Patent Description:
<NPL>, discloses a framework on Rear-end collision warning systems to take into consideration of perception-reaction time effects based on an Artificial Neural Network. <NPL>, discloses a fast deep-learning-based object detection approach for identifying and recognizing road obstacles types, as well as interpreting and predicting complex traffic situations. <CIT> discloses a method and a system for vision-centric deep-learning-based road situation analysis. <CIT> discloses a hierarchical arrangement of one or more artificial neural networks for recognizing visual feature pattern extraction and output labeling. <CIT> discloses a system to display graphical images upon a windscreen of a vehicle including night vision that includes a transparent windscreen head up display, a night vision system, and an enhanced vision system manager monitoring data from the night vision system, analyzing the monitored data identifying critical information, and determining display requirements based upon the critical information. <CIT> discloses systems and methods to provide an Advanced Warning System (AWS) for a driver of a vehicle, by capturing traffic scene types from a single camera video. <CIT> discloses a driver behavior monitoring system that includes: a video camera; an image processing system; and a driver alert system. <NPL>, discloses a framework for the generation of training data for machine learning models used to recognize road traffic objects. <CIT> discloses a similar system.

Therefore, there is a need for an approach for generating synthetic image data for machine learning.

According to one embodiment, a computer-implemented method comprises determining, by a processor, a set of parameters for indicating at least one action by one or more objects. The at least one action, for instance, is a dynamic movement of the one or more objects through a geographic space over a period of time. The method also comprises processing the set of parameters to generate synthetic image data. The synthetic image data includes a computer-generated image sequence of the one or more objects performing the at least one action in the geographic space over the period of time. The method further comprises automatically labeling the synthetic image data with at least one label representing the at least one action, the set of parameters, or a combination thereof. The method further comprises providing the labeled synthetic image data for training or evaluating a machine learning model to detect the at least one action.

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 determine a set of parameters for indicating at least one action by one or more objects. The at least one action, for instance, is a dynamic movement of the one or more objects through a geographic space over a period of time. The apparatus is also caused to process the set of parameters to generate synthetic image data. The synthetic image data includes a computer-generated image sequence of the one or more objects performing the at least one action in the geographic space over the period of time. The apparatus is further caused to automatically label the synthetic image data with at least one label representing the at least one action, the set of parameters, or a combination thereof. The apparatus is further caused to provide the labeled synthetic image data for training or evaluating a machine learning model to detect the at least one action.

According to another embodiment, a non-transitory computer-readable storage medium carries one or more sequences of one or more instructions which, when executed by one or more processors, cause, at least in part, an apparatus to determine a set of parameters for indicating at least one action by one or more objects. The at least one action, for instance, is a dynamic movement of the one or more objects through a geographic space over a period of time. The apparatus is also caused to process the set of parameters to generate synthetic image data. The synthetic image data includes a computer-generated image sequence of the one or more objects performing the at least one action in the geographic space over the period of time. The apparatus is further caused to automatically label the synthetic image data with at least one label representing the at least one action, the set of parameters, or a combination thereof. The apparatus is further caused to provide the labeled synthetic image data for training or evaluating a machine learning model to detect the at least one action.

According to another embodiment, an apparatus comprises means for determining, by a processor, a set of parameters for indicating at least one action by one or more objects. The at least one action, for instance, is a dynamic movement of the one or more objects through a geographic space over a period of time. The method also comprises processing the set of parameters to generate synthetic image data. The synthetic image data includes a computer-generated image sequence of the one or more objects performing the at least one action in the geographic space over the period of time. The method further comprises automatically labeling the synthetic image data with at least one label representing the at least one action, the set of parameters, or a combination thereof. The method further comprises providing the labeled synthetic image data for training or evaluating a machine learning model to detect the at least one action.

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.

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 generating synthetic image data for machine learning 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. 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 generating synthetic image data for machine learning, according to one embodiment. Machine learning-based computer vision systems have enabled a variety of image recognition based services and applications. For example, in the automotive field, computer vision and machine learning have enabled real-time mapping and sensing of a vehicle's environment, particularly with respect to autonomous or semi-autonomous vehicles. Such an understanding of the environment provides increased safety and situational awareness while driving in a vehicle (e.g., a vehicle <NUM>) by, for instance, providing 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 important for a vehicle <NUM> to safely operate. For example, vehicles <NUM> generally must avoid both static obstacles (e.g., guard rails, medians, signs, lamp posts, etc.) and dynamic obstacles (e.g., other vehicles, pedestrians, animals, road debris, etc.), and these obstacles are dynamic may move, change, and/or appear in real-time. The dynamic nature of the movements or actions present significant technical challenges for training and evaluating machine learning models (e.g., a machine learning system <NUM> in combination with a computer vision system <NUM>) to detect the actions in image sequences or videos (e.g., as captured in real-time from camera-equipped vehicles <NUM>).

One technique that has shown significant ability in image recognition is the use of convolutional neural networks (e.g., CNNs) or equivalent machine learning models/algorithms. For example, neural networks have shown unprecedented ability to recognize objects and actions in image data (e.g., individual images and/or image sequences/videos), understand the semantic meaning of image data, and classify the image data according to semantic categories. However, CNNs and other machine learning models often need significant amounts of labeled training evaluation datasets to achieve a target level of feature prediction performance. Obtaining such training and/or evaluation data with labeled ground truth examples of dynamic movements or actions can be challenging because actions generally cannot be adequately depicted in a single image. Instead, an image sequence of at least two image frames (or a video or video clip) are needed to provide examples of actions or dynamic movements that can be learned by a machine learning system <NUM>.

For example, labeled datasets for training CNNs or equivalent in the automotive scenario (e.g., for achieving crash detection prediction) are relatively scarce. As used herein, a labeled dataset is image data (e.g., image sequences) that has been annotated with one or more labels that represent ground truth classes for the depicted action or dynamic movement. Traditionally, in the automotive scenario, real-world footage from accidents at given locations are often annotated (e.g., typically by a human) for used as labeled training or evaluation data for accident or collision detection. However, this real-world footage generally does not cover a large amount of possibilities of how an accident may occur on a single location, let alone in a variety of different locations. In addition, it can be rare or otherwise dangerous to obtain video footage of particularly dangerous situations (e.g., accidents between vehicles, accidents with pedestrians, dangerous vehicle maneuvers, etc.). This limited data can reduce the generalizability or prediction accuracy of the resulting machine learning model.

In addition, tagging/labeling video clips in a dataset as positive/negative for a characteristic, such as "dangerous driving behavior" requires a large manual effort. This manual effort can be slow and error prone because it relies on individual human annotators to review and label the video clips. Differences in perception and interpretation of movements or actions in the video clips by human annotators can then lead to potentially different or inconsistent labeling. Therefore, such datasets often involve a high production cost and are limited to the available videos.

For example, under traditional approaches, image or video training datasets are offered as file packages. These packages typically are constrained to images captured at a specific location that have been labeled (mostly) by hand to identify objects instead of the actions or movements of the objects as would be used to train a machine learning model to detect actions. A state of the art example is the KITTI training set (e.g., produced by the Kalsruhe Institute of Technology and Toyota Technological Institute at Chicago), which includes labeled imagery of real drives through Karlsruhe in Germany. The KITTI dataset, however, has shortcomings with respect to training to detect actions or movements such as when performing detection collision and other similar automotive applications. The shortcomings include providing only limited geographic coverage (e.g., Kalsruhe, Germany) which can potentially reduce generalizability of the trained machine learning model. In addition, labeling of the data has to be done by human review of the entire dataset. In addition, the dataset is static, and a new dataset requires new drives and new labelling effort.

To address these problems, a system <NUM> of <FIG> introduces a capability to generate synthetic image data (e.g., image sequences or videos) based on user defined parameters. In one embodiment, the parameters can describe the types of actions or movements that synthetic image data is to depict. The system can then use the parameters to automatically generate and label the synthetic image data. This labeled synthetic data can be then be used for training or evaluating machine learning models (e.g., CNNs or equivalent) to predict or detect actions or dynamic movements of objects in input image sequences or videos. The various embodiments of generating synthetic image data described herein provide advantages over traditional real-life video dataset because the real-life video datasets only encompass a finitie and limited number of locations and scenarios.

Generally, synthetic datasets of sufficient image quality perform similarly to real world imagery when used to train CNN detectors for detecting a specific situation, action, or movement. In one embodiment, a target image quality level can be achieved by modern graphics and simulation engines, such as driving simulators. Accordingly, in one embodiment, instead of providing a set of images gathered through cameras at a real-world location, the system <NUM> can provide a Synthetic Training Dataset Generation Service (e.g., via a synthetic data platform <NUM>) to generate labeled synthetic image data tailored to user-defined parameters to represent various actions/movements, geographic locations, environmental conditions, object types, etc. By way of example, the user for such a service can include, but is not limited to: (<NUM>) a human user defining the dataset parameters over a user interface of an application/webpage/cloud service, etc.; or (<NUM>) a client application/device/system, providing the dataset parameters over an application programming interface (API).

In contrast to traditional systems, the system <NUM> (e.g., via the synthetic data platform <NUM>) is able to generate a dataset with one or more labeled classes of actions or situations including a dynamic movement (rather than classes of objects which is the case with traditional systems). In other words, the system <NUM> generates a dataset of labeled traffic incident video clips or video clips labeled with any other action or situation involving dynamic movement for training a CNN detector (or any other equivalent machine learning algorithm). The dataset is synthetic, i.e., generated using any rendering and/or physics engine or method known in the art. By way of example, such engines include, but are not limited to, driving simulators, game engines, and/or the like. In one embodiment, the engines provide for simulating the physics of objects, rendering a video clip, collision detection, artificial intelligence (e.g., to simulate driving behavior, pedestrian behavior, animal behavior, etc.), and/or the like. Examples of game engines include, but are not limited to, the Unreal Engine, Unity Engine, etc. In one embodiment, the engines can generate the synthetic image datasets from user parameters at varying levels of detail from abstract representations (e.g., simple shapes) to photorealistic representations. It is noted, that the synthetic image data can be used for any stage of the machine learning pipeline from training to evaluation/validation.

In one embodiment, multiple road geometries and features can be randomly generated by varying a set of randomizable parameters. In yet another embodiment, the system <NUM> can automatically label the synthetic image data because the system <NUM> would know the actions or situations requested by the user based on the corresponding specified parameters. In addition, the system <NUM> can further label the synthetic dataset by using the rendering/physics engine to simulate and identify when the paths of the objects depicted in the synthetic image sequence will intercept or come close to intercepting, without having to render the actual collisions, near misses, or potential collisions. In other words, rendering of the 3D objects inside a 3D world specified by user parameters allows for generating video content, which can be used to train a CNN or equivalent machine learning model. In one embodiment, any action or situation involving a dynamic movement by one or more objects (e.g., vehicles, pedestrians, etc.) can also be simulated and automatically labeled in the same manner (e.g. dangerous driving behavior of cars nearby, potential dangerous trajectory of a pedestrian/vehicle nearby, etc.).

In one embodiment, the synthetic image data can be generic using generic geographic locations (e.g., not corresponding to real-world locations) or actual real-world locations. For example, when a CNN or equivalent machine learning model is trained using generic imaging, imagery specific to one route will not be considered by the detector to increase generalizability with respect to the detected action or movement. In some cases, this may raise the issue that geometries specific to one route cannot be assessed by the trained machine learning model to a target level of accuracy or specificity. Accordingly, in cases where specific routes or locations are to be considered, the CNN or equivalent machine learning model can be trained using a synthetic dataset generated based on the geometry and other map-stored characteristics of a specific road, route, or location. In this way, a vehicle <NUM> could have further, previously unavailable insights of e.g. safety hazards specific to a route.

The various embodiments of generating synthetic image data describe herein provides several advantages. For example, the achievable sample variety is many times higher than the available variety from traditional real-world video sources, thereby advantageously increasing the technical performance of the trained machine learning mode. In addition, the production cost of automatically tagging synthetic image data sets as positive/negative cases decreases substantially in comparison to traditional human or manual labeling. Further, the learning acquired from synthetic data only is transferable and relevant to detect events in real-life videos with high accuracy.

<FIG> is a diagram of the components of a synthetic data platform, according to one embodiment. By way of example, the synthetic data platform <NUM> includes one or more components for generating synthetic image data for machine learning according to the various embodiments described herein. It is contemplated that the functions of these components may be combined or performed by other components of equivalent functionality. In this embodiment, the synthetic data platform <NUM> includes a parameter module <NUM>, an image generator <NUM>, a labeling module <NUM>, and a data delivery module <NUM>. The above presented modules and components of the synthetic data platform <NUM> can be implemented in hardware, firmware, software, or a combination thereof. Though depicted as a separate entity in <FIG>, it is contemplated that the synthetic data platform system <NUM> may be implemented as a module of any of the components of the system <NUM> (e.g., a component of the machine learning system <NUM>, computer vision system <NUM>, services platform <NUM>, services 119a-119n (also collectively referred to as services <NUM>), etc.). In another embodiment, one or more of the modules <NUM>-<NUM> may be implemented as a cloud based service, local service, native application, or combination thereof. The functions of the synthetic data platform <NUM> and the modules <NUM>-<NUM> are discussed with respect to <FIG> below.

<FIG> is a flowchart of a process for generating synthetic image data for machine learning, according to one embodiment. In various embodiments, the synthetic data platform <NUM> and/or any of the modules <NUM>-<NUM> of the synthetic data 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 synthetic data platform <NUM> and/or the modules <NUM>-<NUM> can provide means for accomplishing various parts of the process <NUM>, as well as means for accomplishing embodiments of other processes described herein in conjunction with other components of the system <NUM>. Although the process <NUM> is illustrated and described as a sequence of steps, its contemplated that various embodiments of the process <NUM> may be performed in any order or combination and need not include all of the illustrated steps.

As discussed above, synthetic datasets perform similarly to real-world imagery when used to train CNN or equivalent machine learning detectors for detecting a specific action or situation including dynamic movements. Such synthetic image data can easily be achieved by modern simulation engines, such as driving simulators or game engines. As a result, instead of providing a set of images gathered through cameras at a real-world location, a user of the synthetic data platform <NUM> (e.g., supporting a Synthetic Training Dataset Generation Service) can provide the parameters for generating a desired synthetic image dataset. As previously discussed, the user can include, but is not limited to: (<NUM>) a human user defining the dataset parameters over a user interface of an application/webpage/cloud service, etc.; and/or (<NUM>) A client application/device/system, providing the dataset parameters over an API. For example, in step <NUM> of the process <NUM>, the parameter module <NUM> determines a set of parameters (e.g., from a user) for indicating at least one action by one or more objects. <FIG> is a diagram illustrating an example user interface for inputting parameters for generating synthetic image data, according to one embodiment. As shown, the user interface <NUM> provides user interface elements for specifying the action to simulate, the objects involved, and other parameters such as environment geographic parameters, environmental parameters, and dataset delivery parameters. Examples of these parameters are discussed in more detail below.

The at least one action is a dynamic movement of the one or more objects through a geographic space over a period of time. This is in contrast to traditional systems that generate classes of objects. Instead the embodiments of the synthetic data platform <NUM> described herein generate classes of actions which can then be used to automatically label the synthetic imaged data. In other words, the synthetic data platform <NUM> is able to generate a dataset with one or more labeled classes of actions. As noted above, an action or a situation can be defined by the dynamics of an object over time. In this way, actions can have various classes depending on how the action is defined. For example, when the action is a car movement, the resulting synthetic image data or dataset can include two classes: "car moving" versus "car standing still". As another example related to car movement, there can be an x number of classes corresponding to various speed ranges: <NUM>-<NUM> mph, <NUM>-<NUM> mph, <NUM>-<NUM> mph, and so on. Examples of other more complex actions can include, but are not limited: "Bicycle will collide with a pedestrian in t seconds", "dangerous bypassing of one car of another car", "car zig-zagging in its lane", etc. It is noted that the actions described herein are not generally recognizable in one image frame, but requires a sequence of two or more frames or images. This sequence of two or more images/frames is referred to herein as an image sequence. In the various embodiments described herein, this image sequence can also be referred to synonymously as a video or video clip.

In one embodiment, the at least one action can be related to an automotive scenario. As such, the one or more objects that are to perform the specified action or movement can include, but are not limited to, a vehicle, a pedestrian, a cyclist, an animal, a road, road debris, a road object, or a combination thereof. It is noted that the various embodiments described herein are also applicable to generating synthetic image data for any other use case in which a machine learning model is being trained to detect an action or movement (e.g., drone flight, human movement detection, non-vehicular movement detection, etc.). Accordingly, the specified object or action by the object can be related to another action or movement use case, and is not limited to the automotive use case.

In one embodiment, the parameter module <NUM> selects the geographic area based on an area of interest, an origin-destination pair, a navigation route, a road geometry, an accident rate, or a combination thereof. In other words, the user need not specify an exact geographic location to initiate the creation of the synthetic image data. Instead, the user can specify attributes (e.g., area of interest, origin-destination pair, route, road geometry, etc.) that can be used by the parameter module <NUM> to determine the appropriate geographic area. In one embodiment, the geographic area can be a real-world location or a generic location that is modeled to include the specified attributes. For example, in cases where real-world locations are used, the image generator <NUM> queries a geographic database for map data associated with the geographic area. The image generator <NUM> then renders the geographic area in the computer-generated image sequence based on the map data. The map data, for instance, can include HD surface maps of the terrain and/or locations of geographic features (e.g., roads, road objects, points of interest, etc.) that can be used to render a 3D rendering of the location in the synthetic image data.

In one embodiment, the set of parameters includes an action parameter describing a type of the at least one action. In an automotive scenario, the type of the at least one action includes failing to drive at a safe distance, driving above the speed limit, failing to yield, running a red light, driving in the wrong direction, driving while impaired, an imminent collision, an accident, a pedestrian or animal crossing or about to cross, safe or dangerous overtaking, dangerous driving behavior, or a combination thereof. The dataset should include labeled examples of these situations. Some situations not mentioned above may be entered in the system by defining object interactions as custom parameters. In other words, the user can specify another type of action other than the ones listed above using custom parameters.

In one embodiment, the general issue with datasets is that they usually only contain situations which have been foreseen and labeled. By generating the training sets on request, new scene objects and interactions can be defined. Taking the previous list as an example of preprogrammed situations, a traditional system generally would not be suited to generating labeled imagery of a cyclist performing a sudden and risky overtake because such footage would be rare or dangerous to obtain. Instead, a user could define the basic and generic pattern of such an overtake using, for instance, custom parameters if the action is not already preprogramed into the synthetic data platform <NUM>. For example, the user can specify the cyclist action to be based on the following: - a single lane road with a car driving slowly. The action can further specify parameters to indicate that the cyclist then accelerates to overtake the slowly driving car with less than lm sideways distance from car by straying into the opposite lane. In one embodiment, variations of such a scenario could be generated by the synthetic data platform <NUM> and automatically labeled accordingly, e.g., with on-coming traffic, at an intersection, vehicles ahead of the overtaken vehicle, etc. Variations of the situation would be labeled as positive as well.

A single video can also be an example of different simultaneous actions/situations, for which it can be labeled as positive for some situations and negative for other at the same time. The labeling could also include a relative figure (e.g. <NUM>% chance of a collision) rather than a binary label.

In one embodiment, the set of parameters includes an object parameter describing a type of the one or more objects that are to be simulated to perform a specified action or movement in the synthetic image data. In an automotive scenario, example objects can include, but are not limited to vehicles, pedestrians, animals, road debris, other objects, etc. The object parameters can be further defined based on an initial high-level object type. Example object parameters can include, but are not limited to: (<NUM>) vehicle-related parameters; (<NUM>) pedestrian-related parameters; and (<NUM>) parameters related to other objects.

For example, vehicle-related parameters can be used to describe the types of vehicles as well as the physical and performance characteristics that can be used to determine their appearance, movement, behavior, etc. that is simulated for rendering in the computer-generated video or image sequence included in the synthetic image data. In one embodiment, the vehicle-related parameters can specify the type of vehicle such as, but not limited to: cars, trucks, bicycles, motorcycles, airplanes, aerial drones, boats, ships, trains, etc. Further defining vehicle-related parameters can specify: (<NUM>) different makes, models, colors, shapes, etc.; (<NUM>) different speeds and movement paths (legal and illegal) of the vehicles; and/or the like. In one embodiment, the vehicle-related parameters can be based on the selected geographic are or location. For example, the specific types of vehicles and/or their characteristics can be automatically determined based on the location. For example, urban centers can be rendered to include more passenger vehicles, taxis, smaller delivery trucks, while rural interstate highways can be populated with tractor-trailer trucks, long-haul vehicles, etc..

Similarly, pedestrian-related parameters can be used to describe the types, characteristics, behaviors, capabilities, etc. of pedestrians that are to be rendered in the synthetic image data as part of a selected action or movement to render. For example, these pedestrian-related parameters include, but are not limited to all person types (e.g., age, sex, size, ethnicity, with pets, etc.. In one embodiment, the pedestrian parameter values can also be derived according to the selected location. For example, demographic data can be retrieved for the selected geographic area from the geographic database <NUM> or other equivalent database. In this way, for instance, if the geographic area or location is a neighborhood street, more children can be selected as pedestrians to render. If the geographic location is a downtown urban location, then a pedestrian mix of mostly adults can be rendered. In addition, different pedestrian behaviors (e.g., walking, running, playing, erratic, etc.), appearance (e.g., clothing, accessories, on-person equipment, etc.), and/or the like may also be derived from location.

In addition to vehicles and pedestrians, parameters describing other objects to include in the synthetic image data can be specified. These other object parameters can indicate the types of objects as well as their behavior, appearance, and/or other characteristics. These other objects can include, but are not limited to: (<NUM>) animals (e.g., wild, domestic, etc.), (<NUM>) road debris, (<NUM>) road signs, (<NUM>) road objects (e.g., lane markings, guard rails, sidewalks, etc.), (<NUM>) nearby structures; (<NUM>) nearby terrain; and/or the like. In addition, the parameters can indicate characteristics such as, but not limited to, sizes, speeds, trajectories, etc. of the objects. The parameters can also specify whether the objects are involved in the selected action or movement (e.g., in the action path), involved in some side or secondary action occurring separately from the main selected action, presented as background objects, etc. In one embodiment, the other objects and/or their characteristics, behaviors, appearances, etc. may also be derived from the selected geographic location. For example, more wild animals (e.g., deer) can be rendered in computer-generated videos depicting rural roads.

In one embodiment, the set of parameters includes an environmental parameter describing the geographic space. Environmental parameters set the scene by specifying locations, general scene characteristics, visible background items, etc. For example, the locations can be specified using geographic parameters such as, but not limited to: Area of interest (defined by polygon, city, region, state, country ZIP-code); Origin-Destination pairs (to generate multiple possible routes); Specific routes (selected on basis of e.g. most traveled, statistically accident prone, etc.); Road geometry complexity (curves, bridges, tunnels, intersections, etc. may provide more interesting cases than straight roads); Accident rates; etc..

Other environmental parameters can include parameters related to weather/visibility conditions such as but not limited to: clear, cloudy, fog, rain, snow, hail, glare, darkness, etc. In one embodiment, environmental parameters can also include parameters for specifying presence/absence and/or other characteristics of background objects such as, but not limited to: advertisements, background people, animals, cloud formations, horizon features - which types, where, and how many.

In one embodiment, a synthetic image generator <NUM> can generate 3D scenarios in image sequences or videos by composing a renderable reality, in which actions can be simulated, for example, using available physics/rendering engines (e.g., driving simulators, game engines, etc.). Accordingly, in step <NUM>, the image generator <NUM> processes the set of parameters to generate synthetic image data. The synthetic image data, for instance, includes a computer-generated image sequence of the one or more objects performing the at least one action in the geographic space over the period of time. In other words, situations or actions are simulated inside of the defined environments based on the user defined parameters indicating the action, objects, etc. to be simulated.

In one embodiment, the exact scene locations can be selected at random if no specific geographic area or location is specified by the user. In either case, if the geographic location is a real-world location, the synthetic image data can be constructed from actual 3D map data (with roads, buildings, roadside features, etc.). Objects derived from the object parameters would populate the scene and interact as defined by the action or situation (for generating labels) or in other patterns. Custom interactions would also be included in the synthetic image dataset. In one embodiment, the synthetic image generator <NUM> can create the computer-generated image sequence or video to include background imagery consistent with outdoor reality and the user defined parameters.

<FIG> is a diagram illustrating example synthetic image data, according to one embodiment. More specifically, <FIG> illustrates an example video clip <NUM> in which a user has specified parameters describing a situation or action in which a vehicle <NUM> makes a dangerous left lane passing move in a school zone causing a potential collision with a pedestrian <NUM> within five seconds. The image generator <NUM> simulates the action and renders the video clip <NUM> that shows the dynamic movements involved in the dangerous overtaking and potential collision from the perspective of another vehicle traveling in the right lane that is being overtaken.

In one embodiment, the most basic version of background imagery can be a surface and a horizon (e.g., based on terrain topology corresponding to a selected location). Then the synthetic image generator <NUM> can render additional features such as a road topology occupying the surface. In one embodiment, the road topology can be produced from a set of basic geometries combined in endless permutations. In other embodiments, the road topology can be extracted from map databases, such as a geographic database <NUM> (e.g., with a high definition (HD) map data).

Depending on the user defined parameters, the 3D world generated by the synthetic image generator <NUM> can be complemented using random scenery objects such as buildings, trees, road furniture (signs, lights, etc.), parked vehicles, advertisement posters, etc. Such objects can also be obtained from a map containing 3D information, such as the geographic database <NUM>. In addition, variable 3D moving objects (or designed to illustrate the action of interest), such as pedestrians, cyclists, vehicles, animals, debris can be generated to follow realistic (but still random) trajectories/scenarios in the scene using a pre-defined engine.

In one embodiment, the image generator <NUM> can distinguish between user-specified and randomizable parameters. For example, any of the parameters used by the synthetic data platform <NUM> can be classified as either user specified or randomizable. In one embodiment, any parameter that is not specified by a user can be selected as randomizable. As previously discussed, randomizing the values of certain parameters enable the machine learning system <NUM> be trained to be more generalizable with respect to a particular feature. If more specific prediction for a given feature is specified, then parameters or characteristics associated with the feature can be classified as not-randomizable (hence of limited or pre-defined variability). Accordingly, in one embodiment, the image generator <NUM> can determine one or more randomizable rendering variables associated with the computer-generated image sequence. The image generator then randomizes the one or more randomizable rendering variables to generate the synthetic image data. For example, in order to generate a viable dataset for machine learning (e.g., for CNNs), the video samples included in the synthetic image data may include random changes to several randomizable variables, such as, but not limited to: road shapes, road surfaces, road paint quality, road paint color, visibility/weather conditions, variable background, variable one/multiple lane trajectories for a road, variable one/multiple trajectories of objects (such as random pedestrian crossings, animals on the road, flying debris, rolling balls, etc.), and/or the like.

<FIG> is a diagram illustrating the rendering of the synthetic image data under different selectable or randomizable conditions, according to one embodiment. In the example of <FIG>, the weather condition for the action or situation depicted in the video clip <NUM> of <FIG> has been randomized to show foggy conditions. Accordingly, under the variation of the action shown in the <FIG>, the image generator <NUM> simulates the same dangerous overtaking and potential collision of <FIG> but a rendering of foggy conditions in the video clip <NUM>. In this way, the image generator can generate multiple instances or clips of a same or similar action under any number of different scenarios.

In one embodiment, the image generator <NUM> varies a level of detail or an abstractness of a rendering of the one or more objects, the geographic space, or other objects in the computer-generated image sequence based on a target generalizability of the machine learning model, and/or available computing resources. In other words, the image generator <NUM> can vary the realism of the computer-generated video or image sequence depending on the feature that is to be predicted. In the various embodiment described herein the synthetic image data is being generated for a machine learning system <NUM> to predict actions or movements. Accordingly, in some embodiments, the image generator <NUM> need not render at least some of the objects and/or the scenes in which the action or movement occurs using photo-realistic rendering (e.g., which is more computationally expensive). For example, instead of rendering a photo-realistic representation of a pedestrian or object moving across a vehicle's path, a more abstract representation can be used (e.g., a silhouette, a block, a simple shape, etc.). In this way, the machine learning system <NUM> can be trained on the action or movement made by the object rather than on the specific appearance of the object making the movement. The simpler rendering also advantageously reduces the computer resources needed to generate the synthetic image data. However, in cases where the visual appearance of the object performing the selected action is important, then a more realistic rendering can be used.

<FIG> is a diagram illustrating the rendering of the synthetic image data with different abstractions to increase the generalizability of a machine learning model, according to one embodiment. In the example of <FIG>, the image generator <NUM> abstracts the dangerous overtaking and potential collision scenario as depicted in the video clip <NUM> of <FIG> to generate an abstracted video clip <NUM>. In the abstracted video clip <NUM>, the image generator <NUM> renders only a simple horizon line without any background objects (e.g., telephone poles and mountains). In addition, instead of rendering realistic 3D representations of the vehicle <NUM> and pedestrian <NUM>, the image generator renders them as simple geographic shapes (e.g., a square <NUM> to represent the vehicle <NUM>, and a rounded rectangle <NUM> to represent the pedestrian <NUM>).

In step <NUM>, after generating the image sequence or video in the synthetic image data, the labeling module <NUM> automatically labels the synthetic image data with at least one label representing the at least one action, the set of parameters, or a combination thereof. In one embodiment, the labeled synthetic image can be stored in a training database <NUM> or equivalent. Because the synthetic image data is generated by the synthetic data platform <NUM> based on specified parameters, the platform <NUM> already has information to precisely label the computer-generated image sequence or video without any manual effort. In this way, the synthetic data platform <NUM> advantageously eliminates or reduces the manual or human resources that traditional approaches to annotating or labeling machine learning datasets entail. Moreover, by eliminating the manual labeling, the synthetic data platform <NUM> also advantageously reduces human error of inconsistency resulting from a subjective interpretation by the human labelling the action. In one embodiment, the image generator <NUM> has data indicating the precise frames or images in the image sequence that correspond to the action or movement requested by a user through its simulation and rendering of the scene. In this way, the image generator <NUM> can interact with the labeling module <NUM> to define labels to represent the designated action and/or parameters and then associated the labels with one or more frames of the computer-generated video.

For example, if a user requests that the synthetic data platform <NUM> create a computer-generated video depicting a dangerous overtaking maneuver and potential collision as in the example of <FIG> above as shown in <FIG>. The labeling module <NUM> can automatically label the video to indicate which frames of the video were generated to show the maneuver. In one embodiment, the labeling of the synthetic image data comprises labeling a span of frames of the computer-generated image sequence as a positive case or a negative case of the at least one action, or labeling the span of frames with a variable parameter value associated with the at least one action (e.g., <NUM>% probability of a collision). In the example of <FIG>, the labeling module <NUM> automatically labels the video clip <NUM> to with two labels: (<NUM>) a first label to indicate dangerous overtaking is associated with frames <NUM>-<NUM> of the video clip <NUM>; and (<NUM>) a second label to indicate that there is an <NUM>% probability of an imminent collision with a pedestrian within <NUM> seconds is associated with frames <NUM>-<NUM> of the video clip <NUM>. It is noted that the example fo <FIG> presents the labels in human readable form, but in one embodiment, the labels generally would be represented numerically and provide as indexed binary data.

In one embodiment, the labeling module <NUM> can use the rendering or physics engine used to simulate the action to predict further potential scenarios our outcomes that may result from the simulation action even if the potential scenarios or outcomes are not be rendered or depicted in the computer-generated video. In other words, as the trajectories of the different objects are planned by a simulation after taking in the different parameters, the simulation can determine if the objects in the 3D scene will collide or potentially collide at any point, without having to render the 3D scene in detail. This, in turn, provides an indication for automatically labeling the computer-generated video clip as positive/negative for containing a collision or potential collision. A frame or image sequence can then be labeled as positive if a collision is to happen in <= x seconds from the action depicted in the sequence. For example, the labeling module <NUM> can determine that the at least one action will result in a collision, a near miss, a potential collision, or a combination thereof between the one or more objects within a time threshold. The labeling module <NUM> can then label the synthetic image data to further indicate the collision, the near miss, the potential collision, or a combination thereof.

It is contemplated that the possible identifiable situations are not limited to collisions or potential collisions, but to any situation or outcome that can be inferred by analyzing a time instance before the situation happens. Using embodiments of this inference capability, multiple variations of a situation or action at any given location can be generated in a simpler manner. In this way, the training set can advantageously grow with a high variety and with an almost unlimited amount of samples, which is not possible using actual video samples.

After automatically labeling the synthetic image data (e.g., as either positive or negative cases) of a certain situation, they can be exported. For example, in step <NUM>, the data delivery module <NUM> provides the labeled synthetic image data for training or evaluating a machine learning model to detect the at least one action. In one embodiment, the set of parameters provided by the user can include a dataset parameter for describing a technical property of the synthetic image data that is to be used for providing the synthetic image data. By way of example, the technical property includes a perspective, a frame size, a frame rate, a resolution, an image sequence length, a format or codec, a delivery option, or a combination thereof of the labeled synthetic image data.

In one embodiment, the labeled synthetic image data can be provided as a download, as a data stream, via a physical media, or a combination thereof continuously or by a batch process. Accordingly, a user can choose to obtain the dataset from the data delivery module <NUM> either per: download; physical media (e.g., CD/DVD/Blu-ray Disc, HDD, SSD, SD card, USB memory device etc.); streamed; and/or the like. For example, streaming of labeled synthetic image data offers the possibility to provide an essentially infinite stream of image variations for actions simulated for a specific geographic region/route. In one embodiment, the data delivery module <NUM> use access credentials, metering, and/or equivalent to enable users to access the synthetic data platform <NUM> and, in some cases, to charge the user for the synthetic data service. A client application (e.g., a client application <NUM> executing on a user device <NUM>) may connect to the service via a web link (e.g., a URL) to access the synthetic image data stream. Alternatively, the data delivery module <NUM> can push the labeled synthetic image dataset to a pre-defined web address (e.g., a client URL). In this way, CNN or other machine learning detectors can advantageously constantly train and improve their detections if permanently plugged into the synthetic data platform <NUM>'s streaming or other data delivery service.

In addition to providing labeled synthetic image data as a service, the data delivery module <NUM> can provide the data as a product. For example, the resulting set of labeled synthetic image sequences from the synthetic data platform <NUM> can be delivered as a bulk package for download, on media or offered for download, as described above. In yet another embodiment, the synthetic data platform <NUM> itself can be a product that is integrated into other third-party services and/or content providers. For example, the synthetic data platform <NUM> (as a product) can be incorporated into a third-party services platform <NUM>, any of the services 119a-119n (also collectively referred to as services <NUM>) of the services platform <NUM>, the content providers 121a-<NUM> (also collectively referred to as content providers <NUM>), and/or equivalent.

In one embodiment, the synthetic data platform <NUM> can generate labeled synthetic image datasets as service for training models to safely navigate vehicles in planned routes. For example, routes planned in advance typically cover specific areas of the map. While there are many variables on a route, the route geometry and buildings are rather constant. Accordingly, a labeled synthetic image training set could be generated according to the various embodiments described herein to include all of the more permanent elements of a route while randomizing features such as road quality, road paint, other vehicles, pedestrians, cyclist, advertisement posters, etc. A CNN or other machine learning detector mounted on a vehicle <NUM> could receive the synthetic image dataset prior to driving the route, which would enable the dataset to further train the machine learning detector of the vehicle <NUM> for the specifics of a route, thereby advantageously increasing accuracy and safety significantly.

<FIG> is a diagram illustrating an example user interface <NUM> of a CNN detector trained to detect potential collisions using synthetic image data, according to one embodiment. In this example, the vehicle <NUM> is equipped with a system <NUM> including a CNN detector for detecting potential collisions. Although not depicted <FIG>, in some embodiments, the system <NUM> and/or CNN detector can comprise an application <NUM> executing on a user device <NUM> (e.g., a mobile phone) that can be mounted in the vehicle <NUM> or held by a user. In the example of <FIG>, synthetic image data such as the video clip <NUM> of <FIG> is streamed or otherwise delivered to train and or evaluate the CNN detector subsequently placed in the vehicle <NUM>. Based on this training, when the vehicle <NUM> approaches a school crossing zone <NUM> at which a vehicle <NUM> is making a dangerous maneuver that might result in a collision with a pedestrian <NUM>. The vehicle <NUM> captures a video clip of the scene which is processed by the onboard CNN detector, resulting in a prediction of an imminent collision between the vehicle <NUM> and pedestrian <NUM>. The system <NUM> can then warn the driver of the imminent third-party collision in an alert message <NUM>. Alternatively, if the vehicle <NUM> is an autonomous vehicle operating in autonomous mode, the system <NUM> can interact with the vehicle control system to automatically modify the vehicle <NUM>'s operation to avoid the upcoming collision (e.g., changing direction, slowing down, or honking to alert the other vehicle <NUM> and/or pedestrian <NUM>).

In summary, allowing users of this synthetic data platform <NUM> to choose or describe the labeled action (e.g., dynamic movements and potentially other behaviors) by selected objects along with parameters such as camera point-of-view/position and additional variability (like environmental variables), provides the potential to learn and predict complex behaviors for autonomous driving and other applications beyond the automotive scenario. These other applications include, but are not limited to: (<NUM>) alerting pedestrians when it is unsafe to cross the road; (<NUM>) alerting bicyclists t seconds before potential collisions; (<NUM>) street cameras capturing motorcycles dangerous in-lane bypassing; etc..

Returning to <FIG>, as shown and discussed above, the system <NUM> includes the synthetic data platform <NUM> for providing labeled synthetic image data for training machine learning models (e.g., a CNN) of the machine learning system <NUM>. In some use cases, the system <NUM> can include a computer vision system <NUM> configured to use machine learning to detect actions or dynamic movement of objects depicted in image sequences or videos. For example, with respect to driving, navigation, mapping, and/or other similar applications, the computer vision system <NUM> can detect collisions, dangerous situations (e.g., dangerous overtaking, following too closely, dangerous weaving, etc.) in input image sequences and generate associated prediction confidence values, according to the various embodiments described herein. In one embodiment, the machine learning system <NUM> includes a neural network or other machine learning models to make predictions of detected actions and related features. In one embodiment, the neural network of the machine learning system <NUM> is a CNN which consists of multiple layers of collections of one or more neurons (which are configured to process a portion of an input image such as a grid cell or receptive field). 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 an input image delineated by a respective a grid cell generated as described above.

In one embodiment, the synthetic data platform <NUM> has connectivity or access to a training database <NUM> for storing the labeled synthetic image data generated according to the various embodiments described herein, and as well as a geographic database <NUM> for retrieving mapping data and/or related attributes for creating computer-generated videos of user-specified actions. In one embodiment, the geographic database <NUM> can include electronic or digital representations of mapped geographic features to facilitate generating of synthetic image data. In one embodiment, the synthetic data platform <NUM>, machine learning system <NUM> and/or computer vision system <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 that rely on machine learning models trained using synthetic image data. By way of example, the services <NUM> include, but are not limited to, 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 synthetic data platform <NUM>, machine learning system <NUM> and/or of the computer vision system <NUM> employing labeled synthetic image data for machine learning.

In one embodiment, the synthetic data platform <NUM>, machine learning system <NUM>, and/or computer vision system <NUM> may be platforms with multiple interconnected components. The synthetic data platform <NUM>, machine learning system <NUM>, and/or computer vision system <NUM> may include multiple servers, intelligent networking devices, computing devices, components and corresponding software for generating labeled synthetic image data for machine learning. In addition, it is noted that the synthetic data platform <NUM>, machine learning system <NUM>, and/or computer vision system <NUM> may be separate entities of the system <NUM>, a part of the one or more services <NUM>, a part of the services platform <NUM>, or included within the user devices <NUM> and/or vehicle <NUM>.

In one embodiment, content providers <NUM> may provide content or data (e.g., including geographic data, 3D models, parametric representations of mapped features, etc.) to the synthetic data platform <NUM>, the machine learning system <NUM>, the computer vision system <NUM>, the services platform <NUM>, the services <NUM>, the user devices <NUM>, the vehicle <NUM>, and/or an application <NUM> executing on the user device <NUM>. The content provided may be any type of content, such as map content, textual content, audio content, video content, image content, etc. used for generating labeled synthetic image data. In one embodiment, the content providers <NUM> may provide content that may also aid in generating synthetic image data. In one embodiment, the content providers <NUM> may also store content associated with the synthetic data platform <NUM>, geographic database <NUM>, machine learning system <NUM>, computer vision system <NUM>, services platform <NUM>, services <NUM>, user device <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 user device <NUM> and/or vehicle <NUM> may execute a software application <NUM> to capture image data or other observation data for processing by the redundant feature detection engine according the embodiments described herein. By way of example, the application <NUM> may also be any type of application that is executable on the user device <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 machine learning system <NUM> and/or computer vision system <NUM> and perform one or more functions associated with providing a redundant feature detection engine alone or in combination with the machine learning system <NUM>.

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

In one optional embodiment, the user device <NUM> and/or vehicle <NUM> are configured with various sensors for generating or collecting environmental image data (e.g., for processing by the machine learning system <NUM> and/or computer vision system <NUM>), related geographic data, etc. In one embodiment, the sensed data represent sensor data associated with a geographic location or coordinates at which the sensor data was collected. In this way, the sensor data can act as observation data that can be process by the machine learning system <NUM> that has been trained and/or evaluated using the synthetic image data generated by the synthetic data platform <NUM>. 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 optional sensors of the user device <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 user device <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 user device <NUM> and/or vehicle <NUM> may include GPS or other satellite-based receivers to obtain geographic coordinates or signal for determine the coordinates from satellites <NUM>. 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..

In another optional embodiment, 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 synthetic data platform <NUM>, machine learning system <NUM>, computer vision system <NUM>, services platform <NUM>, services <NUM>, user device <NUM>, vehicle <NUM>, and/or content providers <NUM> optionally 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 signs include, e.g., encoding and/or decoding parametric representations into object models of signs. In one embodiment, geographic features (e.g., two-dimensional or three-dimensional features) are represented using polygons (e.g., two-dimensional features) or polygon extrusions (e.g., three-dimensional features). For example, the edges of the polygons correspond to the boundaries or edges of the respective geographic feature. In the case of a building, a two-dimensional polygon can be used to represent a footprint of the building, and a three-dimensional polygon extrusion can be used to represent the three-dimensional surfaces of the building. It is contemplated that although various embodiments are discussed with respect to two-dimensional polygons, it is contemplated that the embodiments are also applicable to three-dimensional polygon extrusions. Accordingly, the terms polygons and polygon extrusions as used herein can be used interchangeably.

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

"Node" - A point that terminates a link.

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

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

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

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

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

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

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

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

In exemplary embodiments, the road segment data records <NUM> are links or segments representing roads, streets, or paths, as can be used in the calculated route or recorded route information for determination of one or more personalized routes. The node data records <NUM> are end points corresponding to the respective links or segments of the road segment data records <NUM>. The road link data records <NUM> and the node data records <NUM> represent a road network, such as used by vehicles, 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).

In one embodiment, the geographic database <NUM> can also include synthetic image records <NUM> for storing labeled synthetic image data (e.g., as an alternate or in addition to storage in the training database <NUM>, data used for generating the labeled synthetic image data, and or any related data. In one embodiment, the synthetic image 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 associate the synthetic image data with specific geographic locations. In this way, the labeled synthetic image data can also be associated with the characteristics or metadata of the corresponding record <NUM>, <NUM>, and/or <NUM>.

In one embodiment, 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 user device <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) format) to organize and/or configure the data for performing navigation-related functions and/or services, such as route calculation, route guidance, map display, speed calculation, distance and travel time functions, and other functions, by a navigation device, such as by a vehicle <NUM> or user device <NUM>, for example. The navigation-related functions can correspond to vehicle navigation, pedestrian navigation, or other types of navigation. The compilation to produce the end user databases can be performed by a party or entity separate from the map developer. For example, a customer of the map developer, such as a navigation device developer or other end user device developer, can perform compilation on a received geographic database in a delivery format to produce one or more compiled navigation databases.

The processes described herein for generating labeled synthetic image data for machine learning 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 generate labeled synthetic image data for machine learning as described herein and includes a communication mechanism such as a bus <NUM> for passing information between other internal and external components of the computer system <NUM>. Information (also called data) is represented as a physical expression of a measurable phenomenon, typically electric voltages, but including, in other embodiments, such phenomena as magnetic, electromagnetic, pressure, chemical, biological, molecular, atomic, sub-atomic and quantum interactions. For example, north and south magnetic fields, or a zero and non-zero electric voltage, represent two states (<NUM>, <NUM>) of a binary digit (bit). Other phenomena can represent digits of a higher base. A superposition of multiple simultaneous quantum states before measurement represents a quantum bit (qubit). A sequence of one or more digits constitutes digital data that is used to represent a number or code for a character. In some embodiments, information called analog data is represented by a near continuum of measurable values within a particular range.

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

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

<FIG> illustrates a chip set <NUM> upon which an embodiment of the invention may be implemented. Chip set <NUM> is programmed to generate labeled synthetic image data for machine learning as described herein and includes, for instance, the processor and memory components described with respect to <FIG> incorporated in one or more physical packages (e.g., chips). By way of example, a physical package includes an arrangement of one or more materials, components, and/or wires on a structural assembly (e.g., a baseboard) to provide one or more characteristics such as physical strength, conservation of size, and/or limitation of electrical interaction. It is contemplated that in certain embodiments the chip set can be implemented in a single chip.

The processor <NUM> and accompanying components have connectivity to the memory <NUM> via the bus <NUM>. The memory <NUM> includes both dynamic memory (e.g., RAM, magnetic disk, writable optical disk, etc.) and static memory (e.g., ROM, CD-ROM, etc.) for storing executable instructions that when executed perform the inventive steps described herein to generate labeled synthetic image data for machine learning. 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>, according to one embodiment. Generally, a radio receiver is often defined in terms of front-end and back-end characteristics. The front-end of the receiver encompasses all of the Radio Frequency (RF) circuitry whereas the 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 generate labeled synthetic image data for machine learning. 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 computer-implemented method (<NUM>) comprising:
determining (<NUM>), by a processor, a set of parameters for indicating at least one action by one or more objects, wherein the at least one action is a dynamic movement of the one or more objects through a geographic space over a period of time;
processing (<NUM>) the set of parameters to generate synthetic image data, wherein the synthetic image data includes a computer-generated image sequence of the one or more objects performing the at least one action in the geographic space over the period of time;
automatically labeling (<NUM>) the synthetic image data with at least one label representing the at least one action, the set of parameters, or a combination thereof, wherein the labeling of the synthetic image data comprises labeling a span of frames of the computer-generated image sequence as a whole as a positive case or a negative case of the at least one action, or labeling the span of frames as a whole with a variable parameter value associated with the at least one action; and
providing (<NUM>) the labeled synthetic image data for training or evaluating a machine learning model to detect the at least one action.