Integration of positional data and overhead images for lane identification

A method and apparatus for lane detection using overhead images and positional data. A server receives positional data from a vehicle and computes a continuous trajectory. The server receives an overhead image of a road section. The server crops and processes the overhead image to remove unwanted portions. The server identifies edge features using the continuous trajectory and steerable filters. The server identifies lanes in the overhead image using a maximization algorithm, the edge filters, and the continuous trajectory.

FIELD

The following disclosure relates to mapping, imagery, and vehicle navigation services.

BACKGROUND

Modern vehicles require accurate navigational systems. A vehicle may eliminate many dangerous unknowns by identifying exactly where the vehicle is on the road in real time, along with its immediate surroundings. A high definition (HD) map is a crucial component of assisted or automatic driving technology. Vehicles may include many sensors, but a HD map may be the most important tool vehicles use.

An HD map is needed not only to allow a vehicle to precisely position itself laterally and longitudinally, but to enable the car to maneuver correctly. While sensors in vehicles may detect objects out around 100 meters, a car traveling at 80 miles per hour only has a sensing horizon of 3 seconds. Vehicles need highly accurate and up to date maps to extend sensor range and “peek” around the corner.

Existing mapping services may use inaccurate estimates based on sets of data which are not designed to offer the precise measurements required. For example, centerline estimation may attempt a measurement of the width of a road and then just split it in two. For generalized routing, this may be adequate to offer a rough estimate of a distance from point A to point B. However, for assisted driving, errors of even a few centimeters may be hazardous.

Sensors in vehicles may be able to detect lanes and lane markings in real time using image processing and light detection and ranging (lidar) based systems. These systems are useful for obstacle avoidance and detecting the movements of other vehicles. When used alone though, on-board sensor systems may exhibit large blind spots and may be unable to predict events or maneuvers even a short distance away.

On-board sensors, however, when combined with high definition maps may allow for assisted and highly automated vehicle operation. High definition maps may allow a vehicle to identify precisely where it is with respect to the road (or the world) far beyond what the Global Positioning System can do, and without its errors. The map allows the vehicle to plan precisely where the vehicle may go, and to accurately execute that plan because the vehicle is following the map. By identifying precisely where a vehicle is on the road to the centimeter, and understanding the surroundings, a mapping system may bring advanced safety to an ever-changing environment.

SUMMARY

A method for lane identification. The method comprising a server receives positional data points from a vehicle. The server computes a continuous trajectory using the positional data points. The server receives an overhead image. The server crop and processes the image to remove any unwanted objects. The server identifies edge features in the image. The server uses the edge features and continuous trajectory to identify lanes.

An apparatus comprising at least one processor and at least one memory. Computer program code causes the apparatus to receive a path of a vehicle and an overhead image of the path. The apparatus crops the image and removes any unwanted objects. The apparatus identifies edge features. The apparatus identifies lanes using the path and the edge features.

A non-transitory computer readable medium comprising instructions to identify a lane. The instructions are operable to receive positional data and an overhead image. The instructions are operable to computer a continuous trajectory from the positional data. The instructions are operable to identify an edge feature in the overhead image. The instructions are operable to identify a lane using the edge feature and the continuous trajectory.

DETAILED DESCRIPTION

The following embodiments relate to using overhead images and global positioning system (GPS) information to efficiently locate and identify lanes in a roadway image. A trajectory of a vehicle derived from GPS is used to estimate road regions in the overhead image. Objects such as trees and vehicles are removed from the overhead image. Edge features are identified using a steerable filter (using the trajectory). Lanes are detected by finding the maximum of the sum of filter responses. Portions of the lanes which are covered or hidden in the overhead images are then estimated from the detected lanes.

Existing algorithms generally use satellite images for centerline estimation. Embodiments herein integrate GPS information and overhead images together to greatly improve the efficiency and accuracy of lane identification. Other systems use information captured from sensors onboard vehicles. Sensors may offer alerts when a car is moving too close to another object, but additional information from map coverage provides a more complete picture, eradicating sensor blind spots. A sensor may miss lane markings that are not clearly visible to the vehicle, lack the ability to identify if a car is in lane three or four of an eight lane highway, or not identify the matrix environment of an intersection with no lane markings. Additionally, these sensors and algorithms are generally used for real time navigation and only estimate lanes of roads at a certain distance in front of a vehicle. Embodiments herein generate and populate maps with lane-level accuracy for large regions such as cities and countries.

Certain embodiments may be configured to identify multiple lanes in the roadway. Existing systems may focus only on the driven lane. Further, certain embodiment are capable of handling inconsistent lane widths and merge/split situations where the markings deviate from a GPS trajectory.

FIG. 1illustrates an example of a system for lane identification. The system comprises one or more devices122, a network127, and a mapping system121. The system may also include a device for capturing overhead imagery129such as an airplane or satellite. The mapping system121may include a server125and a database123. The device122(here shown as a vehicle) may collect data including positional data. The device122transmits data through the network127to the mapping system121or the server125. The database123may store the positional data, overhead image data received from the device for capturing aerial overhead imagery129and other related data.

The device122may be a mobile device or a tracking device that provides samples of data for the location of a vehicle. The device122may be a mobile phone running specialized applications that collect positional data as people travel roads as part of their daily lives. The device122may also be integrated in or with a vehicle. Switches, sub-systems or sensors that are either standard or optional equipment on a vehicle may collect and aggregate information regarding the operation of the vehicle. The device122may be sensors located on the perimeter of the vehicle in order to detect the relative distance of the vehicle from lane or roadways, the presence of other vehicles, pedestrians, traffic lights, potholes and any other objects, or a combination thereof. The device122and/or other sensor(s) such as lidar or cameras may collect data from one or more vehicles and aggregate the data into positional data. The positional data may be transmitted over the network to the mapping system121. The device122may also be configured to receive location and routing information from the mapping system121. Such data may be used to navigation or operate a vehicle or device122.

The mapping system121may include a database123and a server125. The mapping system may be comprised of multiple servers, workstations, databases, and other machines connected together and maintained by a map developer. The mapping system121may be configured to provide up to date information and maps to external map databases or mapping applications. The mapping system121collects data from multiple sources, such as through the network127, in order to maintain up to date roadway conditions. Data such as sensor data, weather, road conditions, traffic flow, and historical data is processed to determine current and future traffic conditions. The database123(also referred to as a traffic database or map database or geographic database) may include geographic data used for traffic and/or navigation-related applications. The geographic data may include overhead imagery of roads and other forms of transportation. The overhead images may be associated with other data representing a road network or system including road segment data and node data. For example, road segment data may be overlaid on overhead imagery. The road segment data represent roads. The node data represent the ends or intersections of the roads. The road segment data and the node data indicate the location of the roads and intersections as well as various attributes of the roads and intersections. Other formats than road segments and nodes may be used for the geographic data. The geographic data may include structured cartographic data or pedestrian routes.

In certain embodiments high definition overhead images, such as aerial or satellite photography may be stored in the database123. Aerial or satellite photography may be received from the device for capturing overhead imagery129. Overhead imagery such as aerial or satellite photography may be collected from a third party responsible for operating the device for capturing overhead imagery129. Overhead images collected or received from different sources may be combined to provide a continuous non-interrupted view of a region. Composite overhead images may updated as additional overhead images or information is collected.

The network127may include wired networks, wireless networks, or combinations thereof. The wireless network may be a cellular telephone network, LTE (Long-Term Evolution), 4G LTE, a wireless local area network, such as an 802.11, 802.16, 802.20, WiMax (Worldwide Interoperability for Microwave Access) network, or wireless short range network. Further, the network127may be a public network, such as the Internet, a private network, such as an intranet, or combinations thereof, and may utilize a variety of networking protocols now available or later developed including, but not limited to transmission control protocol/internet protocol (TCP/IP) based networking protocols.

FIG. 2illustrates a flow chart of a method for lane identification. As presented in the following sections, the acts may be performed using any combination of the components indicated inFIG. 1,FIG. 8, orFIG. 9. The following acts may be performed by the server125, the device122, the mapping system121, or a combination thereof. Additional, different, or fewer acts may be provided. The acts are performed in the order shown or other orders. The acts may also be repeated. Certain acts may be skipped. For example, act A107may be skipped depending on the content and size of the overhead image. Act A101may be skipped or modified in that the database123may already contain high definition overhead images for the specified region.

At act A101the server125receives an overhead image of one or more road sections. Overhead imagery is the process of taking photos of the ground from an elevated position such as from an airplane (aerial imagery). Aerial imagery may be collected using various methods including fixed-winged manned aircraft, balloons, kites, and a whole host of other methods. Another form of remotely capturing images taken of the earth's surface is known as satellite imagery (images taken from earth's orbit by satellites). Aerial and Satellite images may be captured with high resolution. In certain embodiments, the overhead images may be high definition. Satellite images may be combined with aerial images (including higher resolution images taken from airplane) to build composite images. Images may be georeferenced and stored alongside other georeferenced information in the database123. Overhead imagery may be taken on demand of a region covering a section of roads. The database123may contain previously captured overhead images which may be updated as additional overhead images are received.

At act A103, the server125receives positional data measured from a device122. The device (also referred to as a vehicle)122may be a mobile device or a tracking device that provides samples of data for the location of a vehicle. The device122may be part of a dedicated probe vehicle which travels the roadways collecting data in order to update the map database123. Positional data may be collected at the device122by use of GPS receivers or Global Navigation Satellite System (GNSS) receivers. Positional data may also be collected from positional sensors in the device122or imbedded in the vehicle. Positional data may be derived from other data such as WIFI, lidar, or cellular signals. Positional data may be collected at regular intervals. Positional data may also be collected at a change in heading of the vehicle or device122. Positional data may be supplemented with additional sensor data such as wireless or radar sensors in order to more accurately identify the position of the vehicle.

The positional data is collected at the device122and transmitted to the mapping system. The positional data may be transmitted as it is collected. The positional data may be stored locally in the device122and only transmitted once a threshold amount has been collected or transmission bandwidth is available.

The positional data may be received by the server125or the map database123. The positional data may be received real time as the vehicle or device122travels along a roadway. In such a situation, the data may be identified as comprising a related set of data points. In certain embodiments, the data may be received as more than one data point. For example, data may be received once a day from the device122or only when bandwidth is available. Such data may include more than one trip covering the same road segment. Positional data may also be collected from more than one device122which may include overlapping trips covering the same road region or segment. Each set of data may be treated as a separate set of data or the data may be averaged together to create a single set. The mapping system may determine that positional data is overlapping if the data points are within a certain threshold of one another. For example, two vehicles driving in the same lane may not produce identical sets of data even though both vehicles are generally driving the same route. Related data points may be compared with one another to create an average or median value. Other algorithms may be used to combine sets of data. The sets of data may also be combined after the continuous trajectory has been calculated for each set as described below.

At act A105, the server125computes a continuous trajectory derived from the positional data. The server125links each of the positional data points (locations) together to create a continuous path. The first data point is connected to the second data point which is connected to the third data point and so on. The result of linking the data points is a rough estimation of a vehicle's path. The server125further computes normal directions for the continuous trajectories. For the locations at the end of each trajectory, the server125uses normal directions for the last line segments. For other locations in the middle, the server125uses the average of normal directions of two consecutive line segments. If the server125is missing a positional data point, the server125may derive the data from the existing path or historical data. The server125may combine more than one set of positional data to compute the trajectory. For example, the server125may use an algorithm (or the average or median) to combine the two or more sets of data. The server125may ignore or replace data that is outside a normal distribution of errors. For example, if a positional data point is outside the expected path in light of the other data points (such as when there is a gross GPS error), that data point may be disregarded or replaced.

At act A107, the server125segments or crops the overhead image and removes objects. The server125identifying the regions of the overhead image within a threshold distance of the continuous trajectory. The continuous trajectory computed in A105may be overlaid on the overhead image. The overhead image and continuous trajectory may share positional coordinates which may be used to properly overlay the two. Once the two have been combined, the server125may crop the overhead image to generate an estimated road region. The server125roughly segments out road regions based on the vehicle GPS locations and the maximum road width. The goal of this step is to remove or crop other regions such as buildings and narrow down the search range of lanes. The maximum road width may be a value that is stored in the map database123. For example, one of the attributes stored in the map database123may be road width. The maximum may also be calculated from alternative information stored in the map database123. The map database123may indicate that a certain road segment is four lanes. The widths of vehicle lanes typically vary from 9 to 15 feet (in the United States for example). To be certain that everything is included, the maximum road width may be a multiple of the standard lane such as four times the width. The server125may segment (crop) out the regions outside the maximum road width (here using 9-15 feet as the typical width and a multiple of four—4 time 15 equals 60 feet) on each side of the positional data. The server125may also use an estimated center of the road to determine the road region. For example, using the lanes widths given above, the server125may only include the regions less than three time the maximum width (3 times 15 equal 45 feet) from the centerline. Different multiples and different maximum widths may be used to calculate the maximum road width. For different road segments, the database123may store the maximum road width. The server125may also use preexisting data regarding the buildings, sidewalks, or other identified features such as curbs to determine a road region. The following steps are applied to this estimated road regions in order to increase efficiency and limit noise.

The server may then remove objects that may interfere with the lane determinations or objects that may provide false positives when detecting features such as lines. Multiple methods may be use to remove unwanted objects.

Vehicles in the overhead image may generate false lane features. In certain embodiments the server125removes vehicles from the overhead images. There are many detection techniques could be applied in this process. The server125may use adaptive boosting (Adaboost) based object detection technique to detect vehicles. Adaboost is a machine learning meta-algorithm. Adaboost combines a set of weak classifiers to make a strong classifier. The server125may apply other techniques such as support vector machines (SVMs), other types of machine learning, perceptrons, and nearest neighbor, or other types of neural network systems to process the image. SVMs are algorithms that analyze data and recognize patterns, used for classification and regression analysis. Machine learning algorithms use existing identified objects to teach itself. After learning on existing objects, objects in images may be classified or detected. Once a vehicle is detected in the overhead images, the pixels that make up the vehicle may be identified (and removed) from the overhead image. The threshold for vehicle identification may be higher or lower depending on the type of road segment that was imaged. Different types of road segments may have varying volumes of vehicles in the overhead image at any time. Certain road segments may not need for vehicles to be removed depending on the type of road and time when the overhead image was taken. For example, lesser traveled roads such as rural or farm roads may have few to no vehicles present. Running the vehicle detection algorithms on the overhead image may return false positives or not return anything at all.

In addition to vehicles, other objects such as trees may cause issues during the later act of edge detection. Trees often have random textures that may generate multiple edge features. Hence, it may be beneficial to remove them from the overhead image. The server125may detect trees that have green (or a green shade) leaves. Hence, color based segmentation is applied that could be K-means and mean shift clustering algorithms. As with vehicle detection, multiple methods including one or more algorithms may be used to detect and then eliminate trees or foliage from the overhead image. For example, a pixel-level classifier may be trained to classify a {tree, non-tree} label to each pixel in the overhead image. The pixel-level classification is then refined by a partitioning algorithm to generate clean image masks of tree and non-tree regions.

Other objects, if they can be identified, may be isolated and removed from the overhead image. These steps may generate binary image masks for a road region that mainly contains road surfaces (e.g., ‘1’ indicates vehicle or tree, ‘0’ indicates road surface). The road surface mask may then be used to detect edge features.

In A109, the server125identifies edge features in the road region. The server125may use the road surface mask or what remains of the overhead image data once the road region has been identified and any extraneous objects have been removed. The server125may use edge detection to identify points in the overhead image at which the overhead image brightness changes sharply or has discontinuities. In certain embodiments, as only edges along the road tangent direction could be the lanes, the server125uses adaptive steerable filters to extract edge features. The angular portions or interpolation functions of these filters vary according to the road tangent directions. The radial portion of the filters could be fixed since the overhead images have fixed resolution and lane widths may be similar. The output of this step is a response map of these steerable filters.

One example of a steerable filter is the directional derivative of 2D Gaussian filter, G1φ, where subscript 1 indicates the first derivative (note that the second derivative also could be used) and superscript φ is the orientation. G1φmay be generated by a lienar combination of two basis filters, G10and G1π/2, which are filters along x and y direction respectively.
G1φ=cos(φ)G10+sin(φ)G1π/2EQUATION #1:

The server125first computes road tangent direction for each node of GPS trajectory. The tangent directions of pixels in the overhead images are interpolated based on the distances to these nodes. Therefore, every pixel in the overhead image has a direction φi. φ is a 2D array with the same size of the overhead image. cos(φ) and sin(φ) are the per-element operations, and multiplication in the above equation is also conducted per element.

r=[G1φ]2measures the orientation strength along the direction φ (The server125may ignore the Hilbert transform of G1, which may be used to measure the orientation strength). This may be referred to as the filter response. riis the filter response for the i-th pixel.

Other methods for edge detection may be used such as canny edge detection, thresholding, edge thinning, image gradients, among others.

In act A111, the server125identifies lanes in the road region. Once the server125has computed the response map in A109, the server125may start the process to detect lanes. The server125translates the continuous trajectory along two normal directions. The server125then uses a maximization process for steps along the continuous trajectory. A step size along the normal direction could be as few as three to four pixels or as many as several hundred depending on the resolution of the image. At each step, the server125computes the sum of responses of these two translated continuous trajectories (i.e., Σriand Σri′ where riis the filter response for a pixel on the translated continuous trajectory).

If the distance between two translated trajectories is close to the distance between two lanes by a threshold, the lanes are detected by finding the maximum of the sum of filter responses. The real distance between two lanes could be measured in advance as the overhead image resolution is fixed. The server125continue this maximization process by further translating continuous trajectories along two normal directions. This iteration could detect multiple lanes within the road regions segmented in act A107.

Each maximization process could be summarized by the following equation:

where ri∈Ct, ri′∈Ct′, Ctand Ct′are the new trajectories translated from the GPS trajectory by t and t′, riand ri′ are filter responses of ith pixel on Ctand Ct′, tprevand tprev′ are the previous estimated lanes during the maximization step, d is the threshold that is proportional to the lane width. In the first iteration, tprevand tprev′ are the same.

In act A113, the identified lanes are overlaid on a map or the full overhead image. In some situations, the most road regions could be invisible (e.g., completely blocked by trees and vehicles). As a result, the lanes in these road regions cannot be estimated from an overhead image. The server125may connect end points of visible lanes based on the original continuous trajectory. For example, the server125may assume that over a short period that the driver is traveling parallel to a lane line. Using this assumption, the server125may extend identified lane lines through space that is unknown due to an obstruction.

The server125may also identify the type of lane from the overhead image. Different types of lanes may be identified from their markings or the lane layout. For example, a high occupancy vehicle or carpool lane may be marked with a diamond icon, or separated from other lanes by double broken white lines or a continuous pair of double yellow lines. Bike lanes may be identified by their width or markings on the pavement. A motorcycle lane may be identified by the markings on the pavement. A bus lane may be identified by the color or markings on the pavement. A parking lane is reserved for parallel parking of vehicles and may be identified by the orientation of the markings on the pavement. A shoulder, sometimes called an emergency lane or a breakdown lane, may be identified by its width or fill (gravel for example) or by markings such as rumble strips. Lane markings vary widely from country to country. In certain situations, yellow lines separate traffic going opposite directions and white separates lanes of traffic traveling the same direction. In certain embodiments, lane identification takes into consideration local customs and rules.

The detected lane data may be added to the map database and associated with the geographic data. These acts may be repeated for different road sections. The detected lanes may be compared against previously calculated lane data. Multiple passes over a roadway made be made over time in order to detect changes to the lane layout. On the ground data may also be used to update and improve lane detection.

The detected lanes may be transmitted to a navigation device122along with other map updates. The detected lanes may be used to determine a precise route for a vehicle from a starting point to a destination. The detected lanes may also be used for assisted or automated operation of a vehicle. The detected lanes may be used to identify shoulders and safe biking routes. The detected lanes may also be used to accurately identify or model turns or off-ramps. Accurate detected lanes may also serve as validation devices for on-board vehicle sensors.

FIG. 3Ais a representation of an overhead image. The overhead image includes a roadway surface330, shoulders340and a lane divider350. In an actual overhead image, the lines including the roadway markings for the shoulder and lane may not be as clear. The overhead image may be comprised of pixels and may be in black and white or in color.

InFIG. 3B, a vehicle301travels along a roadway lane330. At certain intervals, the vehicles collects positional data regarding the vehicle's path. These measurements are illustrated by the dots320-325as the vehicle moves from A to B. This positional data is transmitted to the server125though the network.

InFIG. 4A, the positional data is linked by the server125to create a continuous trajectory405illustrated by the dotted line. The continuous trajectory is a representation of the path the vehicle took. The accuracy of the continuous trajectory405depends on the frequency that the positional data is collected. In certain embodiments, the server125may smooth out the continuous trajectory to more accurately represent the operation of a vehicle. For example, the intersection between the data points320-325are not smooth, but angular. Normal operation of a vehicle might take a smooth turn.

FIG. 4Bshows adaptive steerable filters generated using the continuous trajectory. The filters411-414are oriented in line with the continuous trajectory. Oriented filers may be used in imaging processing tasks such as texture analysis, edge detection, and image enhancement. Using the adaptive steerable filters allows the server125to detect and identify the edges that may represent lanes (edges that are parallel with the continuous trajectory). Once the edges have been identified, the server125uses the edges and the trajectory to identify lanes in the roadway.

FIG. 5Aillustrates an overhead image of a roadway. The overhead image contain extraneous objects such as buildings520, trees510, and vehicles530. These objects may cause errors or issues when the server125determines edges.

FIG. 5Billustrates the overhead image of5A cropped down to only include a swath of area within a certain distance of the roadway. By cropping (or segmenting) the overhead image, the resulting overhead image is a more useable size. Information outside the roadway is not necessary to determine the lanes of the roadway.

FIG. 6Aillustrates the overhead image of5B after the trees have been removed. As illustrated inFIG. 5B, Trees often have random textures that may generate multiple edge features. In order not to produce multiple false positives for edges, it is beneficial to remove the pixels that make up the trees. The server125may detect trees that have green (or a green shade) leaves and removed those pixels (green pixels rarely indicating a lane). Other algorithms may be used to detect and then eliminate trees or foliage from the overhead image.

FIG. 6Billustrates the overhead image of6A after the vehicles have been removed. Vehicles in the overhead image may generate a large amount of false lane features. In certain embodiments the server125removes vehicles from the overhead images using object detection techniques such as Adaboost or SVM. The server may learn a vehicle model and attempt to classify pixels in the image as vehicles or non-vehicles. A database is built from existing images of vehicles, which the server is then trained on.

The overhead image illustrated inFIG. 6Bmay be the overhead image data that is used to determine the edge features and subsequently lanes. Buildings, trees, and vehicle pixels have been removed from the overhead image. Edges may be detected in the overhead image using a steerable filter. Certain edges may then be identified as lanes.

FIG. 7illustrates an example overhead image700of an overhead image with lanes. The lanes710have been determined using the process above. However, large sections of the roadway are blocked by trees. This section may be estimated using the existing lanes710and the trajectory715to produce estimated lanes705.

FIG. 8illustrates an example server125ofFIG. 1. The server125includes a processor800, a communication interface805, and a memory801. The server125may be coupled to a database123. Additional, different, or fewer components may be provided in the server125. The server125may be a host for a website or web service such as a mapping service and/or a navigation service. The mapping service may provide maps generated from the geographic data of the database123, and the navigation service may generate routing or other directions from the geographic data of the database123.

The term server is used herein to collectively include the computing devices for creating, maintaining, indexing, and updating the one or more databases123and indexes. Any computing device may be substituted for the device122. The computing device may be a host for a website or web service such as a mapping service or a navigation service. The mapping service may provide maps generated from the geographic data of the database123, and the navigation service may calculate routing or other directions from the geographic data of the databases123.

The server125or processor800may be configured to detect lanes in an overhead image. The overhead image may be stored in memory801or the database123. The overhead image may be received through the communication interface. The overhead image may be received from a device for overhead images. Positional data may be received from the device through the network127. The server125or processor300may be configured to process the overhead image and the positional data to determine lanes.

The controller200and/or processor800may include a general processor, digital signal processor, an application specific integrated circuit (ASIC), field programmable gate array (FPGA), analog circuit, digital circuit, combinations thereof, or other now known or later developed processor. The controller200and/or processor800may be a single device or combinations of devices, such as associated with a network127, distributed processing, or cloud computing.

The memory204and/or memory801may be a volatile memory or a non-volatile memory. The memory204and/or memory801may include one or more of a read only memory (ROM), random access memory (RAM), a flash memory, an electronic erasable program read only memory (EEPROM), or other type of memory. The memory204and/or memory801may be removable from the mobile device122, such as a secure digital (SD) memory card.

The communication interface205and/or communication interface305may include any operable connection. An operable connection may be one in which signals, physical communications, and/or logical communications may be sent and/or received. An operable connection may include a physical interface, an electrical interface, and/or a data interface. The communication interface205and/or communication interface805provides for wireless and/or wired communications in any now known or later developed format.

In addition to the data describe above, the map database123may include 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 may 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 may 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 may be matched with respective map or geographic records via position or Global Positioning System (GPS) data associations (such as using known or future map matching or geo-coding techniques).

The road segment data records are links or segments representing roads, streets, or paths, which may be used for determination of one or more routes. The node data records are points corresponding to the respective links or segments of the road segment data records. The road link data records and the node data records represent a road network, such as used by vehicles, cars, and/or other entities. Alternatively, for example, the map database123may 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.

The road or link segments and nodes may 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 map database123may include data about the POIs and their respective locations in the POI data records. The map database123may 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 may be part of the POI data or may 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 database123may include historical and current event data (e.g., traffic incidents, constructions, scheduled events, unscheduled events, etc.) associated with the POI data records or other records of the database123.

The database123may be maintained by a content provider (e.g., a map developer). By way of example, the map developer may collect vehicle, roadway, and traffic data to generate and enhance the database123. Data may be obtained from multiple sources, such as municipalities or respective geographic authorities. In addition, the map developer may employ field personnel to travel by vehicle along roads throughout the geographic region to observe features and/or record information about them, positional data for example. Also, remote sensing, such as aerial or satellite photography, may be used. The database123may integrate data collected from device or sensors. The database123may store information derived from the collected information such as lane boundaries, curbs, or other identifiable road features.

FIG. 9illustrates a device configured to collect and transmit positional data. The device122may be referred to as a navigation device or a vehicle. The device122includes a controller200, a memory204, an input device203, a communication interface205, position circuitry207, movement circuitry208, and an output interface211. The output interface211may present visual or non-visual information such as audio information. Additional, different, or fewer components are possible for the device122. The device122is a smart phone, a mobile phone, a personal digital assistant (PDA), a tablet computer, a notebook computer, a personal navigation device (PND), a portable navigation device, and/or any other known or later developed mobile device. In an embodiment, a vehicle may be considered a device, or the device may be integrated into a vehicle.

The positioning circuitry207, which is an example of a positioning system, is configured to determine a geographic position of the mobile device122. The positioning circuitry may include a GPS receiver or a GNSS receiver. The movement circuitry208, which is an example a movement tracking system, is configured to determine movement of a mobile device122. The position circuitry207and the movement circuitry208may be separate systems, or segments of the same positioning or movement circuitry system. The movement circuitry208may include a gyroscope, an accelerometer, or an inertial measurement unit. In an embodiment, components as described herein with respect to the mobile device122may be implemented as a static device.

The input device203may be one or more buttons, keypad, keyboard, mouse, stylist pen, trackball, rocker switch, touch pad, voice recognition circuit, or other device or component for inputting data to the mobile device122. The input device203and the output interface211may be combined as a touch screen, which may be capacitive or resistive. The output interface211may be a liquid crystal display (LCD) panel, light emitting diode (LED) screen, thin film transistor screen, or another type of display. The output interface211may also include audio capabilities, or speakers.

Lane information may be used to directly or indirectly navigation a vehicle. The navigation device122may be integrated into an autonomous vehicle or a highly assisted driving (HAD) vehicle. The navigation device122may be configured as a navigation system for an autonomous vehicle or a HAD. An autonomous vehicle or HAD may undertake maneuvers in response to lane information determined by the server125.

As described herein, an autonomous vehicle may refer to a self-driving or driverless mode in which no passengers are required to be on board to operate the vehicle. An autonomous vehicle may be referred to as a robot vehicle or an automated vehicle. The autonomous vehicle may include passengers, but no driver is necessary. These autonomous vehicles may park themselves or move cargo between locations without a human operator. Autonomous vehicles may include multiple modes and transition between the modes.

As described herein, a highly assisted driving (HAD) vehicle may refer to a vehicle that does not completely replace the human operator. Instead, in a highly assisted driving mode, the vehicle may perform some driving functions and the human operator may perform some driving functions. Vehicles may also be driven in a manual mode in which the human operator exercises a degree of control over the movement of the vehicle. The vehicles may also include a completely driverless mode. Other levels of automation are possible.

The autonomous or highly automated driving vehicle may include sensors for identifying the surrounding and location of the car. The sensors may include GPS, light detection and ranging (lidar), radar, and cameras for computer vision. Proximity sensors may aid in parking the vehicle. The proximity sensors may detect the curb or adjacent vehicles. The autonomous or highly automated driving vehicle may optically track and follow lane markings or guide markings on the road.

Lane information is included in HD maps which allow highly automated vehicles to precisely locate themselves on the road and they also serve as a foundation for real-time data about the road environment. A HAD vehicle needs to know where it is on the road with respect to the lanes, and thus where the lanes are. It needs to plot a course to stay properly in the lane, and needs to plot courses to move to other lanes and roads. It is also beneficial for a vehicle to know the geometry of where that it is turning to, how many lanes are there on the next road, what is beyond the truck that is blocking a view, etc. Lane information from a HD map may also validate information detected real time at the vehicle using sensors such as radar, lidar, and cameras.