CN112015832B - 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 prediction tree visualization method, a device, electronic equipment and a storage medium, wherein the method comprises the following steps: obtaining a driving auxiliary log, wherein the driving auxiliary log at least records the corresponding relation of road network prediction tree data, auxiliary road network data and auxiliary road network data; 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 drawing a road network prediction tree corresponding to the road network prediction tree data on the electronic map layer based on the self-vehicle position 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, and provides possibility for facilitating understanding of the road network prediction tree and 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, a road network prediction tree visualization device, electronic equipment and a storage medium.
Background
The road network prediction tree is tree-shaped data describing a road network structure in a certain geographical area range in front of the vehicle, and can be generated based on an electronic horizon system and output to a vehicle auxiliary driving system (such as an advanced driving auxiliary system ADAS), so that the normal operation of an auxiliary driving function of the vehicle is ensured, 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 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 contents of the road network prediction tree, and it takes much time for even professionals to analyze the contents of the road network prediction tree, which results in low efficiency in testing the electronic horizon system.
Disclosure of Invention
In view of this, embodiments of the present invention provide a method, an apparatus, an electronic device, and a storage medium for visualizing a road network prediction tree, so as to implement visualization of the road network prediction tree, and provide possibility for improving efficiency of testing an electronic horizon system.
In order to achieve the above object, the embodiment of the present invention provides the following technical solutions:
A road network prediction tree visualization method comprises the following steps:
obtaining a driving auxiliary log, wherein the driving auxiliary log at least records the corresponding relation of road network prediction tree data, auxiliary road network data and auxiliary road network data;
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 drawing a road network prediction tree corresponding to the road network prediction tree data on the electronic map layer based on the self-vehicle position 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 a road network prediction tree visualization device, which comprises:
The log acquisition module is used for acquiring a driving auxiliary log, and the driving auxiliary log at least records 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;
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 own vehicle position 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 invention also provides a storage medium which 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 acquire the driving auxiliary log, wherein the driving auxiliary log 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; because the road network prediction tree data records the relative positions of all elements of the road network prediction tree and the auxiliary road network data records the geographic positions of the elements, the embodiment of the invention can determine the geographic positions of all 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 vehicle position 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, so that the visualization of the road network prediction tree on the electronic map layer is realized. 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 simple in understanding the content of the abstract road network prediction tree, but also can be combined with the road network prediction tree visually displayed on the electronic map layer to understand the content of the road network prediction tree, thereby providing possibility for facilitating understanding the road network prediction tree and improving the test efficiency of the electronic horizon system.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings that are required to be used in the embodiments or the description of the prior art will be briefly described below, and it is obvious that the drawings in the following description are only embodiments of the present application, and that other drawings can be obtained according to the provided drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic diagram of an implementation of an electronic horizon system to build a road network prediction tree;
FIG. 2 is an example diagram of a road network prediction tree;
FIG. 3 is a schematic diagram of an architecture of an electronic horizon system;
FIG. 4 is a block diagram of an electronic device;
FIG. 5 is a flowchart of a method for visualizing a road network prediction tree according to an embodiment of the present invention;
FIG. 6 is an example diagram of a road network prediction tree drawn above an electronic map layer;
FIG. 7 is another flowchart of a method for visualizing a road network prediction tree according to an embodiment of the present invention;
FIG. 8 is a flowchart for determining the geographic location of each element of a road network prediction tree according to an embodiment of the present invention;
fig. 9 is a block diagram of a road network prediction tree visualization device according to an embodiment of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
FIG. 1 is a schematic diagram of an alternative implementation of an electronic horizon system for constructing a road network prediction tree, as shown in FIG. 1, where 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 the road network prediction tree; the road network prediction tree can describe the road topology structure, the road attribute and the like of a certain geographic area in front of the vehicle through a tree-shaped data structure, namely the road network prediction tree can describe the road topology structure and represent the relative position relationship of the road;
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 an auxiliary driving function of the vehicle is guaranteed, and the safety and the comfort of the driving of the vehicle are improved.
As one example, a road network prediction tree mainly includes: MPP (Most Probable Path, maximum likelihood path) and non-MPP; referring to an alternative example of the road network prediction tree shown in fig. 2, the solid line of fig. 2 may represent MPP, which may be a predicted maximum likelihood travel path of a vehicle, and one path may include at least one road segment; for example, the MPP may include at least one MPP segment (the solid line of the two-point connection in fig. 2 may represent one MPP segment), and the MPP segments are connected to form the MPP;
The dashed line in fig. 2 may represent a non-MPP, which may be a predicted Path that the vehicle is not traveling with the maximum likelihood, which may be extended from an MPP segment of the MPP, i.e., which may be a Sub-Path (Sub Path) in the road network prediction tree connected to the MPP segment; a non-MPP may include at least one non-MPP segment (a dashed line connecting two points in the figure may represent a non-MPP segment);
It can be seen that the depth of the road network prediction tree is in positive correlation with the length of the MPP, i.e. the longer the MPP is, the deeper the depth of the road network prediction tree is, the positive correlation between the breadth of the road network prediction tree and the length of the non-MPP is, i.e. the longer the non-MPP is, the wider the breadth of the road network prediction tree is; the depth of the road network prediction tree represents the furthest field of view distance of the vehicle, and the breadth can represent the bifurcation level of the road segments.
At present, because of the limitation 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 are required 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 the vehicle where the vehicle is located through positioning information, takes the current road section of the vehicle as a starting road section (the starting road section can be an initial MPP road section), expands MPP and non-MPP of a road network prediction tree from the starting road section until the horizons (visual field) length of the expanded MPP is not less than an MPP length threshold, and stops expanding the non-MPP road section on a road section branch of the non-MPP road section when the non-MPP road section with the horizons length not less than the non-MPP length threshold exists; the horizonn length of the road segment represents a view length of the road segment, and the horizonn length of the road segment may be a road segment distance from a terminal position of the road segment to a vehicle position;
The road network prediction tree can be constructed in various modes, as long as the horizons length of the MPP of the road network prediction tree finally constructed is not smaller than the MPP length threshold, the horizons length of the non-MPP is not smaller than the non-MPP length threshold, and the mode of constructing the road network prediction tree is not limited by the embodiment of the invention.
In an alternative implementation, fig. 3 shows an alternative architecture schematic of an electronic horizon system, as shown in fig. 3, which may include: an electronic horizon terminal and an electronic horizon cloud server; when the electronic horizon system is applied to the vehicle, the electronic horizon terminal can be arranged on the vehicle, and the electronic horizon cloud server can be arranged on the network side;
In one example of constructing a road network prediction tree, an electronic horizon terminal may send a request to an electronic horizon cloud server to construct the road network prediction tree, where the request may carry vehicle positioning information and road network data to implement construction of the road network prediction tree by the electronic horizon cloud server; 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 a vehicle (such as an ADAS system of the vehicle);
In another example, the electronic horizon terminal may also independently construct a road network prediction tree based on vehicle positioning information and road network data.
It can be seen that the electronic horizon system has the effect of providing a road network prediction tree in order to assist driving decisions; 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, because the road network prediction tree is generated based on a specific protocol, it is difficult for non-professional staff to understand the content of the road network prediction tree, and even professional staff needs to spend a great deal of time to analyze the content of the road network prediction tree, so that the test efficiency of the electronic horizon system is lower.
The inventors of the present invention have found through studies that: the difficulty in understanding the contents of the road network prediction tree can be reduced by visually displaying the road network prediction tree, and possibility is provided for improving the test efficiency of the electronic horizon system. Meanwhile, in order to display the image of the electronic horizon system product, the realization of the visualization of the road network prediction tree is also particularly necessary. Based on the above, the embodiment of the invention provides a road network prediction tree visualization method, a device, electronic equipment and a storage medium, so as to realize the visualization of the road network prediction tree.
In order to realize the visualization of the road network prediction tree, the embodiment of the invention provides a road network prediction tree visualization method, and in an optional implementation, the road network prediction tree visualization method can be applied to electronic equipment which at least has data processing capability and display capability, and in an example, the electronic equipment can be terminal computing equipment such as a PC (personal computer), a smart phone, a tablet computer and 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 communication with each other through the communication bus 40;
alternatively, the communication interface 20 may be an interface of a communication module, and 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 (Application SPECIFIC INTEGRATED Circuit), or one or more integrated circuits configured to implement embodiments of the present invention.
Memory 30 may comprise high-speed RAM memory or may further comprise non-volatile memory (non-volatile memory), such as at least one disk memory;
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 embodiment of the present invention may implement visualization of the road network prediction tree by drawing the road network prediction tree on top of the electronic map layer. Optionally, fig. 5 shows an optional flow of a method for visualizing a road network prediction tree according to an embodiment of the present invention, and referring to fig. 5, the flow may include:
step S100, obtaining a driving assistance log; and the driving auxiliary log at least records the corresponding relation of the road network prediction tree data, the auxiliary road network data and the road network prediction tree 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 the embodiment of the invention, the driving auxiliary log (such as an electronic horizon log) can at least record 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.
The road network prediction tree data can be regarded as construction data of the road network prediction tree, and the road network prediction tree constructed by the electronic horizon system can be restored through the road network prediction tree data. Although the road network prediction tree data can represent a road network structure (such as a road segment topological structure) of a certain geographic area in front of a vehicle, the road network prediction tree data represents the relative positions of all elements of the road network prediction tree and cannot represent the geographic positions (such as absolute positions of longitude and latitude coordinates and the like) of all elements in the road network prediction tree; taking the element as an example of a road segment, the road network prediction tree data can represent the relative position of the road segment in the road network prediction tree, but the geographic position of the road segment cannot be represented.
In order to draw a road network prediction tree on an electronic map layer, the embodiment of the invention needs to determine the geographic positions of elements in the road network prediction tree so as to match the elements of the road network prediction tree on the electronic map layer through the geographic positions of the elements in the road network prediction tree, thereby drawing the road network prediction tree on the electronic map layer; the embodiment of the invention can determine the geographic position of each element of the road network prediction tree 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 of the element (such as the geographic position of a road section) based on an electronic horizon protocol and combining a positioning means to form auxiliary road network data; and record the auxiliary road network data in a driving auxiliary log, such as an electronic horizon log generated based on an electronic horizon protocol.
Further, the embodiment of the invention can record the corresponding relation 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 can record the corresponding relation between the relative positions of all elements of the road network prediction tree in the road network prediction tree data and the geographic positions of all elements in the auxiliary road network data; and records the correspondence in a driving assistance log (e.g., in an electronic horizon log).
And step S110, 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.
After driving auxiliary logs are acquired, 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 are determined, the road network prediction tree data record the relative positions of elements of the road network prediction tree, the auxiliary road network data record the geographic positions of the elements, and the road network prediction tree is drawn on an electronic map layer for passing through the geographic positions of the elements of the road network prediction tree.
And step 120, drawing a road network prediction tree corresponding to the road network prediction tree data on the electronic map layer based on the own vehicle position in the road network prediction tree data and the geographic position of each element of the road network prediction tree.
After determining the geographic position of each element of the road network prediction tree, the embodiment of the invention can match the self-vehicle position (namely the drawing position of the self-vehicle on the electronic map layer) on the electronic map layer according to the self-vehicle position 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 drawing the road network prediction tree corresponding to the road network prediction tree data 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 may be road segment attributes of the road segments, the road segment attributes may be road segment information such as a form of the road segments, a grade of the road segments, and the content contained in the road segment attributes may be expanded through fields contained in the attributes. The embodiment of the invention can further correlate 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.
By way of example, fig. 6 shows an example of a road network prediction tree drawn above an electronic map layer, to which reference may be made, with bold lines in fig. 6 representing the drawn road network prediction tree.
The road network prediction tree construction method provided by the embodiment of the invention can acquire the driving auxiliary log, wherein the driving auxiliary log 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; because the road network prediction tree data records the relative positions of all elements of the road network prediction tree and the auxiliary road network data records the geographic positions of the elements, the embodiment of the invention can determine the geographic positions of all 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 vehicle position 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, so that the visualization of the road network prediction tree on the electronic map layer is realized. 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 simple in understanding the content of the abstract road network prediction tree, but also can be combined with the road network prediction tree visually displayed on the electronic map layer to understand the content of the road network prediction tree, thereby providing possibility for facilitating understanding the road network prediction tree and improving the test efficiency of the electronic horizon system.
As an alternative implementation, the road network prediction tree data may be transmitted by an ADAS V2 protocol (second generation advanced driving assistance system protocol), and the road network prediction tree data transmitted by the 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 based on an electronic horizon protocol and combine with a positioning means 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 the corresponding relation between the relative positions of all elements of the road network prediction tree in the road network prediction tree data and the geographic positions of all elements in the auxiliary road network data, and record the corresponding relation in an electronic horizon log generated based on the electronic horizon protocol.
As an alternative implementation, the road network prediction tree data of the electronic horizon log may include a road segment relative position and a road segment attribute of the road network prediction tree; 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, so that the form of the road network prediction tree is determined; the road section attribute can be used for associating road section information such as road section morphology, road section grade, road section bending degree and the like with each road section of the road network prediction tree; optionally, fig. 7 shows another flowchart of a method for visualizing a road network prediction tree according to an embodiment of the present invention, and referring to fig. 7, the flowchart may include:
step S200, acquiring an electronic horizon log; the electronic horizon log at least comprises 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.
Optionally, the electronic horizon log may be an optional form of a driving assistance log, and the description of step S200 may refer to step S100; still further, the road network prediction tree data may further include road segment attributes of the road network prediction tree.
Step S210, determining the geographic position of each road section of the road network prediction tree according to the corresponding relation between the relative position of each road section of the road network prediction tree in the road network prediction tree data and the geographic position of each road section in the auxiliary road network data.
Optionally, in an example, the embodiment of the present invention may organize a road network according to the auxiliary road network data, for example, according to the geographical location of the road segment recorded by the auxiliary road network data, and organize a corresponding road network; 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 corresponding relation between the road network prediction tree data and the auxiliary road network data recorded by the electronic horizon log, the road network prediction tree data can be mapped to the road network by the road network prediction tree road sections in the road network prediction tree data, and the geographic position of the road network prediction tree road sections in the road network prediction tree data can be determined.
And step S220, drawing a road network prediction tree on the electronic map layer based on the self-vehicle position 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 self-vehicle position 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 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, namely the shape of the drawn road network prediction tree is the shape of the 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 segment attributes for each road segment 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 offset between the road section relative ID of the road network prediction tree and the road section; the geographic locations of the elements of the auxiliary road network data record may include: the road segment ID and the corresponding geographic position of each road segment in the auxiliary road network data;
accordingly, the correspondence may include: offset between the relative IDs of road segments of the road network prediction tree in the road network prediction tree data and the road segments, and the corresponding relation of the road segment IDs in the auxiliary road network data;
Optionally, fig. 8 illustrates an alternative flow of determining the geographic location of elements of the road network prediction tree, as shown in fig. 8, which may include:
and step S300, determining the corresponding road segment ID of the road segment in the road network prediction tree in the auxiliary road network data according to the offset between the road segment relative ID of the road network prediction tree in the road network prediction tree data and the road segment 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 the road segment ID corresponding to the road segment in the road network prediction tree, and corresponding geographic position in the auxiliary road network data.
After determining the road segment ID corresponding to the road segment 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 segment ID of the road segment, thereby determining the geographic position of the road segment.
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, and the relative position of the road section of the drawn 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 invention can be used for associating the corresponding road section attribute carried by the road network prediction tree data for each road section of the road network prediction tree matched on the electronic map layer.
The above description is an example description of taking a road section as an example, and the embodiment of the invention draws a road network prediction tree on an electronic map layer, and after associating element attributes of each element of the road network prediction tree, statistics information of the element attributes can be also counted, and the statistics information of the element attributes of the road network prediction tree is displayed in a graph form on the electronic map layer; for example, the embodiment of the invention can display the number of grades of each road section, the number of straight road sections, the speed limit information of each road section and the like in the road network prediction tree in a chart form, and the road section attribute information of chart statistics can be set according to the content of the road section attribute and the actual statistical requirement, and the embodiment of the invention is not limited.
It should be noted that it is not necessary for the embodiment of the present invention to select or not select statistics of element attributes and display the statistics of element attributes in the form of a graph.
The foregoing describes the basic content and alternative implementation of the visualization of the road network prediction tree, and it should be further explained that, as the vehicle position changes, the road network prediction tree is in a state of being updated in real time, for example, the MPP segments and non-MPP segments of the road network prediction tree from which the vehicle is driven off will be removed from the road network prediction due to the change of the vehicle position, so that the space of the road network prediction tree is left to expand the MPP segments and non-MPP segments corresponding to the vehicle driving direction; for another example, when the vehicle deviates from the navigation path and runs from the MPP road section to the non-MPP road section, the non-MPP road section where the vehicle is located can be changed into the MPP road section, and updating of the road network prediction tree is realized, so that the length of the MPP and the length of the non-MPP horizons meet the requirement; therefore, under the limitation of the system memory and the flow, the road network prediction tree is in a continuously dynamic state based on the change of the vehicle position, so the road network prediction tree drawn on the electronic map layer can be dynamically changed.
In the embodiment of the invention, the self-vehicle position in the road network prediction tree data and the geographic position of the element of the road network prediction tree are in a dynamic update 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-vehicle position dynamically updated in the road network prediction tree data and the geographic position of the element dynamically updated by the road network prediction tree.
Optionally, according to the embodiment of the invention, the vehicle position in the road network prediction tree drawn above the electronic map layer can be adjusted according to the vehicle position currently updated in the road network prediction tree data, and the elements and/or the corresponding deletion elements in the road network prediction tree drawn above the electronic map layer can be correspondingly added according to the geographic positions of the elements currently added and/or deleted in the road network prediction tree.
As an alternative implementation, the road network prediction tree data may include data of a plurality of road network prediction trees constructed according to time sequence, and the data of the road network prediction tree constructed at the next time point may be considered as update data of the road network prediction tree constructed at the previous time point at other time points except the initial time point;
The data of the road network prediction tree constructed at the initial time point in the data of the road network prediction tree constructed 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 vehicle is located; the data of constructing the road network prediction tree at the next time point adjacent to the initial time point can be regarded as update data for updating the road network prediction tree on the basis of the initially constructed road network prediction tree;
Based on this, the embodiment of the present invention may obtain the data of the road network prediction tree constructed at the next time point after the road network prediction tree at the previous time point is drawn on the electronic map layer, where the data of the road network prediction tree constructed at the next time point may include: the relative position of the updated road section in the road network prediction tree of the next time point; the update section may include: based on the vehicle position adjustment, adding road segments in the road network prediction tree and/or deleting road segments in the road network prediction tree; accordingly, the relative position of the updated road segment may be considered as: based on the self-vehicle position adjustment, the position relation of the added road sections in the road network prediction tree and/or the relative positions of the deleted road sections;
after the data of the road network prediction tree constructed 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; for example, after determining the geographic position of the added road segment, the added road segment is added and 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.
In the case of adding the drawn added road segment, the embodiment of the invention can also associate corresponding road segment attributes with the added road segment drawn.
Optionally, if the electronic horizon log is in the form of a network file, after the electronic horizon log is obtained, the embodiment of the invention may perform preprocessing on data of a plurality of construction road network prediction trees in the electronic horizon log according to time sequence, for example, perform deduplication processing (for example, performing deduplication on construction data with the same time point) from a plurality of construction data included in the electronic horizon log, remove meaningless data (for example, remove construction data without time point), and so on;
If the electronic horizon log is in the local file form, 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 play speed, such as continuous play, piece-by-piece play, skip play and the like of the plurality of pieces of construction data, thereby actively controlling the visual display of the road network prediction tree at each time point.
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, realize the visualization of the road network prediction tree, ensure that the road network prediction tree is understood not only by simply understanding the content of the abstract road network prediction tree, but also by combining the road network prediction tree visually displayed on the electronic map layer, and provide possibility for understanding the road network prediction tree conveniently and improving the test efficiency of the electronic horizon system.
The foregoing describes several embodiments of the present invention, and the various alternatives presented by the various embodiments may be combined, cross-referenced, with each other without conflict, extending beyond what is possible embodiments, all of which are considered to be embodiments of the present invention disclosed and disclosed.
The following describes a road network prediction tree visualization device provided by the embodiment of the present invention, where the road network prediction tree visualization device described below may be regarded as a program functional module required to be set for implementing the road network prediction tree visualization method provided by 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 device according to an embodiment of the present invention, and referring to fig. 9, the road network prediction tree visualization device may include:
The log obtaining module 100 is 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;
The position determining module 200 is configured to determine a geographic position of each element of the road network prediction tree according to the corresponding relationship between the road network prediction tree data and the auxiliary road network data;
And the drawing module 300 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 own vehicle position in the road network prediction tree data and the geographic position of each element of the road network prediction tree.
Optionally, the electronic horizon log may record road network prediction tree data transmitted through an ADAS V2 protocol.
Optionally, the location determining module 200 is configured to determine, according to the correspondence between the road network prediction tree data and the auxiliary road network data, a geographic location of each element of the road network prediction tree, and may specifically include:
And determining the geographic position of each element of the road network prediction tree according to the corresponding relation 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.
Optionally, the relative positions of the elements of the road network prediction tree in the road network prediction tree data include: the offset between the road section relative ID of the road network prediction tree and the road section; the geographic positions of the elements in the auxiliary road network data comprise: the road segment ID and the corresponding geographic position of each road segment in the auxiliary road network data;
The location determining module 200 is configured to determine the geographic location of each element of the road network prediction tree according to the correspondence 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, and specifically includes:
Determining the corresponding road segment ID of the road segment in the road network prediction tree in the auxiliary road network data according to the offset between the road segment relative ID of the road network prediction tree in the road network prediction tree data and the road segment ID in the auxiliary road network data;
and determining the road segment ID corresponding to the road segment in the road network prediction tree, and corresponding geographic position in the auxiliary road network data.
Optionally, the drawing module 300 is configured to draw, on the electronic map layer, a road network prediction tree corresponding to the road network prediction tree data based on the own vehicle position in the road network prediction tree data and the geographic position of each element of the road network prediction tree, and specifically includes:
Matching the vehicle position on the electronic map layer according to the vehicle position 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 self-vehicle position in the road network prediction tree data and the geographic position of the element of the road network prediction tree are in a dynamically updated state; the drawing module 300 is configured to draw, on the electronic map layer, a road network prediction tree corresponding to the road network prediction tree data based on a vehicle position in the road network prediction tree data and a geographic position 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-vehicle position dynamically updated in the road network prediction tree data and the geographic position of the element dynamically updated by 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 the dynamically updated own vehicle position in the road network prediction tree data and the geographic position of the dynamically updated element of the road network prediction tree, and may specifically include:
According to the current updated vehicle position in the road network prediction tree data, adjusting the vehicle position in the road network prediction tree drawn above the electronic map layer, and according to the geographic position of the current added and/or current deleted element in the road network prediction tree, correspondingly adding the element and/or correspondingly deleting the element in the road network prediction tree drawn above the electronic map layer.
Optionally, the road network prediction tree visualization device provided by the embodiment of the present invention may be further used for: statistics information of the element attributes is counted; 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 road network prediction tree is understood not only by simply understanding the content of the abstract road network prediction tree, but also by combining the road network prediction tree visually displayed on the electronic map layer, thereby providing possibility for understanding the road network prediction tree and improving the test efficiency of the electronic horizon system.
The road network prediction tree visualization device provided by the embodiment of the invention can be loaded in an electronic device in a program form, and the hardware structure of the electronic device can be shown as fig. 4, and the 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 invention also provides a storage medium which can store a program for executing the road network prediction tree visualization method provided by the embodiment of the invention.
Alternatively, the program may be for:
obtaining a driving auxiliary log, wherein the driving auxiliary log at least records the corresponding relation of road network prediction tree data, auxiliary road network data and auxiliary road network data;
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 drawing a road network prediction tree corresponding to the road network prediction tree data on the electronic map layer based on the self-vehicle position 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 optional implementation of the program may refer to the corresponding parts of the foregoing, which are not described herein.
Although the embodiments of the present invention are disclosed above, the present invention is not limited thereto. Various changes and modifications may be made by one skilled in the art without departing from the spirit and scope of the invention, and the scope of the invention should be assessed accordingly to that of the appended claims.
Claims (9)
1. The road network prediction tree visualization method is characterized by comprising the following steps of:
obtaining a driving auxiliary log, wherein the driving auxiliary log at least records the corresponding relation of road network prediction tree data, auxiliary road network data and auxiliary road network data;
determining the geographic position of each element of the road network prediction tree according to the corresponding relation 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 drawing a road network prediction tree corresponding to the road network prediction tree data on the electronic map layer based on the self-vehicle position 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 a road network prediction tree according to claim 1, wherein the relative positions of the elements of the road network prediction tree in the road network prediction tree data comprise: the offset between the road section relative ID of the road network prediction tree and the road section; the geographic positions of the elements in the auxiliary road network data comprise: the road segment ID and the corresponding geographic position of each road segment in the auxiliary road network data;
The determining the geographic position of each element of the road network prediction tree according to the corresponding relation 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 comprises the following steps:
Determining the corresponding road segment ID of the road segment in the road network prediction tree in the auxiliary road network data according to the offset between the road segment relative ID of the road network prediction tree in the road network prediction tree data and the road segment ID in the auxiliary road network data;
and determining the road segment ID corresponding to the road segment in the road network prediction tree, and corresponding geographic position in the auxiliary road network data.
3. The road network prediction tree visualization method according to claim 1 or 2, wherein the drawing the road network prediction tree corresponding to the road network prediction tree data on the electronic map layer based on the own vehicle position in the road network prediction tree data and the geographic position of each element of the road network prediction tree comprises:
Matching the vehicle position on the electronic map layer according to the vehicle position 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.
4. The method for visualizing a road network prediction tree according to claim 1, wherein the own vehicle position in the road network prediction tree data and the geographic position of the element of the road network prediction tree are in a dynamically updated state; the drawing the road network prediction tree corresponding to the road network prediction tree data on the electronic map layer based on the own vehicle position in the road network prediction tree data and the geographic position of each element of the road network prediction tree comprises the following steps:
And dynamically updating the drawn road network prediction tree on the electronic map layer based on the self-vehicle position dynamically updated in the road network prediction tree data and the geographic position of the element dynamically updated by the road network prediction tree.
5. The method for visualizing a road network prediction tree according to claim 4, wherein dynamically updating the plotted road network prediction tree on top of the electronic map layer based on the dynamically updated own vehicle position in the road network prediction tree data and the geographic position of the dynamically updated element of the road network prediction tree comprises:
According to the current updated vehicle position in the road network prediction tree data, adjusting the vehicle position in the road network prediction tree drawn above the electronic map layer, and according to the geographic position of the current added and/or current deleted element in the road network prediction tree, correspondingly adding the element and/or correspondingly deleting the element in the road network prediction tree drawn above the electronic map layer.
6. The method for visualizing a road network prediction tree as in claim 3, further comprising:
statistics information of the element attributes is counted;
And displaying the statistical information.
7. A road network prediction tree visualization device, comprising:
The log acquisition module is used for acquiring a driving auxiliary log, and the driving auxiliary log at least records 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;
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 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 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 own vehicle position in the road network prediction tree data and the geographic position of each element of the road network prediction tree.
8. An electronic device comprising at least one memory and at least one processor; the memory stores a program that the processor invokes to perform the road network prediction tree visualization method of any one of claims 1-6.
9. A storage medium storing a program for executing the road network prediction tree visualization method according to any one of claims 1 to 6.
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