Method and apparatus for the detection and labeling of features of an environment through contextual clues

Described herein are methods of detecting and labeling features within an image of an environment. Methods may include: receiving sensor data from an image sensor, where the sensor data is representative of a first image including an aerial view of a geographic region; detecting, using a perception module, at least one vehicle within the image of the geographic region; identifying an area around the at least one vehicle as a road segment in response to detecting the at least one vehicle; based on the identification of the area around the vehicle as a road segment, identifying features within the area as road features based on a context of the area; generating a map update for the road features of the road segment; and causing a map database to be updated with the road features of the road segment.

TECHNOLOGICAL FIELD

Example embodiments of the present invention relate generally to the detection and labeling of objects in an environment, and more particularly, to automatically identify features of an environment based on contextual clues.

BACKGROUND

Road geometry modelling is very useful for map creation and identification of features in environments, such as lane lines or road signs along a road segment. Such feature identification may facilitate autonomous vehicle navigation along a prescribed path. Traditional methods for modelling of road geometry and object or feature detection are resource intensive, often requiring significant amounts of human measurement and calculation. Such methods are thus time consuming and costly. Exacerbating this issue is the fact that many modern day applications require the analysis of large amounts of data, and therefore are not practical without quicker or less costly techniques.

Some current methods for road geometry and environment modelling rely upon feature detection from image data to perform object identification, but these methods have deficiencies. For instance, some systems designed for object detection/identification around a vehicle exist, but may be unreliable. Further, the reliability of object and feature detection may not be known such that erroneous object detection or lack of object detection may adversely impact autonomous or semi-autonomous driving.

BRIEF SUMMARY

Accordingly, a method, apparatus, and computer program product are provided for automatically detecting and labeling of features in an environment, and more particularly, to automatically identify features of an environment based on contextual clues. In a first example embodiment, an apparatus is provided including at least one processor and at least one memory including computer program code. The at least one memory and the computer program code may be configured to, with the at least one processor, cause the apparatus to: receive sensor data from an image sensor, where the sensor data is representative of a first image including an aerial view of a geographic region; detect, using a perception module, at least one vehicle within the image of the geographic region; identify an area around the at least one vehicle as a road segment in response to detecting the at least one vehicle; based on the identification of the area around the vehicle as a road segment, identify features within the area as road features based on a context of the area; generate a map update for the road features of the road segment; and cause a map database to be updated with the road features of the road segment.

Causing the apparatus to detect, using the perception module at least one vehicle within the image of the geographic region may include causing the apparatus to identify at least one object within the image of the environment as a vehicle in response to the at least one object corresponding to a learned template of a vehicle. Causing the apparatus to identify an area around the at least one vehicle as a road segment in response to detecting the at least one vehicle may include causing the apparatus to: identify a location of the at least one vehicle; determine if a mapped road is within a predefined distance of the location of the at least one vehicle; and identify an area around the at least one vehicle as a road segment in response to detecting the at least one vehicle and the location of the at least one vehicle being within the predefined distance of a mapped road. The apparatus may be caused to provide for autonomous control of a vehicle based, at least in part, on the map update of the road features of the road segment.

The road features of the road segment may include information associated with driving restrictions along the road segment, where causing the apparatus to provide for autonomous control of the vehicle based, at least in part, on the map update may include causing the apparatus to provide autonomous control of the vehicle along the road segment based on the driving restrictions. The perception module may include an auto-encoder, where the auto-encoder may be trained based on a plurality of manually identified vehicles. Causing the apparatus to identify an around the at least one vehicle as a road segment may include causing the apparatus to: apply a dilation algorithm to probe and expand the area around the at least one vehicle in identifying the area as a road segment; and apply a spline-based curve fitting model to extract lane level geometry of the road segment.

Embodiments described herein may provide a computer program product including at least one non-transitory computer-readable storage medium having computer-executable program code instructions stored therein. The computer-executable program code instructions may include program code instructions to: receive sensor data from an image sensor, where the sensor data is representative of a first image including an aerial view of a geographic region; detect, using a perception module, at least one vehicle within the image of the geographic region; identify an area around the at least one vehicle as a road segment in response to detecting the at least one vehicle; based on the identification of the area around the vehicle as a road segment, identify features within the area as road features based on a context of the area; generate a map update for the road features of the road segment; and cause a map database to be updated with the road features of the road segment.

According to some embodiments, the program code instructions to detect, using the perception module, at least one vehicle within the image of the geographic region may include program code instructions to: identify at least one object within the image of the environment as a vehicle in response to the at least one object corresponding to a learned template of a vehicle. The program code instructions to identify an area around the at least one vehicle as a road segment in response to detecting the at least one vehicle may include program code instructions to: identify a location of the at least one vehicle; determine if a mapped road is within a predefined distance of the location of the at least one vehicle; and identify an area around the at least one vehicle as a road segment in response to detecting the at least one vehicle and the location of the at least one vehicle being within the predefined distance of a mapped road. The computer program product may include program code instructions to provide for autonomous control of a vehicle based, at least in part, on the map update of the road features of the road segment.

The road features of the road segment may include information associated with driving restrictions along the road segment. The program code instructions to provide for autonomous control of the vehicle based, at least in part, on the map update may include program code instructions to provide for autonomous control of the vehicle along the road segment based on the driving restrictions. The perception module may include an auto-encoder, where the auto-encoder is trained based on a plurality of manually identified vehicles. The program code instructions to identify an area around the at least one vehicle as a road segment may include program code instructions to: apply a dilation algorithm to probe and expand the area around the at least one vehicle in identifying the area as a road segment; and apply a spline-based curve fitting model to extract lane level geometry of the road segment.

Embodiments described herein may provide a method including: receiving sensor data from an image sensor, where the sensor data is representative of a first image including an aerial view of a geographic region; detecting, using a perception module, at least one vehicle within the image of the geographic region; identifying an area around the at least one vehicle as a road segment in response to detecting the at least one vehicle; based on the identification of the area around the vehicle as a road segment, identifying features within the area as road features based on a context of the area; generating a map update for the road features of the road segment; and causing a map database to be updated with the road features of the road segment. According to some embodiments, detecting, using the perception module, at least one vehicle within the image of the geographic region may include identifying at least one object within the image of the environment as a vehicle in response to the at least one object corresponding to a learned template of a vehicle.

According to some methods, identifying an area around the at least one vehicle as a road segment in response to detecting the at least one vehicle may include: identifying a location of the at least one vehicle; determining if a mapped road is within a predefined distance of the location of the at least one vehicle; and identifying an area around the at least one vehicle as a road segment in response to detecting the at least one vehicle and the location of the at least one vehicle being within the predefined distance of a mapped road. Methods may include providing for autonomous vehicle control of a vehicle based, at least in part, on the map update of the road features of the road segment. The road features of the road segment may include information associated with driving restrictions along the road segment, where providing for autonomous control of the vehicle based, at least in part, on the map update may include providing autonomous control of the vehicle along the road segment based on the driving restrictions. Identifying an area around the at least one vehicle as a road segment may include: applying a dilation algorithm to probe and expand the area around the at least one vehicle in identifying the area as a road segment; and applying a spline-based curve fitting model to extract lane level geometry of the road segment.

Embodiments described herein may provide an apparatus including: means for receiving sensor data from an image sensor, where the sensor data is representative of a first image including an aerial view of a geographic region; means for detecting, using a perception module, at least one vehicle within the image of the geographic region; means for identifying an area around the at least one vehicle as a road segment in response to detecting the at least one vehicle; based on the identification of the area around the vehicle as a road segment, means for identifying features within the area as road features based on a context of the area; means for generating a map update for the road features of the road segment; and means for causing a map database to be updated with the road features of the road segment. According to some embodiments, the means for detecting, using the perception module, at least one vehicle within the image of the geographic region may include means for identifying at least one object within the image of the environment as a vehicle in response to the at least one object corresponding to a learned template of a vehicle.

According to some embodiments, the means for identifying an area around the at least one vehicle as a road segment in response to detecting the at least one vehicle may include: means for identifying a location of the at least one vehicle; means for determining if a mapped road is within a predefined distance of the location of the at least one vehicle; and means for identifying an area around the at least one vehicle as a road segment in response to detecting the at least one vehicle and the location of the at least one vehicle being within the predefined distance of a mapped road. An apparatus may include means for providing for autonomous vehicle control of a vehicle based, at least in part, on the map update of the road features of the road segment. The road features of the road segment may include information associated with driving restrictions along the road segment, where the means for providing for autonomous control of the vehicle based, at least in part, on the map update may include means for providing autonomous control of the vehicle along the road segment based on the driving restrictions. The means for identifying an area around the at least one vehicle as a road segment may include: means for applying a dilation algorithm to probe and expand the area around the at least one vehicle in identifying the area as a road segment; and means for applying a spline-based curve fitting model to extract lane level geometry of the road segment.

DETAILED DESCRIPTION

A method, apparatus and computer program product are provided in accordance with an example embodiment of the present invention to facilitate perception system training for automatically detecting features of an environment. In the context of mapping, objects or features of the environment may include roads, lane geometry, road signs, buildings, etc. Perception systems may detect these features and objects in an environment and understand the features and objects in the context of the environment. In order for a perception system to reliably detect features and objects, large volumes of training data may be collected from a data capture platform and objects and features of images of the environment may be positively identified in order for the perception system to understand how to identify objects and features of the environment. Conventionally, humans may perform the positive identification of objects and features as manual labelers of objects and features in the images. Such manual labeling may include identification and classification of objects within an image of an environment, together with providing an indication of a location of the objects within the image of the environment.

Embodiments of the present disclosure relate to automated feature detection in satellite imagery to augment the creation of high-definition (HD) maps, particularly in areas in which available map data is sparse. A segmented satellite image or image captured by an aerial vehicle from the overhead perspective may be used to invert lane geometry, road signs, etc., that are relevant features to the autonomous driving use case.

In order to automatically detect and segment features, such as regions that correspond to roads in a satellite or overhead image, large scale datasets may need to be hand-labeled and manually curated. The road section needs to be manually labeled at a pixel-level, resulting in a laborious and time consuming process. Once a dataset is curated, a segmentation method, typically implemented as a deep-learning network, may be used to identify regions that correspond to features such as roads in the satellite imagery. Embodiments described herein propose a method to simplify the labeling process and to bootstrap the segmentation scheme in order to improve the throughput of the data labelers and ultimately, the HD map constructed via segmented satellite data.

The automatic detection of features of an environment may further be complicated through learning data produced in one area, such as proximate a first city, is used to detect features in another region or city. For example, road segments in a first region may be exclusively asphalt, which is black in color, whereas another region may have roads that are substantially concrete, which is gray/white in color. Learning data generated from one region may be ineffective at detecting roads in another region, which may lead to incorrect labeling of features in the environment. Embodiments described herein avoid such issues through use of contextual cues that may be readily identified.

FIG.1is a schematic diagram of an example apparatus configured for performing some of the operations described herein and/or which may benefit from example embodiments of the present disclosure. Apparatus20is an example embodiment that may be embodied by or associated with any of a variety of computing devices that include or are otherwise associated with a device configured for providing an advanced driver assistance features. For example, the computing device may be an Advanced Driver Assistance System module (ADAS) which may at least partially control autonomous or semi-autonomous features of a vehicle with the assistance of establishing object location using a perception system trained according to example embodiments described herein; however embodiments of the apparatus may be embodied or partially embodied as a mobile terminal, such as a personal digital assistant (PDA), mobile telephone, smart phone, personal navigation device, tablet computer, camera or any combination of the aforementioned systems. In an example embodiment for facilitating autonomous or partially autonomous vehicle control, the apparatus20may embodied or partially embodied by an electronic control unit of a vehicle that supports safety-critical systems such as the powertrain (engine, transmission, electric drive motors, etc.), steering (e.g., steering assist or steer-by-wire), and braking (e.g., brake assist or brake-by-wire). Optionally, the computing device may be a fixed computing device, such as a built-in vehicular navigation device, assisted driving device, or the like.

Optionally, the apparatus may be embodied by or associated with a plurality of computing devices that are in communication with or otherwise networked with one another such that the various functions performed by the apparatus may be divided between the plurality of computing devices that operate in collaboration with one another.

The apparatus20may be equipped with any number of sensors21, such as a global positioning system (GPS), accelerometer, image sensor, LiDAR (Light Distancing and Ranging) sensor, radar, and/or gyroscope. Any of the sensors may be used to sense information regarding the movement, positioning, or orientation of the device for use in navigation assistance, as described herein according to example embodiments. In some example embodiments, such sensors may be implemented in a vehicle or other remote apparatus, and the information detected may be transmitted to the apparatus20, such as by near field communication (NFC) including, but not limited to, Bluetooth™ communication, or the like.

The apparatus20of an example embodiment may also include or otherwise be in communication with a user interface28. The user interface may include a touch screen display, a speaker, physical buttons, and/or other input/output mechanisms. In an example embodiment, the processor24may comprise user interface circuitry configured to control at least some functions of one or more input/output mechanisms. The processor and/or user interface circuitry comprising the processor may be configured to control one or more functions of one or more input/output mechanisms through computer program instructions (for example, software and/or firmware) stored on a memory accessible to the processor (for example, memory device24, and/or the like).

The apparatus20of an example embodiment may also optionally include a communication interface22that may be any means such as a device or circuitry embodied in either hardware or a combination of hardware and software that is configured to receive and/or transmit data from/to other electronic devices in communication with the apparatus, such as by NFC, described above. Additionally or alternatively, the communication interface22may be configured to communicate over Global System for Mobile Communications (GSM), such as but not limited to Long Term Evolution (LTE). In this regard, the communication interface22may include, for example, an antenna (or multiple antennas) and supporting hardware and/or software for enabling communications with a wireless communication network. Additionally or alternatively, the communication interface22may include the circuitry for interacting with the antenna(s) to cause transmission of signals via the antenna(s) or to handle receipt of signals received via the antenna(s). In some environments, the communication interface22may optionally support wired communication may alternatively support vehicle to vehicle or vehicle to infrastructure wireless links.

The apparatus20may support a mapping or navigation application so as to present maps or otherwise provide navigation, driver assistance, or some degree of autonomous control of a vehicle. For example, the apparatus20may provide for display of a map and/or instructions for following a route within a network of roads via user interface28. In order to support a mapping application, the computing device may include or otherwise be in communication with a geographic database, such as may be stored in memory26. For example, the geographic database includes node data records, road segment or link data records, point of interest (POI) data records, and other data records. More, fewer or different data records can be provided. In one embodiment, the other data records include cartographic data records, routing data, and maneuver data. One or more portions, components, areas, layers, features, text, and/or symbols of the POI or event data can be stored in, linked to, and/or associated with one or more of these data records. For example, one or more portions of the POI, event data, or recorded route information can be matched with respective map or geographic records via position or GPS data associations (such as using known or future map matching or geo-coding techniques), for example. Furthermore, other positioning technology may be used, such as electronic horizon sensors, radar, LiDAR, ultrasonic and/or infrared sensors.

In example embodiments, a navigation system user interface may be provided to provide driver assistance to a user traveling along a network of roadways. Optionally, embodiments described herein may provide assistance for autonomous or semi-autonomous vehicle control. Autonomous vehicle control may include driverless vehicle capability where all vehicle functions are provided by software and hardware to safely drive the vehicle along a path identified by the vehicle. Semi-autonomous vehicle control may be any level of driver assistance from adaptive cruise control, to lane-keep assist, or the like. Identifying objects along road segments or road links that a vehicle may traverse may provide information useful to navigation and autonomous or semi-autonomous vehicle control by establishing barriers defining roadway width, identifying roadway curvature, locating signs and identifying information communicated by the sign, or any boundary related details of the road links that may be traversed by the vehicle.

Autonomous vehicles, or vehicles with some level of autonomous controls, provide some degree of vehicle control that was previously performed by a person driving a vehicle. Removing some or all of the responsibilities of driving from a person and automating those responsibilities requires a high degree of confidence in performing those responsibilities in a manner at least as good as a human driver. For example, maintaining a vehicle's position within a lane by a human involves steering the vehicle between observed lane markings and determining a lane when lane markings are faint, absent, or not visible due to weather (e.g., heavy rain, snow, bright sunlight, etc.). A vehicle with autonomous capability to keep the vehicle within a lane as it travels along a road segment must also be able to identify the lane based on the lane markings or other features that are observable. As such, the autonomous vehicle must be equipped with sensors sufficient to observe road features, and a controller that is capable of processing the signals from the sensors observing the road features, interpret those signals, and provide vehicle control to maintain the lane position of the vehicle based on the sensor data.

A perception system may be used to interpret the information gathered by the sensors of a vehicle, such as one or more image sensors, to identify objects and features of a roadway. The perception system may be trained through a neural network using training data that identifies objects and features to facilitate real-time identification of objects and features in an environment of the vehicle through the perception system. Maintaining lane position is merely one illustrative example of a function of autonomous or semi-autonomous vehicles that demonstrates the sensor level and complexity of autonomous driving. However, autonomous vehicle capabilities, particularly in fully autonomous vehicles, must be capable of performing all driving functions. As such, the vehicles must be equipped with sensor packages that enable the functionality in a safe manner.

Beyond sensors on a vehicle, autonomous and semi-autonomous vehicles may use HD maps to help navigate and to control a vehicle along its path. These HD maps may provide road geometry, lane geometry, road segment restrictions (e.g., speed limits), lane restrictions (e.g., turn-only lanes), and any other information that may be related to the road segments of a road network. Further, HD maps may be dynamic and may receive updates periodically from map services providers which may be informed by vehicles traveling along the road segments with sensor packages able to identify and update the HD maps. Further, properties of road segments may change at different times of day or different days of the week, such as express lanes which may be in a first direction of travel at a first time of day, and a second direction of travel at a second time of day. HD maps may include this information to provide accurate navigation and to facilitate autonomy along these road segments to supplement a sensor package associated with a vehicle. Embodiments described herein may facilitate the building and updating of HD maps through the perception systems being able to reliably interpret features and objects of a mapped region, and providing that data to a map services provider.

A map service provider database may be used to provide driver assistance via a navigation system and/or through an ADAS having autonomous or semi-autonomous vehicle control features.FIG.2illustrates a communication diagram of an example embodiment of a system for implementing example embodiments described herein. The illustrated embodiment ofFIG.2includes a mobile device104, which may be, for example, the apparatus20ofFIG.2, such as a mobile phone, an in-vehicle navigation system, an ADAS, or the like, and a map data service provider or cloud service108. Each of the mobile device104and map data service provider108may be in communication with at least one of the other elements illustrated inFIG.2via a network112, which may be any form of wireless or partially wireless network as will be described further below. Additional, different, or fewer components may be provided. For example, many mobile devices104may connect with the network112. The map data service provider108may be cloud-based services and/or may operate via a hosting server that receives, processes, and provides data to other elements of the system.

The map data service provider may include a map database110that may include node data, road segment data or link data, point of interest (POI) data, traffic data or the like. The map database110may also include cartographic data, routing data, and/or maneuvering data. According to some example embodiments, the road segment data records may be links or segments representing roads, streets, or paths, as may be used in calculating a route or recorded route information for determination of one or more personalized routes. The node data may be end points corresponding to the respective links or segments of road segment data. The road link data and the node data may represent a road network, such as used by vehicles, cars, trucks, buses, motorcycles, and/or other entities. Optionally, the map database110may contain path segment and node data records or other data that may represent pedestrian paths or areas in addition to or instead of the vehicle road record data, for example. The road/link segments and nodes can be associated with attributes, such as geographic coordinates, street names, address ranges, speed limits, turn restrictions at intersections, and other navigation related attributes, as well as POIs, such as fueling stations, hotels, restaurants, museums, stadiums, offices, auto repair shops, buildings, stores, parks, etc. The map database110can include data about the POIs and their respective locations in the POI records. The map database110may include data about places, such as cities, towns, or other communities, and other geographic features such as bodies of water, mountain ranges, etc. Such place or feature data can be part of the POI data or can be associated with POIs or POI data records (such as a data point used for displaying or representing a position of a city). In addition, the map database110can include event data (e.g., traffic incidents, construction activities, scheduled events, unscheduled events, etc.) associated with the POI data records or other records of the map database110.

The map database110may be maintained by a content provider e.g., the map data service provider and may be accessed, for example, by the content or service provider processing server102. By way of example, the map data service provider can collect geographic data and dynamic data to generate and enhance the map database110and dynamic data such as traffic-related data contained therein. 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, such as via global information system databases. In addition, the map developer can employ field personnel to travel by vehicle along roads throughout the geographic region to observe features and/or record information about them, for example. Also, remote sensing, such as aerial or satellite photography and/or LiDAR, can be used to generate map geometries directly or through machine learning as described herein. However, the most ubiquitous form of data that may be available is vehicle data provided by vehicles, such as mobile device104, as they travel the roads throughout a region. As noted above, sensor data from vehicles may be processed according to example embodiments described herein using a perception system to identify objects and features of a road segment. This data may be used to build and/or update the map database110.

The map database110may be a master map database, such as an HD map database as described further below, stored in a format that facilitates updates, maintenance, and development. For example, the master map database or data in the master map database can be in an Oracle spatial format or other spatial format, such as for development or production purposes. The Oracle spatial format or development/production database can be compiled into a delivery format, such as a geographic data files (GDF) format. The data in the production and/or delivery formats can be compiled or further compiled to form geographic database products or databases, which can be used in end user navigation devices or systems.

As mentioned above, the map data service provider108map database110may be a master geographic database, but in alternate embodiments, a client side map database may represent a compiled navigation database that may be used in or with end user devices (e.g., mobile device104) to provide navigation and/or map-related functions. For example, the map database110may be used with the mobile device104to provide an end user with navigation features and/or to facilitate autonomous or partial autonomous control of a vehicle. In such a case, the map database110can be downloaded or stored on the end user device which can access the map database110through a wireless or wired connection, such as via a processing server102and/or the network112, for example.

The map database110of example embodiments may be generated from a plurality of different sources of data. For example, municipalities or transportation departments may provide map data relating to road ways, while geographic information survey systems may provide information regarding areas beyond the road network. Satellite systems may be used to collect over-head bird's eye view images of a mapped region which may be used in conjunction with data from other sources. Additional data may be stored in the map database such as traffic information, routing information, etc. This data may supplement the HD map data that provides an accurate depiction of a network of roads in the geographic region in a high level of detail including road geometries, features along the roads such as signs, rules related to travel along road segments such as speed limits, etc. The data stored in the map database may be gathered from multiple different sources, and one source of data that may help keep the data in the map database fresh is map data provided by vehicles traveling along the road segments of the road network.

Autonomous and semi-autonomous vehicles leverage sensor information relating to roads, objects, and features proximate the roads to determine safe regions of a road to drive and to evaluate their surroundings as they traverse a road segment. Further, autonomous and semi-autonomous vehicles may use high-definition map information to facilitate autonomous driving and to plan autonomous driving routes. These high-definition maps or HD maps may be specifically designed and configured to facilitate autonomous and semi-autonomous vehicle control and may be able to replicate road segments virtually with the inclusion of accurately placed signs and detailed information contained therein along with other features or objects proximate a roadway.

HD maps may have a high precision at resolutions that may be down to a several centimeters and may identify objects proximate a road segment, features of a road segment including lane widths, lane markings, traffic direction, road signs, speed limits, lane restrictions, etc. Autonomous and semi-autonomous vehicles may use these HD maps to facilitate the autonomous control features, such as traveling within a lane of a road segment at a prescribed speed limit, or following instructions of a road sign identified along a road segment. Autonomous vehicles may also be equipped with a plurality of sensors to facilitate autonomous vehicle control. Sensors may include image sensors/cameras, Light Distancing and Ranging (LiDAR), Global Positioning Systems (GPS), Inertial Measurement Units (IMUs), or the like which may measure the surroundings of a vehicle and communicate information regarding the surroundings to a vehicle control module to process and adapt vehicle control accordingly.

HD maps may be generated and updated based in part, on sensor data from vehicles traveling along road segments of a road network. These vehicles may have various degrees of autonomy and may be equipped with a variety of different levels of sensors. Sensors from fully autonomous vehicles, for example, may be used to update map data or generate new map data in a form of crowd-sourced data from vehicles traveling along road segments. Sensor data received can be compared against other sensor data relating to the images captured by sensors to establish the accuracy of sensor data and to confirm the position, size, shape, etc. of features and objects along the road segment. HD maps may optionally be generated and updated based, at least in part, on satellite imagery that provides a bird's eye view of a region and may be used to identify objects and features of the mapped region.

Embodiments described herein may broadly relate to satellite imagery or overhead imagery (e.g., from drone or aircraft) when there is a need to establish the identification and position of an object or feature within an environment, as may be beneficial to the generation and confirmation of HD maps facilitating autonomous or semi-autonomous vehicle control. For example, objects and features along a road segment may be detected through processing of sensor data through a perception system. A perception system may discern objects and features within an environment and be capable of efficiently locating and classifying the objects according to the type of object. However, in order to efficiently locate and classify objects, a perception system may require training data from which object identification can be learned through a machine-learning technique in order to reliably and efficiently identify objects within an environment.

Training data for a perception system may be generated through initial image capture using, for example, satellites or aircraft, and objects within the images may be manually labeled to create a positive and definitive identification of objects within the image. However, in order to train a perception system, a large volume of training data may be needed. Generally, the more training data used, the more accurate the perception system will be. Embodiments described herein provide a method for automatically generating training data for road-segmentation in a fully automated process that requires minimal human labeling efforts.

Embodiments described herein estimate features of an environment within imagery captured from an overhead vantage point without requiring human labeling. An automated label generation process generates labels to identify features of an environment in order to facilitate HD map building, generation, and repair. The automated label generation process relies on identifying support structures or context cues within an environment that are visible to identify features of the image. These support structures or context cues may be, for example cars and/or trucks that are visible within an image. Embodiments rely on the context cues driving the object search that involves the identification of such vehicles within an environment. Identification of cars and/or trucks within an image provides a strong indicator that the area in which the vehicles are located is a drivable surface, and is potentially a roadway. While vehicles may be detected in parking lots or driveways, alignment of a location of a detected vehicle with a map, even if the alignment is rough and not of high precision, can discern between a vehicle that is traveling along a road segment versus a vehicle that is parked in a driveway. The self-supervised learning method described herein may then be used to automatically bootstrap the labeling process of the road segment after identifying image features that are determined to be a roadway.

Methods described herein automatically generate training data for feature segmentation in a fully automated process that requires minimal manual labeling by a human. In order to train a neural network for discerning features of an environment, training data may be automatically generated from substantially random sets of images captured through any available means. As examples described herein focus on the identification of features from an overhead perspective, the sets of images used may be satellite imagery and images captured by aircraft, such as drones or camera-equipped helicopters and airplanes. The training data that is selected should be representative of the typical domains in which the feature labeling is being performed. For example, images of roads and highways may be typical domains for use with autonomous vehicles and perception systems thereof.

Using context in the identification of features of an image of an environment enables features to be estimated based on other elements of the image that can be positively and repeatably identified. For example, vehicles typically drive on roads, and identifying vehicles within an image of an environment as described herein may be done consistently and efficiently. One example process of generating training data for the detection of objects such as vehicles can be implemented in which a human user identifies vehicles within an image by bounding the vehicles, such as by clicking and dragging a box around the found vehicles present in the image. The identified vehicles may each then be segmented from the image, resulting in a mask that may be applied to other regions in the satellite image. A single such labeled example can then be pasted against random satellite images to generate training data for a vehicle detector. This may be used as a vehicle-detection network that is trained to automatically detect vehicles present in overhead perspective images.

Vehicles are examples of objects within an image that may be repeatably identifiable due to relatively consistent shapes and sizes, particularly when viewed from the overhead perspective. For example, tractor-trailer combinations often have a box trailer that is of a standard fifty-three foot length and eight foot width, with a varying length tractor pulling the trailer. The identification of such an object may be relatively easy within an overhead view given relatively standard dimensions and a regular shape. Automobiles, such as cars, minivans, and pick-up trucks, may have less consistent dimensions, but remain relatively similar in width and have a general range of lengths. The identification of vehicles in images may be facilitated through training data in which vehicles are manually identified, and those manually identified vehicles provide templates for a machine learning neural network that becomes capable of automatic identification of vehicles. Vehicle templates may be relatively robust due to commonality in sizes and shapes, such that the volume of training data needed to accurately train a machine learning neural network on how to automatically identify vehicles may be relatively low as compared with the identification of other, less uniform objects.

According to example embodiments described herein, a satellite image or other overhead perspective may be captured and provided for feature identification. The image may be processed by a perception system, which may be embodied by processing server102of the map data service provider108. The perception system, based on the neural network configuration, which may be embodied as an auto-encoder, identifies vehicles within the image. The identification of vehicles in the image provides context cues for the system described herein, as the identification of vehicles suggests that the location of the vehicles are roadways. Embodiments described herein operate under the assumption that the immediate vicinity of the vehicle is a drivable surface, whether it is paved, concrete, gravel, dirt, etc. As such, pixels within a neighborhood of the vehicle are labeled as road regions in the satellite image. Such regions may then be cropped and fed to another segmentation network that learns to segment out the full road from a single input comprising such detected vehicles. A standard dilation algorithm, followed by a spline-based curve fitting model, can be used to extract the lane-level geometry.

FIG.3depicts an example embodiment in which a roadway is identified in a satellite or overhead image. As shown inFIG.3, a portion of a satellite image200is analyzed with the perception module. A vehicle210is detected in the satellite image based on the training data that informs the perception module with respect to objects that correspond to vehicles. Based on the vehicle being identified as a context cue in the image200, embodiments described herein predict the immediate vicinity of the vehicle210to be a road/drivable segment.

The area in the immediate vicinity of the detected vehicle210may then be analyzed under the presumption that the area is a road segment. This analysis may include an auto-encoder network that identifies road features based on the presumption that the identified area is a road segment. Such road features may include lane lines, road signs, or other features specific to roadways.FIG.4illustrates the features detected through the process described herein in which the vehicle210, identified by the perception module using machine learning, indicates that the area immediately around the vehicle is a roadway. As it is established that the vehicle210is in a roadway, roadway specific features are sought. The perception module of the illustrated embodiment detects lane lines220,230,240, and250given that the lines are identified within the context of a roadway. This enables the perception module to identify the lane lines with considerably greater certainty knowing that the area around the vehicle is a roadway. Further, embodiments of the perception system may classify feature types, such as classifying lane line220and250as solid lines, where lane line220may be a solid yellow line while line250may be a solid white line. Further, lines230and240may be identified as broken or dashed lane lines.

According to some embodiments, the identification of a road/drivable segment in the immediate vicinity of a vehicle may not only detect the vehicle, but may correlate a location of the detected vehicle object with a map of a road network of the region in which the vehicle object was located. If the vehicle location does not correspond to the location of a road segment within a predetermined distance, the vehicle object may be presumed to be on a non-drivable area or private area in which drivers are not generally allowed. Such an area may be in a driveway, or parked near a house or business, for example.

While the aforementioned example embodiments relate to the detection of vehicles as context cues, it is appreciated that a variety of context cues can be used to identify a variety of features in an environment. For example, embodiments may be configured to detect bicycles from above using learning data of identified bicycles, which typically have a similar profile when viewed from above from which templates of bicycles are readily generated for use as training data in a perception system. The identification of bicycles may inform embodiments of the present disclosure that the area proximate the identified bicycles is a bicycle-friendly environment. Further, the absence of vehicles in the areas proximate bicycles may indicate that the areas proximate the bicycles are bicycle/pedestrian only paths, where vehicles may be prohibited. Similarly, embodiments may be configured to identify pedestrians from an overhead perspective, such that the identification of pedestrians suggests a pedestrian-friendly environment which may be a sidewalk, pedestrian only path, or the like.

Embodiments described herein may broadly relate to automatic map data generation when there is a need to establish the identification and position of a feature within an environment, as may be beneficial to the generation and confirmation of HD maps facilitating autonomous or semi-autonomous vehicle control. For example, features of a road segment may be detected through processing of sensor data through a perception system as described above. A perception system may discern objects and features within an environment and be capable of efficiently locating and classifying the features according to the type of feature, such as a lane line or lane line type.

As described above, HD maps may be instrumental in facilitating autonomous vehicle control. Building the HD maps may rely on satellite imagery or data or data/imagery captured from a bird's-eye perspective. The sensor data that is received is processed to identify objects and features in the sensor data to properly build and update the HD maps, and to facilitate autonomous control of a vehicle within the mapped region. Certain features within the satellite/aerial imagery may be critical for facilitating autonomous control of a vehicle, such as roadway boundaries, lane lines, road signs, etc. Detecting these features reliably and repeatably may be instrumental in safe autonomous vehicle control.

FIG.5is a flowchart of a method for automatically detecting and labeling of features in an environment, and more particularly, to automatically identify features of an environment based on contextual clues. As shown at310, sensor data is received from an image sensor, where the sensor data is representative of a first image including an aerial view of a geographic region. This image may be captured, for example, by a satellite, drone, or other aircraft of a bird's-eye view of a region. At320, at least one vehicle within the image of the geographic region is detected through use of a perception module, which may be embodied by the processing server102ofFIG.2, for example. An area is identified at330of an area around the at least one vehicle as a road segment in response to detecting the at least one vehicle. Features within the area are identified at340as road features based on a context of the area through identification of the area around the vehicle as a road segment. A map update may be generated at350for the road features of the road segment, and a map database may be updated with the road features of the road segment at360.

In an example embodiment, an apparatus for performing the method ofFIG.5above may comprise a processor (e.g., the processor24) configured to perform some or each of the operations (310-360) described above. The processor may, for example, be configured to perform the operations (310-360) by performing hardware implemented logical functions, executing stored instructions, or executing algorithms for performing each of the operations. Alternatively, the apparatus may comprise means for performing each of the operations described above. In this regard, according to an example embodiment, examples of means for performing operations310-360may comprise, for example, the processor24and/or a device or circuit for executing instructions or executing an algorithm for processing information as described above.