent a point at which the boundary of a polygon intersects the boundary of another polygon.

As shown, the geographic database <NUM> includes node data records <NUM>, road segment or link data records <NUM>, POI data records <NUM>, quality of capture pose data records <NUM>, HD mapping data records <NUM>, and indexes <NUM>, for example. More, fewer or different data records can be provided. In one embodiment, additional data records (not shown) can include cartographic ("carto") data records, routing data, and maneuver data. In one embodiment, the indexes <NUM> may improve the speed of data retrieval operations in the geographic database <NUM>. In one embodiment, the indexes <NUM> may be used to quickly locate data without having to search every row in the geographic database <NUM> every time it is accessed. For example, in one embodiment, the indexes <NUM> can be a spatial index of the polygon points associated with stored feature polygons.

In exemplary embodiments, the road segment data records <NUM> are links or segments representing roads, streets, or paths, as can be used in the calculated route or recorded route information for determination of one or more personalized routes. The node data records <NUM> are end points corresponding to the respective links or segments of the road segment data records <NUM>. The road link data records <NUM> and the node data records <NUM> represent a road network, such as used by vehicles (e.g., vehicles <NUM>), cars, and/or other entities. Alternatively, the geographic database <NUM> can contain path segment and node data records or other data that 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 functional class, a road elevation, a speed category, a presence or absence of road features, geographic coordinates, street names, address ranges, speed limits, turn restrictions at intersections, and other navigation related attributes, as well as POIs, such as gasoline stations, hotels, restaurants, museums, stadiums, offices, automobile dealerships, auto repair shops, buildings, stores, parks, etc. The geographic database <NUM> can include data about the POIs and their respective locations in the POI data records <NUM>. The geographic database <NUM> can also 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 records <NUM> or can be associated with POIs or POI data records <NUM> (such as a data point used for displaying or representing a position of a city).

In one embodiment, the geographic database <NUM> can also include quality of capture pose data records <NUM> for storing the known quality/accuracy/location of survey points <NUM>, accuracy and reliability standards or specifications (e.g., for various autonomous applications), pixel positions of the survey points <NUM> in all previously obtained captures within a certain radius, error associated with the capture observations, error curves, mean/standard of deviation with the number of passes, histograms, percentages, and/or types of situations where accuracy and reliability specifications are not met for automatically selecting a sensor system <NUM> covering an area of interest. By way of example, the quality of capture pose data records <NUM> can be associated with one or more of the node records <NUM>, road segment records <NUM>, and/or POI data records <NUM> to support localization or visual odometry based on the features (e.g., survey points and/or ground control points) stored therein and the corresponding estimated quality of the features. In this way, the quality of capture pose data records <NUM> can also be associated with or used to classify the characteristics or metadata of the corresponding records <NUM>, <NUM>, and/or <NUM>.

In one embodiment, as discussed above, the HD mapping data records <NUM> model road surfaces and other map features to centimeter-level or better accuracy. The HD mapping data records <NUM> also include lane models that provide the precise lane geometry with lane boundaries, as well as rich attributes of the lane models. These rich attributes include, but are not limited to, lane traversal information, lane types, lane marking types, lane level speed limit information, and/or the like. In one embodiment, the HD mapping data records <NUM> are divided into spatial partitions of varying sizes to provide HD mapping data to vehicles <NUM> (e.g., autonomous vehicles) and other end user devices (e.g., a UE <NUM>) with near real-time speed without overloading the available resources of the vehicles <NUM> and/or the UEs <NUM> (e.g., computational, memory, bandwidth, etc. resources).

In one embodiment, the HD mapping data records <NUM> are created from high-resolution 3D mesh or point-cloud data generated, for instance, from LiDAR-equipped vehicles (e.g., one or more vehicles <NUM>). The 3D mesh or point-cloud data are processed to create 3D representations of a street or geographic environment at centimeter-level accuracy for storage in the HD mapping data records <NUM>.

In one embodiment, the HD mapping data records <NUM> also include real-time sensor data collected from probe vehicles in the field (e.g., one or more vehicles <NUM>). The real-time sensor data, for instance, integrates real-time traffic information, weather, and road conditions (e.g., potholes, road friction, road wear, etc.) with highly detailed 3D representations of street and geographic features to provide precise real-time also at centimeter-level accuracy. Other sensor data can include vehicle telemetry or operational data such as windshield wiper activation state, braking state, steering angle, accelerator position, and/or the like.

In one embodiment, the geographic database <NUM> can be maintained by a content provider <NUM> in association with the services platform <NUM> (e.g., a map developer). The map developer can collect geographic data to generate and enhance the geographic database <NUM>. There can be different ways used by the map developer to collect data. These ways can include obtaining data from other sources, such as municipalities or respective geographic authorities. In addition, the map developer can employ field personnel to travel by vehicle (e.g., a vehicle <NUM> and/or a UE <NUM>) along roads throughout a geographic area of interest to observe features and/or record information about them, for example. Also, remote sensing, such as aerial or satellite photography (e.g., from the satellites <NUM>), can be used.

For example, geographic data is 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 a vehicle <NUM> or a UE <NUM>, for example. The navigation-related functions can correspond to vehicle navigation, pedestrian navigation, or other types of navigation. The compilation to produce the end user databases can be performed by a party or entity separate from the map developer. For example, a customer of the map developer, such as a navigation device developer or other end user device developer, can perform compilation on a received geographic database in a delivery format to produce one or more compiled navigation databases.

The processes described herein for automatically selecting the most appropriate sensor system for high-definition map feature accuracy and reliability specifications may be advantageously implemented via software, hardware (e.g., general processor, Digital Signal Processing (DSP) chip, an Application Specific Integrated Circuit (ASIC), Field Programmable Gate Arrays (FPGAs), etc.), firmware or a combination thereof. Such exemplary hardware for performing the described functions is detailed below.

<FIG> illustrates a computer system <NUM> upon which an embodiment of the invention may be implemented. Computer system <NUM> is programmed (e.g., via computer program code or instructions) to automatically select the most appropriate sensor system for high-definition map feature accuracy and reliability specifications as described herein and includes a communication mechanism such as a bus <NUM> for passing information between other internal and external components of the computer system <NUM>. Information (also called data) is represented as a physical expression of a measurable phenomenon, typically electric voltages, but including, in other embodiments, such phenomena as magnetic, electromagnetic, pressure, chemical, biological, molecular, atomic, sub-atomic and quantum interactions. For example, north and south magnetic fields, or a zero and non-zero electric voltage, represent two states (<NUM>, <NUM>) of a binary digit (bit). Other phenomena can represent digits of a higher base. A superposition of multiple simultaneous quantum states before measurement represents a quantum bit (qubit). A sequence of one or more digits constitutes digital data that is used to represent a number or code for a character. In some embodiments, information called analog data is represented by a near continuum of measurable values within a particular range.

A processor <NUM> performs a set of operations on information as specified by computer program code related to automatically selecting the most appropriate sensor system for high-definition feature accuracy and reliability specifications. The computer program code is a set of instructions or statements providing instructions for the operation of the processor and/or the computer system to perform specified functions. The code, for example, may be written in a computer programming language that is compiled into a native instruction set of the processor. The code may also be written directly using the native instruction set (e.g., machine language). The set of operations include bringing information in from the bus <NUM> and placing information on the bus <NUM>. The set of operations also typically include comparing two or more units of information, shifting positions of units of information, and combining two or more units of information, such as by addition or multiplication or logical operations like OR, exclusive OR (XOR), and AND. Each operation of the set of operations that can be performed by the processor is represented to the processor by information called instructions, such as an operation code of one or more digits. A sequence of operations to be executed by the processor <NUM>, such as a sequence of operation codes, constitute processor instructions, also called computer system instructions or, simply, computer instructions. Processors may be implemented as mechanical, electrical, magnetic, optical, chemical or quantum components, among others, alone or in combination.

Computer system <NUM> also includes a memory <NUM> coupled to bus <NUM>. The memory <NUM>, such as a random access memory (RAM) or other dynamic storage device, stores information including processor instructions for automatically selecting the most appropriate sensor system for high-definition feature accuracy and reliability specifications. Dynamic memory allows information stored therein to be changed by the computer system <NUM>. RAM allows a unit of information stored at a location called a memory address to be stored and retrieved independently of information at neighboring addresses. The memory <NUM> is also used by the processor <NUM> to store temporary values during execution of processor instructions. The computer system <NUM> also includes a read only memory (ROM) <NUM> or other static storage device coupled to the bus <NUM> for storing static information, including instructions, that is not changed by the computer system <NUM>. Some memory is composed of volatile storage that loses the information stored thereon when power is lost. Also coupled to bus <NUM> is a non-volatile (persistent) storage device <NUM>, such as a magnetic disk, optical disk or flash card, for storing information, including instructions, that persists even when the computer system <NUM> is turned off or otherwise loses power.

Information, including instructions for automatically selecting the most appropriate sensor system for high-definition feature accuracy and reliability specifications, is provided to the bus <NUM> for use by the processor from an external input device <NUM>, such as a keyboard containing alphanumeric keys operated by a human user, or a sensor. A sensor detects conditions in its vicinity and transforms those detections into physical expression compatible with the measurable phenomenon used to represent information in computer system <NUM>. Other external devices coupled to bus <NUM>, used primarily for interacting with humans, include a display device <NUM>, such as a cathode ray tube (CRT) or a liquid crystal display (LCD), or plasma screen or printer for presenting text or images, and a pointing device <NUM>, such as a mouse or a trackball or cursor direction keys, or motion sensor, for controlling a position of a small cursor image presented on the display <NUM> and issuing commands associated with graphical elements presented on the display <NUM>. In some embodiments, for example, in embodiments in which the computer system <NUM> performs all functions automatically without human input, one or more of external input device <NUM>, display device <NUM> and pointing device <NUM> is omitted.

Computer system <NUM> also includes one or more instances of a communications interface <NUM> coupled to bus <NUM>. Communication interface <NUM> provides a one-way or two-way communication coupling to a variety of external devices that operate with their own processors, such as printers, scanners and external disks. In general, the coupling is with a network link <NUM> that is connected to a local network <NUM> to which a variety of external devices with their own processors are connected. For example, communication interface <NUM> may be a parallel port or a serial port or a universal serial bus (USB) port on a personal computer. In some embodiments, communications interface <NUM> is an integrated services digital network (ISDN) card or a digital subscriber line (DSL) card or a telephone modem that provides an information communication connection to a corresponding type of telephone line. In some embodiments, a communication interface <NUM> is a cable modem that converts signals on bus <NUM> into signals for a communication connection over a coaxial cable or into optical signals for a communication connection over a fiber optic cable. As another example, communications interface <NUM> may be a local area network (LAN) card to provide a data communication connection to a compatible LAN, such as Ethernet. For wireless links, the communications interface <NUM> sends or receives or both sends and receives electrical, acoustic or electromagnetic signals, including infrared and optical signals, that carry information streams, such as digital data. For example, in wireless handheld devices, such as mobile telephones like cell phones, the communications interface <NUM> includes a radio band electromagnetic transmitter and receiver called a radio transceiver. In certain embodiments, the communications interface <NUM> enables connection to the communication network <NUM> for automatically selecting the most appropriate sensor system for high-definition feature accuracy and reliability specifications.

<FIG> illustrates a chip set <NUM> upon which an embodiment of the invention may be implemented. Chip set <NUM> is programmed to automatically select the most appropriate sensor system for high-definition map feature accuracy and reliability specifications as described herein and includes, for instance, the processor and memory components described with respect to <FIG> incorporated in one or more physical packages (e.g., chips). By way of example, a physical package includes an arrangement of one or more materials, components, and/or wires on a structural assembly (e.g., a baseboard) to provide one or more characteristics such as physical strength, conservation of size, and/or limitation of electrical interaction. It is contemplated that in certain embodiments the chip set can be implemented in a single chip.

The processor <NUM> and accompanying components have connectivity to the memory <NUM> via the bus <NUM>. The memory <NUM> includes both dynamic memory (e.g., RAM, magnetic disk, writable optical disk, etc.) and static memory (e.g., ROM, CD-ROM, etc.) for storing executable instructions that when executed perform the inventive steps described herein to automatically select the most appropriate sensor system for high-definition map feature accuracy and reliability specifications. The memory <NUM> also stores the data associated with or generated by the execution of the inventive steps.

<FIG> is a diagram of exemplary components of a mobile terminal (e.g., a vehicle <NUM>, a UE <NUM>, or component thereof) capable of operating in the system of <FIG>, according to one embodiment. Generally, a radio receiver is often defined in terms of front-end and back-end characteristics. The front-end of the receiver encompasses all of the Radio Frequency (RF) circuitry whereas the backend encompasses all of the base-band processing circuitry. Pertinent internal components of the telephone include a Main Control Unit (MCU) <NUM>, a Digital Signal Processor (DSP) <NUM>, and a receiver/transmitter unit including a microphone gain control unit and a speaker gain control unit. A main display unit <NUM> provides a display to the user in support of various applications and mobile station functions that offer automatic contact matching. An audio function circuitry <NUM> includes a microphone <NUM> and microphone amplifier that amplifies the speech signal output from the microphone <NUM>. The amplified speech signal output from the microphone <NUM> is fed to a coder/decoder (CODEC) <NUM>.

The signals may be forwarded from there to a remote telephone which may be another cellular telephone, other mobile phone or a landline connected to a Public Switched Telephone Network (PSTN), or other telephony networks.

The MCU <NUM> receives various signals including input signals from the keyboard <NUM>. The keyboard <NUM> and/or the MCU <NUM> in combination with other user input components (e.g., the microphone <NUM>) comprise a user interface circuitry for managing user input. The MCU <NUM> runs a user interface software to facilitate user control of at least some functions of the mobile station <NUM> to automatically select the most appropriate sensor system for high-definition map feature accuracy and reliability specifications. The MCU <NUM> also delivers a display command and a switch command to the display <NUM> and to the speech output switching controller, respectively. Further, the MCU <NUM> exchanges information with the DSP <NUM> and can access an optionally incorporated SIM card <NUM> and a memory <NUM>. In addition, the MCU <NUM> executes various control functions required of the station. The DSP <NUM> may, depending upon the implementation, perform any of a variety of conventional digital processing functions on the voice signals. Additionally, DSP <NUM> determines the background noise level of the local environment from the signals detected by microphone <NUM> and sets the gain of microphone <NUM> to a level selected to compensate for the natural tendency of the user of the mobile station <NUM>.

An optionally incorporated SIM card <NUM> carries, for instance, important information, such as the cellular phone number, the carrier supplying service, subscription details, and security information. The SIM card <NUM> serves primarily to identify the mobile station <NUM> on a radio network. The card <NUM> also contains a memory for storing a personal telephone number registry, text messages, and user specific mobile station settings.

Claim 1:
A method (<NUM>) implemented by a mapping platform (<NUM>) comprising:
selecting (<NUM>) at least one survey point that has a known physical location;
for a plurality of sensor systems (<NUM>) carrying out the steps of:
initiating (<NUM>) a plurality of passes, wherein each of the plurality of passes captures a plurality of images of the at least one survey point using a sensor system of the plurality of sensor systems (<NUM>);
for each pass of the plurality of passes, calculating (<NUM>) an estimated location of the at least one survey point based on the plurality of images and calculating error data based on the estimated location and the known physical location;
generating (<NUM>) an error curve associated with the sensor system with respect to a number of the plurality of passes based on the error data for said each pass;
providing (<NUM>) an output indicating a target number of passes to meet an error specification based on the error curve for each sensor system of the plurality of sensor systems;
comparing the error curve of a first sensor system of the plurality of sensor systems with at least one other error curve corresponding to at least one other sensor system of the plurality of sensor systems (<NUM>); and
selecting a sensor system based on the comparison.