CN111597986B - Method, apparatus, device and storage medium for generating information - Google Patents
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Abstract
The application discloses a method, a device, equipment and a storage medium for generating information, and relates to the field of automatic driving. The specific implementation scheme is as follows: segmenting a traffic indicator image from the target image; acquiring camera attitude information corresponding to the target image; projecting high-precision map data matched with the target image to a plane where the target image is located according to the camera gesture information to generate a projection image, wherein the matched high-precision map data comprises three-dimensional data of traffic indicators with positions and orientations meeting preset requirements; and generating traffic indicator change information based on the comparison of the traffic indicator image and the projection image, wherein the traffic indicator change information is used for indicating whether the traffic indicator corresponding to the matched high-precision map data is changed or not. Therefore, whether the traffic indicator presented by the high-precision map data is actually changed or not can be judged quickly and timely in a low-cost mode, and a solid data base is provided for automatic updating of the high-precision map of the construction minute.
Description
Technical Field
The embodiment of the application relates to the technical field of computers, in particular to a high-precision map change detection technology in the automatic driving field.
Background
With the development of the automatic driving technology, the core elements (such as traffic lights and the like) in the high-precision map play a role in ensuring the timeliness of the high-precision map and the safety of an automatic driving system along with the change of the actual situation.
The prior art generally utilizes a special map to collect vehicles to quickly cover main roads and transmit collected data back. And analyzing and processing the acquired point cloud and image, carrying out background fusion on target elements on the road by combining the positioning data, and constructing global information of the high-precision map through each local information. However, the method has the problems of long acquisition period, long drawing period, high manufacturing cost and the like.
Disclosure of Invention
Provided are a method, apparatus, device, and storage medium for generating information.
According to a first aspect, there is provided a method for generating information, the method comprising: segmenting a traffic indicator image from the target image; acquiring camera attitude information corresponding to a target image; projecting high-precision map data matched with the target image to a plane where the target image is located according to the camera gesture information to generate a projection image, wherein the matched high-precision map data comprises three-dimensional data of traffic indicators with positions and orientations meeting preset requirements; generating traffic indicator change information based on the comparison of the traffic indicator image and the projection image, wherein the traffic indicator change information is used for indicating whether the traffic indicator corresponding to the matched high-precision map data is changed, and the traffic indicator change information is used for indicating at least one of the following: the traffic indicator increases, the traffic indicator decreases, and the traffic indicator is unchanged.
According to a second aspect, there is provided an apparatus for generating information, the apparatus comprising: a segmentation unit configured to segment a traffic indicator image from a target image; a first acquisition unit configured to acquire camera pose information corresponding to a target image; the projection unit is configured to project high-precision map data matched with the target image to a plane where the target image is located according to the camera gesture information, and generate a projection image, wherein the matched high-precision map data comprises three-dimensional data of traffic indicators with positions and orientations meeting preset requirements; a generation unit configured to generate traffic indicator change information based on a comparison of the traffic indicator image and the projection image, wherein the traffic indicator change information is used for indicating whether a traffic indicator corresponding to the matched high-precision map data is changed, and the traffic indicator change information is used for indicating at least one of the following: the traffic indicator increases, the traffic indicator decreases, and the traffic indicator is unchanged.
According to a third aspect, there is provided an electronic device comprising: at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method as described in any one of the implementations of the first aspect.
According to a fourth aspect, there is provided a non-transitory computer readable storage medium storing computer instructions for enabling a computer to perform a method as described in any of the implementations of the first aspect.
The technology provided by the application can be used for rapidly and timely judging whether the traffic indicator (such as a traffic light and the like) presented by the high-precision map data is actually changed in a low-cost manner, and has good generalization. And a solid data base can be provided for automatic updating of the manufactured minute-level high-precision map. Thus, the problems of long acquisition period, long drawing period, high manufacturing cost and the like of the existing high-precision map updating method are solved.
It should be understood that the description in this section is not intended to identify key or critical features of the embodiments of the disclosure, nor is it intended to be used to limit the scope of the disclosure. Other features of the present disclosure will become apparent from the following specification.
Drawings
The drawings are included to provide a better understanding of the present application and are not to be construed as limiting the application. Wherein:
FIG. 1 is a schematic diagram of a first embodiment according to the present application;
FIG. 2 is a schematic diagram of a second embodiment according to the present application;
FIG. 3 is a schematic diagram of one application scenario in which a method for generating information of an embodiment of the present application may be implemented;
FIG. 4 is a schematic diagram of an apparatus for generating information according to an embodiment of the application;
fig. 5 is a block diagram of an electronic device for implementing a method for generating information of an embodiment of the application.
Detailed Description
Exemplary embodiments of the present application will now be described with reference to the accompanying drawings, in which various details of the embodiments of the present application are included to facilitate understanding, and are to be considered merely exemplary. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the application. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.
Fig. 1 is a schematic diagram 100 showing a first embodiment according to the present application. The method for generating information comprises the steps of:
s101, segmenting a traffic indicator image from the target image.
In the present embodiment, the execution subject for generating information may divide the traffic indicator image from the target image in various ways. The target image may include an image acquired from an in-vehicle camera. The target image may generally include various traffic indicator images. The traffic indicator may include, but is not limited to, at least one of: traffic signal lights of motor vehicles, traffic signal lights of sidewalks, speed limit signs and other traffic sign boards. The above method of image segmentation may include, but is not limited to, at least one of: threshold-based segmentation methods, watershed algorithms, edge-detection-based segmentation methods, wavelet analysis and wavelet transformation-based image segmentation methods, active contour model (Active Contour Models) -based segmentation methods, and deep-learning-based segmentation models.
In some optional implementations of this embodiment, the executing entity may further input the target image into a pre-trained traffic indicator segmentation model, and generate a segmentation result including at least one traffic indicator image. The traffic indicator segmentation model can comprise an encoding network and a decoding network based on hole separable convolution. The traffic indicator segmentation model can use Xreception as a main network, and a hole space pyramid pooling (Atrous Spatial Pyramid Pooling, ASPP) module is added on the basis of the original coding network and decoding network, so that convolution characteristics on multiple scales can be obtained. The traffic indicator segmentation model can use a depth separable convolution structure (depthwise separable convolution), so that network parameters can be reduced, and the robustness of network inference can be improved. In practice, the deep neural network structure of deep Lab v < 3+ > can be used as an initial model, and a machine learning algorithm is utilized to train by adopting a preset training sample set, so that the traffic indicator segmentation model is obtained.
S102, camera attitude information corresponding to the target image is acquired.
In this embodiment, the execution subject may acquire the camera pose information corresponding to the target image in various manners. The camera pose information corresponding to the target image may be a pose of the vehicle-mounted camera. As an example, the execution subject may acquire the camera Pose information by various Pose Estimation (Pose Estimation) methods. The above-described methods of pose estimation may include, for example, but are not limited to, feature-based methods and direct matching methods.
And S103, projecting the high-precision map data matched with the target image to the plane where the target image is located according to the camera posture information, and generating a projection image.
In this embodiment, according to the camera pose information acquired in S102, the execution subject may project high-precision map data matched with the target image onto a plane where the target image is located, to generate a projection image. The matched high-precision map data can comprise three-dimensional data of traffic indicators with positions and orientations meeting preset requirements. The matching high-precision map data may be high-precision map data including data consistent with a traffic indicator indicated by the target image. The preset requirements can be preset according to the actual application scene. For example, the above-mentioned preset demand may be in a range of not more than 200 meters in front of the vehicle traveling direction. Optionally, the preset requirements may further include a correspondence between the location of the traffic indicator and the vehicle lane, so as to exclude interference of traffic markers of non-vehicle lanes.
In this embodiment, the execution subject may determine the matching high-precision map data based on the positioning data and the coordinate and orientation information included in the high-precision map. The above positioning data may be acquired in various ways, for example, from EXIF (Exchangeable image file format ) information of the target image or from a vehicle positioning system corresponding to the on-vehicle camera. Since the high-precision map data often includes three-dimensional data corresponding to the point cloud, the execution subject may project the high-precision map data matched with the target image according to the coordinate transformation matrix indicated by the camera pose information acquired in S102, thereby generating the projection image.
In some optional implementations of this embodiment, the executing body may generate the projection image according to the following steps:
first, shooting direction and position information corresponding to a target image are acquired.
In these implementations, the execution subject may acquire the shooting direction and the position information corresponding to the target image in various ways. As an example, the execution subject may acquire the position information from the positioning data. Then, track information may be generated from the positioning data, and a travel direction may be generated from the track information. Then, the execution body may determine a direction corresponding to the traveling direction as a shooting direction.
And secondly, selecting high-precision map data matched with shooting direction and position information from preset high-precision map data as a candidate data set by utilizing a pre-constructed high-dimensional index tree structure.
In these implementations, the execution subject may search, as the candidate data set, high-precision map data matching the shooting direction and position information acquired in the first step using a pre-built high-dimensional index tree structure. The high-dimensional index tree structure may include a high-precision map data query database indexed according to the trajectory and camera pose. The high-dimensional index tree structure may include, for example, a K-D tree (K-dimensional tree).
And thirdly, projecting the candidate data set to a plane where the target image is located according to the camera attitude information, and generating a projection image.
In these implementations, the execution subject may project the candidate data set selected in the second step onto the plane where the target image is located according to the coordinate transformation matrix indicated by the camera pose information acquired in the S102, so as to generate a projection image.
Based on the optional implementation manner, the execution subject can quickly screen the matched high-precision map data through a pre-constructed high-dimensional index tree data structure, so that the time complexity of retrieval can be effectively reduced, and a data basis can be provided for the subsequent generation of quick and accurate traffic indicator change.
In some optional implementations of this embodiment, the execution body may further project the matched high-precision map data onto a plane on which the target image is located, and then post-process the traffic indicator image obtained by projection, so that an image obtained by post-processing is used as the projection image. Wherein the post-processing may include, but is not limited to, at least one of: and (3) expanding the area, diluting the points on the contour curve, performing right angle treatment on the points on the contour curve, repeating the deletion of the dotted line and the like.
Based on the optional implementation manner, the projected traffic indicator image can be optimized and corrected, such as the situation that irregular and unreasonable geometric bodies appear is avoided, and the storage space waste caused by excessive data redundancy can be reduced.
S104, generating traffic indicator change information based on the comparison of the traffic indicator image and the projection image.
In the present embodiment, the execution subject may generate the traffic indicator change information in various ways based on the comparison between the traffic indicator image and the projection image obtained in S103. The traffic indicator change information may be used to indicate whether or not the traffic indicator corresponding to the matched high-precision map data is changed. The traffic indicator change information may be used to indicate at least one of: the traffic indicator increases, the traffic indicator decreases, and the traffic indicator is unchanged.
As an example, in response to determining that there is a traffic indicator in the traffic indicator image that coincides with the traffic indicator (e.g., traffic light) indicated by the projection image obtained in S103, the execution subject may generate traffic indicator change information for indicating that the traffic indicator is unchanged, that is, high-precision map data coincides with the actual situation. As yet another example, in response to determining that there is no traffic indicator in the traffic indicator image that coincides with the traffic indicator (e.g., traffic light) indicated by the projection image obtained in S103, the executing body may generate traffic indicator change information for indicating that the traffic indicator is reduced, that is, the corresponding traffic indicator is multi-labeled in the high-precision map data.
In some optional implementations of this embodiment, the executing entity may further use a grid search (grid search) method to determine whether the traffic indicator indicated by the traffic indicator image is consistent with the traffic indicator indicated by the projection image obtained in step 103.
In some optional implementations of this embodiment, the executing entity may further generate traffic indicator change information by:
first, in response to determining that no traffic indicator image exists in the projection image, inputting the traffic indicator image into a pre-trained traffic indicator fine classification model, and generating category information to which the traffic indicator belongs.
In these implementations, in response to determining that the traffic indicator image is not present in the projected image, the executing body may input the traffic indicator image to a pre-trained traffic indicator fine classification model, thereby generating category information to which the traffic indicator belongs. Wherein, the above-mentioned category information may be used to indicate whether the traffic indicator is displayed in a high-precision map. The pre-trained traffic indicator fine classification model may include various neural networks trained by machine learning. The training sample set of traffic indicator fine classification models described above may include positive samples and negative samples. The positive sample may include a traffic indicator image (e.g., traffic light, speed limit sign, etc.) of the motor vehicle lane and category information for indicating that the traffic indicator is displayed in a high-precision map. The negative examples may include traffic indicator images of non-motor vehicle lanes (e.g., non-motor vehicle signal lights, crosswalk signal lights, no-non-motor vehicle entry signs, etc.) and category information indicating that the traffic indicator is not displayed in a high-precision map.
Alternatively, the traffic indicator subdivision model may be a single training model, or may be a network layer near the output end in the traffic indicator subdivision model in the alternative implementation of S101, which is not limited herein.
And a second step of generating traffic indicator change information for indicating an increase in the traffic indicator in response to determining that the generated category information is for indicating that the traffic indicator is displayed in the high-precision map.
In these implementations, in response to determining that the generated category information is used to indicate that the traffic indicator is displayed in the high-precision map, the executing entity may generate traffic indicator change information that is used to indicate that the traffic indicator is added, i.e., that the corresponding traffic indicator is missed in the high-precision map data.
And a third step of generating traffic indicator change information for indicating that the traffic indicator is not changed in response to determining that the generated category information is for indicating that the traffic indicator is not displayed in the high-precision map.
In these implementations, in response to determining that the generated category information is used to indicate that the traffic indicator is not displayed in the high-precision map, the executing entity may generate traffic indicator change information that is used to indicate that the traffic indicator is unchanged, i.e., the high-precision map data is consistent with the actual situation.
Based on the optional implementation manner, whether the generated traffic indicator change information meets the acquisition and production requirements of the high-precision map can be further determined through the traffic indicator fine classification model, so that the accuracy of the generated traffic indicator change information for indicating the traffic indicator of the high-precision map missed sign can be further improved.
In some optional implementations of this embodiment, the foregoing execution body may further execute the following steps:
first, in response to generation of traffic indicator change information for indicating an increase in traffic indicator, supplementary data associated with the matched high-precision map data is acquired.
In these implementations, the executing entity may further obtain supplemental data associated with the matching high-precision map data in response to generating traffic indicator change information for indicating an increase in traffic indicator. Wherein the supplementary data may include high-precision map data matched with the position of the target image. The supplementary data may generally include high-precision map data that does not fully satisfy the preset requirements. As an example, the above-described preset requirement is in a range of not more than 200 meters in front of the vehicle traveling direction. The above-described supplementary data may generally include high-precision map data located in a range of not more than 200 meters forward (not more than 20 deg. in the left-right offset direction) of the vehicle traveling direction.
And a second step of changing the generated traffic indicator changing information for indicating that the traffic indicator is increased to traffic indicator changing information for indicating that the traffic indicator is not changed in response to determining that the three-dimensional data of the traffic indicator matching the traffic indicator image for indicating that the traffic indicator is increased in the target image exists in the supplementary data.
Based on the above-mentioned optional implementation manner, the matching target of the traffic indicator can be further expanded by increasing the data volume of the matched high-precision map, so that the accuracy of the generated traffic indicator change information for indicating the high-precision map missed-label traffic indicator can be further improved.
According to the method provided by the embodiment of the application, the high-precision map data matched with the target image is projected to the plane where the target image is located and compared, so that the traffic indicator change information for indicating whether the traffic indicator is changed is generated, whether the traffic indicator (such as a traffic light) presented by the high-precision map data is actually changed or not is quickly and timely judged in a low-cost mode, and the method has good generalization. And a solid data base can be provided for automatic updating of the manufactured minute-level high-precision map.
With continued reference to fig. 2, fig. 2 is a schematic diagram 200 according to a second embodiment of the present application. The method for generating information comprises the steps of:
s201, segmenting a traffic indicator image from the target image.
S202, camera attitude information corresponding to the target image is acquired.
And S203, projecting the high-precision map data matched with the target image to the plane where the target image is located according to the camera posture information, and generating a projection image.
The above S201, S202, and S203 are consistent with S101, S102, and S103 and their optional implementation manners in the foregoing embodiments, and the above description of S101, S102, and S103 and their optional implementation manners also applies to S201, S202, and S203, which are not repeated herein.
S204, traffic indicator changing sub-information is generated according to the comparison of the traffic indicator image and the projection image.
In this embodiment, the execution subject of the method for generating information may generate the traffic indicator changing sub-information in a manner consistent with the method described in S104 and its alternative implementation in the foregoing embodiment. The traffic indicator change sub-information may be used to indicate whether the traffic indicator corresponding to the matched high-precision map data is changed.
S205, the expanded images of the target number of images associated with the target image are acquired.
In this embodiment, the execution subject may acquire the expanded image of the target number of sheets associated with the target image in various ways. The high-precision map data matched with the extended image is generally consistent with the high-precision map data matched with the target image. As an example, the execution subject may acquire the extended image associated with the target image from an in-vehicle camera that captures the target image. For example, the vehicle equipped with the in-vehicle camera may continuously capture images while traveling, and the extended image associated with the target image may be a plurality of images adjacent to the target image in the image sequence.
S206, segmenting the traffic indicator image from the target number of expanded images.
In this embodiment, the execution subject may divide the traffic indicator image from the target number of expanded images in a manner consistent with the method described in S101 in the foregoing embodiment.
S207, generating the traffic indicator changing sub-information of the target number according to the comparison of the traffic indicator images segmented in the target number expansion image and the projection image.
In this embodiment, the execution subject may generate the target number of traffic indicator change sub-information in a manner consistent with the methods described in S102 to S104 and alternative implementations thereof in the foregoing embodiments.
S208, the generated plurality of traffic indicator change sub-information are counted, and traffic indicator change information is generated.
In this embodiment, the executing body may count the generated plurality of traffic indicator sub-information in various manners. As an example, the execution subject may count traffic indicator change sub-information indicating that the traffic indicator is changed and the number of traffic indicator change sub-information indicating that the traffic indicator is not changed, respectively. The execution entity may then generate traffic indicator change information that matches the traffic indicator change indicated by the greater number of traffic indicator change sub-information. As yet another example, the execution body may further determine whether a ratio of the number of traffic indicator changing sub-information indicating the traffic indicator change to the target number is greater than a preset proportion threshold. In response to determining to be greater than, the executing entity may generate traffic indicator change information indicating a traffic indicator change. In response to determining not to be greater than, the executing entity may generate traffic indicator modification information indicating that the traffic indicator is not modified.
In some optional implementations of this embodiment, in response to determining that the number of traffic indicator change sub-information indicating whether the traffic indicator is changed does not satisfy the preset condition for generating traffic indicator change information indicating whether the traffic indicator is changed, the executing entity may further characterize traffic indicator change information that is not determined whether the traffic indicator is changed. In these implementations, the executing entity may send information prompting for characterizing the artificial takeover or re-execute the method for generating information for the region corresponding to the target image.
As can be seen from fig. 2, the flow 200 of the method for generating information in this embodiment embodies the step of determining the traffic indicator change information to be finally generated by the statistical result of the traffic indicator change sub-information determined by the target number of expanded images. Therefore, the scheme described in the embodiment can determine the change of the traffic indicator through the plurality of associated images of the target image, so that the credibility of the change information of the traffic indicator is improved, and further, the accuracy of the high-precision map is guaranteed.
With continued reference to fig. 3, fig. 3 is a schematic illustration of an application scenario of a method for generating information according to an embodiment of the application. In the application scenario of fig. 3, the autonomous vehicle 301 may take a target image 302 with an onboard camera during travel. The autonomous vehicle 301 may then upload the target image 302 to the background server 303. Thereafter, the background server 303 may segment the traffic signal image 304 from the target image 302 using an image segmentation method. The backend server 303 may also obtain camera pose information 305 of the onboard camera from the autonomous vehicle 301. Then, the background server 303 may project high-precision map data corresponding to the shooting position of the target image 302 onto the plane where the target image 302 is located according to the coordinate conversion matrix indicated by the acquired camera pose information 305, and generate a projection image 306. Finally, the background server may compare the traffic light image 304 with the projected image 306 to generate traffic indicator modification information 307. The traffic indicator change information 307 may be used to indicate an increase in traffic indicator.
At present, one of the prior art generally carries out background fusion on the acquired point cloud and the image, and utilizes each piece of local information to construct global information of a high-precision map, so that the problems of long acquisition period, long drawing period, high manufacturing cost and the like are caused. According to the method provided by the embodiment of the application, the high-precision map data matched with the target image is projected to the plane where the target image is located and compared, so that the traffic indicator change information for indicating whether the traffic indicator is changed is generated, whether the traffic indicator presented by the high-precision map data is actually changed or not is quickly and timely judged in a low-cost mode, and the method has good generalization. And a solid data base can be provided for automatic updating of the manufactured minute-level high-precision map.
With further reference to fig. 4, as an implementation of the method shown in the above figures, the present disclosure provides an embodiment of an apparatus for generating information, which corresponds to the method embodiment shown in fig. 1, and which is particularly applicable in various electronic devices.
As shown in fig. 4, the apparatus 400 for generating information provided in the present embodiment includes a dividing unit 401, a first acquiring unit 402, a projecting unit 403, and a generating unit 404. Wherein the segmentation unit 401 is configured to segment the traffic indicator image from the target image; a first acquisition unit 402 configured to acquire camera pose information corresponding to a target image; the projection unit 403 is configured to project high-precision map data matched with the target image to a plane where the target image is located according to the camera gesture information, and generate a projection image, wherein the matched high-precision map data comprises three-dimensional data of traffic indicators with positions and orientations meeting preset requirements; a generating unit 404 configured to generate traffic indicator change information based on a comparison of the traffic indicator image and the projection image, wherein the traffic indicator change information is used for indicating whether a traffic indicator corresponding to the matched high-precision map data is changed, and wherein the traffic indicator change information is used for indicating at least one of the following: the traffic indicator increases, the traffic indicator decreases, and the traffic indicator is unchanged.
In the present embodiment, in the apparatus 400 for generating information: the specific processes of the dividing unit 401, the first acquiring unit 402, the projecting unit 403 and the generating unit 404 and the technical effects thereof may refer to the relevant descriptions of steps S101, S102, S103 and S104 in the corresponding embodiment of fig. 1, and are not repeated here.
In some optional implementations of this embodiment, the projection unit 403 may include a first acquisition module (not shown in the figure), a selection module (not shown in the figure), and a projection module (not shown in the figure). The first acquiring module may be configured to acquire shooting direction and position information corresponding to a target image; the selecting module may be configured to select, from preset high-precision map data, high-precision map data matching with shooting direction and position information as a candidate data set by using a pre-constructed high-dimensional index tree data structure; the projection module may be configured to project the candidate data set onto a plane where the target image is located according to the camera pose information, and generate a projection image.
In some optional implementations of this embodiment, the generating unit 404 may include a first comparing module (not shown in the figure), a second obtaining module (not shown in the figure), a dividing module (not shown in the figure), a second comparing module (not shown in the figure), and a first generating module (not shown in the figure). The first comparison module may be configured to generate traffic indicator change sub-information according to a comparison between the traffic indicator image and the projection image. The second obtaining module may be configured to obtain a target number of extension images associated with the target image. The high-precision map data matched with the extended image can be consistent with the high-precision map data matched with the target image. The segmentation module may be configured to segment the traffic indicator image from the target number of expanded images. The second comparison module may be configured to generate the target number of traffic indicator change sub-information according to a comparison between the traffic indicator image segmented in the target number of expanded images and the projection image. The first generation module may be configured to count the generated plurality of traffic indicator change sub-information to generate traffic indicator change information.
In some optional implementations of this embodiment, the generating unit 404 may include a classification module (not shown in the figure), a second generating module (not shown in the figure), and a third generating module (not shown in the figure). The classification module may be configured to input the traffic indicator image into a pre-trained traffic indicator fine classification model to generate category information to which the traffic indicator belongs in response to determining that the traffic indicator image does not exist in the projection image. Wherein, the above-mentioned category information may be used to indicate whether the traffic indicator is displayed in a high-precision map. The second generation module may be configured to generate traffic indicator change information for indicating an increase in the traffic indicator in response to determining that the generated category information is for indicating that the traffic indicator is displayed in the high-precision map. The third generation module may be configured to generate traffic indicator change information indicating that the traffic indicator is not changed in response to determining that the generated category information is used to indicate that the traffic indicator is not displayed in the high-precision map.
In some optional implementations of this embodiment, the apparatus 400 for generating information may further include: a second acquisition unit (not shown in the figure), a modification unit (not shown in the figure). Wherein the above-described second acquisition unit may be configured to acquire the supplementary data associated with the matched high-precision map data in response to generation of traffic indicator change information for indicating an increase in the traffic indicator. Wherein the supplementary data may include high-precision map data matched with the position of the target image. The above-described changing unit may be configured to change the generated traffic indicator changing information for indicating that the traffic indicator is increased to the traffic indicator changing information for indicating that the traffic indicator is not changed in response to determining that the three-dimensional data of the traffic indicator matching the traffic indicator image indicating that the traffic indicator is increased in the target image exists in the supplementary data.
The apparatus provided by the above embodiment of the present application segments the traffic indicator image from the target image by the segmentation unit 401. Then, the first acquisition unit 402 acquires camera pose information corresponding to the target image. Then, the projection unit 403 projects high-precision map data matching the target image onto the plane where the target image is located according to the camera pose information, generating a projection image. The matched high-precision map data comprise three-dimensional data of traffic indicators with positions and orientations meeting preset requirements. The generation unit 404 generates traffic indicator change information based on the comparison of the traffic indicator image and the projection image. The traffic indicator change information is used for indicating whether the traffic indicator corresponding to the matched high-precision map data is changed or not. Wherein the traffic indicator change information is used for indicating at least one of the following: the traffic indicator increases, the traffic indicator decreases, and the traffic indicator is unchanged. Therefore, whether the traffic indicator (such as a traffic light and the like) presented by the high-precision map data is actually changed or not is rapidly and timely judged in a low-cost mode, and the generalization is good. And a solid data base can be provided for automatic updating of the manufactured minute-level high-precision map.
Referring now to FIG. 5, the present application also provides an electronic device, a readable storage medium, and a computer program product according to embodiments of the present application.
As shown in fig. 5, is a block diagram of an electronic device for generating information according to an embodiment of the present application. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device may also represent various forms of mobile devices, such as an automatic control system of an autonomous vehicle, personal digital processing, cellular telephones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the applications described and/or claimed herein.
As shown in fig. 5, the electronic device includes: one or more processors 501, memory 502, and interfaces for connecting components, including high-speed interfaces and low-speed interfaces. The various components are interconnected using different buses and may be mounted on a common motherboard or in other manners as desired. The processor may process instructions executing within the electronic device, including instructions stored in or on memory to display graphical information of the GUI on an external input/output device, such as a display device coupled to the interface. In other embodiments, multiple processors and/or multiple buses may be used, if desired, along with multiple memories and multiple memories. Also, multiple electronic devices may be connected, each providing a portion of the necessary operations (e.g., as a server array, a set of blade servers, or a multiprocessor system). One processor 501 is illustrated in fig. 5.
Memory 502 is a non-transitory computer readable storage medium provided by the present application. Wherein the memory stores instructions executable by the at least one processor to cause the at least one processor to perform the method for generating information provided by the present application. The non-transitory computer readable storage medium of the present application stores computer instructions for causing a computer to execute the method for generating information provided by the present application.
The memory 502 is a non-transitory computer readable storage medium, and may be used to store a non-transitory software program, a non-transitory computer executable program, and modules, such as program instructions/modules (e.g., the dividing unit 401, the first acquiring unit 402, the projecting unit 403, and the generating unit 404 shown in fig. 4) corresponding to the method for generating information in the embodiment of the present application. The processor 501 executes various functional applications of the server and data processing, i.e., implements the methods for generating information in the above-described method embodiments, by running non-transitory software programs, instructions, and modules stored in the memory 502.
Memory 502 may include a storage program area that may store an operating system, at least one application program required for functionality, and a storage data area; the storage data area may store data created according to the use of the electronic device for generating information, and the like. In addition, memory 502 may include high-speed random access memory, and may also include non-transitory memory, such as at least one magnetic disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, memory 502 may optionally include memory located remotely from processor 501, which may be connected to the electronic device for generating information via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The electronic device for generating information may further include: an input device 503 and an output device 504. The processor 501, memory 502, input devices 503 and output devices 504 may be connected by a bus or otherwise, for example in fig. 5.
The input device 503 may receive input numeric or character information and generate key signal inputs related to user settings and function controls of the electronic device used to generate the information, such as a touch screen, a keypad, a mouse, a trackpad, a touchpad, a pointer stick, one or more mouse buttons, a trackball, a joystick, and the like. The output devices 504 may include a display device, auxiliary lighting devices (e.g., LEDs), and haptic feedback devices (e.g., vibration motors), among others. The display device may include, but is not limited to, a Liquid Crystal Display (LCD), a Light Emitting Diode (LED) display, and a plasma display. In some implementations, the display device may be a touch screen.
Various implementations of the systems and techniques described here can be realized in digital electronic circuitry, integrated circuitry, application specific ASIC (application specific integrated circuit), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs, the one or more computer programs may be executed and/or interpreted on a programmable system including at least one programmable processor, which may be a special purpose or general-purpose programmable processor, that may receive data and instructions from, and transmit data and instructions to, a storage system, at least one input device, and at least one output device.
These computing programs (also referred to as programs, software applications, or code) include machine instructions for a programmable processor, and may be implemented in a high-level procedural and/or object-oriented programming language, and/or in assembly/machine language. As used herein, the terms "machine-readable medium" and "computer-readable medium" refer to any computer program product, apparatus, and/or device (e.g., magnetic discs, optical disks, memory, programmable Logic Devices (PLDs)) used to provide machine instructions and/or data to a programmable processor, including a machine-readable medium that receives machine instructions as a machine-readable signal. The term "machine-readable signal" refers to any signal used to provide machine instructions and/or data to a programmable processor.
To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and pointing device (e.g., a mouse or trackball) by which a user can provide input to the computer. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic input, speech input, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a background component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such background, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), wide Area Networks (WANs), and the internet.
The computer system may include a client and a server. The client and server are typically remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other.
According to the technical scheme provided by the embodiment of the application, the traffic indicator changing information for indicating whether the traffic indicator is changed can be generated. Therefore, whether the traffic indicator (such as a traffic light) presented by the high-precision map data is actually changed or not is rapidly and timely judged in a low-cost mode, and the method has good generalization. And a solid data base can be provided for automatic updating of the manufactured minute-level high-precision map.
It should be appreciated that various forms of the flows shown above may be used to reorder, add, or delete steps. For example, the steps described in the present application may be performed in parallel, sequentially, or in a different order, provided that the desired results of the disclosed embodiments are achieved, and are not limited herein.
The above embodiments do not limit the scope of the present application. It will be apparent to those skilled in the art that various modifications, combinations, sub-combinations and alternatives are possible, depending on design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present application should be included in the scope of the present application.
Claims (10)
1. A method for generating information, comprising:
segmenting a traffic indicator image from the target image;
acquiring camera attitude information corresponding to the target image;
projecting high-precision map data matched with the target image to a plane where the target image is located according to the camera gesture information to generate a projection image, wherein the matched high-precision map data comprises three-dimensional data of traffic indicators with positions and orientations meeting preset requirements;
Generating traffic indicator change information based on the comparison of the traffic indicator image and the projection image, wherein the traffic indicator change information is used for indicating whether the traffic indicator corresponding to the matched high-precision map data is changed, and the traffic indicator change information is used for indicating at least one of the following: the traffic indicator increases, the traffic indicator decreases, and the traffic indicator is unchanged;
obtaining supplemental data associated with the matched high-precision map data in response to generating traffic indicator change information for indicating an increase in traffic indicators, wherein the supplemental data includes high-precision map data matched to a location of the target image;
in response to determining that three-dimensional data of traffic indicators matching the traffic indicator image indicating an increase in traffic indicators in the target image exists in the supplemental data, changing the generated traffic indicator change information indicating an increase in traffic indicators to traffic indicator change information indicating that the traffic indicators are unchanged.
2. The method of claim 1, wherein the projecting high-precision map data matched with the target image onto a plane in which the target image is located according to the camera pose information, generating a projection image, comprises:
Acquiring shooting direction and position information corresponding to the target image;
selecting high-precision map data matched with the shooting direction and the position information from preset high-precision map data as a candidate data set by utilizing a pre-constructed high-dimensional index tree data structure;
and projecting the candidate data set to the plane where the target image is located according to the camera posture information, and generating the projection image.
3. The method of claim 1, wherein the generating traffic indicator modification information based on the comparison of the traffic indicator image and the projection image comprises:
generating traffic indicator change sub-information according to the comparison of the traffic indicator image and the projection image;
acquiring the number of target expansion images associated with the target image, wherein the high-precision map data matched with the expansion images are consistent with the high-precision map data matched with the target image;
segmenting a traffic indicator image from the target number of expanded images;
generating traffic indicator change sub-information of the target number according to the comparison of the traffic indicator images segmented in the target number expansion image and the projection image;
And counting the generated plurality of traffic indicator change sub-information to generate the traffic indicator change information.
4. The method of claim 1, wherein the generating traffic indicator modification information based on the comparison of the traffic indicator image and the projection image comprises:
in response to determining that the traffic indicator image does not exist in the projection image, inputting the traffic indicator image into a pre-trained traffic indicator fine classification model, and generating category information to which a traffic indicator belongs, wherein the category information is used for indicating whether the traffic indicator is displayed in a high-precision map;
generating traffic indicator change information for indicating an increase in the traffic indicator in response to determining that the generated category information is for indicating that the traffic indicator is displayed in the high-precision map;
in response to determining that the generated category information is used to indicate that the traffic indicator is not displayed in the high-precision map, traffic indicator change information is generated that is used to indicate that the traffic indicator is unchanged.
5. An apparatus for generating information, comprising:
a segmentation unit configured to segment a traffic indicator image from a target image;
A first acquisition unit configured to acquire camera pose information corresponding to the target image;
the projection unit is configured to project high-precision map data matched with the target image to a plane where the target image is located according to the camera gesture information, and generate a projection image, wherein the matched high-precision map data comprises three-dimensional data of traffic indicators with positions and orientations meeting preset requirements;
a generation unit configured to generate traffic indicator change information based on a comparison of the traffic indicator image and the projection image, wherein the traffic indicator change information is used for indicating whether a traffic indicator corresponding to the matched high-precision map data is changed, and the traffic indicator change information is used for indicating at least one of the following: the traffic indicator increases, the traffic indicator decreases, and the traffic indicator is unchanged;
a second acquisition unit configured to acquire supplementary data associated with the matched high-precision map data in response to generation of traffic indicator change information for indicating an increase in traffic indicator, wherein the supplementary data includes high-precision map data matched with a position of the target image;
And a changing unit configured to change the generated traffic indicator changing information for indicating that the traffic indicator is increased to traffic indicator changing information for indicating that the traffic indicator is not changed in response to determining that three-dimensional data of the traffic indicator matching the traffic indicator image indicating that the traffic indicator is increased in the target image exists in the supplementary data.
6. The apparatus of claim 5, the projection unit comprising:
the first acquisition module is configured to acquire shooting direction and position information corresponding to the target image;
the selecting module is configured to select high-precision map data matched with the shooting direction and the position information from preset high-precision map data by utilizing a pre-constructed high-dimensional index tree data structure as a candidate data set;
and the projection module is configured to project the candidate data set to the plane where the target image is located according to the camera gesture information, and generate the projection image.
7. The apparatus of claim 5, the generating unit comprising:
a first comparison module configured to generate traffic indicator alteration sub-information based on a comparison of the traffic indicator image and the projection image;
A second acquisition module configured to acquire a target number of extended images associated with the target image, wherein high-precision map data matched by the extended images is consistent with high-precision map data matched by the target image;
a segmentation module configured to segment a traffic indicator image from the target number of expanded images;
the second comparison module is configured to generate traffic indicator changing sub-information of the target number according to the comparison of the traffic indicator images segmented in the target number expansion image and the projection image;
the first generation module is configured to count the generated plurality of traffic indicator change sub-information and generate the traffic indicator change information.
8. The apparatus of claim 5, the generating unit comprising:
a classification module configured to input the traffic indicator image to a pre-trained traffic indicator sub-classification model in response to determining that the traffic indicator image does not exist in the projection image, and generate category information to which a traffic indicator belongs, wherein the category information is used for indicating whether the traffic indicator is displayed in a high-precision map;
A second generation module configured to generate traffic indicator change information for indicating an increase in the traffic indicator in response to determining that the generated category information is for indicating that the traffic indicator is displayed in a high-precision map;
and a third generation module configured to generate traffic indicator change information for indicating that the traffic indicator is not changed in response to determining that the generated category information is for indicating that the traffic indicator is not displayed in the high-precision map.
9. An electronic device, comprising:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein,,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-4.
10. A non-transitory computer readable storage medium storing computer instructions for causing the computer to perform the method of any one of claims 1-4.
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| CN113514053B (en) * | 2021-07-13 | 2024-03-26 | 阿波罗智能技术(北京)有限公司 | Method and device for generating sample image pair and method for updating high-precision map |
| CN113706704A (en) * | 2021-09-03 | 2021-11-26 | 北京百度网讯科技有限公司 | Method and equipment for planning route based on high-precision map and automatic driving vehicle |
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