Patent Description:
<CIT> discloses a robot system includes a mobile robot having a controller executing a control system for controlling operation of the robot, a cloud computing service in communication with the controller of the robot, and a remote computing device in communication with the cloud computing service, the remote computing device communicates with the robot through the cloud computing service.

<CIT> discloses a systems and methods are provided for embedded data inference. The systems and methods may process camera and other sensor data in by leveraging processing and storage capacity of one or more devices nearby or in the cloud to augment or update the sensor processing of an embedded device. The joint processing may be used in stationary cameras or in vehicular systems such as cars and drones, and may improve crop assessments, navigation, and safety.

<CIT> discloses an exemplary methods, apparatuses, and systems infer a context of a user or device. A computer vision parameter is configured according to the inferred context. Performing a computer vision task, in accordance with the configured computer vision parameter. The computer vision task may by at least one of: a visual mapping of an environment of the device, a visual localization of the device or an object within the environment of the device, or a visual tracking of the device within the environment of the device.

Therefore, there is a need for an approach for dynamic adaptation of an in-vehicle feature detector to provide increased accuracy, speed, and/or generality.

According to one embodiment, a method for dynamic adaptation of an in-vehicle feature detector comprising retrieving, by a vehicle comprising a vehicle feature detector, a feature detection model, precomputed weights for the feature detection model, or a combination thereof embedded in a data layer of map data representing a geographic area from which a training data set was collected to generate the feature detection model, the precomputed weights, or a combination thereof. Deploying the feature detection model, the precomputed weights, or a combination thereof to adapt the in-vehicle feature detector based on determining that the in-vehicle feature detector is in the geographic area, plans to travel in the geographic area, or a combination thereof. The in-vehicle feature detector uses the feature detection model, the precomputed weights, or a combination thereof to process sensor data collected while in the geographic area to detect one or more features from imagery-of the sensor data. Matching, by the in-vehicle feature detector, the detected one or more features from the imagery-of the sensor data to one or more mapped features of the map data to determine a current location of the vehicle.

According to another embodiment, an apparatus comprises at least one processor, and at least one memory including computer program code for one or more computer programs, the at least one memory and the computer program code configured to, with the at least one processor, cause, at least in part, the apparatus to retrieve, by a vehicle comprising a vehicle feature detector, a feature detection model, precomputed weights for the feature detection model, or a combination thereof embedded in a data layer of map data representing a geographic area from which a training data set was collected to generate the feature detection model, the precomputed weights, or a combination thereof. Deploy the feature detection model, the precomputed weights, or a combination thereof to adapt the in-vehicle feature detector based on determining that the in-vehicle feature detector is in the geographic area, plans to travel in the geographic area, or a combination thereof. The in-vehicle feature detector uses the feature detection model, the precomputed weights, or a combination thereof to process sensor data collected while in the geographic area to detect one or more features from imagery-of the sensor data. Match, by the in-vehicle feature detector, the detected one or more features from the imagery-of the sensor data to one or more mapped features of the map data to determine a current location of the vehicle.

According to another embodiment, a computer comprising instruction when executed by a processor, causing the processor to retrieve, by a vehicle comprising a vehicle feature detector, a feature detection model, precomputed weights for the feature detection model, or a combination thereof embedded in a data layer of map data representing a geographic area from which a training data set was collected to generate the feature detection model, the precomputed weights, or a combination thereof. Deploy the feature detection model, the precomputed weights, or a combination thereof to adapt the in-vehicle feature detector based on determining that the in-vehicle feature detector is in the geographic area, plans to travel in the geographic area, or a combination thereof. The in-vehicle feature detector uses the feature detection model, the precomputed weights, or a combination thereof to process sensor data collected while in the geographic area to detect one or more features from imagery-of the sensor data. Match, by the in-vehicle feature detector, the detected one or more features from the imagery-of the sensor data to one or more mapped features of the map data to determine a current location of the vehicle.

According to an example embodiment of any of the above mentioned embodiments, the precomputed weights are calculated during an offline training of the feature model using the training data set.

According to an example embodiment of any of the above mentioned embodiments, the feature detection model is a neural network, and the precomputed weights are associated with a plurality of connections among a plurality of interconnected processing elements of the neural network.

According to an example embodiment of any of the above mentioned embodiments, one or more detection classes of the feature detection model are further based on the training data set collected from the geographic area.

According to an example embodiment of the any of above mentioned embodiments, the in-vehicle feature detector uses the feature detection model, the precomputed weights, or a combination thereof corresponding to a map tile in which the in-vehicle feature detector is located or expected to be located.

According to another embodiment, a sending, by the vehicle, a location-based request for one or more map tiles of the geographic database to the geographic database or to a mapping platform being connected to the geographic database. The retrieving of a map tile from the geographic database is in response to the location-based request, where the retrieving of a map tile from the geographic database comprises retrieving a map layer for the one or more map tiles of the geographic database, the map layer storing a feature detection model, precomputed weights for the feature detection model, or a combination thereof respectively for the one or more map tiles. The feature detection model, the precomputed weights, or a combination thereof are generated from a training data set respectively from the one or more map tiles. In particular, the precomputed weights are calculated during an offline training of the feature model using the training data set.

According to an example embodiment of any of the above mentioned embodiments, the embodiment comprises retrieving of the map tile comprises pre-fetching the map tile based on a planned location, a planned route, or a combination thereof of a vehicle equipped with the in-vehicle feature detector.

According to an example embodiment of any of the above mentioned embodiments, the embodiment comprises storing the feature detection model, the precomputed weights, or a combination thereof in the at least one memory of the apparatus, another memory of the in-vehicle feature detector, or a combination thereof.

According to an example embodiment of any of the above mentioned embodiments, the stored feature detection model, the stored precomputed weights, or a combination thereof are used to dynamically adjust the in-vehicle feature detector when the in-vehicle feature detector is in or expected to be in the geographic area.

According to an example embodiment of any of the above mentioned embodiments, the map tile is among a plurality of map tiles of the geographic database, wherein each of the plurality of map tiles includes a respective data layer comprising a respective feature detection model, respective precomputed weights of the respective feature detection model, or a combination thereof.

According to an example embodiment of any of the above mentioned embodiments, the feature detection model, the precomputed weights, or a combination thereof are generated with respect to one or more operational conditions, and wherein the in-vehicle feature detector is configured with the feature detection model, the precomputed weights, or a combination thereof when the in-vehicle feature detector is operating or expected to operate under the one or more operational conditions.

According to another embodiment, a computer-implemented method comprises embedding a feature detection model, precomputed weights for the feature detection model, or a combination thereof in a data layer of map data representing a geographic area from which a training data set was collected to generate the feature detection model, the precomputed weights, or a combination thereof.

According to another embodiment, a (e.g. computer-implemented) method comprises embedding (e.g. by a processor) a feature detection model, precomputed weights for the feature detection model, or a combination thereof in a data layer of map data representing a geographic area from which a training data set was collected to generate the feature detection model, the precomputed weights, or a combination thereof; and deploying the feature detection model, the precomputed weights, or a combination thereof to adapt an in-vehicle feature detector based on determining that the in-vehicle feature detector is in the geographic area, plans to travel in the geographic area, or a combination thereof.

According to another embodiment, an apparatus comprises at least one processor, and at least one memory including computer program code for one or more computer programs, the at least one memory and the computer program code configured to, with the at least one processor, cause, at least in part, the apparatus to embed a feature detection model, precomputed weights for the feature detection model, or a combination thereof in a data layer of map data representing a geographic area from which a training data set was collected to generate the feature detection model, the precomputed weights, or a combination thereof; and to deploy the feature detection model, the precomputed weights, or a combination thereof to adapt an in-vehicle feature detector based on determining that the in-vehicle feature detector is in the geographic area, plans to travel in the geographic area, or a combination thereof.

According to another embodiment, an apparatus comprises means for embedding a feature detection model, precomputed weights for the feature detection model, or a combination thereof in a data layer of map data representing a geographic area from which a training data set was collected to generate the feature detection model, the precomputed weights, or a combination thereof. The apparatus further comprises means for deploying the feature detection model, the precomputed weights, or a combination thereof to adapt an in-vehicle feature detector based on determining that the in-vehicle feature detector is in the geographic area, plans to travel in the geographic area, or a combination thereof.

According to another embodiment, a non-transitory computer-readable storage medium carries one or more sequences of one or more instructions (e.g. a computer program) which, when executed by one or more processors, cause, at least in part, an apparatus to embed a feature detection model, precomputed weights for the feature detection model, or a combination thereof in a data layer of map data representing a geographic area from which a training data set was collected to generate the feature detection model, the precomputed weights, or a combination thereof; and to deploy the feature detection model, the precomputed weights, or a combination thereof to adapt an in-vehicle feature detector based on determining that the in-vehicle feature detector is in the geographic area, plans to travel in the geographic area, or a combination thereof.

According to an example embodiment of any of the above mentioned embodiments, the in-vehicle feature detector uses the feature detection model, the precomputed weights, or a combination thereof to process sensor data collected while in the geographic area to detect one or more features.

According to an example embodiment of any of the above mentioned embodiments, the map data is organized into a tile-based structure, and wherein the geographic area corresponds to one map tile of the tile-based structure.

According to an example embodiment of any of the above mentioned embodiments, the embodiment comprises embedding another feature detection model, other precomputed weights for the another feature detection model, or a combination thereof respectively in each map tile of the tile-based structure, wherein the another feature detection model, the other precomputed weights, or a combination are based on a respective training data set collected from said each map tile.

According to an example embodiment of any of the above mentioned embodiments, the feature detection model, the precomputed weights, or a combination there are further based on one or more operational conditions of the in-vehicle feature detector, and wherein the deploying of the feature detection model, the precomputed weights, or a combination thereof is further based on determining that the in-vehicle feature detector is operating or expected to operate under the one or more operational conditions.

According to an example embodiment of any of the above mentioned embodiments, the feature detection model, the precomputed weights, or a combination thereof are embedded in a data layer of the map data.

According to an example embodiment of any of the above mentioned embodiments, the feature detection model, the precomputed weights, or a combination thereof are determined in an offline mode; and wherein the deploying of the feature detection model, the precomputed weights, or a combination thereof is performed dynamically in an online mode.

According to an example embodiment of any of the above mentioned embodiments, the in-vehicle feature detector is part of computer vision system of a vehicle.

In addition, for various example embodiments of the invention, 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 as relevant to any embodiment of the invention.

For various example embodiments of the invention, 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 embodiments of the invention, 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 embodiments of the invention, 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 embodiments, 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 scope of the invention as defined by the claims. 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 dynamic adaptation of an in-vehicle feature detector 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 dynamic adaptation of an in-vehicle feature detector, according to one embodiment. One of the major components of self-driving functionality in the modern automotive industry is high-definition (HD) maps (e.g., such as the HD map data stored in a geographic database <NUM> as shown in <FIG>). These HD maps allow highly automated vehicles (e.g., a vehicle <NUM>) to precisely localize themselves on the road, e.g., by using in-vehicle feature detectors <NUM> to process sensor data collected by onboard sensors <NUM> to detect road objects <NUM> or other road features previously mapped and included in the HD map data to perform visual odometry.

However, visual odometry typically requires that HD maps provide at least centimeter-level mapping accuracy to ensure safe operation of autonomous, semi-autonomous, or highly-assisted driving (HAD) vehicles <NUM>. By way of example, in order to map roads at <NUM>-to-<NUM> centimeter accuracy, map service providers (e.g., operating a mapping platform <NUM>) can use advanced sensors (e.g., LiDAR technology) to collect billions of three-dimensional (3D) points and model road surfaces down to the number of lanes and their width. In this way, HD maps capture important details such as the slope and curvature of the road, lane markings and roadside objects such as sign posts, including what that signage denotes. For example, intelligent vehicles <NUM> (e.g., vehicles <NUM> with in-vehicle feature detectors <NUM>) with sensors <NUM> (e.g., cameras, radar, LiDAR, etc.) capture sensor data as they drive in a road network, and can transmit the data to the mapping platform <NUM> (e.g., a cloud-based mapping service). In one embodiment, the transmitted data can include output from the in-vehicle feature detectors <NUM> that identifies potential features and/or objects identified in the sensor data that can be used by the mapping platform <NUM> can generate HD maps.

As a result, the data collection stage in the HD map building process can be heavily relying upon in-vehicle feature detectors <NUM>. Traditional, in-vehicle feature detectors <NUM> are mainly based on computer vision neural network technologies that have been under active development for over the last <NUM> years. For example, the topology of deep convolutional networks can be represented by multiple layers of interconnected processing elements. These layers generally require significant compute power and memory allocation. In addition, extending the number of features (e.g., detection classes) that can be detected by a feature detector <NUM> typically requires more capacity from the underlying deep neural network. Given that embedded processors installed in vehicles are limited in computational capacity (e.g., as minimized for low power consumption, thermal dissipation, cost, etc.), it becomes clear that deployed in-vehicle feature detectors <NUM> will often have a trade-off between quality of detections, speed/throughput, and generality against available compute power and memory allocations.

Stated another way, given a fixed computational budget and other constraints, a traditional in-vehicle feature detector <NUM> can be built as either (<NUM>) a specialized neural network that has good quality under a specific or limited set of operational conditions and locales, or (<NUM>) a general-purpose model that will have lower quality but more consistent performance across varying conditions and locales. Therefore, providing a feature detection system that provides specificity across a broad range of locales and/or operational conditions within a resource-constrained compute environment presents significant technical challenges.

Current and next-generation vehicles <NUM> are examples of such compute resource-constrained environments. For example, many current and next-generation vehicles <NUM> with advanced driver-assistance systems (ADAS) technology (e.g., autonomous, semi-autonomous, and HAD vehicles <NUM>) use "smart camera" systems that have embedded computing units available for deploying feature detection algorithms or models (e.g., in-vehicle feature detectors <NUM>). A representative embedded computing device used in in-vehicle feature detectors <NUM> may currently be limited to <NUM>,<NUM> MFLOPS (MFLOPS = million floating point operations per second). Contemporary efficient deep neural networks may need <NUM> billion operations for a single inference (e.g., processing a single image or sensor observation). As a result, in order to process images from the camera's video stream at <NUM> frames per second (e.g., or other sensor data stream), the in-vehicle feature detector <NUM> would require <NUM>,<NUM> MFLOPS or 20x the computational capacity of this example computing device. A higher-capacity computing device may be employed for in-vehicle feature detectors <NUM> in next-generation vehicles, but there would still be limits with respect to what can be computed within a few watts of power that can be budgeted for a vehicle's computer vision system (e.g., including the in-vehicle feature detector <NUM>).

One traditional approach to addressing this technical problem includes building region-specific feature detection models for the in-vehicle feature detector <NUM>. For example, a feature detection model specialized for a specific geographic region can be developed using a much smaller neural network than a model which has the same detection accuracy but works equally well in any geographic region. However, region-specific feature detection models have limitations as shown in the examples of <FIG> and <FIG>.

In the examples of <FIG> and <FIG>, a vehicle <NUM> includes an in-vehicle feature detector <NUM> that uses a region-specific feature detection model trained using sensor data (e.g., imagery data) collected from California to improve feature detections in California. <FIG> illustrates an image <NUM> captured by a camera sensor <NUM> of the vehicle <NUM>. The in-vehicle feature detector <NUM> performs well when applied to imagery of a California highway (e.g., the image <NUM>) and is able to detect features such as lane lines 203a-203e and signs 205a-205b shown in the feature detection output <NUM>. However, the region-specific feature model fails on an image <NUM> of <FIG> captured on an urban road in Germany, and detects only fragmented lines 223a-223d that do not accurately represent the actual lane lines of the German road. The failure occurred because the in-vehicle feature detector <NUM> is using a feature detection model or model parameters (e.g., model interconnection weights) that was mainly trained on California highway drives.

One traditional way to address the problem would be to add samples from German urban areas to the training set used to create this region-specific feature detection model. However, such an approach would require using a higher-capacity neural network to achieve the desired quality in both regions. To achieve good quality results over all geographic regions where this particular vehicle is expected to operate would require a deep network too large to be deployed to a low-power computing device used for in-vehicle feature detectors <NUM>. Moreover, traditional processes to update feature detection models in vehicles are often complex, e.g., requiring, firmware or software updates of embedded systems that have to be installed by vehicle dealers or other equivalent means.

To address these technical problems, a system <NUM> of <FIG> introduces a capability to maximize feature detection quality when an in-vehicle feature detector <NUM> is implemented using a computing device with a fixed set of computational resource constraints. In one embodiment, the system <NUM> creates different sets of specialized feature detection models or different sets of parameters for those models. These specialized detections models or sets of parameters (e.g., model weights) are specific to delineated geographic areas to produce less resource intensive feature detection models that can function within the fixed set of computational resource constraints of the in-vehicle feature detector. The system <NUM> then uses the best feature detection model or model parameters from this set to adapt an in-vehicle feature detector <NUM> given the location and/or operational conditions (e.g., day/night, weather, time, visibility, etc.) at the time the feature detector <NUM> is being used. In one embodiment, the specialized feature detection models or model parameters are embedded in map data corresponding to the geographic area (e.g., a map tile encompassing the geographic area) to which the specialized feature detection models are best suited. In this way, the specialized feature detection models or model parameters can be deployed to the in-vehicle feature detector <NUM> based on delivery of the corresponding of the map data or map tile to the computer vision system <NUM> or other navigation/mapping service or application of the vehicle <NUM> (e.g., a navigation system, UE <NUM>, or other device or component associated with the vehicle <NUM>).

In other words, the system <NUM> makes it possible to dynamically adapt in-vehicle feature detectors <NUM> to changing locations and/or operational conditions by installing a new set of model parameters (e.g., weights) and/or new feature detection models as needed. For example, with respect to feature detection models based on neural networks or equivalent, weights are calculated for deep neural networks during training. Training process can be quite lengthy, computationally intensive, and therefore, is usually executed offline by the mapping platform <NUM>. However, if computation resources, bandwidth, etc. are available to perform the training process with low latency, the mapping platform <NUM> can perform all or a portion of the training in an online (e.g., real-time or near real-time) mode. By way of example, most feature detectors <NUM> used in computer vision systems <NUM> are based on supervised learning models, and often require a large collection of annotated images or other similar sensor data for training (generally referred to as training data sets).

In one embodiment, the system <NUM> trains specialized or region-specific feature detection models or derives the parameters to use for those models for individual map tiles of HD maps (e.g., the geographic database <NUM>) that are organized according to a tile-based structure. In this way, a prior knowledge of the geolocation of a map tile, would allow for tailoring of individual training sets to region-specific landscape, urban areas, road marking, signage, etc. present in the geographic areas of each map tile. This tailoring can advantageously increase the specificity of the resulting feature detection model while minimizing the computation resource requirements of the model (e.g., by reducing model size or complexity by reducing a need to maintaining the same prediction accuracy over a wider variety of input data, or eliminating detection classes or features not likely to be applicable to a given map tile area). In addition, different sets of weights or models can be precomputed to address various operational conditions in each map tile, e.g., day/night, weather, time of the year, etc..

In one embodiment, the system stores or embeds tile-specific feature detection models or precomputed per-tile sets of model weights/parameters in a designated data layer of the geographic database <NUM> or other equivalent map data as shown in <FIG>. In the example of <FIG>, the geographic database <NUM> is organized into a tile-based structure consisting of map tiles <NUM> that represent respective portions or a mapped area. The area of each map tile <NUM> is delineated by geographic coordinates along a longitude coordinate axis <NUM> and a latitude coordinate axis <NUM>. In one embodiment, each map tile <NUM> is made of multiple layers <NUM> including a feature detection model layer <NUM> for storing feature detection models and/or precomputed model weights or parameters generated using a training data set collected from a given map tile <NUM>. Each map tile <NUM> can also include one or more other layers such as, but not limited to, a localization model layer <NUM> (e.g., storing data to aid in vehicle localization), a lane model layer <NUM> (e.g., storing data representing lanes that are present in a given map tile <NUM>), a road model layer <NUM> (e.g., storing data on the geometry, attributes, etc. of roads that are present in a given map tile <NUM>), and/or the like.

In one embodiment, embedding the feature detection model and/or model weights/parameters in the map tiles <NUM> or its layers would allow for the utilization of the map tile structure to deliver the specialized feature detection models and/or precomputed weights to in-vehicle feature detectors <NUM> as a part of prefetching map tiles required for a drive or other use by one or more systems of the vehicle <NUM>. In other words, the system <NUM> can seamlessly deliver a feature detection models or model weights trained for a given geographic area (e.g., to achieve a more specific, compact, or efficient model) to an in-vehicle feature detector <NUM> when a map data or map tiles <NUM> of that given geographic area is delivered to the feature detector <NUM> or the corresponding vehicle <NUM>. Once downloaded to a vehicle <NUM> (e.g., as part of fetching corresponding map tile <NUM>), these models or sets of weights can be stored (e.g., as a part of a local cache of map tiles <NUM> maintained at the vehicle <NUM>) and later used to adapt in-vehicle feature detectors <NUM> to new locations and/or operational conditions as the corresponding vehicle <NUM> travels to the new locations (e.g., by updating the model or model weights used by the in-vehicle feature detector <NUM> to correspond to the current location or operational condition).

As noted, in one embodiment, the system <NUM> can embed either or both feature detector models or the model weights or parameters. Embedding of feature detector models themselves enables the system <NUM> to dynamically adjust the net architectures of the models used in the in-vehicle feature detectors <NUM>. For example, to add or alter detection classes, it is possible to dynamically add or replace deep neural network decoders or models that include the added or altered detection classes. Therefore, the system <NUM> can handle feature detection models or decoders the same way the system <NUM> handles model weights by precomputing per-tile models or decoders off-line in the cloud, storing them in a map layer, and then dispatching them dynamically for in-vehicle deployment as part of the map tile delivery pipeline. Embodiments of the model and/or model weight generation and delivery processes are described in further detail below with respect to <FIG>.

<FIG> is a flowchart of a process for generating a tile-based geographic database for dynamic adaptation of an in-vehicle feature detector <NUM>, according to one embodiment. In one embodiment, the mapping platform <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> can provide means for accomplishing various parts of the process <NUM>. In addition or alternatively, a services platform <NUM> and/or one or more services 117a-117n (also collectively referred to as services <NUM>) may perform any combination of the steps of the process <NUM> in combination with the mapping platform <NUM>, or as standalone components. Although the process <NUM> is illustrated and described as a sequence of steps, it is contemplated that various embodiments of the process <NUM> may be performed in any order or combination and need not include all of the illustrated steps.

As discussed above, autonomous, semi-autonomous, and/or HAD driving has quickly become an area of intense interest, with recent advances in machine learning, computer vision and computing power enabling real-time mapping and sensing of a vehicle <NUM>'s environment using an in-vehicle feature detector <NUM> in combination with HD maps (e.g., a tile-based geographic database <NUM>). Such an understanding of the environment enables autonomous, semi-autonomous, or highly assisted driving in a vehicle <NUM> in at least two distinct ways.

First, real-time sensing of the environment provides information about potential obstacles, the behavior of others on the road, and safe, drivable areas. An understanding of where other cars are and what they might do is critical for a vehicle <NUM> to safely plan a route. Moreover, vehicles <NUM> generally must avoid both static (lamp posts, e.g.) and dynamic (cats, deer, e.g.) obstacles, and these obstacles may change or appear in real-time. More fundamentally, vehicles <NUM> can use a semantic understanding of what areas around them are navigable and safe for driving. Even in a situation where the world is completely mapped in high resolution, exceptions will occur in which a vehicle <NUM> might need to drive off the road to avoid a collision, or where a road's geometry or other map attributes like direction of travel have changed. In this case, detailed mapping may be unavailable, and the vehicle <NUM> has to navigate using real-time sensing of road features or obstacles based solely on its in-vehicle feature detector <NUM> (e.g., included as part of a computer vision system <NUM>).

A second application of vision techniques in autonomous driving is localization of the vehicle <NUM> with respect to a map of reference landmarks stored in the geographic database <NUM>. Understanding one's location on a map enables planning of a route, both on fine and coarse scales. On a coarse scale, navigation maps allow vehicles <NUM> to know what roads to use to reach a particular destination. However, on a finer scale, the geographic database <NUM> allows vehicles <NUM> to know what lanes to be in and when to make lane changes. Knowing this information is important for planning an efficient and safe route, for in complicated driving situations maneuvers need to be executed in a timely fashion, and sometimes before they are visually obvious. In addition, localization with respect to a map enables the incorporation of other real-time information into route planning. Such information could include traffic, areas with unsafe driving conditions (e.g., ice, fog, potholes), and temporary road changes like construction.

With respect to lane localization and also generally with respect to autonomous driving, high accuracy and real-time localization of vehicles <NUM> are needed. Traditionally, most vehicle navigation systems accomplish this localization using GPS, which generally provides a real-time location with a <NUM>% confidence interval of <NUM> meters. However, in complicated urban environments, reflection of GPS signals can further increase this error, such that one's location may be off by as much as <NUM> meters. Given that the width of many lanes is <NUM>-<NUM> meters, this accuracy is not sufficient to properly localize a vehicle <NUM> (e.g., an autonomous vehicle) so that it can make safe route planning decisions. Other sensors, such as inertial measurement units (IMUs) can increase the accuracy of localization by taking into account vehicle movement, but these sensors tend to drift and still do not provide sufficient accuracy for localization.

In general, a localization accuracy of around <NUM> is needed for safe driving in many areas. One way to achieve this level of accuracy is to use visual odometry, in which features are detected from imagery by in-vehicle feature detectors <NUM>. These features can then be matched to the mapped features stored in the geographic database <NUM> to determine a current location. By way of example, traditional feature-based localization that both detect features and localize against them generally rely on low-level features. However, low-level features typically used in these algorithms (e.g., Scale-Invariant Feature Transform (SIFT) or Oriented FAST and rotated BRIEF (ORB)) tend to be brittle and not persist in different environmental and lighting conditions. As a result, they often cannot be used to localize a vehicle <NUM> under different operational conditions (e.g., on different days in different weather conditions). Aside from reproducibility, the ability to detect and store higher level features of different types (e.g., lane features such as lane markings, lane lines, etc.) can provide better and more accurate localization. Accordingly, in one embodiment, the in-vehicle feature detector <NUM> can use feature detector models or decoders trained to detect such higher-level features on a region-specific basis as discussed with respect to the various embodiments described herein. The process <NUM> describes embodiments for generating such region-specific feature detection models and embedding them in the geographic database <NUM> for delivery to and dynamic adaptation of in-vehicle feature detectors <NUM>.

To initiate the process <NUM>, in step <NUM>, the mapping platform <NUM> collects training data sets from geographic areas of the geographic database <NUM>. The geographic areas correspond to discrete areas of the map of the geographic database <NUM> for which a region-specific or specialized feature detection model or set of model weights is to be generated. In one embodiment, the map data is organized into a tile-based structure, and the geographic area corresponds to one map tile of the tile-based structure. It is noted, however, that a map-tile is provided by way of example, and the embodiments described herein can use any other means to segment the geographic database <NUM> into discrete geographic areas for generating region-specific feature detection models and/or model weights.

<FIG> illustrates an example of collecting training data sets from a tile-based representation of a geographic area, according to one embodiment. In the example of <FIG>, map tiles 501a-501d (also collectively referred to as map tiles <NUM>) correspond to geographic areas mapped in the geographic database <NUM>. In each of the map tiles <NUM>, respective data collection vehicles 503a-503d (also collectively referred to as data collections vehicles <NUM> collect sensor data (e.g., imagery data, radar data, LiDAR data) as they travel within each respective map tile <NUM>. The mapping platform <NUM> then collects the sensor data from each set of data collection vehicles <NUM> for each map tile <NUM> respectively as training data sets 505a-505b (also collectively referred to as training data sets <NUM>). Each training data set <NUM> includes sensor readings or observations (e.g., images from onboard camera sensors) unique to each corresponding map tile.

After data collection, in step <NUM>, the mapping platform <NUM> generates feature detection models, weights or parameters for the feature detection models, or a combination thereof as models/weights 507a-507d (also collectively referred as models/weights <NUM>) from each of the corresponding training data sets <NUM>. It is contemplated that any type of feature detection model (e.g., neural networks, support vector machines (SVM), decision trees, RandomForest, etc.) can be used in the embodiments described herein. For example, convolutional neural networks have shown unprecedented ability to recognize objects in images, understand the semantic meaning of images, and segment images according to these semantic categories. Therefore, neural networks can be used by the in-vehicle feature detector <NUM> in combination with the computer vision system <NUM> to detect features for vehicle localization and other similar driving applications.

In one embodiment, the processing of the received training data sets <NUM> to generate per-tile models/weights <NUM> includes annotating the received training data sets <NUM> with one or more feature labels. The resulting labeled training data sets <NUM> represent, for instance, ground truth data for generating the respective models/weights <NUM>. For example, with respect to a use case of feature detection from imagery data, the training or ground data truth data can include a set of images that have been manually marked or annotated with feature labels to indicate examples of the features or objects of interest. A manually marked feature that is an object (e.g., lane markings, road signs, etc.), for instance, can be a polygon or polyline representation of the feature that a human labeler has visually detected in the image. In one embodiment, the polygon, polyline, and/or other feature indicator can outline or indicate the pixels or areas of the image that the labeler designates as depicting the labeled feature.

In one embodiment, the mapping platform <NUM> can incorporate a supervised learning model (e.g., neural network, logistic regression model, RandomForest model, and/or any equivalent model) to provide feature matching probabilities or statistical patterns that are learned from the labeled training data sets <NUM> for each map tile <NUM>. For example, during training, the mapping platform <NUM> uses a learner module that feeds feature sets from each individual training data set <NUM> into the feature detection model to compute a predicted matching feature using an initial set of model parameters (e.g., an initial set of model weights). The learner module then compares the predicted matching probability and the predicted feature to the ground truth data (e.g., the manually annotated feature labels) in the respective training data set <NUM>. The learner module then computes an accuracy of the predictions for the initial set of model parameters or weights. If the accuracy or level of performance does not meet a threshold or configured level, the learner module incrementally adjusts the model parameters or weights until the model generates predictions at a desired or configured level of accuracy with respect to the manually annotated labels in each of the training data sets <NUM> (e.g., the ground truth data). This results in producing a respective model or set of weights <NUM> for each training data set <NUM>, and correspondingly for each map tile <NUM> from which the training data set <NUM> was collected. In other words, a "trained" feature prediction model is a classifier with model parameters or weights adjusted to make accurate predictions with respect to the labeled sensor data set.

<FIG> is a diagram of an example of neural network and connection weights for dynamic adaptation of an in-vehicle feature detector, according to one embodiment. In the example of <FIG>, the feature detection model being trained is a neural network <NUM> including two nodes in an input layer <NUM>, four nodes in a hidden layer <NUM>, and two nodes in the output layer <NUM> (e.g., corresponding to two detection classes). Each of the nodes in one layer is connected to each other node in the next layer via one or more interconnections <NUM>, and includes an activation function that is responds based on a respective weight values of each of the interconnections <NUM>. After training, the set of weight values for all of the interconnections <NUM> represents the precomputed weights <NUM> corresponding to the respective training data set <NUM> on which the neural network <NUM> was trained. Continuing with the example of <FIG>, each of the training data sets 505a-505d is used to train the neural network <NUM> separately to respectively generate weights 507a-507d that are specialized for each respective map tile 501a-501d.

As discussed above, the embodiments described herein are not limited to generating different sets of model weights, but can also be used to generate different feature detection models with different architectures. In other words, different numbers of input nodes, hidden nodes, or output nodes can be used depending on the training data sets <NUM> or other characteristics of the map tile <NUM>. For example, in heterogenous geographic environments such as urban centers, there can be many more feature or objects of interests (e.g., different signs, lane markings, road types, etc.) than in a map tile including only rural highways. Therefore, additional detection classes can be supported by adding additional nodes in the output layer of the neural network. Like the model weights, these architectural differences can be precomputed from the training data sets <NUM> for each respective map tile <NUM>.

In one embodiment, the feature detection model, the precomputed weights, or a combination there can further based on one or more operational conditions of the in-vehicle feature detector <NUM>, and/or the vehicle <NUM>. By way of example, operational conditions can include, but are not limited to, environmental or contextual conditions (e.g., day versus night, weather, time of day, season, vehicle type, sensor type, etc.) that can potentially affect feature detection performance. To address this potential issue, the mapping platform <NUM> can collect training data under each operational condition of interest alone or in combination with the map tile geographic boundaries. This operational condition-based training data can then be used to train generate models and model weights on a per condition basis as described with respect to embodiments of training on a per-tile basis.

In step <NUM>, the mapping platform <NUM> generates a map layer for one or more map tiles <NUM> of a tile-based geographic database <NUM> to store the generated feature detection model, precomputed weights for the feature detection model, or a combination thereof (e.g., models/weights <NUM>) respectively for the one or more map tiles <NUM>. For example, the mapping platform <NUM> embeds the feature detection model, precomputed weights for the feature detection model, or a combination thereof in a data layer of map data (e.g., the geographic database <NUM>) representing a geographic area from which a respective training data set <NUM> was collected to generate the feature detection model, the precomputed weights, or a combination thereof. In one embodiment, the mapping platform <NUM> continue processing other map tiles in the geographic database <NUM> until all or designated map tiles <NUM> are processed. In other words, the mapping platform <NUM> embeds another feature detection model, other precomputed weights for the other feature detection model, or a combination thereof respectively in each map tile <NUM> of the tile-based structure of the geographic database <NUM>. This other feature detection model, other precomputed weights, or a combination are based on a respective training data set <NUM> collected from each map tile <NUM>.

In step <NUM>, the mapping platform <NUM> dynamically deploys the feature detection model, the precomputed weights, or a combination thereof to adapt an in-vehicle feature detector <NUM> based on determining that the in-vehicle feature detector is in the geographic area, plans to travel in the geographic area, or a combination thereof. In one embodiment, the dynamic deployment occurs as part of online delivering or streaming of map tile data to the vehicle <NUM>. As a result, if the vehicle <NUM> has map tile data for a given location, the vehicle <NUM> will also have (e.g., in a layer of the map tile data) a corresponding specialized or region-specific feature detection model or weights best suited to detect features in the map tile while minimizing the feature detection model resource load on the in-vehicle feature detector <NUM>.

The dynamic adaption process from the perspective of the computer vision system <NUM> and/or in-vehicle feature detector <NUM> of the vehicle <NUM> is described with respect to <FIG>, which is a flowchart of a process for dynamically adapting an in-vehicle feature detector based on a tile-based geographic database, according to one embodiment. In one embodiment, the computer vision system <NUM> and/or in-vehicle feature detector <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 computer vision system <NUM> and/or in-vehicle feature detector <NUM> can provide means for accomplishing various parts of the process <NUM>. In addition or alternatively, a user equipment (UE) device <NUM> (e.g., personal navigation device, mobile device, etc.), executing an application <NUM>, may perform any combination of the steps of the process <NUM> alone or in combination with the computer vision system <NUM> and/or in-vehicle feature detector <NUM>. Although the process <NUM> is illustrated and described as a sequence of steps, it is contemplated that various embodiments of the process <NUM> may be performed in any order or combination and need not include all of the illustrated steps.

As described above, the computer vision system <NUM> uses advances in machine learning, sensors, and data analytics to provide for greater environmental sensing and modeling to facilitate, for instance, autonomous driving. The computer vision system <NUM> includes an in-vehicle detector <NUM> that uses a feature detection model to process sensor data to detect road or other environmental conditions as the vehicle <NUM> travels. In-vehicle feature detectors <NUM> typically are compute resource constrained so that they traditionally had to compromise between prediction accuracy and generality. The dynamic adaption process <NUM> enables the in-vehicle feature detector <NUM> to avoid or reduce this compromise by providing specialized feature detection models and/or model weights on a per map-tile or per geographic area basis according to the embodiments described, for instance, in the process <NUM> of <FIG>.

To initiate a dynamic adaption of the in-vehicle feature detector <NUM> on the vehicle <NUM> or client-side, in step <NUM>, the computer vision system <NUM> or in-vehicle feature detector <NUM> determines a location and/or operational condition of the in-vehicle feature detector <NUM>. For example, a current or expected location of the vehicle can be determined using, e.g., location sensors, planned locations, planned routes, inputs via a navigation device, etc. The map data corresponding to the current or expected map tiles can then be requested for delivery to the vehicle <NUM> (e.g., via a location based request to the mapping platform <NUM> and/or geographic database <NUM>). Alternatively, in cases where, the entire geographic database <NUM> is pre-stored in the vehicle <NUM>, all corresponding feature models and/or model weights also be pre-stored since they are contained in a data layer of the geographic database <NUM>.

In step <NUM>, the computer vision system <NUM> retrieves a map tile from a geographic database <NUM> (e.g., either from the geographic database online over a communication network <NUM>, or from in-vehicle map storage or cache). As discussed previously, the retrieved map tile includes a data layer storing a feature detection model, precomputed weights for the feature detection model, or a combination thereof specialized for the geographic area corresponding to the map tile. By way of example, the feature detection model, the precomputed weights, or a combination thereof are generated using a training data set collected from a geographic area represented by the map tile. In addition, the feature detection model, the precomputed weights, or a combination thereof can be generated with respect to one or more operational conditions. In this way, the feature model or model weights corresponding to the current or expected operational condition can also be retrieved. In one embodiment, the retrieving of the map tile comprises pre-fetching the map tile based on a planned location, a planned route, or a combination thereof of the vehicle <NUM>.

In step <NUM>, the computer vision system <NUM> adapts an in-vehicle feature detector <NUM> with the feature detection model, the precomputed weights, or a combination thereof based on determining that the in-vehicle feature detector is in or expected to be in the geographic area. In one embodiment, adapting the in-vehicle feature detector <NUM> based the precomputed weights or parameters include updating the interconnection weights or parameters currently in used by a feature detection model of the in-vehicle feature detector <NUM> to the precomputed weights retrieved from the map tile data layer. Similarly, with respect to adapting the in-vehicle feature detector <NUM> based on the model includes replace the architecture or type of model currently in use with the feature detection model stored in the map tile data layer. In one embodiment, the adaptation is referred to as occurring "dynamically" because the change in model or model weights is automatically triggered by changes in location and/or operational condition of the vehicle <NUM>, in-vehicle feature detector <NUM>, and/or computer vision system <NUM>.

In one embodiment, the computer vision system <NUM> stores the feature detection model, the precomputed weights, or a combination thereof in the at least one memory of the apparatus, the in-vehicle feature detector <NUM>, or a combination thereof. The stored feature detection model, the stored precomputed weights, or a combination thereof are used to dynamically adjust the in-vehicle feature detector when the in-vehicle feature detector is in or expected to be in the geographic area. In this way, the specialized feature detection models or model weights can be stored (temporarily or permanently) until the triggering condition for their use is detected (e.g., detecting that the vehicle <NUM> has entered a map tile area and/or operational condition).

Once within the corresponding map tile area and/or operational condition, in step <NUM>, the computer vision system <NUM> uses the adapted in-vehicle feature detector to process sensor data collected from the geographic area to detect one or more features.

Returning to <FIG>, as shown, the system <NUM> includes the in-vehicle feature detector <NUM>, computer vision system <NUM>, and/or mapping platform <NUM> for providing dynamic adaptation of the in-vehicle feature detector <NUM> according the various embodiments described herein. In some use cases, with respect to autonomous, navigation, mapping, and/or other similar applications, the in-vehicle feature detector <NUM> can detect road features (e.g., lane lines, signs, etc.) in input sensor data and generate associated prediction confidence values (e.g., confidence metrics, uncertainty values, etc.), according to the various embodiments described herein. In one embodiment, the in-vehicle feature detector <NUM> and/or mapping platform <NUM> can include one or more feature detection models such as, but not limited to, neural networks, SVMs, decision trees, etc. to make feature predictions. For example, when the sensor data include images used for environment modeling, the features of interest can include lane lines in image data to support localization of, e.g., a vehicle <NUM> within the sensed environment. In one embodiment, the neural network of the system <NUM> is a traditional convolutional neural network which consists of multiple layers of collections of one or more neurons (e.g., processing nodes of the neural network) which are configured to process a portion of input sensor data. In one embodiment, the receptive fields of these collections of neurons (e.g., a receptive layer) can be configured to correspond to the area of the input sensor data.

In one embodiment, the in-vehicle feature detector <NUM> and/or mapping platform <NUM> also have connectivity or access to the geographic database <NUM> which stores representations of mapped geographic features to facilitate autonomous driving and/or other mapping/navigation-related applications or services. The geographic database <NUM> can also store specialized feature detection models and/or model weights in conjunction with map data according to the various embodiments described herein.

In one embodiment, the in-vehicle feature detector <NUM> and/or mapping platform <NUM> have connectivity over a communication network <NUM> to the services platform <NUM> that provides one or more 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> uses the output of the in-vehicle feature detector <NUM> and/or mapping platform <NUM> (e.g., detected features) to model an environment of the vehicle <NUM>, localize the vehicle <NUM> or UE <NUM> (e.g., a portable navigation device, smartphone, portable computer, tablet, etc.) to provide services <NUM> such as navigation, mapping, other location-based services, etc..

In one embodiment, the in-vehicle feature detector <NUM>, computer vision system <NUM>, and/or mapping platform <NUM> may be platforms with multiple interconnected components. The in-vehicle feature detector <NUM>, computer vision system <NUM>, and/or mapping platform <NUM> may include multiple servers, intelligent networking devices, computing devices, components and corresponding software for providing parametric representations of lane lines. In addition, it is noted that the in-vehicle feature detector <NUM>, computer vision system <NUM>, and/or 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 the UE <NUM> and/or vehicle <NUM>.

In one embodiment, content providers 125a-<NUM> (collectively referred to as content providers <NUM>) may provide content or data (e.g., including geographic data, parametric representations of mapped features, etc.) to the geographic database <NUM>, the in-vehicle feature detector <NUM>, the mapping platform <NUM>, the services platform <NUM>, the services <NUM>, the UE <NUM>, the vehicle <NUM>, and/or an application <NUM> executing on the UE <NUM>. The content provided may be any type of content, such as map content, 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 classifying of lane lines and/or other features in image data, and estimating the quality of the detected features. In one embodiment, the content providers <NUM> may also store content associated with the geographic database <NUM>, in-vehicle feature detector <NUM>, mapping platform <NUM>, services platform <NUM>, services <NUM>, UE <NUM>, and/or vehicle <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, the UE <NUM> and/or vehicle <NUM> may execute a software application <NUM> to collect, encode, and/or decode feature data detected in image data to select training observations for machine learning models according the embodiments described herein. By way of example, the application <NUM> may also be any type of application that is executable on the UE <NUM> and/or vehicle <NUM>, such as autonomous driving applications, mapping applications, location-based service applications, navigation applications, content provisioning services, camera/imaging application, media player applications, social networking applications, calendar applications, and the like. In one embodiment, the application <NUM> may act as a client for the in-vehicle feature detector <NUM> and/or mapping platform <NUM> and perform one or more functions associated with in-vehicle data selection for feature detection model creation and maintenance.

By way of example, the UE <NUM> is 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 the UE <NUM> can support any type of interface to the user (such as "wearable" circuitry, etc.). In one embodiment, the UE <NUM> may be associated with the vehicle <NUM> or be a component part of the vehicle <NUM>.

The UE <NUM> and/or vehicle <NUM> are configured with various sensors for generating or collecting environmental sensor data (e.g., for processing by the in-vehicle feature detector <NUM> and/or mapping platform <NUM>), related geographic data, etc. including but not limited to, optical, radar, ultrasonic, LiDAR, etc. sensors. In one embodiment, the sensed data represent sensor data associated with a geographic location or coordinates at which the sensor data was collected. By way of example, the sensors may include a global positioning sensor for gathering location data (e.g., GPS), 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 road sign information, images of road obstructions, 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.

Other examples of sensors of the UE <NUM> and/or vehicle <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 the vehicle along a path of travel, moisture sensors, pressure sensors, etc. In a further example embodiment, sensors about the perimeter of the UE <NUM> and/or vehicle <NUM> may detect the relative distance of the vehicle 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, the UE <NUM> and/or vehicle <NUM> may include GPS or other satellite-based receivers to obtain geographic coordinates from satellites for determining current location and time. Further, the location can be determined by visual odometry, triangulation systems such as A-GPS, Cell of Origin, or other location extrapolation technologies. In yet another embodiment, the sensors can determine the status of various control elements of the car, 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..

The communication network <NUM> of 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 in-vehicle feature detector <NUM>, mapping platform <NUM>, services platform <NUM>, services <NUM>, UE <NUM>, vehicle <NUM>, and/or content providers <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 parametric representation of lanes include, e.g., encoding and/or decoding parametric representations into lane lines. In one embodiment, the geographic database <NUM> include 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 Light Detection and Ranging (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 sign posts, including what the signage denotes. By way of example, the HD mapping data enable highly automated vehicles to precisely localize themselves on the road.

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.

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.

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.

The geographic database <NUM> is stored as a hierarchical or multi-level tile-based projection or structure. More specifically, in one embodiment, the geographic database <NUM> may be defined according to a normalized Mercator projection. Other projections may be used. By way of example, the map tile grid of a Mercator or similar projection is a multilevel grid. Each cell or tile in a level of the map tile grid is divisible into the same number of tiles of that same level of grid. In other words, the initial level of the map tile grid (e.g., a level at the lowest zoom level) is divisible into four cells or rectangles. Each of those cells are in turn divisible into four cells, and so on until the highest zoom or resolution level of the projection is reached.

The map tile grid may be numbered in a systematic fashion to define a tile identifier (tile ID). For example, the top left tile may be numbered <NUM>, the top right tile may be numbered <NUM>, the bottom left tile may be numbered <NUM>, and the bottom right tile may be numbered <NUM>. In one embodiment, each cell is divided into four rectangles and numbered by concatenating the parent tile ID and the new tile position. A variety of numbering schemes also is possible. Any number of levels with increasingly smaller geographic areas may represent the map tile grid. Any level (n) of the map tile grid has <NUM>(n+<NUM>) cells. Accordingly, any tile of the level (n) has a geographic area of A/<NUM>(n+<NUM>) where A is the total geographic area of the world or the total area of the map tile grid <NUM>. Because of the numbering system, the exact position of any tile in any level of the map tile grid or projection may be uniquely determined from the tile ID.

The system <NUM> may identify a tile by a quadkey determined based on the tile ID of a tile of the map tile grid. The quadkey, for example, is a one-dimensional array including numerical values. In one embodiment, the quadkey may be calculated or determined by interleaving the bits of the row and column coordinates of a tile in the grid at a specific level. The interleaved bits may be converted to a predetermined base number (e.g., base <NUM>, base <NUM>, hexadecimal). In one example, leading zeroes are inserted or retained regardless of the level of the map tile grid in order to maintain a constant length for the one-dimensional array of the quadkey. In another example, the length of the one-dimensional array of the quadkey may indicate the corresponding level within the map tile grid <NUM>. In one embodiment, the quadkey is an example of the hash or encoding scheme of the respective geographical coordinates of a geographical data point that can be used to identify a tile in which the geographical data point is located.

As shown, the geographic database <NUM> includes node data records <NUM>, road segment or link data records <NUM>, POI data records <NUM>, feature detection data records <NUM>, HD mapping data records <NUM>, and indexes <NUM>, for example. More, fewer or different data records can be provided. 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.

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, 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 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).

The geographic database <NUM> can also include feature detection data records <NUM> for training data, region-specific feature detection models, pre-computed model weights or parameters, annotated observations, computed featured distributions, sampling probabilities, and/or any other data generated or used by the system <NUM> according to the various embodiments described herein. By way of example, the feature detection 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 stored therein and the corresponding estimated quality of the features. In this way, the 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, the feature detection data records <NUM> are stored as a data layer of the hierarchical tile-based structure of the geographic database <NUM> according to the various embodiments described herein. In one embodiment, the geographic database <NUM> can provide the tile-based feature detection data records <NUM> to dynamic adaptation of the in-vehicle feature detector <NUM>.

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> and other end user devices with near real-time speed without overloading the available resources of the vehicles <NUM> and/or devices (e.g., computational, memory, bandwidth, etc. resources).

The HD mapping data records <NUM> are created from high-resolution 3D mesh or point-cloud data generated, for instance, from LiDAR-equipped vehicles. 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>.

The HD mapping data records <NUM> also include real-time sensor data collected from probe vehicles in the field. 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.

The geographic database <NUM> can be maintained by the 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., vehicle <NUM> and/or UE <NUM>) along roads throughout the geographic region to observe features and/or record information about them, for example. Also, remote sensing, such as aerial or satellite photography, can be used.

For example, geographic data is compiled (such as into a platform specification format (PSF)) 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 UE <NUM>. 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 providing dynamic adaptation of the in-vehicle feature detector <NUM> 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 provide dynamic adaptation of the in-vehicle feature detector <NUM> 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, 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 providing dynamic adaptation of the in-vehicle feature detector <NUM>. 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 providing dynamic adaptation of the in-vehicle feature detector <NUM>. 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 providing dynamic adaptation of the in-vehicle feature detector <NUM>, 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.

Special purpose hardware, such as an application specific integrated circuit (ASIC) <NUM>, is coupled to bus <NUM>.

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 providing dynamic adaptation of the in-vehicle feature detector <NUM>.

<FIG> illustrates a chip set <NUM> upon which the invention may be implemented. Chip set <NUM> is programmed to provide dynamic adaptation of the in-vehicle feature detector <NUM> 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 chip set <NUM> includes a communication mechanism such as a bus <NUM> for passing information among the components of the chip set <NUM>.

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 provide dynamic adaptation of the in-vehicle feature detector <NUM>. 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 station (e.g., handset) capable of operating in the system of <FIG>. 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 back-end 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 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 provide dynamic adaptation of the in-vehicle feature detector <NUM>. 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 for dynamic adaptation of an in-vehicle feature detector comprising:
retrieving, by a vehicle comprising a vehicle feature detector, a feature detection model, precomputed weights for the feature detection model, or a combination thereof embedded in a data layer of map data representing a geographic area from which a training data set was collected to generate the feature detection model, the precomputed weights, or a combination thereof;
deploying the feature detection model, the precomputed weights, or a combination thereof to adapt the in-vehicle feature detector based on determining that the in-vehicle feature detector is in the geographic area, plans to travel in the geographic area, or a combination thereof;
wherein the in-vehicle feature detector uses the feature detection model, the precomputed weights, or a combination thereof to process sensor data collected while in the geographic area to detect one or more features from imagery-of the sensor data, and
matching, by the in-vehicle feature detector, the detected one or more features from the imagery-of the sensor data to one or more mapped features of the map data to determine a current location of the vehicle.