Road roughness identification method, device, vehicle, equipment and storage medium

Abstract

The present application provides a method, device, vehicle, equipment and storage medium for identifying the roughness of a road surface. The method includes: obtaining a target point cloud of a target area, the target point cloud includes multiple target point cloud points, and each target point cloud point carries a coordinate in a preset coordinate system, the first direction and the second direction are the coordinate axis directions of the preset coordinate system, the first direction is parallel to the forward direction of the vehicle, the second direction is perpendicular to the first direction and parallel to the road surface, and the target area belongs to the road surface area; dividing the target point cloud into N point cloud layers along the first direction, where N is an integer greater than 1; for each point cloud layer, determining the roughness corresponding to the point cloud layer according to the coordinates of all target point cloud points in the point cloud layer; identifying the roughness of the road surface according to the roughness corresponding to the N point cloud layers. In this way, the roughness of the road surface can be identified through the target point cloud scanned by the laser radar, so as to provide early warning and improve driving safety.

Description

Technical Field

[0001] The present application belongs to the field of vehicle technology, and in particular, relates to a method, device, vehicle, equipment and storage medium for identifying road roughness. Background Art

[0002] During the driving process, the actual road conditions of the vehicle are relatively complex, and the vehicle may encounter roads with poor road conditions. For example, the vehicle may encounter a relatively rough road ahead during driving, which affects driving safety. Summary of the invention

[0003] The embodiments of the present application provide a method, device, vehicle, equipment and storage medium for identifying the roughness of a road surface, so as to provide an early warning and improve driving safety.

[0004] In a first aspect, an embodiment of the present application provides a method for identifying road roughness, the method comprising:

[0005] Acquire a target point cloud of a target area, wherein the target point cloud includes a plurality of target point cloud points, and each target point cloud point carries coordinates in a preset coordinate system, a first direction and a second direction are coordinate axis directions of the preset coordinate system, the first direction is parallel to a forward direction of the vehicle, the second direction is perpendicular to the first direction and parallel to a road surface, and the target area belongs to a road surface area;

[0006] Dividing the target point cloud into N point cloud layers along the first direction, where N is an integer greater than 1;

[0007] For each point cloud layer, determining the ruggedness corresponding to the point cloud layer according to the coordinates of all target point cloud points in the point cloud layer;

[0008] The ruggedness of the road surface is identified according to the ruggedness corresponding to the N point cloud layers.

[0009] In a second aspect, an embodiment of the present application provides a device for identifying road roughness, the device comprising:

[0010] an acquisition module, configured to acquire a target point cloud of a target area, wherein the target point cloud includes a plurality of target point cloud points, and each target point cloud point carries coordinates in a preset coordinate system, wherein a first direction and a second direction are coordinate axis directions of the preset coordinate system, wherein the first direction is parallel to a forward direction of the vehicle, and the second direction is perpendicular to the first direction and parallel to a road surface, and the target area belongs to a road surface area;

[0011] A division module, used for dividing the target point cloud into N point cloud layers along the first direction, where N is an integer greater than 1;

[0012] A determination module, for determining, for each point cloud layer, a ruggedness corresponding to the point cloud layer according to the coordinates of all target point cloud points in the point cloud layer;

[0013] The recognition module is used to identify the ruggedness of the road surface according to the ruggedness corresponding to the N point cloud layers.

[0014] In a third aspect, an embodiment of the present application further provides a vehicle, the vehicle comprising a device for identifying the degree of roughness of a road surface, wherein the device for identifying the degree of roughness of a road surface is used to implement the above method.

[0015] In a fourth aspect, an embodiment of the present application provides an electronic device, the device comprising:

[0016] A processor and a memory storing programs or instructions;

[0017] The above method is implemented when the processor executes the program or instruction.

[0018] In a fifth aspect, an embodiment of the present application provides a readable storage medium, on which a program or instruction is stored, and the program or instruction implements the above method when executed by a processor.

[0019] In a sixth aspect, an embodiment of the present application provides a computer program product. When instructions in the computer program product are executed by a processor of an electronic device, the electronic device executes the above method.

[0020] The road surface roughness identification method, device, vehicle, equipment and storage medium of the embodiment of the present application can obtain the target point cloud of the target area, the target point cloud includes multiple target point cloud points, and each target point cloud point carries the coordinates in the preset coordinate system, the first direction and the second direction are the coordinate axis directions of the preset coordinate system, the first direction is parallel to the forward direction of the vehicle, the second direction is perpendicular to the first direction and parallel to the road surface, and the target area belongs to the road surface area; the target point cloud is divided into N point cloud layers along the first direction, N is an integer greater than 1; for each point cloud layer, the ruggedness corresponding to the point cloud layer is determined according to the coordinates of all target point cloud points in the point cloud layer; the ruggedness of the road surface is identified according to the ruggedness corresponding to the N point cloud layers. In this way, the ruggedness of the road surface can be identified through the target point cloud scanned by the laser radar, so as to provide early warning and improve driving safety. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the technical solution of the embodiments of the present application, the following is a brief introduction to the drawings required for use in the embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0022] Figure 1 It is a flowchart of a method for identifying road roughness provided by an embodiment of the present application;

[0023] Figure 2 is a point cloud schematic diagram of a flat road surface in the road surface roughness identification method provided in an embodiment of the present application;

[0024] Figure 3 is a point cloud schematic diagram of road surface roughness in the road surface roughness identification method provided in an embodiment of the present application;

[0025] Figure 4 is a schematic diagram of the structure of a device for identifying road roughness provided by another embodiment of the present application;

[0026] Figure 5 It is a structural diagram of an electronic device provided by yet another embodiment of the present application. DETAILED DESCRIPTION

[0027] The features and exemplary embodiments of various aspects of the present application will be described in detail below. In order to make the purpose, technical solutions and advantages of the present application clearer, the present application will be further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only intended to explain the present application, rather than to limit the present application. For those skilled in the art, the present application can be implemented without the need for some of these specific details. The following description of the embodiments is only to provide a better understanding of the present application by illustrating the examples of the present application.

[0028] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the statement "include..." do not exclude the presence of other identical elements in the process, method, article or device including the elements.

[0029] In order to solve the problems in the prior art, the embodiments of the present application provide a method, device, vehicle, equipment and storage medium for identifying the roughness of a road surface. The method for identifying the roughness of a road surface provided by the embodiments of the present application is first introduced below.

[0030] Figure 1FIG. 1 is a flow chart of a method for identifying road roughness provided by an embodiment of the present application. Figure 1 As shown, the method for identifying the roughness of a road surface may include the following steps:

[0031] Step 101, obtaining a target point cloud of a target area, the target point cloud includes a plurality of target point cloud points, and each target point cloud point carries a coordinate in a preset coordinate system, a first direction and a second direction are coordinate axis directions of the preset coordinate system, the first direction is parallel to the forward direction of the vehicle, the second direction is perpendicular to the first direction and parallel to the road surface, and the target area belongs to a road surface area;

[0032] Step 102, dividing the target point cloud into N point cloud layers along a first direction, where N is an integer greater than 1;

[0033] Step 103, for each point cloud layer, determining the ruggedness corresponding to the point cloud layer according to the coordinates of all target point cloud points in the point cloud layer;

[0034] Step 104 , identifying the roughness of the road surface according to the roughness corresponding to the N point cloud layers.

[0035] The specific implementation of each of the above steps will be described in detail below.

[0036] In an embodiment of the present application, a method for identifying road roughness is able to.

[0037] The specific implementation methods of the above steps are introduced below.

[0038] In step 101, it is understood that a laser radar is generally provided on the vehicle, and a laser scan can be performed on the road ahead of the vehicle to obtain a point cloud of the road ahead. Based on this, a target point cloud of a target area can be obtained by the laser radar, and the target area can be a road area. It is understood that the target area can be all road areas within the scanning range of the laser radar, or a partial area within the scanning range set according to actual needs.

[0039] The target point cloud may include multiple target point cloud points, and each target point cloud point carries coordinates in a preset coordinate system. For example, the position of the laser radar can be used as the origin of the preset coordinate system, the vehicle's forward direction (i.e., the first direction) is the X-axis, and the direction perpendicular to the vehicle's forward direction and parallel to the road surface (i.e., the second direction) is the Y-axis.

[0040] In step 102, for the convenience of calculation, the target point cloud may be divided into N point cloud layers along a first direction, and then the ruggedness corresponding to each point cloud layer may be determined.

[0041] In step 103, if Figure 2 and Figure 3 As shown, for a flat road, the point cloud distribution is relatively uniform, while for a rugged road, the point cloud distribution is relatively scattered.

[0042] Based on this, for each point cloud layer, the ruggedness corresponding to the point cloud layer can be determined according to the coordinates of all target point cloud points in the point cloud layer. For example, the coordinate difference of these point cloud points in the first direction (X-axis coordinate difference) can be calculated according to the coordinates of all target point cloud points. The smaller the coordinate difference, the more evenly all the target point cloud points of the point cloud layer are distributed, that is, the lower the ruggedness of the point cloud layer is. The larger the coordinate difference, the more dispersed all the target point cloud points of the point cloud layer are distributed, that is, the higher the ruggedness of the point cloud layer is.

[0043] The coordinate differences of these point cloud points in the second direction (Y-axis coordinate differences) can also be calculated. Similarly, the distribution of all target point cloud points of the point cloud layer can be determined, thereby determining the ruggedness of the point cloud layer.

[0044] In step 104, the ruggedness of the road surface can be identified according to the ruggedness corresponding to the N point cloud layers. For example, the ruggedness with the largest number of ruggedness corresponding to the N point cloud layers can be determined as the ruggedness of the road surface. The average value of the ruggedness can also be calculated according to the ruggedness corresponding to the N point cloud layers, and the average value is determined as the ruggedness of the road surface. The specific setting can be based on actual needs and is not specifically limited here.

[0045] The road surface roughness identification method of the embodiment of the present application can obtain the target point cloud of the target area, the target point cloud includes multiple target point cloud points, and each target point cloud point carries the coordinates in the preset coordinate system, the first direction and the second direction are the coordinate axis directions of the preset coordinate system, the first direction is parallel to the forward direction of the vehicle, the second direction is perpendicular to the first direction and parallel to the road surface, and the target area belongs to the road surface area; the target point cloud is divided into N point cloud layers along the first direction, N is an integer greater than 1; for each point cloud layer, the roughness corresponding to the point cloud layer is determined according to the coordinates of all target point cloud points in the point cloud layer; the roughness of the road surface is identified according to the roughness corresponding to the N point cloud layers. In this way, the roughness of the road surface can be identified through the target point cloud scanned by the laser radar, so as to provide early warning and improve driving safety.

[0046] In some embodiments, the above step 101 may include the following steps:

[0047] Obtain the initial point cloud within the scanning range of the laser radar;

[0048] Determine the target point cloud that meets the preset area conditions from the initial point cloud.

[0049] It is understandable that, generally speaking, the laser radar is installed at the center of the vehicle's head to collect point clouds of the road in front of the vehicle. The laser radar has a wide scanning range, but the farther away from the laser radar, the sparser the distribution of the point cloud.

[0050] For pothole detection on the road, it is often necessary to detect the area within a certain range in front of the vehicle's lane and the adjacent lane. If the detection distance is too far, on the one hand, it will increase the consumption of computing power, and on the other hand, the sparsity of the point cloud may affect the accuracy of pothole detection and cause misjudgment.

[0051] Based on this, in this embodiment, the initial point cloud within the scanning range of the laser radar can be obtained first. Then, the target point cloud that meets the preset area condition can be determined from the initial point cloud. The preset area condition can be an area with coordinates within a preset range. For example, the target area can be a region with the laser radar as the coordinate origin and the X axis extending from the laser radar to the distance of the road surface. max The Y axis is parallel to the road surface and the range is [-y max ,y max ] The initial point cloud whose coordinates are within this range is the target point cloud of the target area.

[0052] In some embodiments, the above step 103 may include the following steps:

[0053] For each point cloud layer, determine the difference in distance from the laser radar corresponding to the point cloud layer based on the coordinates of all target point cloud points in the point cloud layer;

[0054] The ratio of the difference to a preset threshold corresponding to the point cloud layer is determined as a ruggedness parameter of the point cloud layer, and the ruggedness parameter is used to indicate the degree of ruggedness.

[0055] In this embodiment, for each point cloud layer, the difference in distance from the laser radar corresponding to the point cloud layer can be determined based on the coordinates of all target point cloud points in the point cloud layer. In some examples, the average distance between all target point cloud points and the laser radar (that is, the average value of the X-axis coordinate values ​​of all target point cloud points) can be calculated first, and then the difference between the minimum X-axis coordinate value and the maximum X-axis coordinate value of all target point cloud points and the average value can be calculated respectively, and the average value is taken as the difference in distance from the laser radar corresponding to the point cloud layer. In other examples, the difference between the maximum X-axis coordinate value and the minimum X-axis coordinate value of all target point cloud points can also be directly calculated as the difference in distance from the laser radar corresponding to the point cloud layer. The calculation method of the difference in distance from the laser radar corresponding to a specific point cloud layer can be set according to actual needs, and is not specifically limited here.

[0056] Determine the difference ΔX of the distance lidar corresponding to the point cloud layer (1，2...N) After that, the difference ΔX (1，2...N)The preset threshold ΔX corresponding to the point cloud layer (1，2...N)Default The ratio of is determined as the roughness parameter P of the point cloud layer. (1，2...N) , where the ruggedness parameter can be used to indicate the degree of ruggedness.

[0057] The calculation formula of the roughness parameter can be as follows:

[0058] P i =ΔX i / ΔX iDefault

[0059] Among them, P i is the roughness parameter of the i-th point cloud layer, ΔX i is the difference in distance from the lidar corresponding to the i-th point cloud layer, ΔX iDefault is the preset threshold corresponding to the i-th point cloud layer, and the value range of i is 1 to N.

[0060] It is understandable that the preset threshold corresponding to each point cloud layer can be the same or different, and can be pre-set based on empirical values ​​in combination with the actual conditions such as the type of laser radar and the installation location. The preset threshold corresponding to each point cloud layer can also be obtained based on the point cloud calculation of the laser radar scanning the flat road surface. It is understandable that the preset threshold corresponding to each point cloud layer based on the point cloud calculation of the flat road surface is consistent with the calculation method of the difference between the distance of each point cloud layer and the laser radar. In this way, the preset threshold corresponding to each point cloud layer calculated based on the point cloud of the flat road surface is more accurate, thereby improving the accuracy of road roughness recognition.

[0061] In some embodiments, for each point cloud layer, determining the distance difference of the laser radar corresponding to the point cloud layer according to the coordinates of all target point cloud points in the point cloud layer may include the following steps:

[0062] For each point cloud layer, determining a maximum coordinate value and a minimum coordinate value in a first direction from coordinates of all target point cloud points in the point cloud layer;

[0063] The difference between the maximum coordinate value and the minimum coordinate value is determined as the difference in distance lidar corresponding to the point cloud layer.

[0064] In an embodiment of the present application, in order to simplify the calculation process of the difference of the distance laser radar corresponding to each point cloud layer, save computing power and improve the efficiency of identifying the roughness of the road surface, for each point cloud layer, the maximum coordinate value and the minimum coordinate value in the first direction can be determined from the coordinates of all target point cloud points in the point cloud layer, and the difference between the maximum coordinate value and the minimum coordinate value can be directly determined as the difference of the distance laser radar corresponding to the point cloud layer.

[0065] In some embodiments, the above step 104 may include the following steps:

[0066] According to the roughness parameter of each point cloud layer, determine the average value of the roughness parameters of N point cloud layers;

[0067] determining an average value of the roughness parameters as the roughness parameter of the road surface;

[0068] The roughness of the road surface is identified according to the roughness parameters of the road surface.

[0069] In this embodiment, after the ruggedness parameter of each point cloud layer is determined, the average value of the ruggedness parameters of the N point cloud layers may be calculated, and the average value of the ruggedness parameter may be determined as the ruggedness parameter P of the road surface, so as to identify the ruggedness of the road surface.

[0070] It can be understood that the closer P is to 1, the smoother the road surface is, and the larger P is, the more rugged the road surface is. Based on this, the road surface roughness parameter P can be directly output so that the driver can determine the ruggedness of the road ahead according to the P value, so as to prepare in advance, drive carefully, and improve driving safety.

[0071] In some embodiments, the method of identifying the roughness of the road surface according to the roughness parameter of the road surface may include the following steps:

[0072] A target level of road surface roughness is determined based on road surface roughness parameters and a preset corresponding relationship, wherein the preset corresponding relationship includes a corresponding relationship between road surface roughness parameters and the target level.

[0073] In this embodiment, in order to further more intuitively identify the roughness of the road surface, the roughness parameters may be classified in advance and a preset corresponding relationship may be generated. For example, the preset corresponding relationship includes:

[0074] The roughness parameter P of the road surface The degree of roughness of the road surface 1～p1 Low level p1~p2 Intermediate p2 and above High Level

[0075] At this time, the target level of the road surface roughness can be determined according to the road surface roughness parameter and the preset corresponding relationship. For example, if the road surface roughness parameter is greater than 1 and less than or equal to p1, the target level of the road surface roughness can be determined to be a low level. If the road surface roughness parameter is greater than p1 and less than or equal to p2, the target level of the road surface roughness can be determined to be a medium level. If the road surface roughness parameter is greater than p2, the target level of the road surface roughness can be determined to be a high level.

[0076] The target level can be directly output so that the driver can more intuitively know the ruggedness of the road ahead, so that he can prepare in advance, drive carefully and improve driving safety.

[0077] Based on the road surface roughness identification method provided in the above embodiment, the present application also provides an embodiment of a road surface roughness identification device.

[0078] Figure 4 A schematic structural diagram of a road surface roughness identification device provided in another embodiment of the present application is shown. For ease of explanation, only the parts related to the embodiment of the present application are shown.

[0079] Reference Figure 4 , the road surface roughness recognition device 400 may include:

[0080] An acquisition module 401 is used to acquire a target point cloud of a target area, wherein the target point cloud includes a plurality of target point cloud points, and each target point cloud point carries coordinates in a preset coordinate system, wherein a first direction and a second direction are coordinate axis directions of the preset coordinate system, wherein the first direction is parallel to a forward direction of the vehicle, and the second direction is perpendicular to the first direction and parallel to a road surface, and the target area belongs to a road surface area;

[0081] A division module 402 is used to divide the target point cloud into N point cloud layers along a first direction, where N is an integer greater than 1;

[0082] A determination module 403 is used to determine, for each point cloud layer, the ruggedness corresponding to the point cloud layer according to the coordinates of all target point cloud points in the point cloud layer;

[0083] The acquisition module 404 is used to identify the ruggedness of the road surface according to the ruggedness corresponding to the N point cloud layers.

[0084] In some embodiments, the acquisition module 401 may also be used to:

[0085] Obtain the initial point cloud within the scanning range of the laser radar;

[0086] Determine the target point cloud that meets the preset area conditions from the initial point cloud.

[0087] In some embodiments, the determination module 403 may also be used to:

[0088] For each point cloud layer, determine the difference in distance from the laser radar corresponding to the point cloud layer based on the coordinates of all target point cloud points in the point cloud layer;

[0089] The ratio of the difference to a preset threshold corresponding to the point cloud layer is determined as a ruggedness parameter of the point cloud layer, and the ruggedness parameter is used to indicate the degree of ruggedness.

[0090] In some embodiments, the determination module 403 may also be used to:

[0091] For each point cloud layer, determining a maximum coordinate value and a minimum coordinate value in a first direction from coordinates of all target point cloud points in the point cloud layer;

[0092] The difference between the maximum coordinate value and the minimum coordinate value is determined as the difference in distance lidar corresponding to the point cloud layer.

[0093] In some embodiments, the identification module 404 may also be used to:

[0094] According to the roughness parameter of each point cloud layer, determine the average value of the roughness parameters of N point cloud layers;

[0095] determining an average value of the roughness parameters as the roughness parameter of the road surface;

[0096] The roughness of the road surface is identified according to the roughness parameters of the road surface.

[0097] In some embodiments, the identification module 404 may also be used to:

[0098] A target level of road surface roughness is determined based on road surface roughness parameters and a preset corresponding relationship, wherein the preset corresponding relationship includes a corresponding relationship between road surface roughness parameters and the target level.

[0099] It should be noted that the information interaction, execution process, etc. between the above-mentioned devices / units are based on the same concept as the method embodiment of the present application, and are devices corresponding to the above-mentioned road roughness identification method. All implementation methods in the above-mentioned method embodiment are applicable to the embodiments of the device. Its specific functions and technical effects can be found in the method embodiment part, which will not be repeated here.

[0100] The technicians in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In practical applications, the above-mentioned function allocation can be completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated in a processing unit, or each unit can exist physically separately, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, which will not be repeated here.

[0101] An embodiment of the present application also provides a vehicle, which may include an in-vehicle conference system, and the in-vehicle conference system may include a road surface roughness identification device for implementing the above-mentioned road surface roughness identification method.

[0102] Figure 5A schematic diagram of the hardware structure of an electronic device provided in yet another embodiment of the present application is shown.

[0103] The device may include a processor 501 and a memory 502 storing programs or instructions.

[0104] When the processor 501 executes the program, the steps in any of the above method embodiments are implemented.

[0105] Exemplarily, the program may be divided into one or more modules / units, one or more modules / units are stored in the memory 502, and executed by the processor 501 to complete the present application. One or more modules / units may be a series of program instruction segments capable of completing a specific function, and the instruction segments are used to describe the execution process of the program in the device.

[0106] Specifically, the processor 501 may include a central processing unit (CPU), or an application specific integrated circuit (ASIC), or may be configured to implement one or more integrated circuits of the embodiments of the present application.

[0107] The memory 502 may include a large capacity memory for data or instructions. By way of example and not limitation, the memory 502 may include a hard disk drive (HDD), a floppy disk drive, a flash memory, an optical disk, a magneto-optical disk, a magnetic tape, or a universal serial bus (USB) drive or a combination of two or more of these. In appropriate cases, the memory 502 may include a removable or non-removable (or fixed) medium. In appropriate cases, the memory 502 may be inside or outside the integrated gateway disaster recovery device. In a specific embodiment, the memory 502 is a non-volatile solid-state memory.

[0108] The memory may include read-only memory (ROM), random access memory (RAM), magnetic disk storage media devices, optical storage media devices, flash memory devices, electrical, optical or other physical / tangible memory storage devices. Thus, typically, the memory includes one or more tangible (non-transitory) readable storage media (e.g., memory devices) encoded with software including computer executable instructions, and when the software is executed (e.g., by one or more processors), it is operable to perform the operations described with reference to the method according to an aspect of the present disclosure.

[0109] The processor 501 implements any one of the methods in the above embodiments by reading and executing the program or instruction stored in the memory 502 .

[0110] In one example, the electronic device may further include a communication interface 503 and a bus 510. The processor 501, the memory 502, and the communication interface 503 are connected via the bus 510 and communicate with each other.

[0111] The communication interface 503 is mainly used to implement communication between various modules, devices, units and / or equipment in the embodiments of the present application.

[0112] Bus 510 includes hardware, software or both, and the parts of online data flow billing equipment are coupled to each other. For example, but not limitation, bus may include accelerated graphics port (AGP) or other graphics bus, enhanced industrial standard architecture (EISA) bus, front-end bus (FSB), hypertransport (HT) interconnection, industrial standard architecture (ISA) bus, infinite bandwidth interconnection, low pin count (LPC) bus, memory bus, micro channel architecture (MCA) bus, peripheral component interconnection (PCI) bus, PCI-Express (PCI-X) bus, serial advanced technology attachment (SATA) bus, video electronics standard association local (VLB) bus or other suitable bus or two or more of these combinations. In appropriate cases, bus 510 may include one or more buses. Although the present application embodiment describes and shows a specific bus, the present application considers any suitable bus or interconnection.

[0113] In addition, in combination with the method in the above embodiment, the embodiment of the present application can provide a readable storage medium for implementation. The readable storage medium stores a program or instruction; when the program or instruction is executed by a processor, any one of the methods in the above embodiment is implemented. The readable storage medium can be read by a machine such as a computer.

[0114] An embodiment of the present application further provides a chip, which includes a processor and a communication interface, wherein the communication interface is coupled to the processor, and the processor is used to run programs or instructions to implement the various processes of the above-mentioned method embodiment, and can achieve the same technical effect. To avoid repetition, it will not be repeated here.

[0115] It should be understood that the chip mentioned in the embodiments of the present application can also be called a system-level chip, a system chip, a chip system or a system-on-chip chip, etc.

[0116] An embodiment of the present application provides a computer program product, which is stored in a readable storage medium. The program product is executed by at least one processor to implement the various processes of the above-mentioned method embodiment and can achieve the same technical effect. To avoid repetition, it will not be repeated here.

[0117] It should be clear that the present application is not limited to the specific configuration and processing described above and shown in the figures. For the sake of simplicity, a detailed description of the known method is omitted here. In the above embodiments, several specific steps are described and shown as examples. However, the method process of the present application is not limited to the specific steps described and shown, and those skilled in the art can make various changes, modifications and additions, or change the order between the steps after understanding the spirit of the present application.

[0118] The functional modules shown in the above-described block diagram can be implemented as hardware, software, firmware or a combination thereof. When implemented in hardware, it can be, for example, an electronic circuit, an application specific integrated circuit (ASIC), appropriate firmware, a plug-in, a function card, etc. When implemented in software, the elements of the present application are programs or code segments that are used to perform the required tasks. Programs or code segments can be stored in machine-readable media, or transmitted on a transmission medium or a communication link by a data signal carried in a carrier wave. "Machine-readable media" can include any medium capable of storing or transmitting information. Examples of machine-readable media include electronic circuits, semiconductor memory devices, ROM, flash memory, erasable ROM (EROM), floppy disks, CD-ROMs, optical disks, hard disks, optical fiber media, radio frequency (RF) links, etc. Code segments can be downloaded via computer grids such as the Internet, intranets, etc.

[0119] It should also be noted that the exemplary embodiments mentioned in this application describe some methods or systems based on a series of steps or devices. However, this application is not limited to the order of the above steps, that is, the steps can be performed in the order mentioned in the embodiment, or in a different order from the embodiment, or several steps can be performed simultaneously.

[0120] Aspects of the present disclosure are described above with reference to the flowchart and / or block diagram of the method, device (system) and program product according to the embodiment of the present disclosure. It should be understood that each box in the flowchart and / or block diagram and the combination of each box in the flowchart and / or block diagram can be implemented by a computer program or instruction. These programs or instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device to produce a machine so that these instructions executed by the processor of the computer or other programmable data processing device enable the implementation of the function / action specified in one or more boxes of the flowchart and / or block diagram. Such a processor can be, but is not limited to, a general-purpose processor, a special-purpose processor, a special application processor, or a field programmable logic circuit. It can also be understood that each box in the block diagram and / or flowchart and the combination of boxes in the block diagram and / or flowchart can also be implemented by dedicated hardware that performs a specified function or action, or can be implemented by a combination of dedicated hardware and computer instructions.

[0121] The above is only a specific implementation of the present application. Those skilled in the art can clearly understand that for the convenience and simplicity of description, the specific working processes of the systems, modules and units described above can refer to the corresponding processes in the aforementioned method embodiments, and will not be repeated here. It should be understood that the protection scope of the present application is not limited to this. Any technician familiar with the technical field can easily think of various equivalent modifications or replacements within the technical scope disclosed in this application, and these modifications or replacements should be included in the protection scope of this application.

Claims

1. A method for identifying road roughness, characterized in that: include: Acquire a target point cloud of a target area, wherein the target point cloud includes a plurality of target point cloud points, and each target point cloud point carries coordinates in a preset coordinate system, a first direction and a second direction are coordinate axis directions of the preset coordinate system, the first direction is parallel to a forward direction of the vehicle, the second direction is perpendicular to the first direction and parallel to a road surface, and the target area belongs to a road surface area; Dividing the target point cloud into N point cloud layers along the first direction, where N is an integer greater than 1; For each point cloud layer, determining the ruggedness corresponding to the point cloud layer according to the coordinates of all target point cloud points in the point cloud layer; Identifying the ruggedness of the road surface according to the ruggedness corresponding to the N point cloud layers; For each point cloud layer, determining the ruggedness corresponding to the point cloud layer according to the coordinates of all target point cloud points in the point cloud layer includes: For each point cloud layer, determine the difference of the distance laser radar corresponding to the point cloud layer according to the coordinates of all target point cloud points in the point cloud layer; Determine a ratio of the difference to a preset threshold corresponding to the point cloud layer as a ruggedness parameter of the point cloud layer, where the ruggedness parameter is used to indicate a ruggedness degree; The step of determining, for each point cloud layer, a distance laser radar difference corresponding to the point cloud layer according to the coordinates of all target point cloud points in the point cloud layer, includes: For each point cloud layer, determining a maximum coordinate value and a minimum coordinate value in the first direction from coordinates of all target point cloud points in the point cloud layer; The difference between the maximum coordinate value and the minimum coordinate value is determined as the difference in distance between the point cloud layer and the laser radar.

2. The method according to claim 1, characterized in that The step of obtaining a target point cloud of a target area includes: Obtain the initial point cloud within the scanning range of the laser radar; A target point cloud that meets a preset area condition is determined from the initial point cloud.

3. The method according to claim 1, characterized in that The identifying the ruggedness of the road surface according to the ruggedness corresponding to the N point cloud layers includes: Determining an average value of the rugged parameters of the N point cloud layers according to the rugged parameter of each point cloud layer; determining an average value of the roughness parameters as the roughness parameter of the road surface; The roughness of the road surface is identified according to the roughness parameter of the road surface.

4. The method according to claim 3, characterized in that The step of identifying the roughness of the road surface according to the roughness parameter of the road surface comprises: A target level of the roughness of the road surface is determined according to the roughness parameter of the road surface and a preset corresponding relationship, wherein the preset corresponding relationship includes a corresponding relationship between the roughness parameter of the road surface and the target level.

5. A device for identifying road roughness, characterized in that: The device comprises: an acquisition module, configured to acquire a target point cloud of a target area, wherein the target point cloud includes a plurality of target point cloud points, and each target point cloud point carries coordinates in a preset coordinate system, wherein a first direction and a second direction are coordinate axis directions of the preset coordinate system, wherein the first direction is parallel to a forward direction of the vehicle, and the second direction is perpendicular to the first direction and parallel to a road surface, and the target area belongs to a road surface area; A division module, used for dividing the target point cloud into N point cloud layers along the first direction, where N is an integer greater than 1; A determination module, for determining, for each point cloud layer, a ruggedness corresponding to the point cloud layer according to the coordinates of all target point cloud points in the point cloud layer; An identification module, used for identifying the ruggedness of the road surface according to the ruggedness corresponding to the N point cloud layers; The determining module is also used for: For each point cloud layer, determine the difference of the distance laser radar corresponding to the point cloud layer according to the coordinates of all target point cloud points in the point cloud layer; Determine a ratio of the difference to a preset threshold corresponding to the point cloud layer as a ruggedness parameter of the point cloud layer, where the ruggedness parameter is used to indicate a ruggedness degree; The determining module is also used for: For each point cloud layer, determining a maximum coordinate value and a minimum coordinate value in the first direction from coordinates of all target point cloud points in the point cloud layer; The difference between the maximum coordinate value and the minimum coordinate value is determined as the difference in distance between the point cloud layer and the laser radar.

6. A vehicle, characterized in that: The vehicle comprises a device for identifying the degree of roughness of a road surface, wherein the device for identifying the degree of roughness of a road surface is used to implement the method according to any one of claims 1-4.

7. An electronic device, characterized in that: The device comprises: a processor and a memory storing programs or instructions; When the processor executes the program or instruction, the method according to any one of claims 1 to 4 is implemented.

8. A readable storage medium, characterized in that: The readable storage medium stores a program or instruction, and when the program or instruction is executed by a processor, the method according to any one of claims 1 to 4 is implemented.

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