CN112015832A - Road network prediction tree visualization method and device, electronic equipment and storage medium - Google Patents
Road network prediction tree visualization method and device, electronic equipment and storage medium Download PDFInfo
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Abstract
The embodiment of the invention provides a road network forecast tree visualization method, a road network forecast tree visualization device, electronic equipment and a storage medium, wherein the method comprises the following steps: acquiring a driving assistance log, wherein the driving assistance log at least records road network prediction tree data, auxiliary road network data and a corresponding relation between the road network prediction tree data and the auxiliary road network data; determining the geographical position of each element of the road network prediction tree according to the corresponding relation between the road network prediction tree data and the auxiliary road network data; and drawing the road network prediction tree corresponding to the road network prediction tree data on the electronic map layer based on the position of the vehicle in the road network prediction tree data and the geographic position of each element of the road network prediction tree. The embodiment of the invention can realize the visualization of the road network prediction tree, is convenient for understanding the road network prediction tree and provides possibility for improving the test efficiency of the electronic horizon system.
Description
Technical Field
The embodiment of the invention relates to the technical field of auxiliary driving, in particular to a road network prediction tree visualization method and device, electronic equipment and a storage medium.
Background
The road network prediction tree is tree data describing a road network structure of a certain geographical area range in front of a vehicle, and the road network prediction tree can be generated and output to a vehicle driving assistance system (such as an advanced driving assistance system ADAS) based on an electronic horizon system, so that the normal operation of the driving assistance function of the vehicle is guaranteed, and the safety and the comfort of the vehicle driving are improved.
In order to test the performance of the electronic horizon system, the content of a road network prediction tree needs to be analyzed; however, since the road network prediction tree is generated based on a specific protocol, it is difficult for a non-professional to understand the contents of the road network prediction tree, and even a professional takes a lot of time to analyze the contents of the road network prediction tree, which results in inefficient testing of the electronic horizon system.
Disclosure of Invention
In view of this, embodiments of the present invention provide a road network prediction tree visualization method, apparatus, electronic device and storage medium, so as to implement visualization of a road network prediction tree and provide possibility for improving efficiency of testing an electronic horizon system.
In order to achieve the above purpose, the embodiments of the present invention provide the following technical solutions:
a road network prediction tree visualization method comprises the following steps:
acquiring a driving assistance log, wherein the driving assistance log at least records road network prediction tree data, auxiliary road network data and a corresponding relation between the road network prediction tree data and the auxiliary road network data;
determining the geographical position of each element of the road network prediction tree according to the corresponding relation between the road network prediction tree data and the auxiliary road network data;
and drawing the road network prediction tree corresponding to the road network prediction tree data on the electronic map layer based on the position of the vehicle in the road network prediction tree data and the geographic position of each element of the road network prediction tree.
The embodiment of the present invention further provides a road network forecast tree visualization device, including:
the system comprises a log acquisition module, a log acquisition module and a driving assistance module, wherein the log acquisition module is used for acquiring a driving assistance log, and the driving assistance log at least records road network prediction tree data, auxiliary road network data and a corresponding relation between the road network prediction tree data and the auxiliary road network data;
the geographic position determining module is used for determining the geographic position of each element of the road network prediction tree according to the corresponding relation between the road network prediction tree data and the auxiliary road network data;
and the drawing module is used for drawing the road network prediction tree corresponding to the road network prediction tree data on the electronic map layer based on the position of the vehicle in the road network prediction tree data and the geographic position of each element of the road network prediction tree.
The embodiment of the invention also provides electronic equipment, which comprises at least one memory and at least one processor; the memory stores a program, and the processor calls the program to execute the road network prediction tree visualization method.
The embodiment of the present invention further provides a storage medium, where the storage medium stores a program for executing the road network prediction tree visualization method.
The road network prediction tree construction method provided by the embodiment of the invention can obtain driving auxiliary logs, wherein the driving auxiliary logs comprise road network prediction tree data, auxiliary road network data and a corresponding relation between the road network prediction tree data and the auxiliary road network data; the road network prediction tree data records the relative positions of the elements of the road network prediction tree, and the auxiliary road network data records the geographic positions of the elements, so the embodiment of the invention can determine the geographic positions of the elements of the road network prediction tree according to the corresponding relation between the road network prediction tree data and the auxiliary road network data; furthermore, based on the position of the vehicle in the road network prediction tree data and the geographic position of each element of the road network prediction tree, the road network prediction tree corresponding to the road network prediction tree data can be drawn on the electronic map layer, and the road network prediction tree can be visualized on the electronic map layer. Therefore, the road network prediction tree visualization method provided by the embodiment of the invention can realize the visualization of the road network prediction tree, so that the understanding of the road network prediction tree is not only the simple understanding of the abstract contents of the road network prediction tree, but also the understanding of the contents of the road network prediction tree can be realized by combining the road network prediction tree which is displayed in an visualized manner on the electronic map layer, and the possibility of improving the test efficiency of the electronic horizon system is provided for facilitating the understanding of the road network prediction tree.
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In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly introduced below, it is obvious that the drawings in the following description are only embodiments of the present application, and for those skilled in the art, other drawings can be obtained according to the provided drawings without creative efforts.
FIG. 1 is a schematic diagram of an electronic horizon system for implementing a road network forecast tree;
FIG. 2 is an exemplary graph of a road network prediction tree;
FIG. 3 is a schematic diagram of an electronic horizon system;
FIG. 4 is a block diagram of an electronic device;
fig. 5 is a flowchart of a road network prediction tree visualization method according to an embodiment of the present invention;
FIG. 6 is an exemplary diagram of a road network prediction tree drawn over layers of an electronic map;
fig. 7 is another flowchart of a road network prediction tree visualization method according to an embodiment of the present invention;
FIG. 8 is a flowchart of determining the geographical location of each element of the road network forecast tree according to the embodiment of the present invention;
fig. 9 is a block diagram of a road network prediction tree visualization apparatus according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Fig. 1 is a schematic diagram of an alternative implementation of the electronic horizon system for constructing a road network forecast tree, as shown in fig. 1, the electronic horizon system may combine road network data provided by a map navigation application and positioning information provided by a positioning device (e.g., a positioning sensor) to construct a road network forecast tree; the road network prediction tree can describe road section topological structures, road section attributes and the like of a certain geographic area in front of the vehicle through a tree data structure, namely the road network prediction tree can describe the road section topological structures and express the relative position relation of road sections;
the electronic horizon system can transmit the road network prediction tree to an auxiliary driving system of the vehicle, so that the normal operation of the auxiliary driving function of the vehicle is ensured, and the safety and the comfort of vehicle driving are improved.
As an example, the road network prediction tree mainly includes: MPP (Most Probable Path) and non-MPP; referring to fig. 2, which is an alternative example of the road network prediction tree, the solid line of fig. 2 may represent MPP, which may be a predicted maximum likelihood driving path of a vehicle, and one path may include at least one road segment; for example, the MPP may include at least one MPP section (a solid line connecting two points in fig. 2 may represent one MPP section), and each MPP section is connected to form an MPP;
fig. 2 is a dashed line that may represent a non-MPP, which may be a predicted Path on which the vehicle is not likely to travel to the maximum, and may be extended from an MPP segment of the MPP, that is, the non-MPP may be a Sub-Path (Sub Path) connected to the MPP segment in the road network prediction tree; one non-MPP may include at least one non-MPP section (a dotted line connecting two points in the drawing may indicate one non-MPP section);
it can be seen that the depth of the road network forecast tree is in a positive correlation with the length of the MPP, that is, the deeper the MPP is, the deeper the depth of the road network forecast tree is, and the extent of the road network forecast tree is in a positive correlation with the length of the non-MPP, that is, the wider the non-MPP is, the wider the extent of the road network forecast tree is; the depth of the road network prediction tree represents the farthest visual field distance of the vehicle, and the breadth can represent the road section branching level.
At present, due to the limitations of system memory and flow, the road network prediction tree cannot be infinitely expanded, namely the size of the road network prediction tree is set, so that an MPP length threshold value and a non-MPP length threshold value need to be set; on the basis, the construction logic of the road network prediction tree mainly comprises the following steps: the electronic Horizon system determines a current road section of a vehicle where a self-vehicle is located through positioning information, the current road section of the vehicle is used as an initial road section (the initial road section can be an initial MPP road section), the MPP and non-MPP of a road network prediction tree are expanded from the initial road section until the Horizon length of the expanded MPP is not less than an MPP length threshold, and when the non-MPP road section with the Horizon length not less than the non-MPP length threshold exists, the expansion of the non-MPP road section on a road section branch of the non-MPP road section is stopped; the Horizon length of the road segment represents a view length of the road segment, which may be a distance of a destination location of the road segment to a vehicle location;
the road network prediction tree is constructed in various ways, as long as the Horizon length of the MPP of the finally constructed road network prediction tree is not less than the MPP length threshold, and the Horizon length of the non-MPP is not less than the non-MPP length threshold, the embodiment of the invention is not limited to the way of constructing the road network prediction tree.
In an alternative implementation, fig. 3 shows an alternative architectural schematic of an electronic horizon system, which may include, as shown in fig. 3: the system comprises an electronic horizon terminal and an electronic horizon cloud server; when the electronic horizon system is applied to a vehicle, the electronic horizon terminal may be disposed on the vehicle, and the electronic horizon cloud server may be disposed on a network side;
in an example of building the road network prediction tree, the electronic horizon terminal may send a request for building the road network prediction tree to the electronic horizon cloud server, where the request may carry vehicle positioning information and road network data, so that the electronic horizon cloud server may implement building the road network prediction tree; the road network prediction tree constructed by the electronic horizon cloud server can be fed back to the electronic horizon terminal, and the electronic horizon terminal can provide the road network prediction tree for vehicles (such as ADAS systems of the vehicles);
in another example, the electronic horizon terminal may also independently construct a road network forecast tree based on the vehicle positioning information and the road network data.
Therefore, the electronic horizon system has the function of providing a road network prediction tree so as to assist the driving decision; at present, in order to test the performance of the electronic horizon system, the content of the road network prediction tree needs to be analyzed, however, since the road network prediction tree is generated based on a specific protocol, it is difficult for non-professionals to understand the content of the road network prediction tree, even though the professionals need to spend a lot of time analyzing the content of the road network prediction tree, so that the test efficiency of the electronic horizon system is low.
The inventor of the invention finds out through research that: through visual display of the road network prediction tree, the difficulty in understanding the content of the road network prediction tree can be reduced, and the possibility is provided for improving the testing efficiency of the electronic horizon system. Meanwhile, it is especially necessary to visually display the electronic horizon system products and to realize the visualization of the road network prediction tree. Based on this, the embodiment of the invention provides a road network forecast tree visualization method and device, an electronic device and a storage medium, so as to realize the visualization of a road network forecast tree.
In order to implement visualization of a road network prediction tree, an embodiment of the present invention provides a road network prediction tree visualization method, and in an optional implementation, the road network prediction tree visualization method may be applied to an electronic device, where the electronic device at least has data processing capability and display capability, and in an example, the electronic device may be a terminal computing device such as a PC (personal computer), a smart phone, a tablet computer, or the like; alternatively, as shown in fig. 4, the electronic device may include: at least one processor 10, at least one communication interface 20, at least one memory 30, at least one communication bus 40 and at least one display device 50;
in the embodiment of the present invention, the number of the processor 10, the communication interface 20, the memory 30, the communication bus 40 and the display device 50 is at least one, and the processor 10, the communication interface 20, the memory 30 and the display device 50 complete the communication with each other through the communication bus 40;
alternatively, the communication interface 20 may be an interface of a communication module, which may be used for network communication; a communication module such as a mobile communication module, a wifi communication module, etc.;
the processor 10 may be a Central Processing Unit (CPU), or an application Specific Integrated circuit (asic), or one or more Integrated circuits configured to implement embodiments of the present invention.
a display device 50 such as a display screen or the like having display capability;
in the embodiment of the present invention, the memory 30 may store a program for implementing the road network prediction tree visualization method provided in the embodiment of the present invention, and the processor 10 may call the program stored in the memory 30 to execute the road network prediction tree visualization method provided in the embodiment of the present invention.
In an optional implementation of the disclosure of the embodiment of the present invention, the road network prediction tree may be drawn on the electronic map layer to realize visualization of the road network prediction tree. Optionally, fig. 5 shows an optional flow of the road network prediction tree visualization method provided in the embodiment of the present invention, and referring to fig. 5, the flow may include:
step S100, acquiring a driving assistance log; the driving support log records at least road network prediction tree data, auxiliary road network data and the corresponding relation between the road network prediction tree data and the auxiliary road network data.
Alternatively, the driving assistance log may be, for example, an electronic horizon log, which may be considered as log information of the electronic horizon system based on an electronic horizon protocol; in an embodiment of the present invention, the driving support log (e.g., an electronic horizon log) may record at least road network prediction tree data, auxiliary road network data, and a correspondence relationship between the road network prediction tree data and the auxiliary road network data.
The road network prediction tree data may be considered as construction data of a road network prediction tree, and the road network prediction tree constructed by the electronic horizon system may be restored by the road network prediction tree data. Although the road network prediction tree data can represent a road network structure (such as a road section topological structure) in a certain geographic area in front of the vehicle, the road network prediction tree data represents the relative position of each element of the road network prediction tree, and cannot represent the geographic position (such as the absolute position of the geographic position, for example, longitude and latitude coordinates) of each element in the road network prediction tree; taking the above-mentioned elements as the road segments, the road network prediction tree data may represent the relative positions of the road segments in the road network prediction tree, but may not represent the geographic positions of the road segments.
In order to draw the road network prediction tree on the electronic map layer, the embodiment of the invention needs to determine the geographic position of each element in the road network prediction tree, so that each element of the road network prediction tree is matched on the electronic map layer through the geographic position of each element in the road network prediction tree, and the road network prediction tree is drawn on the electronic map layer; according to the embodiment of the invention, the geographical position of each element of the road network prediction tree can be determined by means of the auxiliary road network data and the corresponding relation between the road network prediction tree data and the auxiliary road network data.
In the embodiment of the invention, the auxiliary road network data can record the geographic position of the element; the embodiment of the invention can determine the geographic position (such as the geographic position of a road section) of an element by combining a positioning means based on an electronic horizon protocol to form auxiliary road network data; and recording the auxiliary road network data in a driving assistance log, such as an electronic horizon log generated based on an electronic horizon protocol.
Further, the embodiment of the present invention may record the correspondence between the road network prediction tree data and the auxiliary road network data based on the electronic horizon protocol, for example, the electronic horizon log may record the relative positions of each element of the road network prediction tree in the road network prediction tree data and the correspondence between the geographical positions of each element in the auxiliary road network data; and records the correspondence in a driving assistance log (e.g., in an electronic horizon log).
Step S110, determining the geographical position of each element of the road network forecast tree according to the corresponding relation between the road network forecast tree data and the auxiliary road network data.
After the driving assistance log is obtained and the road network prediction tree data, the assistance road network data and the corresponding relation between the road network prediction tree data and the assistance road network data are determined, the road network prediction tree data records the relative position of the elements of the road network prediction tree, the assistance road network data records the geographic position of the elements, and the road network prediction tree is drawn on the electronic map layer for passing through the geographic position of the elements of the road network prediction tree.
And step S120, drawing the road network prediction tree corresponding to the road network prediction tree data on the electronic map layer based on the position of the vehicle in the road network prediction tree data and the geographic position of each element of the road network prediction tree.
After the geographic position of each element of the road network prediction tree is determined, the embodiment of the invention can match the position of the vehicle (namely the drawing position of the vehicle on the electronic map layer) on the electronic map layer according to the position of the vehicle in the road network prediction tree data, and match each element of the road network prediction tree on the electronic map layer according to the geographic position of each element of the road network prediction tree, thereby determining the drawing position of each element of the road network prediction tree on the electronic map layer, and realizing that the road network prediction tree corresponding to the road network prediction tree data is drawn on the electronic map layer.
Further, the road network prediction tree data may further record element attributes of the elements, for example, the element attributes are link attributes of the links, the link attributes may be link information such as forms of the links, link levels, and the like, and contents included in the link attributes may be extended by fields included in the attributes. The embodiment of the invention can further associate the elements of the road network prediction tree with the corresponding element attributes in the road network prediction tree drawn on the electronic map layer.
For example, fig. 6 shows an example of a road network prediction tree drawn on top of an electronic map layer, which may be referred to, and the bold line in fig. 6 indicates the drawn road network prediction tree.
The road network prediction tree construction method provided by the embodiment of the invention can obtain driving auxiliary logs, wherein the driving auxiliary logs comprise road network prediction tree data, auxiliary road network data and a corresponding relation between the road network prediction tree data and the auxiliary road network data; the road network prediction tree data records the relative positions of the elements of the road network prediction tree, and the auxiliary road network data records the geographic positions of the elements, so the embodiment of the invention can determine the geographic positions of the elements of the road network prediction tree according to the corresponding relation between the road network prediction tree data and the auxiliary road network data; furthermore, based on the position of the vehicle in the road network prediction tree data and the geographic position of each element of the road network prediction tree, the road network prediction tree corresponding to the road network prediction tree data can be drawn on the electronic map layer, and the road network prediction tree can be visualized on the electronic map layer. Therefore, the road network prediction tree visualization method provided by the embodiment of the invention can realize the visualization of the road network prediction tree, so that the understanding of the road network prediction tree is not only the simple understanding of the abstract contents of the road network prediction tree, but also the understanding of the contents of the road network prediction tree can be realized by combining the road network prediction tree which is displayed in an visualized manner on the electronic map layer, and the possibility of improving the test efficiency of the electronic horizon system is provided for facilitating the understanding of the road network prediction tree.
As an alternative implementation, the road network prediction tree data may be transmitted by ADAS V2 protocol (second generation advanced driving assistance system protocol), and the road network prediction tree data transmitted by ADAS V2 protocol may be recorded in an electronic horizon log generated based on the electronic horizon protocol; meanwhile, the electronic horizon system can determine the geographic position of the element by combining a positioning means based on an electronic horizon protocol to form auxiliary road network data, and the auxiliary road network data can be recorded in an electronic horizon log generated based on the electronic horizon protocol; based on the electronic horizon protocol, the electronic horizon system can establish a corresponding relationship between the relative position of each element of the road network prediction tree in the road network prediction tree data and the geographic position of each element in the auxiliary road network data, and record the corresponding relationship in an electronic horizon log generated based on the electronic horizon protocol.
As an optional implementation, the road network prediction tree data of the electronic horizon log may include the relative position of the road segments of the road network prediction tree and the road segment attributes; the road section topological structure of the road network prediction tree can be determined through the road section relative position of the road network prediction tree, and the form of the road network prediction tree is determined; the road section information such as the road section form, the road section grade, the road section bending degree and the like can be associated with each road section of the road network prediction tree through the road section attributes; optionally, fig. 7 shows another flowchart of a road network prediction tree visualization method provided in the embodiment of the present invention, and referring to fig. 7, the flowchart may include:
s200, acquiring an electronic horizon log; the electronic horizon log at least comprises road network prediction tree data, auxiliary road network data and the corresponding relation between the road network prediction tree data and the auxiliary road network data.
Alternatively, the electronic horizon log may be an alternative form of the driving assistance log, and the description of step S200 may refer to step S100; further, the road network prediction tree data may further include road segment attributes of the road network prediction tree.
Step S210, determining the geographical position of each road segment of the road network prediction tree according to the corresponding relationship between the relative position of each road segment of the road network prediction tree in the road network prediction tree data and the geographical position of each road segment in the auxiliary road network data.
Optionally, in an example, the embodiment of the present invention may organize road networks according to the auxiliary road network data, for example, organize corresponding road networks according to the geographic locations of the road segments recorded by the auxiliary road network data; because the positions of the positioning road sections at different time points in the electronic horizon log are different, the organized road network can be dynamically changed; furthermore, based on the correspondence between the road network predicted tree data and the auxiliary road network data recorded by the electronic horizon log, the method can map the road segments of the road network predicted tree in the road network predicted tree data to the road network and determine the geographic position of the road segments of the road network predicted tree in the road network predicted tree data.
Step S220, drawing the road network prediction tree on the electronic map layer based on the position of the vehicle in the road network prediction tree data and the geographic position of each road section of the road network prediction tree, wherein the relative position of the road section of the drawn road network prediction tree corresponds to the relative position of the road section in the road network prediction tree data.
According to the embodiment of the invention, the road network prediction tree can be drawn on the electronic map layer based on the position of the vehicle in the road network prediction tree data and the geographic position of each road section of the road network prediction tree, and the relative position of the drawn road section of the road network prediction tree corresponds to the relative position of the road section in the road network prediction tree data, namely the form of the drawn road network prediction tree is the form of a road section topological structure represented by the relative position of the road section in the road network prediction tree data.
Further, the embodiment of the invention can associate corresponding road section attributes for each road section in the drawn road network prediction tree.
In an alternative implementation, the relative positions of the elements of the road network prediction tree recorded in the road network prediction tree data may include: the method comprises the steps that the road network predicts the offset between the relative ID of the road sections of the tree and the road sections; the geographic location of the elements of the auxiliary road network data record may include: assisting road section IDs and corresponding geographic positions of road sections in road network data;
correspondingly, the correspondence relationship may include: the offset between the relative road section ID of the road network prediction tree in the road network prediction tree data and the road section and the corresponding relation of the road section ID in the auxiliary road network data;
optionally, fig. 8 shows an alternative process for determining the geographical location of each element of the road network prediction tree, as shown in fig. 8, the process may include:
step S300, determining the corresponding road section ID of the road section in the road network prediction tree in the auxiliary road network data according to the offset between the road section relative ID of the road network prediction tree in the road network prediction tree data and the corresponding relation of the road section ID in the auxiliary road network data.
The link relative ID may be an ID indicating the relative position of each link on the basis of the relative positional relationship of the links.
Step S310, determining a road segment ID corresponding to the road segment in the road network prediction tree, and a corresponding geographical location in the auxiliary road network data.
After determining the road section ID corresponding to the road section in the road network prediction tree according to the corresponding relation, the embodiment of the invention can determine the geographic position corresponding to the road section ID of the road section, thereby determining the geographic position of the road section.
After determining the geographic position of each road section of the road network prediction tree data, the embodiment of the invention can match the position of the vehicle on the electronic map layer according to the position of the vehicle in the road network prediction tree data, and match each road section of the road network prediction tree on the electronic map layer according to the geographic position of each road section of the road network prediction tree, thereby drawing a corresponding road network prediction tree, wherein the relative position of the drawn road section of the road network prediction tree corresponds to the relative position between the road sections in the road network prediction tree data;
further, the embodiment of the present invention may associate the road segment attributes carried by the road network prediction tree data with each road segment of the road network prediction tree matched on the electronic map layer.
The above description is an exemplary description of an element taking a road segment as an example, in the embodiment of the present invention, after a road network prediction tree is drawn on an electronic map layer, and element attributes of each element of the road network prediction tree are associated, statistical information of the element attributes may also be counted, and the statistical information of the element attributes of the road network prediction tree is shown in a graph form on the electronic map layer; for example, in the embodiment of the present invention, the number of each road segment level, the number of straight road segments, the speed limit information of each road segment, and the like in the road network prediction tree may be displayed in a graph form, and the road segment attribute information of the graph statistics may be set according to the content of the road segment attribute and the actual statistics requirement, which is not limited in the embodiment of the present invention.
It should be noted that, it is not necessary for the embodiment of the present invention to count the statistical information of the element attributes and display the statistical information in the form of a graph, and the embodiment of the present invention may select or not select the statistical information of the element attributes and display the statistical information of the element attributes as needed.
The above description describes basic contents and optional implementation manners for visualizing the road network prediction tree, and it should be further explained that the road network prediction tree is in a real-time updating state with the change of the own vehicle position, for example, due to the change of the own vehicle position, the MPP road segments and non-MPP road segments that the vehicle drives away from in the road network prediction tree are removed from the road network prediction, so that the space of the road network prediction tree is left to expand the MPP road segments and non-MPP road segments corresponding to the driving direction of the vehicle; for another example, when the vehicle deviates from the navigation path and travels from the MPP road section to the non-MPP road section, the non-MPP road section where the vehicle is located may be changed to the MPP road section, and the road network prediction tree is updated, so that the lengths of the MPP and the non-MPP Horizon meet the requirement; therefore, under the limitation of system memory and flow, the road network prediction tree is in a constantly dynamic state based on the change of the position of the vehicle, so that the road network prediction tree drawn on the electronic map layer can be dynamically changed.
In the embodiment of the invention, the self-parking position in the road network prediction tree data and the geographic position of the elements of the road network prediction tree are in a dynamic updating state; correspondingly, the embodiment of the invention can dynamically update the drawn road network prediction tree on the electronic map layer based on the self-position dynamically updated in the road network prediction tree data and the geographical position of the dynamically updated element of the road network prediction tree.
Optionally, in the embodiment of the present invention, the location of the vehicle in the road network prediction tree drawn on the electronic map layer may be adjusted according to the current updated location of the vehicle in the road network prediction tree data, and an element may be correspondingly added and/or deleted in the road network prediction tree drawn on the electronic map layer according to the geographic location of the currently added and/or deleted element in the road network prediction tree.
As an alternative implementation, the road network prediction tree data may include a plurality of pieces of data for constructing the road network prediction tree in time sequence, and at other time points except the initial time point, the data for constructing the road network prediction tree at the next time point may be regarded as the update data of the data for constructing the road network prediction tree at the previous time point;
the data of the road network prediction tree constructed at the initial time point in the data of the plurality of road network prediction trees can be used for initially constructing the road network prediction tree, namely, the road network prediction tree is constructed from the current road section of the vehicle where the self-parking position is located; the data for constructing the road network prediction tree at the next time point adjacent to the initial time point can be considered as update data for updating the road network prediction tree on the basis of the initially constructed road network prediction tree;
based on this, in the embodiment of the present invention, after the road network prediction tree at the previous time point is drawn on the electronic map layer, the data for constructing the road network prediction tree at the next time point is obtained, where the data for constructing the road network prediction tree at the next time point may include: updating the relative position of the road section in the road network prediction tree at the next time point; the updating the road segment may include: based on the self-parking position adjustment, the road network predicts the added road sections in the tree and/or the deleted road sections in the road network prediction tree; accordingly, updating the relative position of the road segment may be considered as: based on the position adjustment of the self-vehicle, the road network predicts the position relation of the added road sections in the tree and/or deletes the relative positions of the road sections;
after data of constructing the road network prediction tree at the next time point is obtained, the road network prediction tree drawn at the last time point can be updated on the electronic map by the embodiment of the invention; for example, after determining the geographic position of the added road segment, the added road segment is additionally drawn on the road network prediction tree drawn on the electronic map layer according to the geographic position of the added road segment, and/or after determining the geographic position of the deleted road segment, the deleted road segment is deleted on the road network prediction tree drawn on the electronic map layer according to the geographic position of the deleted road segment.
Under the condition of adding the drawing and adding the road sections, the embodiment of the invention can also associate the corresponding road section attributes with the added road sections with the added drawing.
Optionally, if the electronic horizon line log is in a network file form, after the electronic horizon line log is obtained, the embodiment of the present invention may perform preprocessing on data of a plurality of constructed road network prediction trees in time sequence in the electronic horizon line log, for example, perform deduplication processing (for example, perform deduplication on constructed data with the same time point) from a plurality of constructed data included in the electronic horizon line log, remove meaningless data (for example, remove constructed data without time point), and the like;
if the electronic horizon log is in the form of a local file, the embodiment of the invention can sequentially play a plurality of pieces of construction data included in the electronic horizon log and control the data playing speed, such as continuous playing, strip-by-strip playing, skip playing and the like of the plurality of pieces of construction data, so that the visual display of the road network prediction tree at each time point can be actively controlled.
The road network prediction tree visualization method provided by the embodiment of the invention can draw the road network prediction tree on the electronic map layer, realizes the visualization of the road network prediction tree, and enables the understanding of the road network prediction tree not only to simply understand the abstract contents of the road network prediction tree, but also to be combined with the road network prediction tree which is displayed in an visualization way on the electronic map layer to understand the contents of the road network prediction tree.
While various embodiments of the present invention have been described above, various alternatives described in the various embodiments can be combined and cross-referenced without conflict to extend the variety of possible embodiments that can be considered disclosed and disclosed in connection with the embodiments of the present invention.
In the following, the road network prediction tree visualization apparatus provided in the embodiment of the present invention is introduced, and the road network prediction tree visualization apparatus described below may be regarded as a program function module that is required to implement the road network prediction tree visualization method provided in the embodiment of the present invention. The contents of the road network prediction tree visualization device described below may correspond to the contents of the road network prediction tree visualization method described above.
Fig. 9 is a block diagram of a road network prediction tree visualization apparatus according to an embodiment of the present invention, and referring to fig. 9, the road network prediction tree visualization apparatus may include:
a log obtaining module 100, configured to obtain a driving assistance log, where the driving assistance log records at least road network prediction tree data, auxiliary road network data, and a correspondence between the road network prediction tree data and the auxiliary road network data;
a position determining module 200, configured to determine geographic positions of elements of the road network prediction tree according to a corresponding relationship between the road network prediction tree data and the auxiliary road network data;
the drawing module 300 is configured to draw the road network prediction tree corresponding to the road network prediction tree data on the electronic map layer based on the location of the vehicle in the road network prediction tree data and the geographic location of each element of the road network prediction tree.
Optionally, the electronic horizon log may record road network prediction tree data transmitted by ADAS V2 protocol.
Optionally, the position determining module 200 is configured to determine the geographic position of each element of the road network prediction tree according to the correspondence between the road network prediction tree data and the auxiliary road network data, and may specifically include:
and determining the geographic position of each element of the road network forecast tree according to the corresponding relation between the relative position of each element of the road network forecast tree in the road network forecast tree data and the geographic position of each element in the auxiliary road network data.
Optionally, the relative positions of the elements of the road network prediction tree in the road network prediction tree data include: the method comprises the steps that the road network predicts the offset between the relative ID of the road sections of the tree and the road sections; the geographical positions of the elements in the auxiliary road network data comprise: assisting road section IDs and corresponding geographic positions of road sections in road network data;
a location determining module 200, configured to determine, according to a corresponding relationship between the relative location of each element of the road network prediction tree in the road network prediction tree data and the geographic location of each element in the auxiliary road network data, the geographic location of each element of the road network prediction tree, specifically including:
determining the corresponding road section ID of the road section in the road network prediction tree in the auxiliary road network data according to the offset between the road section relative ID of the road network prediction tree in the road network prediction tree data and the road section and the corresponding relation of the road section ID in the auxiliary road network data;
and determining the road section ID corresponding to the road section in the road network prediction tree and the corresponding geographic position in the auxiliary road network data.
Optionally, the drawing module 300 is configured to draw the road network prediction tree corresponding to the road network prediction tree data on the electronic map layer based on the location of the vehicle in the road network prediction tree data and the geographic location of each element of the road network prediction tree, and specifically includes:
matching the position of the vehicle on the electronic map layer according to the position of the vehicle in the road network prediction tree data, and matching the elements of the road network prediction tree on the electronic map layer according to the geographic position of the elements of the road network prediction tree;
and associating corresponding element attributes carried by the road network prediction tree data for each element of the road network prediction tree matched on the electronic map layer.
Optionally, the location of the vehicle in the road network prediction tree data and the geographic location of the element of the road network prediction tree are in a dynamic update state; the drawing module 300 is configured to draw a road network prediction tree corresponding to road network prediction tree data on an electronic map layer based on a vehicle location in the road network prediction tree data and a geographic location of each element of the road network prediction tree, and specifically includes:
and dynamically updating the drawn road network prediction tree on the electronic map layer based on the self-parking position dynamically updated in the road network prediction tree data and the geographical position of the dynamically updated element of the road network prediction tree.
Optionally, the drawing module 300 is configured to dynamically update the drawn road network prediction tree on the electronic map layer based on a self-location position dynamically updated in the road network prediction tree data and a geographic location of an element dynamically updated in the road network prediction tree, and may specifically include:
and correspondingly adding elements and/or deleting elements in the road network prediction tree drawn on the electronic map layer according to the geographical positions of the elements which are currently added and/or deleted in the road network prediction tree.
Optionally, the road network prediction tree visualization device provided in the embodiment of the present invention may further be configured to: counting the statistical information of the element attributes; and displaying the statistical information.
The road network prediction tree visualization device provided by the embodiment of the invention can realize the visualization of the road network prediction tree, so that the understanding of the road network prediction tree is not only the content of the purely understood abstract road network prediction tree, but also the content of the road network prediction tree can be understood by combining the road network prediction tree which is vividly displayed on an electronic map layer, and the possibility of improving the test efficiency of an electronic horizon system is provided for facilitating the understanding of the road network prediction tree.
The road network forecast tree visualization device provided by the embodiment of the invention can be loaded in an electronic device in a program form, and a hardware structure of the electronic device can be selected as shown in fig. 4, and the road network forecast tree visualization device comprises: at least one memory and at least one processor; the memory stores a program, and the processor calls the program to execute the road network prediction tree visualization method provided by the embodiment of the invention.
The embodiment of the present invention further provides a storage medium, where the storage medium may store a program for executing the road network prediction tree visualization method provided by the embodiment of the present invention.
Optionally, the program may be for:
acquiring a driving assistance log, wherein the driving assistance log at least records road network prediction tree data, auxiliary road network data and a corresponding relation between the road network prediction tree data and the auxiliary road network data;
determining the geographical position of each element of the road network prediction tree according to the corresponding relation between the road network prediction tree data and the auxiliary road network data;
and drawing the road network prediction tree corresponding to the road network prediction tree data on the electronic map layer based on the position of the vehicle in the road network prediction tree data and the geographic position of each element of the road network prediction tree.
Optionally, the extended implementation and the optional implementation of the program may refer to corresponding parts in the foregoing, and are not described herein again.
Although the embodiments of the present invention have been disclosed, the present invention is not limited thereto. Various changes and modifications may be effected therein by one skilled in the art without departing from the spirit and scope of the invention as defined in the appended claims.
Claims (10)
1. A road network prediction tree visualization method is characterized by comprising the following steps:
acquiring a driving assistance log, wherein the driving assistance log at least records road network prediction tree data, auxiliary road network data and a corresponding relation between the road network prediction tree data and the auxiliary road network data;
determining the geographical position of each element of the road network prediction tree according to the corresponding relation between the road network prediction tree data and the auxiliary road network data;
and drawing the road network prediction tree corresponding to the road network prediction tree data on the electronic map layer based on the position of the vehicle in the road network prediction tree data and the geographic position of each element of the road network prediction tree.
2. The method for visualizing road network forecast tree according to claim 1, wherein said determining geographical positions of elements of road network forecast tree according to correspondence between said road network forecast tree data and auxiliary road network data comprises:
and determining the geographic position of each element of the road network forecast tree according to the corresponding relation between the relative position of each element of the road network forecast tree in the road network forecast tree data and the geographic position of each element in the auxiliary road network data.
3. The method for visualizing road network prediction tree as claimed in claim 2, wherein said relative positions of elements of road network prediction tree in said road network prediction tree data comprises: the method comprises the steps that the road network predicts the offset between the relative ID of the road sections of the tree and the road sections; the geographical positions of the elements in the auxiliary road network data comprise: assisting road section IDs and corresponding geographic positions of road sections in road network data;
determining the geographic position of each element of the road network forecast tree according to the corresponding relationship between the relative position of each element of the road network forecast tree in the road network forecast tree data and the geographic position of each element in the auxiliary road network data comprises:
determining the corresponding road section ID of the road section in the road network prediction tree in the auxiliary road network data according to the offset between the road section relative ID of the road network prediction tree in the road network prediction tree data and the road section and the corresponding relation of the road section ID in the auxiliary road network data;
and determining the road section ID corresponding to the road section in the road network prediction tree and the corresponding geographic position in the auxiliary road network data.
4. The method for visualizing road network prediction tree according to any of claims 1-3, wherein said drawing the road network prediction tree corresponding to said road network prediction tree data on top of the electronic map layer based on the location of the vehicle in said road network prediction tree data and the geographic location of each element of the road network prediction tree comprises:
matching the position of the vehicle on an electronic map layer according to the position of the vehicle in the road network prediction tree data, and matching the elements of the road network prediction tree on the electronic map layer according to the geographic position of the elements of the road network prediction tree;
and associating corresponding element attributes carried by the road network prediction tree data for each element of the road network prediction tree matched on the electronic map layer.
5. The road network forecast tree visualization method according to claim 1, wherein the geographic positions of the self-location and elements of the road network forecast tree in said road network forecast tree data are dynamically updated; the drawing the road network prediction tree corresponding to the road network prediction tree data on the electronic map layer based on the position of the vehicle in the road network prediction tree data and the geographic position of each element of the road network prediction tree comprises:
and dynamically updating the drawn road network prediction tree on the electronic map layer based on the self-parking position dynamically updated in the road network prediction tree data and the geographical position of the dynamically updated element of the road network prediction tree.
6. The road network forecast tree visualization method according to claim 5, wherein said dynamically updating the mapped road network forecast tree on top of the electronic map layer based on the dynamically updated self-location in the road network forecast tree data and the geographical location of the dynamically updated element of the road network forecast tree comprises:
and correspondingly adding elements and/or deleting elements in the road network prediction tree drawn on the electronic map layer according to the geographical positions of the elements which are currently added and/or deleted in the road network prediction tree.
7. The road network forecast tree visualization method according to claim 4, further comprising:
counting the statistical information of the element attributes;
and displaying the statistical information.
8. A road network prediction tree visualization device is characterized by comprising:
the system comprises a log acquisition module, a log acquisition module and a driving assistance module, wherein the log acquisition module is used for acquiring a driving assistance log, and the driving assistance log at least records road network prediction tree data, auxiliary road network data and a corresponding relation between the road network prediction tree data and the auxiliary road network data;
the geographic position determining module is used for determining the geographic position of each element of the road network prediction tree according to the corresponding relation between the road network prediction tree data and the auxiliary road network data;
and the drawing module is used for drawing the road network prediction tree corresponding to the road network prediction tree data on the electronic map layer based on the position of the vehicle in the road network prediction tree data and the geographic position of each element of the road network prediction tree.
9. An electronic device comprising at least one memory and at least one processor; the memory stores a program that the processor calls to perform the road network prediction tree visualization method according to any of claims 1-7.
10. A storage medium characterized by storing a program for executing the road network prediction tree visualization method according to any one of claims 1 to 7.
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