Method, device, equipment and medium for determining a type of road surface roughness

Abstract

The present application provides a method, a determining device, equipment and a medium for determining the type of road surface roughness. The method includes: collecting point cloud data of the road surface within a preset range in front of the vehicle through a lidar; for each layer of lateral point cloud, calculating the lateral roughness of the road surface corresponding to the layer of lateral point cloud based on the height value of each point cloud in the layer of lateral point cloud; calculating the longitudinal roughness of the road surface according to the lateral roughness of the road surface corresponding to each layer of lateral point cloud; and determining the type of road surface roughness corresponding to the road surface based on the lateral roughness of the road surface corresponding to each layer of lateral point cloud and the longitudinal roughness of the road surface. Through the determining method and the determining device, the accuracy of determining the type of road surface roughness is improved, and thus the driving safety is improved.

Description

Technical Field

[0001] The present application relates to the technical field of data processing, and more specifically, to a method, device, equipment, and medium for determining the type of road surface roughness. Background Art

[0002] With the rapid development of society and the continuous improvement of people's living standards, the number of automobiles in use has increased significantly. When traveling or sightseeing, more and more people choose to drive by themselves. Therefore, driving comfort has become one of the most concerned matters for current automobile drivers. The roughness of the road surface is very important for the safety and comfort of vehicles driving on the road surface. An on-vehicle lidar can obtain three-dimensional point cloud data around the vehicle. A very important application is to detect the road condition information in front of the vehicle to judge the roughness of the road surface ahead.

[0003] In the existing methods for determining the roughness of the road surface based on point cloud data, usually the road surface reference line is obtained from the point cloud data, and the flatness is judged by the road surface reference line. However, since the gully areas on the road surface appear randomly and the depth of each gully area is not uniform, it is inaccurate to judge the roughness only by the road surface reference line. Summary of the Invention

[0004] In view of this, the purpose of the present application is to provide a method, device, equipment, and medium for determining the type of road surface roughness. By measuring the road surface lateral roughness corresponding to each layer of horizontal point cloud through the point cloud height of each layer of horizontal point cloud, and then measuring and determining the road surface roughness type corresponding to the road surface through the road surface lateral roughness corresponding to each layer of horizontal point cloud and the road surface longitudinal roughness, the accuracy of determining the road surface roughness type is improved, and thus the driving safety is improved.

[0005] In a first aspect, an embodiment of the present application provides a method for determining the type of road surface roughness. The determination method includes:

[0006] Collecting point cloud data of the road surface within a preset range in front of the vehicle through a lidar; wherein, the point cloud data includes multiple layers of horizontal point clouds, and each layer of horizontal point cloud includes multiple point clouds;

[0007] For each layer of horizontal point cloud, calculating the road surface lateral roughness corresponding to the layer of horizontal point cloud based on the height value of each point cloud in the layer of horizontal point cloud;

[0008] Calculating the road surface longitudinal roughness according to the road surface lateral roughness corresponding to each layer of horizontal point cloud;

[0009] Based on the road surface lateral roughness corresponding to each layer of horizontal point cloud and the road surface longitudinal roughness, determining the road surface roughness type corresponding to the road surface.

[0010] Further, calculating the lateral road surface roughness corresponding to the lateral point cloud of this layer based on the height value of each point cloud in this layer of lateral point cloud includes:

[0011] Determine the height value of each point cloud in this layer of lateral point cloud according to the coordinates of each point cloud in this layer of lateral point cloud in the three-dimensional coordinate system;

[0012] Determine the average height value of this layer of lateral point cloud according to the height value of each point cloud in this layer of lateral point cloud;

[0013] Calculate the height variance value of this layer of lateral point cloud according to the height value of each point cloud in this layer of lateral point cloud and the average height value of this layer of lateral point cloud, and determine the height variance value as the lateral road surface roughness.

[0014] Further, calculating the longitudinal road surface roughness according to the lateral road surface roughness corresponding to each layer of lateral point cloud includes:

[0015] Calculate the average longitudinal height by using the average height value of each layer of lateral point cloud;

[0016] Calculate the longitudinal height variance value according to the average longitudinal height and the average height value of each layer of lateral point cloud, and determine the longitudinal height variance value as the longitudinal road surface roughness.

[0017] Further, determining the road surface roughness type corresponding to the road surface based on the lateral road surface roughness corresponding to each layer of lateral point cloud and the longitudinal road surface roughness includes:

[0018] For each layer of lateral point cloud, multiply the lateral road surface roughness corresponding to this layer of lateral point cloud by the weight value corresponding to this layer of lateral point cloud to determine the roughness dimension value corresponding to each layer of lateral point cloud;

[0019] Sum up the roughness dimension values corresponding to each layer of point cloud to obtain the lateral roughness dimension value;

[0020] Determine the product of the longitudinal road surface roughness and the longitudinal weight value as the longitudinal roughness dimension value;

[0021] Add the lateral roughness dimension value and the longitudinal roughness dimension value to obtain the total road surface roughness dimension;

[0022] For each preset roughness type, determine whether the total road surface roughness dimension is within the roughness dimension range corresponding to this preset roughness type;

[0023] If so, determine this preset roughness type as the road surface roughness type corresponding to the road surface.

[0024] In a second aspect, the embodiment of the present application further provides a device for determining a road surface roughness type, and the determining device includes:

[0025] A point cloud data acquisition module, configured to collect point cloud data of a road surface within a preset range in front of the vehicle through a lidar; wherein, the point cloud data includes multiple layers of horizontal point clouds, and each layer of horizontal point clouds includes multiple point clouds;

[0026] A horizontal roughness determination module, configured to calculate the horizontal roughness of the road surface corresponding to each layer of horizontal point clouds based on the height values of each point cloud in each layer of horizontal point clouds;

[0027] A longitudinal roughness determination module, configured to calculate the longitudinal roughness of the road surface according to the horizontal roughness of the road surface corresponding to each layer of horizontal point clouds;

[0028] A road surface roughness type determination module, configured to determine the road surface roughness type corresponding to the road surface based on the horizontal roughness of the road surface corresponding to each layer of horizontal point clouds and the longitudinal roughness of the road surface.

[0029] Further, when the horizontal roughness determination module is configured to calculate the horizontal roughness of the road surface corresponding to each layer of horizontal point clouds based on the height values of each point cloud in each layer of horizontal point clouds, the road surface horizontal roughness determination module is further configured to:

[0030] Determine the height value of each point cloud in each layer of horizontal point clouds according to the coordinates of each point cloud in the three-dimensional coordinate system in each layer of horizontal point clouds;

[0031] Determine the average height value of each layer of horizontal point clouds according to the height values of each point cloud in each layer of horizontal point clouds;

[0032] Calculate the height variance value of each layer of horizontal point clouds according to the height values of each point cloud in each layer of horizontal point clouds and the average height value of each layer of horizontal point clouds, and determine the height variance value as the horizontal roughness of the road surface.

[0033] Further, when the longitudinal roughness determination module is configured to calculate the longitudinal roughness of the road surface according to the horizontal roughness of the road surface corresponding to each layer of horizontal point clouds, the road surface longitudinal roughness determination module is further configured to:

[0034] Calculate the average longitudinal height using the average height value of each layer of horizontal point clouds;

[0035] Calculate the longitudinal height variance value according to the average longitudinal height and the average height value of each layer of horizontal point clouds, and determine the longitudinal height variance value as the longitudinal roughness of the road surface.

[0036] Further, when the road surface roughness type determination module is configured to determine the road surface roughness type corresponding to the road surface based on the horizontal roughness of the road surface corresponding to each layer of horizontal point clouds and the longitudinal roughness of the road surface, the road surface roughness type determination module is further configured to:

[0037] For each layer of horizontal point cloud, multiply the road surface horizontal ruggedness corresponding to the layer of horizontal point cloud by the weight corresponding to the layer of horizontal point cloud to determine the ruggedness dimension value corresponding to each layer of horizontal point cloud;

[0038] Sum up the ruggedness dimension values corresponding to each layer of point cloud to obtain the horizontal ruggedness dimension value;

[0039] Determine the longitudinal ruggedness dimension value as the product of the road surface longitudinal ruggedness and the longitudinal weight;

[0040] Add the horizontal ruggedness dimension value and the longitudinal ruggedness dimension value to obtain the total road surface ruggedness dimension;

[0041] For each preset rugged type, determine whether the total road surface ruggedness dimension is within the ruggedness dimension range corresponding to the preset rugged type;

[0042] If so, determine the preset rugged type as the road surface rugged type corresponding to the road surface.

[0043] In a third aspect, an embodiment of the present application further provides an electronic device, including: a processor, a memory, and a bus. The memory stores machine-readable instructions executable by the processor. When the electronic device runs, the processor communicates with the memory through the bus. When the machine-readable instructions are executed by the processor, the steps of the method for determining the road surface rugged type as described above are executed.

[0044] In a fourth aspect, an embodiment of the present application further provides a computer-readable storage medium. A computer program is stored on the computer-readable storage medium. When the computer program is run by a processor, the steps of the method for determining the road surface rugged type as described above are executed.

[0045] The method, device, equipment, and medium for determining the road surface rugged type provided by the embodiments of the present application. The determination method includes: First, collect point cloud data of the road surface within a preset range in front of the vehicle through a lidar; wherein, the point cloud data includes multiple layers of horizontal point clouds, and each layer of horizontal point cloud includes multiple point clouds; Then, for each layer of horizontal point cloud, calculate the road surface horizontal ruggedness corresponding to the layer of horizontal point cloud based on the height value of each point cloud in the layer of horizontal point cloud; Calculate the road surface longitudinal ruggedness according to the road surface horizontal ruggedness corresponding to each layer of horizontal point cloud; Finally, based on the road surface horizontal ruggedness corresponding to each layer of horizontal point cloud and the road surface longitudinal ruggedness, determine the road surface rugged type corresponding to the road surface.

[0046] The method for determining the type of road surface roughness provided by this application measures the lateral roughness of the road surface corresponding to each layer of lateral point cloud through the point cloud height of each layer of lateral point cloud, and then determines the type of road surface roughness corresponding to the road surface through the lateral roughness of the road surface corresponding to each layer of lateral point cloud and the longitudinal roughness of the road surface, improving the accuracy of determining the type of road surface roughness and thus enhancing driving safety.

[0047] To make the above objects, features, and advantages of this application more obvious and understandable, the following specifically gives preferred embodiments and, in conjunction with the accompanying drawings, makes a detailed description as follows. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] To more clearly illustrate the technical solutions of the embodiments of this application, the following will briefly introduce the drawings required to be used in the embodiments. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as limiting the scope. For those of ordinary skill in the art, other relevant drawings can also be obtained based on these drawings without creative efforts.

[0049] Figure 1 It is a flowchart of a method for determining the type of road surface roughness provided by an embodiment of this application;

[0050] Figure 2 It is a schematic diagram of point cloud data within a preset range provided by an embodiment of this application;

[0051] Figure 3 It is a schematic structural diagram of a device for determining the type of road surface roughness provided by an embodiment of this application;

[0052] Figure 4 It is a schematic structural diagram of an electronic device provided by an embodiment of this application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0053] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the following will clearly and completely describe the technical solutions in the embodiments of this application in conjunction with the drawings in the embodiments of this application. Obviously, the described embodiments are only some, rather than all, of the embodiments of this application. Usually, the components of the embodiments of this application described and shown in the drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the drawings is not intended to limit the scope of the claimed application, but merely represents selected embodiments of this application. Based on the embodiments of this application, every other embodiment obtained by those of ordinary skill in the art without creative efforts falls within the scope of protection of this application.

[0054] First, an application scenario applicable to this application is introduced. This application can be applied to the field of data processing technology.

[0055] With the rapid development of society and the continuous improvement of people's living standards, the number of privately-owned cars has increased significantly. When traveling or sightseeing, more and more people choose to drive by themselves. Therefore, driving comfort has become one of the most concerned matters for current car drivers. The roughness of the road surface is very important for the safety and comfort of vehicles driving on the road surface. On-vehicle lidar can obtain the three-dimensional point cloud data around the vehicle. A very important application is to detect the road condition information in front of the vehicle to judge the roughness of the road surface ahead.

[0056] It has been found through research that in the existing methods for determining the roughness of the road surface based on point cloud data, the road surface reference line is often obtained according to the point cloud data, and the flatness is judged through the road surface reference line. However, since the gully areas on the road surface appear randomly and the depth of each gully area is not uniform, it is inaccurate to judge the roughness only through the road surface reference line.

[0057] Based on this, the embodiments of the present application provide a method for determining the type of road surface roughness to improve the accuracy of determining the type of road surface roughness and improve driving safety.

[0058] Please refer to Figure 1 , Figure 1 which is a flowchart of a method for determining the type of road surface roughness provided by the embodiments of the present application. As Figure 1 shown in it, the determination method provided by the embodiments of the present application includes:

[0059] S101, collecting the point cloud data of the road surface within a preset range in front of the vehicle through a lidar.

[0060] Here, the lidar can be set at the front end of the vehicle to perform laser scanning on the road surface within a preset range that the vehicle is about to pass through in front, and obtain the lidar point cloud data of the road surface. The preset range can be the scanning range of the lidar or a range set according to the actual situation. For example, it is preset that the preset range is from 8 meters to 50 meters in front of the vehicle in the longitudinal range and the range of the vehicle width plus 1 meter in the lateral range. The present application does not make specific limitations on this. The obtained point cloud data includes multiple layers of horizontal point clouds, and each layer of horizontal point clouds includes multiple point clouds. Here, the point cloud data of the road surface can be obtained through a multi-line lidar. Assuming that the lidar has n scanning lines, each line can obtain a layer of horizontal point clouds, and each layer of horizontal point clouds includes multiple point clouds.

[0061] For the above step S101, in specific implementation, the point cloud data of the road surface within a preset range in front of the vehicle is collected through a lidar. Each point cloud in the point cloud data contains three-dimensional coordinate information, and can also include detection distance, reflection intensity, azimuth angle, etc.

[0062] Please refer toFigure 2 , Figure 2 is a schematic diagram of point cloud data within a preset range provided by an embodiment of this application. As shown in Figure 2 , the point cloud data includes M + 1 layers of horizontal point clouds, namely Layer0 to LayerM, and each layer of point clouds includes X point clouds. The point cloud data of the M layers of horizontal point clouds collected by the lidar are respectively:

[0063] Layer0: P01, P02,..., P0X;

[0064] Layer1: P11, P12,..., P1X;

[0065] LayerN: PN1, PN2,..., PNX;

[0066] ...

[0067] LayerM: PM1, PM2,..., PMX.

[0068] S102. For each layer of horizontal point clouds, calculate the road surface lateral roughness corresponding to the layer of horizontal point clouds based on the height value of each point cloud in the layer of horizontal point clouds.

[0069] It should be noted that the height value of the point cloud refers to the coordinate value of the point cloud in the Z-axis direction in the three-dimensional coordinate system. Here, the three-dimensional coordinate system takes the position where the lidar is located as the coordinate origin, the direction perpendicular to the ground is the Z-axis, the vehicle forward direction is the X-axis, and the direction perpendicular to the vehicle forward direction is the Y-axis. The road surface lateral roughness refers to the roughness characterized by the height value of each point cloud in each layer of horizontal point clouds.

[0070] For the above step S102, in specific implementation, for each layer of horizontal point clouds in the point cloud data, calculate the road surface lateral roughness corresponding to the layer of horizontal point clouds according to the height value of each point cloud in the layer of horizontal point clouds.

[0071] Specifically, for the above step S102, the calculating the road surface lateral roughness corresponding to the layer of horizontal point clouds based on the height value of each point cloud in the layer of horizontal point clouds includes:

[0072] Step 1021. Determine the height value of each point cloud in the layer of horizontal point clouds according to the coordinates of each point cloud in the layer of horizontal point clouds in the three-dimensional coordinate system.

[0073] For the above step 1021, in specific implementation, first obtain the coordinates of each point cloud in the layer of horizontal point clouds in the three-dimensional coordinate system, and then determine the height value of each point cloud in the layer of horizontal point clouds according to the coordinates of each point cloud in the three-dimensional coordinate system. Specifically, take the coordinate value of the point cloud in the Z-axis direction in the three-dimensional coordinate system as the height value of the point cloud.

[0074] Step 1022: Determine the average height value of the horizontal point cloud of this layer according to the height value of each point cloud in the horizontal point cloud of this layer.

[0075] For the above-mentioned step 1022, in specific implementation, determine the average height value of the horizontal point cloud of this layer according to the height value of each point cloud in the horizontal point cloud of this layer.

[0076] Continuing Figure 2 from the embodiments in

[0077] Layer0: Z01, Z02... Z0X

[0078] Layer1: Z11, Z12... Z1X

[0079] LayerN: ZN1, ZN2... ZNX;

[0080] …

[0081] LayerM: ZM1, ZM2,..., ZMX.

[0082] Specifically, for the above-mentioned step 1022, determine the average height value of the horizontal point cloud of this layer through the following formula (1):

[0083] A N = (ZN1 + ZN2 +... + ZNX) / X (1);

[0084] where A N represents the average height value of the Nth layer of horizontal point cloud, X represents the number of point clouds in the Nth layer of horizontal point cloud, ZN1 represents the height value of the 1st point cloud in the Nth layer of horizontal point cloud, and ZNX represents the height value of the Xth point cloud in the Nth layer of horizontal point cloud.

[0085] Step 1023: Calculate the height variance value of the horizontal point cloud of this layer according to the height value of each point cloud in the horizontal point cloud of this layer and the average height value of the horizontal point cloud of this layer, and determine the height variance value as the lateral roughness of the road surface.

[0086] For the above-mentioned step 1023, in specific implementation, after the average height value of the horizontal point cloud of this layer is determined, calculate the height variance value of the horizontal point cloud of this layer according to the height value of each point cloud in the horizontal point cloud of this layer and the average height value of the horizontal point cloud of this layer, and determine the obtained height variance value as the lateral roughness of the road surface corresponding to the horizontal point cloud of this layer. Since variance is a measure of the degree of dispersion when measuring a random variable or a set of data in probability theory and statistical variance, the degree of dispersion of the ground height can be measured by the variance value of the height values of the point cloud in the Z-axis direction in the three-dimensional coordinate system.

[0087] Specifically, for step 1023 above, the height variance value of the horizontal point cloud of this layer is calculated through the following formula (2):

[0088]

[0089] where δ N represents the height variance value of the Nth layer of horizontal point cloud.

[0090] S103. Calculate the longitudinal roughness of the road surface according to the transverse roughness of the road surface corresponding to each layer of horizontal point cloud.

[0091] For step S103 above, in specific implementation, after calculating the transverse roughness of the road surface corresponding to each layer of horizontal point cloud in step S102, calculate the longitudinal roughness of the road surface according to the transverse roughness of the road surface corresponding to each layer of horizontal point cloud.

[0092] Specifically, for step S103 above, the calculation of the longitudinal roughness of the road surface according to the transverse roughness of the road surface corresponding to each layer of horizontal point cloud includes:

[0093] Step 1031. Calculate the longitudinal height average value by using the height average value of each layer of horizontal point cloud.

[0094] For step 1031 above, in specific implementation, since the height average value of each layer of horizontal point cloud has been calculated in step 1022 above, here, calculate the longitudinal height average value by using the height average value of each layer of horizontal point cloud.

[0095] Specifically, calculate the longitudinal height average value through the following formula (3).

[0096] A = (A 0 + A 1 + … + A M ) / (M + 1) (3);

[0097] where A represents the longitudinal height average value, A 0 represents the height average value of the first layer of horizontal point cloud, and A M represents the height average value of the (M + 1)th layer of horizontal point cloud.

[0098] Step 1032. Calculate the longitudinal height variance value according to the longitudinal height average value and the height average value of each layer of horizontal point cloud, and determine the longitudinal height variance value as the longitudinal roughness of the road surface.

[0099] For step 1032 above, in specific implementation, after determining the longitudinal height average value, calculate the longitudinal height variance value according to the longitudinal height average value and the height average value of each layer of horizontal point cloud, and determine the obtained longitudinal height variance value as the longitudinal roughness of the road surface.

[0100] Specifically, for step 1023 above, the height variance value of the horizontal point cloud of this layer is calculated through the following formula (4):

[0101]

[0102] where δ Z represents the longitudinal height variance value.

[0103] S104. Based on the lateral road surface roughness corresponding to each layer of horizontal point cloud and the longitudinal road surface roughness, determine the road surface roughness type corresponding to the road surface.

[0104] For the above step S104, in specific implementation, after calculating the lateral road surface roughness and the longitudinal road surface roughness corresponding to each layer of horizontal point cloud, based on the lateral road surface roughness corresponding to each layer of horizontal point cloud and the longitudinal road surface roughness, determine the road surface roughness type corresponding to the road surface. Here, according to the embodiments provided in the present application, the road surface types are classified according to the variance value results of the point cloud height, and the road surface roughness types can be flat road surface, low roughness, medium roughness, and high roughness.

[0105] Specifically, for the above step S104, the determining of the road surface roughness type corresponding to the road surface based on the lateral road surface roughness corresponding to each layer of horizontal point cloud and the longitudinal road surface roughness includes:

[0106] Step 1041. For each layer of horizontal point cloud, multiply the lateral road surface roughness corresponding to this layer of horizontal point cloud by the weight value corresponding to this layer of horizontal point cloud to determine the roughness dimension value corresponding to each layer of horizontal point cloud.

[0107] Here, since there are M + 1 layers of horizontal point cloud in the lateral dimension, the weight value corresponding to each layer of horizontal point cloud is 50% / (M + 1), and the longitudinal weight is 50%.

[0108] For the above step 1041, in specific implementation, for each layer of horizontal point cloud, multiply the lateral road surface roughness corresponding to this layer of horizontal point cloud by the weight value corresponding to this layer of horizontal point cloud to determine the roughness dimension value corresponding to each layer of horizontal point cloud.

[0109] Step 1042. Sum the roughness dimension values corresponding to each layer of point cloud to obtain the lateral roughness dimension value.

[0110] For the above step 1042, in specific implementation, after determining the roughness dimension values corresponding to each layer of horizontal point cloud, sum the roughness dimension values corresponding to each layer of point cloud to obtain the lateral roughness dimension value.

[0111] Step 1043. Determine the longitudinal roughness dimension value as the product of the longitudinal road surface roughness and the longitudinal weight value.

[0112] For the above-mentioned step 1043, in specific implementation, the product of the longitudinal road surface roughness and the longitudinal weight value is determined as the longitudinal roughness dimension value. Here, 50% of the longitudinal road surface roughness is the longitudinal roughness dimension value.

[0113] Step 1044, add the transverse roughness dimension value and the longitudinal roughness dimension value to obtain the total road surface roughness dimension δ of the road surface.

[0114] For the above-mentioned step 1044, in specific implementation, add the obtained transverse roughness dimension value and the obtained longitudinal roughness dimension value to determine the total road surface roughness dimension of the road surface.

[0115] Step 1045, for each preset roughness type, determine whether the total road surface roughness dimension is within the roughness dimension range corresponding to the preset roughness type.

[0116] Step 1046, if so, determine the preset roughness type as the road surface roughness type corresponding to the road surface.

[0117] Here, the preset roughness types may include flat road surface, low roughness, medium roughness, and high roughness. The roughness dimension range refers to the typical variance data value ranges of each different road surface type obtained by calibrating the data of the real road surface under each preset roughness type. As an example, the roughness dimension range of the flat road surface is 0 < δ ≤ T1, the roughness dimension range of the low roughness is T1 < δ ≤ T2, the roughness dimension range of the medium roughness is T2 < δ ≤ T3, and the roughness dimension range of the high roughness is T3 < δ.

[0118] For the above-mentioned steps 1045 - 1046, in specific implementation, for each preset roughness type, determine whether the total road surface roughness dimension is within the roughness dimension range corresponding to the preset roughness type. If not, it is considered that the road surface roughness type corresponding to the current road surface does not belong to the preset roughness type. If so, execute the above-mentioned step 1046, and determine the preset roughness type as the road surface roughness type corresponding to the road surface.

[0119] The method for determining the road surface roughness type provided by the embodiment of the present application, first, collect the point cloud data of the road surface within a preset range in front of the vehicle through a lidar; wherein, the point cloud data includes multiple layers of transverse point clouds, and each layer of transverse point clouds includes multiple point clouds; then, for each layer of transverse point clouds, calculate the transverse road surface roughness corresponding to the layer of transverse point clouds based on the height value of each point cloud in the layer of transverse point clouds; calculate the longitudinal road surface roughness according to the transverse road surface roughness corresponding to each layer of transverse point clouds; finally, based on the transverse road surface roughness corresponding to each layer of transverse point clouds and the longitudinal road surface roughness, determine the road surface roughness type corresponding to the road surface.

[0120] When the road surface is uneven, the height values of the point cloud data reflected by the ground will fluctuate significantly. Therefore, in this application, the lateral roughness of the road surface corresponding to each layer of lateral point cloud is measured by the point cloud height of each layer of lateral point cloud, and then the road surface roughness type corresponding to the road surface is determined by the lateral roughness of the road surface corresponding to each layer of lateral point cloud and the longitudinal roughness of the road surface, which improves the accuracy of determining the road surface roughness type and further improves driving safety.

[0121] Please refer to Figure 3 , Figure 3 which is a schematic structural diagram of a device for determining the road surface roughness type provided by an embodiment of this application. As Figure 3 shown in

[0122] The point cloud data acquisition module 301 is configured to acquire the point cloud data of the road surface within a preset range in front of the vehicle through a lidar; wherein, the point cloud data includes multiple layers of lateral point clouds, and each layer of lateral point clouds includes multiple point clouds;

[0123] The lateral roughness determination module 302 is configured to calculate the lateral roughness of the road surface corresponding to each layer of lateral point cloud based on the height value of each point cloud in the layer of lateral point cloud;

[0124] The longitudinal roughness determination module 303 is configured to calculate the longitudinal roughness of the road surface according to the lateral roughness of the road surface corresponding to each layer of lateral point cloud;

[0125] The road surface roughness type determination module 304 is configured to determine the road surface roughness type corresponding to the road surface based on the lateral roughness of the road surface corresponding to each layer of lateral point cloud and the longitudinal roughness of the road surface.

[0126] Further, when the lateral roughness determination module 302 is configured to calculate the lateral roughness of the road surface corresponding to each layer of lateral point cloud based on the height value of each point cloud in the layer of lateral point cloud, the lateral roughness determination module 302 is further configured to:

[0127] Determine the height value of each point cloud in the layer of lateral point cloud according to the coordinates of each point cloud in the layer of lateral point cloud in the three-dimensional coordinate system;

[0128] Determine the average height value of the layer of lateral point cloud according to the height value of each point cloud in the layer of lateral point cloud;

[0129] Calculate the height variance value of the layer of lateral point cloud according to the height value of each point cloud in the layer of lateral point cloud and the average height value of the layer of lateral point cloud, and determine the height variance value as the lateral roughness of the road surface.

[0130] Further, when the longitudinal roughness determination module 303 is used to calculate the longitudinal roughness of the road surface according to the transverse roughness of the road surface corresponding to each layer of transverse point clouds, the longitudinal roughness determination module 303 is further used for:

[0131] Calculating the average longitudinal height by using the average height of each layer of transverse point clouds;

[0132] Calculating the longitudinal height variance value according to the average longitudinal height and the average height of each layer of transverse point clouds, and determining the longitudinal height variance value as the longitudinal roughness of the road surface.

[0133] Further, when the road surface roughness type determination module 304 is used to determine the road surface roughness type corresponding to the road surface based on the transverse roughness of the road surface corresponding to each layer of transverse point clouds and the longitudinal roughness of the road surface, the road surface roughness type determination module 304 is further used for:

[0134] For each layer of transverse point clouds, multiplying the transverse roughness of the road surface corresponding to this layer of transverse point clouds by the weight corresponding to this layer of transverse point clouds to determine the roughness dimension value corresponding to each layer of transverse point clouds;

[0135] Summing the roughness dimension values corresponding to each layer of point clouds to obtain the transverse roughness dimension value;

[0136] Determining the product of the longitudinal roughness of the road surface and the longitudinal weight as the longitudinal roughness dimension value;

[0137] Adding the transverse roughness dimension value and the longitudinal roughness dimension value to obtain the total roughness dimension of the road surface;

[0138] For each preset roughness type, determining whether the total roughness dimension of the road surface is within the roughness dimension range corresponding to the preset roughness type;

[0139] If so, determining the preset roughness type as the road surface roughness type corresponding to the road surface.

[0140] Please refer to Figure 4 , Figure 4 which is a schematic structural diagram of an electronic device provided by an embodiment of the present application. As Figure 4 shown in

[0141] The memory 420 stores machine-readable instructions executable by the processor 410. When the electronic device 400 runs, the processor 410 communicates with the memory 420 through the bus 430. When the machine-readable instructions are executed by the processor 410, they can execute as described above Figure 1The steps of the method for determining the type of rough road surface in the method embodiment shown can be specifically implemented by referring to the method embodiment, which will not be elaborated here.

[0142] The embodiments of the present application also provide a computer-readable storage medium, on which a computer program is stored. When the computer program is run by a processor, it can execute the steps of the method for determining the type of rough road surface in the method embodiment shown above. Figure 1 The steps of the method for determining the type of rough road surface in the method embodiment shown can be specifically implemented by referring to the method embodiment, which will not be elaborated here.

[0143] Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the above-described systems, devices, and units can refer to the corresponding processes in the foregoing method embodiments, which will not be elaborated here.

[0144] In the several embodiments provided by the present application, it should be understood that the disclosed systems, devices, and methods can be implemented in other ways. The device embodiments described above are merely illustrative. For example, the division of the units is only a logical function division, and there may be other division methods in actual implementation. For another example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed couplings or direct couplings or communication connections to each other can be through some communication interfaces. The indirect couplings or communication connections of the devices or units can be in electrical, mechanical, or other forms.

[0145] The units described as separate components may or may not be physically separated. The components displayed as units may or may not be physical units, that is, they can be located in one place or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0146] In addition, in each embodiment of the present application, the functional units can be integrated into one processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit.

[0147] When the above-mentioned functions are implemented in the form of software function units and sold or used as independent products, they can be stored in a non-volatile computer-readable storage medium executable by a processor. Based on such understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of this application. The aforementioned storage medium includes: various media that can store program codes, such as USB flash drives, mobile hard disks, read-only memories (ROM), random access memories (RAM), magnetic disks, or optical discs.

[0148] It should be noted that: similar reference numerals and letters represent similar items in the following drawings. Therefore, once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings. In addition, the terms "first", "second", "third", etc. are only used for descriptive distinction and cannot be understood as indicating or implying relative importance.

[0149] Finally, it should be noted that: the above-mentioned embodiments are only specific implementation manners of this application, used to illustrate the technical solution of this application, rather than limiting it. The protection scope of this application is not limited thereto. Although this application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: any person skilled in the art within the technical scope disclosed in this application can still modify the technical solutions recorded in the foregoing embodiments, or can easily think of changes, or make equivalent replacements for some of the technical features; and these modifications, changes, or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be covered within the protection scope of this application. Therefore, the protection scope of this application should be subject to the protection scope of the claims.

Claims

1. A method for determining the type of road surface roughness, characterized in that, the determination method includes: collecting point cloud data of the road surface within a preset range in front of the vehicle through a lidar; wherein, the point cloud data includes multiple layers of horizontal point clouds, and each layer of horizontal point clouds includes multiple point clouds; for each layer of horizontal point clouds, calculating the road surface lateral roughness corresponding to the layer of horizontal point clouds based on the height values of each point cloud in the layer of horizontal point clouds; calculating the road surface longitudinal roughness according to the road surface lateral roughness corresponding to each layer of horizontal point clouds; determining the road surface roughness type corresponding to the road surface based on the road surface lateral roughness corresponding to each layer of horizontal point clouds and the road surface longitudinal roughness; the calculating the road surface lateral roughness corresponding to the layer of horizontal point clouds based on the height values of each point cloud in the layer of horizontal point clouds includes: determining the height value of each point cloud in the layer of horizontal point clouds according to the coordinates of each point cloud in the three-dimensional coordinate system in the layer of horizontal point clouds; determining the average height value of the layer of horizontal point clouds according to the height values of each point cloud in the layer of horizontal point clouds; calculating the height variance value of the layer of horizontal point clouds according to the height values of each point cloud in the layer of horizontal point clouds and the average height value of the layer of horizontal point clouds, and determining the height variance value as the road surface lateral roughness; the determining the road surface roughness type corresponding to the road surface based on the road surface lateral roughness corresponding to each layer of horizontal point clouds and the road surface longitudinal roughness includes: for each layer of horizontal point clouds, multiplying the road surface lateral roughness corresponding to the layer of horizontal point clouds by the weight value corresponding to the layer of horizontal point clouds to determine the roughness dimension value corresponding to each layer of horizontal point clouds; summing the roughness dimension values corresponding to each layer of point clouds to obtain the lateral roughness dimension value; determining the longitudinal roughness dimension value as the product of the road surface longitudinal roughness and the longitudinal weight value; adding the lateral roughness dimension value and the longitudinal roughness dimension value to obtain the total road surface roughness dimension; for each preset roughness type, determining whether the total road surface roughness dimension is within the roughness dimension range corresponding to the preset roughness type; if so, determining the preset roughness type as the road surface roughness type corresponding to the road surface.

2. The determination method according to claim 1, characterized in that, the calculating the road surface longitudinal roughness according to the road surface lateral roughness corresponding to each layer of horizontal point clouds includes: calculating the average longitudinal height using the average height value of each layer of horizontal point clouds; calculating the longitudinal height variance value according to the average longitudinal height and the average height value of each layer of horizontal point clouds, and determining the longitudinal height variance value as the road surface longitudinal roughness.

3. A device for determining the type of road surface roughness, characterized in that, the determination device includes: a point cloud data acquisition module, configured to collect point cloud data of the road surface within a preset range in front of the vehicle through a lidar; wherein, the point cloud data includes multiple layers of horizontal point clouds, and each layer of horizontal point clouds includes multiple point clouds; a lateral roughness determination module, configured to calculate the road surface lateral roughness corresponding to each layer of horizontal point clouds based on the height values of each point cloud in the layer of horizontal point clouds; A longitudinal roughness determination module, configured to calculate the longitudinal roughness of the road surface according to the transverse roughness of the road surface corresponding to each layer of transverse point cloud; A road surface roughness type determination module, configured to determine the road surface roughness type corresponding to the road surface based on the transverse roughness of the road surface corresponding to each layer of transverse point cloud and the longitudinal roughness of the road surface; When the transverse roughness determination module is configured to calculate the transverse roughness of the road surface corresponding to each layer of transverse point cloud based on the height value of each point cloud in the layer of transverse point cloud, the transverse roughness determination module is further configured to: Determine the height value of each point cloud in the layer of transverse point cloud according to the coordinates of each point cloud in the layer of transverse point cloud in the three-dimensional coordinate system; Determine the average height value of the layer of transverse point cloud according to the height value of each point cloud in the layer of transverse point cloud; Calculate the height variance value of the layer of transverse point cloud according to the height value of each point cloud in the layer of transverse point cloud and the average height value of the layer of transverse point cloud, and determine the height variance value as the transverse roughness of the road surface; When the road surface roughness type determination module is configured to determine the road surface roughness type corresponding to the road surface based on the transverse roughness of the road surface corresponding to each layer of transverse point cloud and the longitudinal roughness of the road surface, the road surface roughness type determination module is further configured to: For each layer of transverse point cloud, multiply the transverse roughness of the road surface corresponding to the layer of transverse point cloud by the weight value corresponding to the layer of transverse point cloud to determine the roughness dimension value corresponding to each layer of transverse point cloud; Sum the roughness dimension values corresponding to each layer of point cloud to obtain the transverse roughness dimension value; Determine the product of the longitudinal roughness of the road surface and the longitudinal weight value as the longitudinal roughness dimension value; Add the transverse roughness dimension value and the longitudinal roughness dimension value to obtain the total roughness dimension of the road surface; For each preset roughness type, determine whether the total roughness dimension of the road surface is within the roughness dimension range corresponding to the preset roughness type; If so, determine the preset roughness type as the road surface roughness type corresponding to the road surface.

4. The determination device according to claim 3, wherein, When the longitudinal roughness determination module is configured to calculate the longitudinal roughness of the road surface according to the transverse roughness of the road surface corresponding to each layer of transverse point cloud, the longitudinal roughness determination module is further configured to: Calculate the average longitudinal height using the average height value of each layer of transverse point cloud; Calculate the longitudinal height variance value according to the average longitudinal height and the average height value of each layer of transverse point cloud, and determine the longitudinal height variance value as the longitudinal roughness of the road surface.

5. An electronic device, wherein, comprises: A processor, a memory and a bus, the memory stores machine-readable instructions executable by the processor, when the electronic device runs, the processor communicates with the memory through the bus, and when the machine-readable instructions are run by the processor, the steps of the method for determining the road surface roughness type according to any one of claims 1 to 2 are executed.

6. A computer-readable storage medium, wherein, A computer program is stored on the computer-readable storage medium, and when the computer program is run by a processor, it executes the steps of the method for determining the type of road surface roughness according to any one of claims 1 to 2.

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