A method, system and electronic device for generating road data

Abstract

The application discloses a road data generation method, system and electronic equipment. The method comprises the following steps: acquiring an original high-definition map; continuously modifying parameters corresponding to X attribute elements in a set of attribute elements of a road in the original high-definition map, to generate N different high-definition maps; and when N reaches a preset threshold, generating N roads in a simulation scene based on the N different high-definition maps. According to the technical scheme provided in the application, only one original high-definition map is needed to obtain N different high-definition maps, thereby reducing the production cost of the high-definition map, and improving the generation efficiency of various roads in the virtual simulation scene.

Description

Technical Field

[0001] This application relates to the field of electronic and electrical technology, and in particular to a method, system and electronic device for generating road data. Background Technology

[0002] With the rapid development of technology and the internet, autonomous vehicles are gradually becoming mainstream in the automotive industry. Before autonomous vehicles can be commercially deployed on a large scale, thorough functional safety and performance safety tests must be conducted to ensure the safety of drivers and other road users.

[0003] Functional safety and performance safety testing and verification are further divided into autonomous driving simulation testing and autonomous driving real vehicle testing. Among them, autonomous driving simulation testing is driving testing of autonomous driving algorithms in a virtual traffic road simulation scenario.

[0004] Currently, various types of roads are mainly generated from raw high-precision maps produced by road survey data, which in turn generate virtual simulation scenarios of traffic roads.

[0005] However, in the above-mentioned methods for generating virtual simulation scenarios of traffic roads, various sensors and algorithms need to be combined to generate usable original high-precision maps when generating original high-precision maps. This makes the production cost of an original high-precision map high and the production difficulty great, resulting in difficulty in obtaining a large number of original high-precision maps, which in turn leads to low efficiency in generating various types of roads in virtual simulation scenarios of traffic roads. Summary of the Invention

[0006] This application provides a method, system, and electronic device for generating road data to solve the problem of low generation efficiency for various types of roads in traffic road virtual simulation scenarios. The specific implementation scheme is as follows:

[0007] Firstly, this application provides a method for generating road data, the method comprising:

[0008] Obtain the original high-precision map;

[0009] By continuously modifying the parameters corresponding to each of the X attribute elements in the attribute element set of the road in the original high-precision map, N different high-precision maps are generated. Here, the attribute elements are line segments describing the shape of the road, and X and N are both integers greater than zero.

[0010] When N reaches a preset threshold, N roads are generated in the simulation scene based on the N different high-precision maps.

[0011] By continuously modifying the parameters corresponding to the road shapes in the original high-precision map, N different high-precision maps can be generated. Then, based on the N different high-precision maps, N different roads can be generated in the simulation scene. At this time, only one original high-precision map with high production cost needs to be obtained to get N different high-precision maps, thereby reducing the production cost of high-precision maps and improving the generation efficiency of various roads in the virtual simulation scene.

[0012] In one possible implementation, modifying the parameters corresponding to each of the X attribute elements in the attribute element set of roads in the original high-precision map includes:

[0013] The road in the original high-precision map is divided into M sub-road segments, where M is an integer greater than zero;

[0014] Modify the parameters corresponding to each of the X attribute elements in the attribute element set of the K segments in the M segments of the road, where K is an integer greater than zero.

[0015] By cutting the roads in the original high-definition map and modifying the shape of the cut roads, the shape of the roads in the original high-definition map is changed.

[0016] In one possible implementation, the step of cutting the road in the original high-precision map into M sub-road segments includes:

[0017] Obtain all road intersections of the road in the original high-precision map;

[0018] Based on all the road intersections, the road is divided into the M sub-road segments.

[0019] By using all road intersections in the original high-precision map as cutting points, the roads in the original high-precision map are divided into M segments, thus providing the starting and ending points for modifying the parameters representing the location in the attribute elements.

[0020] In one possible implementation, modifying the parameters corresponding to each of the X attribute elements in the attribute element set of the K segment roads in the M segment roads includes:

[0021] Get the set of attribute elements;

[0022] According to the different arrangements of the X attribute elements in the attribute element set, the X attribute elements are combined to obtain L combination methods, where L is an integer greater than zero;

[0023] For each of the K segments of the M segments of the road, the parameters corresponding to each attribute element in each of the L combination methods are modified according to any one of the L combination methods.

[0024] By modifying the parameters of the attribute elements in the sub-roads based on one of the various combinations of attribute elements, the foundation for generating multiple different high-precision maps is laid. Different combinations of attribute elements correspond to different high-precision maps, and different attribute elements can form multiple combinations, thereby quickly and automatically obtaining a large number of different high-precision maps, realizing the generalization of high-precision maps.

[0025] Secondly, this application also provides a road data generation system, the system comprising:

[0026] The acquisition module is used to acquire the original high-precision map;

[0027] The modification module is used to continuously modify the parameters corresponding to each of the X attribute elements in the attribute element set of the road in the original high-precision map, and generate N different high-precision maps. The attribute elements are line segments describing the shape of the road, and X and N are both integers greater than zero.

[0028] The processing module is used to generate N roads in the simulation scene based on the N different high-precision maps when N reaches a preset threshold.

[0029] In one possible implementation, the modification module is specifically used to cut the road in the original high-precision map into M sub-road segments, where M is an integer greater than zero;

[0030] Modify the parameters corresponding to each of the X attribute elements in the attribute element set of the K segments in the M segments of the road, where K is an integer greater than zero.

[0031] In one possible implementation, the modification module is specifically used to obtain all road intersections of the road in the original high-precision map;

[0032] Based on all the road intersections, the road is divided into the M sub-road segments.

[0033] In one possible implementation, the modification module is specifically used to obtain the set of attribute elements;

[0034] According to the different arrangements of the X attribute elements in the attribute element set, the X attribute elements are combined to obtain L combination methods, where L is an integer greater than zero;

[0035] For each of the K segments of the M segments of the road, the parameters corresponding to each attribute element in each of the L combination methods are modified according to any one of the L combination methods.

[0036] Thirdly, this application provides an electronic device, comprising:

[0037] Memory, used to store computer programs;

[0038] When a processor executes a computer program stored in the memory, it implements the above-described method steps for generating road data.

[0039] Fourthly, this application provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the above-described method steps for generating road data.

[0040] For the various aspects of the second to fourth aspects mentioned above, and the technical effects that each aspect may achieve, please refer to the above description of the technical effects that can be achieved for the first aspect or the various possible solutions in the first aspect, which will not be repeated here. Attached Figure Description

[0041] Figure 1 A flowchart of a method for generating road data provided in this application;

[0042] Figure 2 A schematic diagram of a straight road shape provided for this application;

[0043] Figure 3 The road diagram of the original high-precision map provided in this application;

[0044] Figure 4 A schematic diagram illustrating the processing steps of the method for generating road data provided in this application;

[0045] Figure 5 A schematic diagram of a road data generation system provided in this application;

[0046] Figure 6 A schematic diagram of an electronic device provided in this application. Detailed Implementation

[0047] To make the objectives, technical solutions, and advantages of this application clearer, the application will be further described in detail below with reference to the accompanying drawings. The specific operational methods in the method embodiments can also be applied to the device embodiments or system embodiments. It should be noted that in the description of this application, "multiple" is understood as "at least two". "And / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. A connected to B can represent: A and B directly connected, and A and B connected through C. Furthermore, in the description of this application, terms such as "first" and "second" are used only for distinguishing the purpose of description and should not be construed as indicating or implying relative importance or order.

[0048] The embodiments of this application will now be described in detail with reference to the accompanying drawings.

[0049] Currently, various types of roads are mainly generated from raw high-definition maps produced by road survey data, which in turn generate virtual traffic simulation scenarios. However, generating raw high-definition maps requires combining various sensors and algorithms to produce usable maps, making the production cost and difficulty of creating a single raw high-definition map high. This results in a difficulty in obtaining a large number of raw high-definition maps, leading to low efficiency in generating various types of roads in virtual traffic simulation scenarios.

[0050] Therefore, this application proposes a method for generating road data. By continuously modifying the parameters corresponding to the road shape in the acquired original high-precision map, N different high-precision maps are generated. When N reaches a preset threshold, N different roads are generated in the simulation scene based on the N different high-precision maps. Only one original high-precision map is needed to generate N different high-precision maps, thereby improving the generation efficiency of various roads in the traffic road virtual simulation scene.

[0051] Reference Figure 1 The diagram shown is a flowchart of a method for generating road data according to an embodiment of this application. The method includes:

[0052] S1, Obtain the original high-precision map;

[0053] In one possible implementation, the original high-definition map is obtained by creating an original high-definition map in OpenDrive format using road survey information.

[0054] After obtaining the original high-precision map, we also obtain the various attribute elements that describe the shape of the road in the original high-precision map, as well as the parameters corresponding to each attribute element in the attribute element set composed of various attribute elements.

[0055] It should be noted that, in the embodiments of this application, the attribute elements describing the shape of the road in the original high-precision map include straight lines, arcs, spirals, and parametric cubic curves. That is, the set of attribute elements is {straight lines, arcs, spirals, parametric cubic curves}.

[0056] Furthermore, it should be noted that in the embodiments of this application, the parameters corresponding to the straight line in the attribute element set include the start parameter, coordinate parameter, direction parameter, and length parameter. The start parameter represents the s-coordinate value of the starting position of the straight line in the road in the reference line coordinate system (st coordinate system); the coordinate parameter represents the x-coordinate value (i.e., x value) and y-coordinate value (i.e., y value) of the starting position of the straight line in the road in the inertial coordinate system (xy coordinate system); the direction parameter represents the extension direction of the straight line; and the length parameter represents the length of the straight line extending along the extension direction at the starting position.

[0057] For example, refer to Figure 2 The diagram shows a straight road shape. The dashed line represents the road in the original high-precision map. Since the straight line is the first type of road shape, the initial parameter, i.e., the s coordinate value, is 0. The starting position coordinate of the straight line is (1, 2), i.e., the x value in the coordinate parameter is 1, and the y value in the coordinate parameter is 2. The straight line extends diagonally upward to the right at 45 degrees (°), i.e., the direction parameter is 45. The length of the straight line is 3 meters (m), i.e., the length parameter is 3.

[0058] The parameters corresponding to the arc in the attribute element set are the same as those of the straight line, including the start parameter, coordinate parameter, direction parameter and length parameter. In addition, the parameters corresponding to the arc also include the curvature parameter, which represents the arc's radius.

[0059] The parameters corresponding to the spiral in the attribute element set are the same as those of the straight line, including the start parameter, coordinate parameter, direction parameter and length parameter. In addition, the parameters corresponding to the spiral also include the start curvature parameter and the end curvature parameter.

[0060] The parameters of the cubic curve in the attribute element set are the parameters of the cubic polynomial.

[0061] S2, continuously modify the parameters of each of the X attribute elements in the attribute element set of the road in the original high-precision map to generate N different high-precision maps;

[0062] After obtaining the original high-precision map, the roads in the original high-precision map are first divided into M road segments. Then, for the K road segments in the M road segments, the parameters corresponding to the X attribute elements in the attribute element set of the K road segments are modified. Here, M, K, and X are all integers greater than zero.

[0063] It should be noted that in this embodiment, the K segments of the road can be continuous different sub-roads or discontinuous different sub-roads. In this embodiment, the specific road segments selected as the K segments can be adjusted according to the actual application scenario.

[0064] The roads in the original high-precision map were cut using the above method, and the shapes of the cut roads were modified, resulting in changes to the road shapes in the original high-precision map.

[0065] In one possible implementation, when dividing the roads in the original high-precision map into M road segments, all road intersections in the original high-precision map are first obtained; then, based on all road intersections, the roads in the original high-precision map are divided into M road segments.

[0066] It should be noted that, in this embodiment of the application, when dividing the road in the original high-precision map into M sub-road segments based on all road intersections, a sub-road segment can be defined as the road between two adjacent road intersections, or as the road between two road intersections spaced T times apart, where T is an integer greater than zero. In this embodiment of the application, the specific method of dividing the road into sub-road segments based on road intersections can be adjusted according to the actual application scenario.

[0067] For example, refer to Figure 3 The original high-definition map shown is a road diagram. The original high-definition map has five road intersections: A, B, C, D, and E. Between road intersection B and road intersection D is an abandoned parking lot. Vehicles can directly pass through this abandoned parking lot to reach road intersection D from road intersection B. Then, using road intersections A, B, D, and E as the cutting points for sub-roads, we get sub-road segments AB, BD, and DE. Among them, sub-road segments AB and DE are formed by the road between two adjacent road intersections, while sub-road segment BD is formed by the abandoned parking lot between two road intersections separated by one road intersection.

[0068] Using the above method, all road intersections in the original high-precision map are used as cutting points, which divides the roads in the original high-precision map into M segments, thereby providing the starting point and ending point for modifying the parameters representing the location in the attribute elements.

[0069] In one possible implementation, when modifying the parameters corresponding to X attribute elements in the attribute element set of the K segment road within the M segment road, the attribute element set is first obtained; then, X attribute elements are extracted from the attribute element set, for example, in the attribute element set {straight line, arc, spiral, parametric cubic curve}, the straight line and arc attribute elements are extracted.

[0070] Furthermore, according to the different arrangements of the X attribute elements in the attribute element set, the X attribute elements are combined to obtain L combination methods, where L is an integer greater than zero.

[0071] It should be noted that, in this embodiment of the application, when combining X attribute elements according to different arrangements of X attribute elements in the attribute element set, each attribute element may appear multiple times or only once. In this embodiment of the application, the X attribute elements may be arranged and combined according to the actual application scenario.

[0072] For example, the two extracted attribute elements are straight lines and arcs. The straight lines and arcs are combined in different arrangements. Since the length of the sub-road that needs to be modified is short, only four combinations are obtained: {straight line}, {arc}, {straight line, arc}, and {arc, straight line}. In other words, L is 4.

[0073] Finally, for each of the K sub-roads in the M sub-road, the parameters of each attribute element in each of the L combination methods are modified according to any one of the L combination methods.

[0074] It should be noted that, in this embodiment of the application, when modifying the parameters of each attribute element in the combination method corresponding to each sub-road segment according to any of the L combination methods, the different attribute elements need to be connected end to end.

[0075] For example, refer to Figure 3 The original high-precision map road diagram shown is modified by combining {straight lines, arcs, straight lines} to modify the parameters of the attribute elements in the AB segment road. First, the direction parameter α of the AG straight line is set to -10°, then the direction parameter β of the AF straight line is set to 10°, and the lengths of the AC and BD straight lines are set to 1m. At this point, the coordinates of points G and F can be determined as (5, 3) and (5, 2) respectively. Finally, an arc is used to connect points G and F, and the direction parameter γ of the arc is set to 50° and the curvature of the arc is set to 0.08014. At this point, the shape of the arc can be determined, and thus the new road AFGB after the modification of the AB segment road can be obtained.

[0076] By modifying the parameters of the attribute elements in the sub-roads based on one of the various combinations of attribute elements, the above method lays the foundation for generating multiple different high-precision maps. Different combinations of attribute elements correspond to different high-precision maps, and different attribute elements can form multiple combinations, thus enabling the rapid and automatic generation of a large number of different high-precision maps and achieving the generalization of high-precision maps.

[0077] Furthermore, by modifying the parameters corresponding to the X attribute elements in the attribute element set of the K sub-roads within the M sub-road segment, a new high-precision map can be generated. This new high-precision map only changes the road shapes; the rest remains the same as the original map. Figure 1 To.

[0078] Finally, by continuously modifying the parameters corresponding to the X attribute elements in the attribute element set of K segments of M segments, N different high-precision maps can be generated, where N is an integer greater than zero.

[0079] It should be noted that when continuously modifying the parameters of the X attribute elements in the attribute element set of the K sub-roads in the M sub-roads, the K sub-roads selected in one modification process may not be the same as the K sub-roads selected in the previous modification process, and the X elements extracted may also not be the same as the X attribute elements extracted in the previous modification process.

[0080] In one possible implementation, when continuously modifying the parameters corresponding to each of the X attribute elements in the attribute element set of the K segment roads in the M segment roads, when modifying the specific value of the parameter corresponding to each of the X attribute elements, for each parameter corresponding to each attribute element, when modifying the specific value of each parameter for the first time, the specific value is any value between the first preset threshold and the second preset threshold.

[0081] It should be noted that in the embodiments of this application, the first preset threshold and the second preset threshold of different parameters corresponding to the same attribute element can be different. For example, the first preset threshold of the direction parameter corresponding to a straight line is 0 and the second preset threshold is 5, while the first preset threshold of the length parameter corresponding to a straight line is 3 and the second preset threshold is 10.

[0082] Similarly, the first preset threshold and the second preset threshold of the same type of parameter corresponding to different attribute elements can be different. For example, the first preset threshold of the length parameter corresponding to a straight line is 3 and the second preset threshold is 10, while the first preset threshold of the length parameter corresponding to an arc is 2 and the second preset threshold is 8.

[0083] Furthermore, when modifying the specific value of the same parameter in a subsequent step, the specific value after the last modification of the parameter is used as the reference. The current specific value after the parameter is modified is the value after the last modification of the parameter plus or minus the third preset threshold.

[0084] It should be noted that, in the embodiments of this application, the third preset thresholds for different parameters corresponding to the same attribute element may be different, and the third preset thresholds for the same type of parameters corresponding to different attribute elements may also be different.

[0085] For example, refer to Figure 3 The original high-precision map road diagram shown is modified twice using a combination of {arcs} to change the parameters of the attribute elements in the DE segment road. First, the initial range of the direction parameter θ of the DE arc is set to [5, 15], in degrees. In the first modification, the direction parameter θ of the DE arc is set to 10°. According to the data of the original high-precision map, the coordinates of points D and E in the inertial coordinate system are (6, 4) and (6, 9), respectively. At the same time, the curvature of the DE arc is set to 0.1524. At this time, the road shape of the DE segment road after the first modification is arc 1. In the second modification, the specific value of the direction parameter θ after the first modification, 10°, is added to the third preset threshold of 20°. The direction parameter η after the second modification is 30°. The specific values ​​of the other parameters are the same as those after the first modification. At this time, the road shape of the DE segment road after the second modification is arc 2.

[0086] S3, When N reaches a preset threshold, N roads are generated in the simulation scene based on N different high-precision maps;

[0087] After generating N different high-definition maps, determine whether the total number N of generated high-definition maps is less than a preset threshold.

[0088] If the total number N of the generated high-precision maps is less than the preset threshold, then continue to modify the parameters corresponding to each of the X attribute elements in the attribute element set of the K segments of the M segments of roads.

[0089] If the total number of generated high-precision maps is not less than a preset threshold, then stop modifying the parameters corresponding to the X attribute elements in the attribute element set of K segments of the M segments of roads, and generate N roads in the simulation scene based on the generated N different high-precision maps.

[0090] For example, if 100 different high-definition maps are generated based on the original high-definition map, and the total number of high-definition maps is not less than a preset threshold of 100, then stop modifying the parameters corresponding to the attribute elements in the attribute element set of the road in the original high-definition map. Then, based on the 100 different high-definition maps, 100 different roads are generated in the simulation scene.

[0091] It should be noted that, in the embodiments of this application, when generating roads in a simulation scene using a high-precision map, a script-based method or a heuristic-based method can be used. The embodiments of this application do not limit the specific method for generating roads in a simulation scene using a high-precision map.

[0092] Through the above methods, various roads were generated in the simulation scenario based on different high-precision maps, providing a foundation for testing autonomous driving algorithms. Moreover, these roads are more complex than real-world roads. When the autonomous driving algorithm can drive the vehicle safely on such complex roads, it can also drive the vehicle more safely on simpler real-world roads, thus ensuring the safety of the vehicle during autonomous driving.

[0093] In one possible implementation, after generating N roads in the simulation scenario, various autonomous driving algorithms can be loaded; then, based on the N roads generated in the simulation scenario, the various autonomous driving algorithms are tested to verify the safety of these autonomous driving algorithms.

[0094] In summary, the road data generation method proposed in this application generates N different high-precision maps by continuously modifying the parameters corresponding to the road shapes in the acquired original high-precision map. When N reaches a preset threshold, N different roads are generated in the simulation scene based on these N different high-precision maps. This method allows for the generation of N different high-precision maps from only one costly original high-precision map, thereby reducing the production cost of high-precision maps and improving the efficiency of generating various types of roads in the virtual simulation scene.

[0095] Furthermore, by modifying the parameters corresponding to the road shapes in the original high-precision map using preset modification methods, different high-precision maps are automatically and quickly generated. In other words, by modifying the parameters corresponding to the road shapes in the high-precision map using preset methods, the automatic generalization of the original high-precision map is achieved, thereby realizing the automatic generalization of roads in the simulation scene.

[0096] The technical solution of this application will be further explained below with reference to a specific application process.

[0097] like Figure 4 The diagram shows the processing steps of the road data generation method. First, an original high-precision map in OpenDrive format is created using road data.

[0098] Then, obtain the various attribute elements describing the road shape in the original high-precision map, as well as the parameters corresponding to each attribute element in the attribute element set composed of various attribute elements;

[0099] Secondly, based on all the road intersections in the original high-precision map, the road is divided into M sub-road segments;

[0100] Furthermore, according to the preset modification method, the parameters corresponding to the specified attribute elements in the attribute element set of the K segment roads in the M segment roads in the original high-precision map are continuously modified to generate N different high-precision maps. The preset revision method is any one of the L combination methods obtained by combining the specified attribute elements in the attribute element set according to different arrangement orders.

[0101] When the total number N of generated high-precision maps reaches the preset threshold, stop modifying the parameters corresponding to the specified attribute elements in the attribute element set of the K segment roads in the M segment roads of the original high-precision map;

[0102] Finally, based on the generated N high-precision maps, N roads are generated in the simulation scene, where the first high-precision map corresponds to the first road in the simulation scene, and the Nth high-precision map corresponds to the Nth road in the simulation scene.

[0103] By using the above method, based on the acquired original high-precision map, and by continuously modifying the parameters corresponding to the specified attribute elements in the attribute element set describing the road shape in the original high-precision map, N different high-precision maps are generated. Then, based on the N different high-precision maps, N different roads are generated in the simulation scene. Only one original high-precision map with high production cost needs to be obtained to get N different high-precision maps, thereby reducing the production cost of high-precision maps and improving the generation efficiency of various roads in the virtual simulation scene.

[0104] Furthermore, by modifying the parameters corresponding to the road shapes in the original high-precision map using preset modification methods, different high-precision maps are automatically and quickly generated. In other words, by modifying the parameters corresponding to the road shapes in the high-precision map using preset methods, the automatic generalization of the original high-precision map is achieved, thereby realizing the automatic generalization of roads in the simulation scene.

[0105] Based on the same inventive concept, this application also provides a road data generation system, such as... Figure 5The diagram shown is a structural schematic of a road data generation system provided in this application. The system includes:

[0106] Module 501 is used to acquire the original high-precision map;

[0107] Modification module 502 is used to continuously modify the parameters corresponding to each of the X attribute elements in the attribute element set of the road in the original high-precision map, and generate N different high-precision maps. Here, the attribute element is a line segment describing the shape of the road, and X and N are integers greater than zero.

[0108] The processing module 503 is used to generate N roads in the simulation scene based on N different high-precision maps when N reaches a preset threshold.

[0109] In one possible implementation, the modification module 502 is specifically used to cut the roads in the original high-precision map into M sub-road segments, where M is an integer greater than zero;

[0110] Modify the parameters of each of the X attribute elements in the attribute element set of the K segment road in the M segment road, where K is an integer greater than zero.

[0111] In one possible implementation, module 502 is modified to specifically obtain all road intersections in the original high-precision map.

[0112] Based on all road intersections, the road is divided into M sub-road segments.

[0113] In one possible implementation, module 502 is modified specifically for obtaining a set of attribute elements;

[0114] According to the different arrangements of X attribute elements in the attribute element set, combine X attribute elements to obtain L combination ways, where L is an integer greater than zero;

[0115] For each of the K segments within the M-segment road, modify the parameters of each attribute element in each of the L combination methods.

[0116] Based on the same inventive concept, this application also provides an electronic device that can realize the functions of the aforementioned road data generation system. (Refer to...) Figure 6 The aforementioned electronic devices include:

[0117] At least one processor 601 and a memory 602 connected to at least one processor 601. In this embodiment, the specific connection medium between the processor 601 and the memory 602 is not limited. Figure 6The example shown is the connection between processor 601 and memory 602 via bus 600. Bus 600 is... Figure 6 The connections between other components are indicated by thick lines and are for illustrative purposes only, not as limiting information. The 600 bus can be divided into address bus, data bus, control bus, etc., for ease of representation. Figure 6 The term "processor" is represented by a single thick line, but this does not imply that there is only one bus or one type of bus. Alternatively, the processor 601 can also be called a controller; there is no restriction on the name.

[0118] In this embodiment, memory 602 stores instructions executable by at least one processor 601. By executing the instructions stored in memory 602, at least one processor 601 can perform the road data generation method described above. Processor 601 can implement... Figure 6 The system shown illustrates the functions of each module.

[0119] The processor 601 is the control center of the system. It can connect to various parts of the control device through various interfaces and lines. By running or executing instructions stored in memory 602 and calling data stored in memory 602, the system can perform various functions and process data, thereby monitoring the system as a whole.

[0120] In one possible design, processor 601 may include one or more processing units. Processor 601 may integrate an application processor and a modem processor, wherein the application processor mainly handles the operating system, user interface, and applications, and the modem processor mainly handles wireless communication. It is understood that the modem processor may also not be integrated into processor 601. In some embodiments, processor 601 and memory 602 may be implemented on the same chip; in some embodiments, they may also be implemented on separate chips.

[0121] The processor 601 can be a general-purpose processor, such as a central processing unit (CPU), digital signal processor, application-specific integrated circuit, field-programmable gate array or other programmable logic device, discrete gate or transistor logic device, or discrete hardware component, capable of implementing or executing the methods, steps, and logic block diagrams disclosed in the embodiments of this application. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the road data generation method disclosed in the embodiments of this application can be directly manifested as being executed by a hardware processor, or executed by a combination of hardware and software modules within the processor.

[0122] Memory 602, as a non-volatile computer-readable storage medium, can be used to store non-volatile software programs, non-volatile computer-executable programs, and modules. Memory 602 may include at least one type of storage medium, such as flash memory, hard disk, multimedia card, card-type memory, random access memory (RAM), static random access memory (SRAM), programmable read-only memory (PROM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), magnetic storage, magnetic disk, optical disk, etc. Memory 602 can be any other medium capable of carrying or storing desired program code in the form of instructions or data structures that can be accessed by a computer, but is not limited thereto. In the embodiments of this application, memory 602 may also be a circuit or any other device capable of implementing storage functions for storing program instructions and / or data.

[0123] By designing and programming the processor 601, the code corresponding to the road data generation method described in the foregoing embodiments can be embedded into the chip, enabling the chip to execute it during operation. Figure 5 The steps of the method for generating road data in the illustrated embodiment are described. How to design and program the processor 601 is a technique well-known to those skilled in the art and will not be elaborated upon here.

[0124] Based on the same inventive concept, embodiments of this application also provide a storage medium storing computer instructions that, when executed on a computer, cause the computer to perform the road data generation method described above.

[0125] In some possible implementations, various aspects of the road data generation method provided in this application can also be implemented in the form of a program product, which includes program code that, when the program product is run on a device, causes the control device to perform the steps in the road data generation method according to the various exemplary embodiments of this application described above.

[0126] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0127] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0128] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0129] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0130] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of the claims of this application and their equivalents, this application also intends to include such modifications and variations.

Claims

1. A method for generating road data, characterized in that, The method includes: Obtain the original high-precision map; The parameters corresponding to each of the X attribute elements in the attribute element set of the road in the original high-precision map are continuously modified to generate N different high-precision maps. Here, the attribute elements are line segments describing the shape of the road, and X and N are both positive integers. Modifying the parameters corresponding to each of the X attribute elements in the attribute element set of the road in the original high-precision map includes: cutting the road in the original high-precision map into M sub-road segments, where M is a positive integer; obtaining the attribute element set; combining the X attribute elements according to different arrangements of the X attribute elements in the attribute element set to obtain L combination methods, where L is a positive integer; and for each of the K sub-road segments in the M sub-road segments, modifying the parameters corresponding to each attribute element in the combination method corresponding to each sub-road segment according to any of the L combination methods, where K is a positive integer. When N reaches a preset threshold, N roads are generated in the simulation scene based on the N different high-precision maps.

2. The method as described in claim 1, characterized in that, The step of dividing the road in the original high-precision map into M sub-road segments includes: Obtain all road intersections of the road in the original high-precision map; Based on all the road intersections, the road is divided into the M sub-road segments.

3. A road data generation system, characterized in that, The system includes: The acquisition module is used to acquire the original high-precision map; The modification module is used to continuously modify the parameters corresponding to each of the X attribute elements in the attribute element set of roads in the original high-precision map, generating N different high-precision maps. Here, the attribute elements are line segments describing the shape of roads, and X and N are both positive integers. Modifying the parameters corresponding to each of the X attribute elements in the attribute element set of roads in the original high-precision map includes: cutting the roads in the original high-precision map into M sub-road segments, where M is a positive integer; obtaining the attribute element set; combining the X attribute elements according to different arrangements of the X attribute elements in the attribute element set to obtain L combination methods, where L is a positive integer; and for each of the K sub-road segments in the M sub-road segments, modifying the parameters corresponding to each attribute element in the combination method corresponding to each sub-road segment according to any one of the L combination methods, where K is a positive integer. The processing module is used to generate N roads in the simulation scene based on the N different high-precision maps when N reaches a preset threshold.

4. The system as described in claim 3, characterized in that, The modification module is used to obtain all road intersections of the road in the original high-precision map; Based on all the road intersections, the road is divided into the M sub-road segments.

5. An electronic device, characterized in that, include: Memory, used to store computer programs; A processor, when executing a computer program stored in the memory, implements the method steps of any one of claims 1-2.

6. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the steps of the method described in any one of claims 1-2.

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