CN115203483B - Label management method, device, vehicle, storage medium and chip - Google Patents
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Abstract
The disclosure relates to a tag management method, a tag management device, a vehicle, a storage medium and a chip, and belongs to the field of automatic driving, wherein the tag management method comprises the following steps: acquiring a tag set corresponding to vehicle running data, wherein the tag set is used for describing information included in the vehicle running data; constructing the tag set into a target tag tree, wherein the target tag tree comprises a plurality of nodes; storing the target tag tree and the vehicle driving data in a tag tree table in an associated manner; selecting a target node in the plurality of nodes according to a preset rule corresponding to the target scene; extracting a first tag tree from the target tag tree according to the target node; and storing the first tag tree into a first data table. The method not only can realize effective management of the labels, so that the construction level of the labels is clear and easy to use, but also can extract label information required by the current scene more rapidly during automatic driving, and effectively improves the efficiency of label management.
Description
Technical Field
The present disclosure relates to the field of autopilot, and in particular to a tag management method, device, vehicle, storage medium and chip.
Background
In the automatic driving technology, in order to improve the accuracy and generalization capability of an algorithm as high as possible, a great amount of tag data needs to be collected, and how to manage the great amount of tag data and extract real valuable tag data therefrom to optimize and improve the capability of a model is a very challenging task. In the related art, how to manage massive tag data, so that the hierarchical structure of the tag data is clear and easy to use is a problem to be solved in the field.
Disclosure of Invention
In order to overcome the problems in the related art, the present disclosure provides a tag management method, apparatus, vehicle, storage medium, and chip.
According to a first aspect of embodiments of the present disclosure, there is provided a tag management method, the method including:
acquiring a tag set corresponding to vehicle running data, wherein the tag set is used for describing information included in the vehicle running data;
constructing the tag set into a target tag tree, wherein the target tag tree comprises a plurality of nodes;
storing the target tag tree and the vehicle driving data in a tag tree table in an associated manner;
selecting a target node in the plurality of nodes according to a preset rule corresponding to the target scene; and is combined with the other components of the water treatment device,
Extracting a first tag tree from the target tag tree according to the target node;
and storing the first tag tree into a first data table.
Optionally, the method further comprises:
in response to receiving a tag acquisition request, determining an autopilot scenario in the tag acquisition request;
acquiring the first tag tree under the condition that the automatic driving scene corresponds to the target scene; and is combined with the other components of the water treatment device,
and sending the first tag tree to a requester of the tag acquisition request.
Optionally, the sending the first tag tree to the requester of the tag acquisition request includes:
analyzing the first tag tree into a scene tag set;
and sending the scene tag set to a requester of the tag acquisition request.
Optionally, the storing the first tag tree in a first data table includes:
determining a first node of the target nodes;
and binding the first label tree with the target label tree according to the first label corresponding to the first node, and storing the first label tree and the target label tree into a first data table.
Optionally, the method comprises:
in response to determining a second tag change in the first tag tree, retrieving from the tag tree table according to a first tag of the first tag tree, obtaining the target tag tree bound to the first tag tree; and is combined with the other components of the water treatment device,
And changing a second label in the target label tree.
Optionally, the method further comprises:
in response to determining a third tag change in the target tag tree, according to at least one parent node to which the third tag corresponds;
searching in the first data table according to the label corresponding to each father node to obtain a second label tree bound with the label corresponding to any father node; and is combined with the other components of the water treatment device,
and changing a third tag in the second tag tree.
Optionally, the target tag tree further includes a version field, and the method further includes:
and updating the data corresponding to the version field in response to determining the tag change in the target tag tree.
Optionally, the method comprises:
determining authority information in a label change request in response to receiving the label change request;
executing the tag change request under the condition that the authority information meets the preset condition;
wherein the tag change request includes an add tag request, a delete tag request, and a modify tag request.
Optionally, the method further comprises:
generating examination information according to the label change request and sending the examination information to a target terminal under the condition that the authority information does not meet the preset condition;
And responding to the received examination passing information sent by the target terminal, and executing the label changing request.
According to a second aspect of embodiments of the present disclosure, there is provided a tag management apparatus, the apparatus comprising:
the system comprises an acquisition module, a storage module and a display module, wherein the acquisition module is configured to acquire a tag set corresponding to vehicle running data, and the tag set is used for describing information included in the vehicle running data;
a building module configured to build the tag set into a target tag tree, the target tag tree comprising a plurality of nodes;
a first storage module configured to store the target tag tree in association with the vehicle travel data to a tag tree table;
the selecting module is configured to select a target node in the plurality of nodes according to a preset rule corresponding to the target scene;
an extraction module configured to extract a first tag tree from the target tag tree according to the target node;
and a second storage module configured to store the first tag tree to a first data table.
According to a third aspect of embodiments of the present disclosure, there is provided a vehicle comprising:
a processor;
a memory for storing processor-executable instructions;
wherein the processor is configured to:
Acquiring a tag set corresponding to vehicle running data, wherein the tag set is used for describing information included in the vehicle running data;
constructing the tag set into a target tag tree, wherein the target tag tree comprises a plurality of nodes;
storing the target tag tree and the vehicle driving data in a tag tree table in an associated manner;
selecting a target node in the plurality of nodes according to a preset rule corresponding to the target scene; and is combined with the other components of the water treatment device,
extracting a first tag tree from the target tag tree according to the target node;
and storing the first tag tree into a first data table.
According to a fourth aspect of embodiments of the present disclosure, there is provided a computer readable storage medium having stored thereon computer program instructions which, when executed by a processor, implement the steps of the method of any of the first aspects of the present disclosure.
According to a fifth aspect of embodiments of the present disclosure, there is provided a chip comprising a processor and an interface; the processor is configured to read instructions to perform the method of any of the first aspects of the present disclosure.
The technical scheme provided by the embodiment of the disclosure can comprise the following beneficial effects: the obtained tag set corresponding to the vehicle driving data is constructed as the target tag tree, the target tag tree is associated with the vehicle driving data and stored, and according to different scenes, the target node is selected according to the preset rule corresponding to the scene, the sub tag tree corresponding to the different scenes is extracted from the target tag tree comprising the full quantity of tags and stored, so that the effective management of the tags is realized, the construction level of the tags is clear and easy to use, and the tag information required by the current scene can be extracted more quickly during automatic driving, and the efficiency of tag management is effectively improved.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the disclosure and together with the description, serve to explain the principles of the disclosure.
Fig. 1 is a flow chart illustrating a tag management method according to an exemplary embodiment.
FIG. 2 is a schematic diagram of a target tag tree, according to an example embodiment.
Fig. 3 is a block diagram illustrating a tag management apparatus according to an exemplary embodiment.
FIG. 4 is a functional block diagram of a vehicle, shown in an exemplary embodiment.
Fig. 5 is a block diagram illustrating a tag management apparatus according to an exemplary embodiment.
Detailed Description
Reference will now be made in detail to exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, the same numbers in different drawings refer to the same or similar elements, unless otherwise indicated. The implementations described in the following exemplary examples are not representative of all implementations consistent with the present disclosure. Rather, they are merely examples of apparatus and methods consistent with some aspects of the present disclosure as detailed in the accompanying claims.
It should be noted that, all actions of acquiring tag sets, signals, information or driving data in the present application are performed under the condition of conforming to the corresponding data protection rule policy of the country of the location and obtaining the authorization given by the owner of the corresponding device.
Fig. 1 is a flowchart illustrating a tag management method according to an exemplary embodiment, which may be applied to a vehicle, and may also be applied to other electronic devices having information processing capabilities, the disclosure of which is not particularly limited, as shown in fig. 1, including the following steps.
S101, acquiring a tag set corresponding to vehicle running data, wherein the tag set is used for describing information included in the vehicle running data.
The vehicle driving data may be image data collected by a camera disposed on the vehicle, or may be ultrasonic data, laser radar data, or the like, which is not particularly limited in the present disclosure.
The tag set corresponding to the vehicle running data may be obtained by inputting the vehicle running data into a neural network model trained in advance, and then labeling the vehicle running data, or may be obtained by manually labeling, which is not limited in this disclosure.
It will be appreciated that the tag set may be used to describe all information of the characterization of the vehicle travel data, i.e. an abstraction of the real world description, for example, the tag set may include one tag that characterizes a pedestrian object included in the vehicle travel data, another tag that characterizes the speed of movement of the pedestrian object, yet another tag that characterizes the distance information of the pedestrian object from the vehicle, etc.
S102, constructing the tag set into a target tag tree, wherein the target tag tree comprises a plurality of nodes.
Fig. 2 is a schematic diagram of a target tag tree according to an exemplary embodiment, where, as shown in fig. 2, an entity node, an event node, and a meta-information node may be included under a root node of the target tag tree, an attribute node and a status node may be included under the entity node, and a weather node and a temperature node may be included under the meta-information node. Where an entity is a description of a physical object in the real world, such as a vehicle, pedestrian and road, etc. The attribute is attached to an entity such as the size color of the vehicle, the man or woman of the pedestrian. The status is also attached to the entity, such as whether the vehicle is stationary or moving, similar to the attribute but describing a non-trivial dimension. Therefore, the attribute node and the state node are both child nodes under the entity node. Meta information is information that exists in any case and is different from an entity, such as weather, temperature, longitude and latitude, and the like. The labels corresponding to the event nodes are special labels generated after special processing is carried out on some labels which are difficult to be structurally described, for example, a white car on the left side is normally driven on a main road and then suddenly carries out lane changing operation. If structured labels are used for the description, not only is the object described (white car) but also the behavior of the car is described, which may also change over time. This becomes complex and difficult to implement for the whole system. Therefore, it is possible to generate an event tag having temporal persistence by temporal integration based on the vehicle travel data.
Based on the target tag tree, tags used in different links can be integrated in a structuring mode, so that the structure hierarchy structure of the tag set is clear and easy to use.
It should be understood that the above-mentioned target tag tree is only an example, and in practical application, the target tag tree may also include other multiple nodes, or may not include the above nodes, for example, a color node, a size node, etc. may also be included under an attribute node, and a speed node, an acceleration node, etc. may be included under a status node. Alternatively, the target tag tree may not have a meta-information node present. Each node of the constructed target tag tree may be configured according to an actual application requirement, which is not specifically limited in the present disclosure.
S103, storing the target tag tree and the vehicle driving data in a tag tree table in a correlated mode.
The target tag tree includes all tags in the tag set, the target tag tree table may be stored in JSON object format, and the tag tree table and the following data table may be stored in mongo db, where mongo db is a product interposed between a relational database and a non-relational database, and functions in the non-relational database are most abundant and most like the relational database. The data structure supported by it is very loose and can store more complex data types. The biggest characteristic of Mongo is that the query language supported by Mongo is very powerful, the grammar is somewhat similar to the object-oriented query language, almost most functions similar to the query of a relational database list can be realized, and the indexing of data is also supported.
When the association storage is carried out, a field can be added into the data corresponding to the target tag tree, and the data corresponding to the field can carry out unique identification on the vehicle form data, so that the association storage is realized. And the vehicle running data corresponding to the target label tree can be searched based on the target label tree, and the vehicle running data can be extracted so as to meet the requirements of related staff.
S104, selecting a target node in the plurality of nodes according to a preset rule corresponding to the target scene.
It may be appreciated that the target scenario may include a plurality of different autopilot scenarios, for example, a parking scenario, a driving scenario, a lane change scenario, and so on, and the selection rule of the target node may be correspondingly performed for the different autopilot scenarios.
S105, extracting a first tag tree from the target tag tree according to the target node.
For example, if the preset rule characterizes selecting the entity node and the attribute node as the target node, then the attribute tree including only the attribute tags of the entity can be obtained through step S105, and the tag tree including only the state tags can also be obtained.
In addition, for each target node, tag screening can be performed at the target node, for example, if the target node includes an entity node, all tags under non-motor vehicles can be screened out in the entity node, and only motor vehicles are reserved in the entity node.
It will be appreciated that the tags on the tag tree of interest are different for different scenarios, and are too redundant for the tag requester if the complete set of tag trees is exposed. For example, if the target scene corresponds to a parking scene, only the tags related to parking, such as the tags of the garage, the floor material, and the like, need to be paid attention to, and if the target scene corresponds to a driving scene, the tags of pedestrians, vehicles, traffic signs, and the like are paid more attention to.
Therefore, based on the steps S104 and S105, the target node corresponding to the target scene may be determined according to different target scenes, so as to screen the tag at the target node, and further obtain a first tag tree for the target scene, and further the first tag tree may include a parking tag tree, a driving tag tree, and so on.
S106, storing the first tag tree into a first data table.
Similarly, the first data table may include a parking data table, a driving data table, an attribute table, a status table, and the like, corresponding to the different first tag tree.
It can be appreciated that the steps S104 to S106 may be performed multiple times until the tag tree corresponding to each target scene is extracted and stored.
In the embodiment of the disclosure, the obtained tag set corresponding to the vehicle running data is constructed as the target tag tree, the target tag tree is associated with the vehicle running data and stored, and according to different scenes, the target node is selected according to the preset rule corresponding to the scene, and the sub tag tree corresponding to the different scenes is extracted from the target tag tree comprising the total number of tags and stored, so that the effective management of the tags is realized, the construction level of the tags is clear and easy to use, and the tag information required by the current scene can be extracted more rapidly during automatic driving, thereby effectively improving the efficiency of tag management.
In some alternative embodiments, the method further comprises:
in response to receiving a tag acquisition request, determining an autopilot scenario in the tag acquisition request;
acquiring the first tag tree under the condition that the automatic driving scene corresponds to the target scene; and transmitting the first tag tree to a requester of the tag acquisition request.
The tag acquisition request may include current autopilot scenario information, which may, for example, characterize a current vehicle in a park scenario, a drive scenario, and so forth. For different braking driving scenes, a scene tag tree can be obtained from a data table corresponding to the scene, for example, if the current automatic driving scene is a parking scene, a parking tag tree in the parking data table can be obtained from the parking data table according to the automatic driving scene, and the tag tree is sent to a requester of a tag obtaining request.
The requesting party of the tag acquisition request may be, for example, an autopilot controller in a vehicle, a whole vehicle controller, or a device such as a user terminal, which is not limited in the present disclosure.
By adopting the scheme, the tag tree corresponding to the automatic driving scene is acquired according to the automatic driving scene in the tag acquisition request, and the whole set of the tags does not need to be acquired, so that the data volume is effectively reduced, and the processing efficiency of the tag data is improved.
In some alternative embodiments, the sending the first tag tree to the requestor of the tag acquisition request includes:
analyzing the first tag tree into a scene tag set;
and sending the scene tag set to a requester of the tag acquisition request.
The first tag tree may be a data format which cannot be identified by a requester in the JSON object format, so that the first tag tree can be resolved into a format of a conventional tag set, and the resolved tag set is sent to the requester of the tag acquisition request, so that the fact that the requester cannot resolve the first tag tree is avoided, and the processing efficiency of tag data is further improved.
In other alternative embodiments, the storing the first tag tree into a first data table includes:
Determining a first node of the target nodes;
and binding the first label tree with the target label tree according to the first label corresponding to the first node, and storing the first label tree and the target label tree into a first data table.
Alternatively, the first node may be the highest level node. Taking the first label tree as an example, the highest-level node corresponding to the attribute tree is an entity node, and if the label corresponding to the entity node of the attribute tree is "vehicle 01", the label can be further bound with the target label tree.
By adopting the scheme, the first label tree and the target label tree are bound according to the first node and then stored in the first data table, so that the data binding of the first label tree and the target label tree is realized, and when any one label tree is changed, the data change is synchronously carried out on the label tree bound with the first label tree, and the problem of inconsistent data in different data tables caused by the change of the data can be effectively avoided.
Further, the method comprises:
in response to determining a second tag change in the first tag tree, retrieving from the tag tree table according to a first tag of the first tag tree, obtaining the target tag tree bound to the first tag tree; and, changing a second tag in the target tag tree.
For example, taking the first label tree as an attribute tree as an example, if the label corresponding to the color node is changed from blue to black in the attribute tree, further, according to the first label in the attribute tree, namely, according to the label "vehicle 01" corresponding to the entity node of the attribute tree, the corresponding entity label is searched in the label tree table, and the color label under the label of the entity node "vehicle 01" in the target label tree is synchronously changed, so that the color label corresponding to "vehicle 01" in the target label tree is changed to black.
By adopting the scheme, when a certain label in the first label tree is changed, the label bound in the first label tree and the target label tree is searched, so that the target label tree bound with the first label is searched in the label table, and the target label tree is synchronously changed, thereby effectively ensuring that the target label tree comprising the whole quantity of labels is also changed when the sub-label tree is changed, and effectively avoiding the problem of inconsistent data in different data tables caused by data change.
In other possible embodiments, the method further comprises:
In response to determining a third tag change in the target tag tree, according to at least one parent node to which the third tag corresponds;
searching in the first data table according to the label corresponding to each father node to obtain a second label tree bound with the label corresponding to any father node; and, changing a third tag in the second tag tree.
It is understood that the third tag may be any tag in the target tag tree.
For example, if the third tag change is characterized by changing from blue to black, and it is determined that the parent node corresponding to the third tag includes a color node, an attribute node, and an entity node, if the second tag tree bound to the tag is searched as an attribute tree according to only the tag "vehicle 01" corresponding to the entity node, then the tag corresponding to the third tag in the attribute tree may be changed from blue to black.
By adopting the scheme, when a certain label in the target label tree is changed, the labels corresponding to the father nodes of the labels are determined, the label tree bound with the father nodes is searched according to the labels of the father nodes, and the corresponding labels in the label tree are changed, so that when the labels in the target label tree comprising the whole quantity of labels are changed, other label trees comprising the labels are synchronously changed, and the problem of inconsistent data in different data tables caused by data change is effectively avoided.
In some optional embodiments, the target tag tree further includes a version field therein, and the method further includes:
and updating the data corresponding to the version field in response to determining the tag change in the target tag tree.
It will be appreciated that in the tag tree table, a complete set of the target tag tree maintaining all versions may be stored, for example, a "version" field may be added to the target tag tree in JSON object format, and the data under this field may characterize the version of the target tag tree. And when the tag acquisition requester acquires the target tag tree, the latest version of the target tag tree can be sent to the requester according to the data size corresponding to the field.
By adopting the scheme, the version field is added in the target label tree, and when the target label tree is changed, the version field is updated, so that the version of the target label tree can be effectively managed, and further, the requirements of related work on testing and application of different versions of the target label tree are met.
To further understand the above target tag tree by those skilled in the art, the present disclosure also provides a target tag tree shown in accordance with an exemplary embodiment: { "version":4, "id": "xxxxx", "create_time":1638343003, "chip-rens": { "name_cn": "motor vehicle", "name_en": "vehicle", "chip-rens": [ { "name_cn": "car", "name_en": "car", "name_car", "protocol": [ { "name_en": "color", "type": "car color", "format", "string", "name": { name_en ":" red "," name }, { name_en ": size", "name_cn": size "," name_en "," size ":", "name": size "," name_en "," name ":", "name_cn", "size, size and size, size and size and.
The target tag tree is stored in JSON format, the data "xxxx" corresponding to the "id" field in the target tag tree can represent the vehicle running data corresponding to the target tag tree, the data "4" corresponding to the "version" field can represent the version of the target tag tree as 4, and the data corresponding to the "create_time" field can represent the creation time of the target tag tree of the version. Based on the target tag tree object, it may be obtained that the tag tree includes entity tags: motor vehicle tags, non-motor vehicle tags, including car tags under motor vehicle tags, including attribute tags under car tags: color labels and size labels, wherein the color labels have a red color and the size labels have a medium size. A bicycle label is included under the non-motor vehicle label, an attribute label is included under the bicycle label, and a color label is included under the attribute label, wherein the color label has a red value.
In some alternative embodiments, the method comprises:
determining authority information in a label change request in response to receiving the label change request;
executing the tag change request under the condition that the authority information meets the preset condition;
Wherein the tag change request includes an add tag request, a delete tag request, and a modify tag request.
The tag change request may be a request for changing a tag in the target tag tree, or a request for changing a tag in a sub tag tree extracted from the target tag tree.
For example, the user roles of a common staff member, an administrator and a super administrator may be included, the common staff member may only have the authority to view the tag, the administrator may have the authority to view the tag and add the tag, and the super administrator may have the authority to view the tag, delete the tag and modify the tag. In addition, the super administrator also has the authority of user role modification, namely, a user with a certain role as a common worker can be modified into an administrator, so that the super administrator is provided with the authority of adding labels.
If the authority information in the received tag change request indicates that the sender of the request is a common administrator, the authority information can be determined not to meet the preset condition, and if the authority information indicates that the sender of the request is an administrator and the tag change request is an added tag request, the authority information can be determined to meet the preset condition.
By adopting the mode, the permission is pre-configured, whether the permission information in the label changing request meets the preset condition is confirmed when the label changing request is received, and the label changing operation is executed according to the label changing request only when the preset condition is met, so that the labels are added, deleted or modified, the labels can be prevented from being changed by staff without corresponding permission, and the safety performance of label management is effectively improved.
In other alternative embodiments, the method further comprises:
generating examination information according to the label change request and sending the examination information to a target terminal under the condition that the authority information does not meet the preset condition;
and responding to the received examination passing information sent by the target terminal, and executing the label changing request.
For example, if the authority information in the received tag change request indicates that the sender of the request is a common administrator and the tag change request is a tag delete request, the audit information may be generated according to the tag delete request and sent to the target terminal, where the target terminal may be a terminal device of a super administrator, and the audit information may include information of an initiator, a tag to be modified, a tag tree to be modified, a modified tag, a modification reason, and the like. If the superadministrator determines that the tag change request can be performed after reviewing the audit information, audit pass information can be sent to perform the tag change request. Wherein, the examination information can be sent to the target terminal in the form of a work order.
By adopting the scheme, under the condition that the authority information does not meet the preset condition, the corresponding examination information is generated and sent to the auditor, so that the auditor can determine whether to execute the label change request according to the examination information, the security performance of label management is ensured, and meanwhile, staff without corresponding authority can still change the labels in the label tree, so that the flexibility of label management is effectively improved.
Fig. 3 is a block diagram of a tag management apparatus according to an exemplary embodiment, and referring to fig. 3, the tag management apparatus 30 includes:
an acquisition module 31 configured to acquire a tag set corresponding to vehicle travel data, the tag set being used to describe information included in the vehicle travel data;
a building module 32 configured to build the tag set into a target tag tree, the target tag tree comprising a plurality of nodes;
a first storage module 33 configured to store the target tag tree in association with the vehicle travel data to a tag tree table;
a selecting module 34, configured to select a target node from the plurality of nodes according to a preset rule corresponding to the target scene;
An extraction module 35 configured to extract a first tag tree from the target tag tree according to the target node;
a second storage module 36 configured to store the first tag tree to a first data table.
Optionally, the tag management device 30 is configured to:
in response to receiving a tag acquisition request, determining an autopilot scenario in the tag acquisition request;
acquiring the first tag tree under the condition that the automatic driving scene corresponds to the target scene; and is combined with the other components of the water treatment device,
and sending the first tag tree to a requester of the tag acquisition request.
Optionally, the tag management device 30 is configured to:
analyzing the first tag tree into a scene tag set;
and sending the scene tag set to a requester of the tag acquisition request.
Optionally, the second storage module 36 is configured to:
determining a first node of the target nodes;
and binding the first label tree with the target label tree according to the first label corresponding to the first node, and storing the first label tree and the target label tree into a first data table.
Optionally, the tag management device 30 is configured to:
in response to determining a second tag change in the first tag tree, retrieving from the tag tree table according to a first tag of the first tag tree, obtaining the target tag tree bound to the first tag tree; and is combined with the other components of the water treatment device,
And changing a second label in the target label tree.
Optionally, the tag management device 30 is configured to:
in response to determining a third tag change in the target tag tree, according to at least one parent node to which the third tag corresponds;
searching in the first data table according to the label corresponding to each father node to obtain a second label tree bound with the label corresponding to any father node; and is combined with the other components of the water treatment device,
and changing a third tag in the second tag tree.
Optionally, the target tag tree further includes a version field, and the tag management device 30 is configured to:
and updating the data corresponding to the version field in response to determining the tag change in the target tag tree.
Optionally, the tag management device 30 is configured to:
determining authority information in a label change request in response to receiving the label change request;
executing the tag change request under the condition that the authority information meets the preset condition;
wherein the tag change request includes an add tag request, a delete tag request, and a modify tag request.
Optionally, the tag management device 30 is configured to:
Generating examination information according to the label change request and sending the examination information to a target terminal under the condition that the authority information does not meet the preset condition;
and responding to the received examination passing information sent by the target terminal, and executing the label changing request.
The specific manner in which the various modules perform the operations in the apparatus of the above embodiments have been described in detail in connection with the embodiments of the method, and will not be described in detail herein.
The present disclosure also provides a computer readable storage medium having stored thereon computer program instructions which, when executed by a processor, implement the steps of the tag management method provided by the present disclosure.
Referring to fig. 4, fig. 4 is a functional block diagram of a vehicle 400 according to an exemplary embodiment. The vehicle 400 may be configured in a fully or partially autonomous mode. For example, the vehicle 400 may obtain environmental information of its surroundings through the perception system 420 and derive an automatic driving strategy based on analysis of the surrounding environmental information to achieve full automatic driving, or present the analysis results to the user to achieve partial automatic driving.
Vehicle 400 may include various subsystems, such as an infotainment system 410, a perception system 420, a decision control system 430, a drive system 440, and a computing platform 450. Alternatively, vehicle 400 may include more or fewer subsystems, and each subsystem may include multiple components. In addition, each of the subsystems and components of the vehicle 400 may be interconnected by wire or wirelessly.
In some embodiments, the infotainment system 410 may include a communication system 411, an entertainment system 412, and a navigation system 413.
The communication system 411 may include a wireless communication system that may communicate wirelessly with one or more devices directly or via a communication network. For example, the wireless communication system may use 3G cellular communication, such as CDMA, EVD0, GSM/GPRS, or 4G cellular communication, such as LTE. Or 5G cellular communication. The wireless communication system may communicate with a wireless local area network (wireless local area network, WLAN) using WiFi. In some embodiments, the wireless communication system may communicate directly with the device using an infrared link, bluetooth, or ZigBee. Other wireless protocols, such as various vehicle communication systems, for example, wireless communication systems may include one or more dedicated short-range communication (dedicated short range communications, DSRC) devices, which may include public and/or private data communications between vehicles and/or roadside stations.
Entertainment system 412 may include a display device, a microphone and an audio, and a user may listen to the broadcast in the vehicle based on the entertainment system, playing music; or the mobile phone is communicated with the vehicle, the screen of the mobile phone is realized on the display equipment, the display equipment can be in a touch control type, and a user can operate through touching the screen.
In some cases, the user's voice signal may be acquired through a microphone and certain controls of the vehicle 400 by the user may be implemented based on analysis of the user's voice signal, such as adjusting the temperature within the vehicle, etc. In other cases, music may be played to the user through sound.
The navigation system 413 may include a map service provided by a map provider to provide navigation of a travel route for the vehicle 400, and the navigation system 413 may be used with the global positioning system 421 and the inertial measurement unit 422 of the vehicle. The map service provided by the map provider may be a two-dimensional map or a high-precision map.
The perception system 420 may include several types of sensors that sense information about the environment surrounding the vehicle 400. For example, the sensing system 420 may include a global positioning system 421 (which may be a GPS system, or may be a beidou system or other positioning system), an inertial measurement unit (inertial measurement unit, IMU) 422, a lidar 423, a millimeter wave radar 424, an ultrasonic radar 425, and a camera 426. Sensing system 420 may also include sensors (e.g., in-vehicle air quality monitors, fuel gauges, oil temperature gauges, etc.) of the internal systems of monitored vehicle 400. Sensor data from one or more of these sensors may be used to detect objects and their corresponding characteristics (location, shape, direction, speed, etc.). Such detection and identification is a critical function of the safe operation of the vehicle 400.
The global positioning system 421 is used to estimate the geographic location of the vehicle 400.
The inertial measurement unit 422 is used to sense the pose change of the vehicle 400 based on inertial acceleration. In some embodiments, inertial measurement unit 422 may be a combination of an accelerometer and a gyroscope.
The lidar 423 senses objects in the environment in which the vehicle 400 is located using a laser. In some embodiments, lidar 423 may include one or more laser sources, a laser scanner, and one or more detectors, among other system components.
The millimeter wave radar 424 senses objects within the surrounding environment of the vehicle 400 using radio signals. In some embodiments, millimeter-wave radar 424 may be used to sense the speed and/or heading of an object in addition to sensing the object.
The ultrasonic radar 425 may utilize ultrasonic signals to sense objects around the vehicle 400.
The image capturing device 426 is used to capture image information of the surrounding environment of the vehicle 400. The image capturing device 426 may include a monocular camera, a binocular camera, a structured light camera, a panoramic camera, etc., and the image information obtained by the image capturing device 426 may include still images or video stream information.
The decision control system 430 includes a computing system 431 for making an analysis decision based on information acquired by the perception system 420, and the decision control system 430 further includes a vehicle controller 432 for controlling the power system of the vehicle 400, and a steering system 433, a throttle 434, and a braking system 435 for controlling the vehicle 400.
The computing system 431 may be operable to process and analyze the various information acquired by the perception system 420 in order to identify targets, objects, and/or features in the environment surrounding the vehicle 400. The targets may include pedestrians or animals and the objects and/or features may include traffic signals, road boundaries, and obstacles. The computing system 431 may use object recognition algorithms, in-motion restoration structure (Structure from Motion, SFM) algorithms, video tracking, and the like. In some embodiments, computing system 431 may be used to map the environment, track objects, estimate the speed of objects, and so forth. The computing system 431 may analyze the acquired various information and derive a control strategy for the vehicle.
The vehicle controller 432 may be configured to coordinate control of the power battery and the engine 441 of the vehicle to enhance the power performance of the vehicle 400.
The steering system 433 is operable to adjust the heading of the vehicle 400. For example, in one embodiment may be a steering wheel system.
Throttle 434 is used to control the operating speed of engine 441 and thus the speed of vehicle 400.
The brake system 435 is used to control the deceleration of the vehicle 400. The braking system 435 may use friction to slow the wheels 444. In some embodiments, the braking system 435 may convert the kinetic energy of the wheels 444 into electrical current. The brake system 435 may take other forms to slow the rotational speed of the wheels 444 to control the speed of the vehicle 400.
The drive system 440 may include components that provide powered movement of the vehicle 400. In one embodiment, the drive system 440 may include an engine 441, an energy source 442, a transmission 443, and wheels 444. The engine 441 may be an internal combustion engine, an electric motor, an air compression engine, or other types of engine combinations, such as a hybrid engine of a gasoline engine and an electric motor, or a hybrid engine of an internal combustion engine and an air compression engine. The engine 441 converts the energy source 442 into mechanical energy.
Examples of energy sources 442 include gasoline, diesel, other petroleum-based fuels, propane, other compressed gas-based fuels, ethanol, solar panels, batteries, and other sources of electricity. The energy source 442 may also provide energy to other systems of the vehicle 400.
The transmission 443 may transmit mechanical power from the engine 441 to the wheels 444. The transmission 443 may include a gearbox, a differential, and a driveshaft. In one embodiment, the transmission 443 may also include other devices, such as a clutch. Wherein the drive shaft may comprise one or more axles that may be coupled to one or more wheels 444.
Some or all of the functions of the vehicle 400 are controlled by the computing platform 450. The computing platform 450 may include at least one processor 451, and the processor 451 may execute instructions 453 stored in a non-transitory computer readable medium, such as the first memory 452. In some embodiments, computing platform 450 may also be a plurality of computing devices that control individual components or subsystems of vehicle 400 in a distributed manner.
The processor 451 may be any conventional processor, such as a commercially available CPU. Alternatively, the processor 451 may also include a processor such as an image processor (Graphic Process Unit, GPU), a field programmable gate array (FieldProgrammable Gate Array, FPGA), a System On Chip (SOC), an application specific integrated Chip (Application Specific Integrated Circuit, ASIC), or a combination thereof. Although FIG. 4 functionally illustrates a processor, memory, and other elements of a computer in the same block, it will be understood by those of ordinary skill in the art that the processor, computer, or memory may in fact comprise multiple processors, computers, or memories that may or may not be stored within the same physical housing. For example, the memory may be a hard disk drive or other storage medium located in a different housing than the computer. Thus, references to a processor or computer will be understood to include references to a collection of processors or computers or memories that may or may not operate in parallel. Rather than using a single processor to perform the steps described herein, some components, such as the steering component and the retarding component, may each have their own processor that performs only calculations related to the component-specific functions.
In the disclosed embodiment, the processor 451 may perform the tag management method described above.
In various aspects described herein, the processor 451 may be located remotely from the vehicle and in wireless communication with the vehicle. In other aspects, some of the processes described herein are performed on a processor disposed within the vehicle and others are performed by a remote processor, including taking the necessary steps to perform a single maneuver.
In some embodiments, the first memory 452 may contain instructions 453 (e.g., program logic), the instructions 453 being executable by the processor 451 to perform various functions of the vehicle 400. The first memory 452 may also contain additional instructions, including instructions to send data to, receive data from, interact with, and/or control one or more of the infotainment system 410, the perception system 420, the decision control system 430, the drive system 440.
In addition to instructions 453, the first memory 452 may also store data such as road maps, route information, vehicle location, direction, speed, and other such vehicle data, as well as other information. Such information may be used by the vehicle 400 and the computing platform 450 during operation of the vehicle 400 in autonomous, semi-autonomous, and/or manual modes.
The computing platform 450 may control the functions of the vehicle 400 based on inputs received from various subsystems (e.g., the drive system 440, the perception system 420, and the decision control system 430). For example, computing platform 450 may utilize input from decision control system 430 in order to control steering system 433 to avoid obstacles detected by perception system 420. In some embodiments, computing platform 450 is operable to provide control over many aspects of vehicle 400 and its subsystems.
Alternatively, one or more of these components may be mounted separately from or associated with vehicle 400. For example, the first memory 452 may exist partially or completely separate from the vehicle 400. The above components may be communicatively coupled together in a wired and/or wireless manner.
Alternatively, the above components are only an example, and in practical applications, components in the above modules may be added or deleted according to actual needs, and fig. 4 should not be construed as limiting the embodiments of the present disclosure.
An autonomous car traveling on a road, such as the vehicle 400 above, may identify objects within its surrounding environment to determine adjustments to the current speed. The object may be another vehicle, a traffic control device, or another type of object. In some examples, each identified object may be considered independently and based on its respective characteristics, such as its current speed, acceleration, spacing from the vehicle, etc., may be used to determine the speed at which the autonomous car is to adjust.
Alternatively, the vehicle 400 or a sensing and computing device associated with the vehicle 400 (e.g., computing system 431, computing platform 450) may predict the behavior of the identified object based on the characteristics of the identified object and the state of the surrounding environment (e.g., traffic, rain, ice on a road, etc.). Alternatively, each identified object depends on each other's behavior, so all of the identified objects can also be considered together to predict the behavior of a single identified object. The vehicle 400 is able to adjust its speed based on the predicted behavior of the identified object. In other words, the autonomous car is able to determine what steady state the vehicle will need to adjust to (e.g., accelerate, decelerate, or stop) based on the predicted behavior of the object. In this process, the speed of the vehicle 400 may also be determined in consideration of other factors, such as the lateral position of the vehicle 400 in the road on which it is traveling, the curvature of the road, the proximity of static and dynamic objects, and so forth.
In addition to providing instructions to adjust the speed of the autonomous vehicle, the computing device may also provide instructions to modify the steering angle of the vehicle 400 so that the autonomous vehicle follows a given trajectory and/or maintains safe lateral and longitudinal distances from objects in the vicinity of the autonomous vehicle (e.g., vehicles in adjacent lanes on a roadway).
The vehicle 400 may be various types of traveling tools, such as a car, a truck, a motorcycle, a bus, a ship, an airplane, a helicopter, a recreational vehicle, a train, etc., and embodiments of the present disclosure are not particularly limited.
In another exemplary embodiment, a computer program product is also provided, comprising a computer program executable by a programmable apparatus, the computer program having code portions for performing the above-described tag management method when executed by the programmable apparatus.
Fig. 5 is a block diagram illustrating a tag management apparatus according to an exemplary embodiment. For example, the first tag management apparatus 500 may be provided as a server. Referring to fig. 5, the first tag management apparatus 500 includes a processing component 522 that further includes one or more processors and memory resources represented by a second memory 532 for storing instructions, such as applications, that are executable by the processing component 522. The application program stored in the second memory 532 may include one or more modules each corresponding to a set of instructions. Further, the processing component 522 is configured to execute instructions to perform the tag management methods described above.
The first tag management apparatus 500 may further comprise a power component 526 configured to perform power management of the first tag management apparatus 500, a wired or wireless network interface 550 configured to connect the first tag management apparatus 500 to a network, and an input/output interface 558. First tag management apparatus 500 may operate based on an operating system stored in the second memory 532, e.g., windows Server TM ，Mac OS X TM ，Unix TM ，Linux TM ，FreeBSD TM Or the like.
The apparatus may be a stand-alone electronic device or may be part of a stand-alone electronic device, for example, in one embodiment, the apparatus may be an integrated circuit (Integrated Circuit, IC) or a chip, where the integrated circuit may be an IC or may be a collection of ICs; the chip may include, but is not limited to, the following: GPU (Graphics Processing Unit, graphics processor), CPU (Central Processing Unit ), FPGA (Field Programmable Gate Array, programmable logic array), DSP (Digital Signal Processor ), ASIC (Application Specific Integrated Circuit, application specific integrated circuit), SOC (System on Chip, SOC, system on Chip or System on Chip), etc. The integrated circuit or chip may be configured to execute executable instructions (or code) to implement the tag management method described above. The executable instructions may be stored on the integrated circuit or chip or may be retrieved from another device or apparatus, such as the integrated circuit or chip including a processor, memory, and interface for communicating with other devices. The executable instructions may be stored in the memory, which when executed by the processor implement the tag management method described above; alternatively, the integrated circuit or chip may receive executable instructions through the interface and transmit the executable instructions to the processor for execution to implement the tag management method described above.
Other embodiments of the disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the disclosure. This application is intended to cover any adaptations, uses, or adaptations of the disclosure following, in general, the principles of the disclosure and including such departures from the present disclosure as come within known or customary practice within the art to which the disclosure pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the disclosure being indicated by the following claims.
It is to be understood that the present disclosure is not limited to the precise arrangements and instrumentalities shown in the drawings, and that various modifications and changes may be effected without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.
Claims (13)
1. A method of tag management, the method comprising:
acquiring a tag set corresponding to vehicle running data, wherein the tag set is used for describing information included in the vehicle running data;
constructing the tag set into a target tag tree, wherein the target tag tree comprises a plurality of nodes, and the root node of the target tag tree comprises at least one of the following nodes: entity nodes, event nodes and meta-information nodes; the entity node comprises attribute nodes and state nodes; the label under the entity node is used for describing information of at least one entity in vehicles, pedestrians and roads, the label under the event node is a label with time persistence, and the label under the meta-information node is used for describing information which exists in any condition and is different from the entity;
Storing the target tag tree and the vehicle driving data in a tag tree table in an associated manner;
selecting a target node in the plurality of nodes according to a preset rule corresponding to a target scene, and extracting a first tag tree from the target tag tree according to the target node;
storing the first tag tree to a first data table;
selecting a target node in the plurality of nodes according to a preset rule corresponding to a target scene, and extracting a first tag tree from the target tag tree according to the target node, wherein the method comprises the following steps:
determining a target node corresponding to the target scene, and screening tags at the target node to obtain a first tag tree corresponding to the target scene, wherein the first tag tree at least comprises a parking tag tree and a driving tag tree.
2. The method according to claim 1, wherein the method further comprises:
in response to receiving a tag acquisition request, determining an autopilot scenario in the tag acquisition request;
acquiring the first tag tree under the condition that the automatic driving scene corresponds to the target scene; and is combined with the other components of the water treatment device,
and sending the first tag tree to a requester of the tag acquisition request.
3. The method of claim 2, wherein the sending the first tag tree to the requestor of the tag acquisition request comprises:
analyzing the first tag tree into a scene tag set;
and sending the scene tag set to a requester of the tag acquisition request.
4. The method of claim 1, wherein storing the first tag tree to a first data table comprises:
determining a first node of the target nodes;
and binding the first label tree with the target label tree according to the first label corresponding to the first node, and storing the first label tree and the target label tree into a first data table.
5. The method according to claim 4, characterized in that the method comprises:
in response to determining a second tag change in the first tag tree, retrieving from the tag tree table according to a first tag of the first tag tree, obtaining the target tag tree bound to the first tag tree; and is combined with the other components of the water treatment device,
and changing a second label in the target label tree.
6. The method according to claim 4, wherein the method further comprises:
in response to determining a third tag change in the target tag tree, according to at least one parent node to which the third tag corresponds;
Searching in the first data table according to the label corresponding to each father node to obtain a second label tree bound with the label corresponding to any father node; and is combined with the other components of the water treatment device,
and changing a third tag in the second tag tree.
7. The method of claim 4, wherein the target tag tree further comprises a version field therein, the method further comprising:
and updating the data corresponding to the version field in response to determining the tag change in the target tag tree.
8. The method according to any one of claims 1-7, characterized in that the method comprises:
determining authority information in a label change request in response to receiving the label change request;
executing the tag change request under the condition that the authority information meets the preset condition;
wherein the tag change request includes an add tag request, a delete tag request, and a modify tag request.
9. The method of claim 8, wherein the method further comprises:
generating examination information according to the label change request and sending the examination information to a target terminal under the condition that the authority information does not meet the preset condition;
And responding to the received examination passing information sent by the target terminal, and executing the label changing request.
10. A tag management apparatus, the apparatus comprising:
the system comprises an acquisition module, a storage module and a display module, wherein the acquisition module is configured to acquire a tag set corresponding to vehicle running data, and the tag set is used for describing information included in the vehicle running data;
a building module configured to build the tag set into a target tag tree, the target tag tree comprising a plurality of nodes, a root node of the target tag tree comprising at least one of: entity nodes, event nodes and meta-information nodes; the entity node comprises attribute nodes and state nodes; the label under the entity node is used for describing information of at least one entity in vehicles, pedestrians and roads, the label under the event node is a label with time persistence, and the label under the meta-information node is used for describing information which exists in any condition and is different from the entity;
a first storage module configured to store the target tag tree in association with the vehicle travel data to a tag tree table;
the selecting module is configured to select a target node in the plurality of nodes according to a preset rule corresponding to the target scene;
An extraction module configured to extract a first tag tree from the target tag tree according to the target node;
a second storage module configured to store the first tag tree to a first data table;
the selecting module and the extracting module are used for:
determining a target node corresponding to the target scene, and screening tags at the target node to obtain a first tag tree corresponding to the target scene, wherein the first tag tree at least comprises a parking tag tree and a driving tag tree.
11. A vehicle, characterized by comprising:
a processor;
a memory for storing processor-executable instructions;
wherein the processor is configured to:
acquiring a tag set corresponding to vehicle running data, wherein the tag set is used for describing information included in the vehicle running data;
constructing the tag set into a target tag tree, wherein the target tag tree comprises a plurality of nodes, and the root node of the target tag tree comprises at least one of the following nodes: entity nodes, event nodes and meta-information nodes; the entity node comprises attribute nodes and state nodes; the label under the entity node is used for describing information of at least one entity in vehicles, pedestrians and roads, the label under the event node is a label with time persistence, and the label under the meta-information node is used for describing information which exists in any condition and is different from the entity;
Storing the target tag tree and the vehicle driving data in a tag tree table in an associated manner;
selecting a target node in the plurality of nodes according to a preset rule corresponding to the target scene; and is combined with the other components of the water treatment device,
extracting a first tag tree from the target tag tree according to the target node;
storing the first tag tree to a first data table;
selecting a target node in the plurality of nodes according to a preset rule corresponding to a target scene, and extracting a first tag tree from the target tag tree according to the target node, wherein the method comprises the following steps:
determining a target node corresponding to the target scene, and screening tags at the target node to obtain a first tag tree corresponding to the target scene, wherein the first tag tree at least comprises a parking tag tree and a driving tag tree.
12. A computer readable storage medium having stored thereon computer program instructions, which when executed by a processor, implement the steps of the method of any of claims 1-9.
13. A chip, comprising a processor and an interface; the processor is configured to read instructions to perform the method of any one of claims 1-9.
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Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2008209791A (en) * | 2007-02-27 | 2008-09-11 | Toyota Central R&D Labs Inc | Information processor and driving behavior simulator |
| CN111177129A (en) * | 2019-12-16 | 2020-05-19 | 中国平安财产保险股份有限公司 | Label system construction method, device, equipment and storage medium |
| CN111339042A (en) * | 2020-03-26 | 2020-06-26 | 佛山中科芯蔚科技有限公司 | Data operation processing method and system and scheduling server |
| CN111400554A (en) * | 2019-01-02 | 2020-07-10 | 中国移动通信有限公司研究院 | Access method and device for unified tag library |
| CN112287566A (en) * | 2020-11-24 | 2021-01-29 | 北京亮道智能汽车技术有限公司 | Automatic driving scene library generation method and system and electronic equipment |
| CN114445803A (en) * | 2022-02-07 | 2022-05-06 | 苏州挚途科技有限公司 | Driving data processing method and device and electronic equipment |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2020101960A (en) * | 2018-12-21 | 2020-07-02 | ソニー株式会社 | Information processing apparatus, information processing method, and program |
-
2022
- 2022-07-15 CN CN202210836990.7A patent/CN115203483B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2008209791A (en) * | 2007-02-27 | 2008-09-11 | Toyota Central R&D Labs Inc | Information processor and driving behavior simulator |
| CN111400554A (en) * | 2019-01-02 | 2020-07-10 | 中国移动通信有限公司研究院 | Access method and device for unified tag library |
| CN111177129A (en) * | 2019-12-16 | 2020-05-19 | 中国平安财产保险股份有限公司 | Label system construction method, device, equipment and storage medium |
| CN111339042A (en) * | 2020-03-26 | 2020-06-26 | 佛山中科芯蔚科技有限公司 | Data operation processing method and system and scheduling server |
| CN112287566A (en) * | 2020-11-24 | 2021-01-29 | 北京亮道智能汽车技术有限公司 | Automatic driving scene library generation method and system and electronic equipment |
| CN114445803A (en) * | 2022-02-07 | 2022-05-06 | 苏州挚途科技有限公司 | Driving data processing method and device and electronic equipment |
Non-Patent Citations (1)
| Title |
|---|
| 基于标签云系统共现分析的用户兴趣预测模型;周朴雄等;情报杂志;20170518(05);全文 * |
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