Road elevation detection method, device and equipment and storage medium

Abstract

The embodiment of the invention relates to the technical field of pavement elevation detection, and discloses a pavement elevation detection method, device and equipment and a storage medium, the method comprises the following steps: obtaining point cloud data of a target pavement area, the point cloud data comprising laser radar point cloud data and millimeter wave radar point cloud data; dividing the target pavement area into a plurality of grid units; based on the point cloud data, carrying out statistics on elevation information of the point cloud in each grid unit, and based on the elevation information, determining a representative elevation of each grid unit; and a tire contact area is extracted from the target road surface area, and the road surface elevation of the current driving position of the vehicle is determined based on the representative elevations of the grid units in the tire contact area. By applying the technical scheme of the invention, the problems of low pavement elevation sensing precision and poor reliability under extreme weather conditions can be solved.

Description

TECHNICAL FIELD

[0001] Embodiments of the present application relate to the technical field of road surface elevation detection, and in particular to a road surface elevation detection method, device, equipment and storage medium. BACKGROUND

[0002] At present, road surface elevation detection is performed based on a binocular vision sensor. In the road surface elevation detection method, road image data collected by the binocular vision sensor is taken as input, elevation information of a current tire-rolled road surface is obtained through pixel depth calculation, and multi-source data fusion processing of a steering wheel angle, a wheel speed sensor and an inertial measurement unit (IMU) is combined to output predicted elevation information of a future tire-rolled path in real time, which is particularly suitable for complex road condition scenarios including speed bumps, manhole covers, road concave-convex and slope changes, so as to trigger chassis control strategy in advance to optimize the driving experience.

[0003] However, actual test verification shows that the binocular detection method has significant technical limitations: the detection performance is affected by changes in light conditions and extreme weather (such as strong light, heavy rain, and haze), which easily leads to failure of the binocular vision sensor; the effective detection range is limited by the baseline parameters of the binocular vision sensor, and usually only covers an area of 10-15 meters in front of the vehicle, and the road surface elevation detection accuracy is actually measured to be ±8 cm, with an error exceeding the expectation. SUMMARY

[0004] In view of the above problems, embodiments of the present application provide a road surface elevation detection method, device, equipment and storage medium to solve the problems of low road surface elevation perception accuracy and poor reliability in extreme weather conditions in the prior art.

[0005] According to an aspect of an embodiment of the present application, a road surface elevation detection method is provided, which comprises:

[0006] Obtaining point cloud data of a target road surface area, the point cloud data including laser radar point cloud data and millimeter wave radar point cloud data;

[0007] Dividing the target road surface area into a plurality of grid cells;

[0008] Based on the point cloud data, the elevation information of the point cloud in each grid cell is counted, and based on the elevation information, the representative elevation of each grid cell is determined;

[0009] Extracting a tire contact area from the target road surface area, and determining the road surface elevation of the current driving position of the vehicle based on the representative elevation of the grid cell in the tire contact area.

[0010] According to another aspect of an embodiment of the present application, a road surface elevation detection device is provided, which comprises:

[0011] An acquisition module is configured to acquire point cloud data of a target road surface region, the point cloud data including laser radar point cloud data and millimeter wave radar point cloud data;

[0012] A division module is configured to divide the target road surface region into a plurality of grid cells.

[0013] A first determination module is configured to count elevation information of the point cloud in each grid cell based on the point cloud data, and determine a representative elevation of each grid cell based on the elevation information.

[0014] A second determination module is configured to extract a tire contact region from the target road surface region, and determine a road surface elevation of a current driving position of the vehicle based on the representative elevation of the grid cell in the tire contact region.

[0015] According to another aspect of the embodiments of the present application, a road surface elevation detection device is provided, comprising:

[0016] A processor, a memory, a communication interface and a communication bus, the processor, the memory and the communication interface complete communication with each other through the communication bus.

[0017] The memory is configured to store at least one executable instruction, and the executable instruction causes the processor to perform the operations of the road surface elevation detection method as described above.

[0018] According to still another aspect of the embodiments of the present application, a computer readable storage medium is provided, the storage medium stores at least one executable instruction, and the executable instruction causes the road surface elevation detection device / apparatus to perform the operations of the road surface elevation detection method as described above.

[0019] The embodiments of the present application solve the problems of low road surface elevation perception accuracy and poor reliability in extreme weather conditions by using point cloud data of laser radar and millimeter wave radar. Laser radar provides high-precision and high-density point cloud data, while millimeter wave radar can still work stably in extreme weather environment. The fusion of the data of the two can generate high-precision and dense road surface point cloud data in any weather condition, thereby ensuring the all-weather working ability of the perception system. In addition, the representative elevation of the grid cell in the tire contact region is used as the road surface elevation true value of the current position of the vehicle, which provides more accurate road surface elevation data and helps to establish a more accurate vehicle dynamics model, which is very important for vehicle stability control, rollover prevention and other safety functions.

[0020] The above description is only a summary of the technical solutions of the embodiments of the present application, in order to more clearly understand the technical means of the embodiments of the present application, the embodiments of the present application can be implemented according to the content of the specification, and in order to make the above and other purposes, features and advantages of the embodiments of the present application more obvious and easy to understand, the specific embodiments of the present application are described below. Attached Figure Description

[0021] The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings:

[0022] Figure 1 A flowchart illustrating a first embodiment of the road surface elevation detection method provided by the present invention is shown.

[0023] Figure 2 A flowchart illustrating a second embodiment of the road surface elevation detection method provided by the present invention is shown.

[0024] Figure 3 A schematic diagram of the tire contact area provided by the present invention is shown;

[0025] Figure 4 A schematic diagram of the trajectory point elevation sequence provided by the present invention is shown;

[0026] Figure 5 A flowchart of the road surface elevation detection method provided by the present invention is shown;

[0027] Figure 6 A schematic diagram of an embodiment of the road surface elevation detection device provided by the present invention is shown;

[0028] Figure 7 A schematic diagram of an embodiment of the road surface elevation detection device provided by the present invention is shown;

[0029] Figure 8 A structural schematic diagram of an embodiment of the vehicle provided by the present invention is shown. Detailed Implementation

[0030] Exemplary embodiments of the invention will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the invention are shown in the drawings, it should be understood that the invention can be implemented in various forms and should not be limited to the embodiments set forth herein.

[0031] Figure 1 A flowchart of a first embodiment of the road surface elevation detection method of the present invention is shown, which can be executed by a domain controller in a vehicle, such as a chassis domain controller. Figure 1 As shown, the method includes the following steps:

[0032] Step 110: Obtain point cloud data of the target road surface area. The point cloud data includes lidar point cloud data and millimeter-wave radar point cloud data.

[0033] The point cloud data of the target road surface area is actually three-dimensional space information of the road surface in front of or below the vehicle collected by the vehicle-mounted sensor. Among them, the laser radar point cloud data provides high-precision and high-density road surface details, but the performance may decrease in bad weather such as rain, snow and fog; the millimeter wave radar point cloud data can still maintain stable detection in extreme weather, and after fusion, complete and reliable road surface three-dimensional point cloud can be obtained under any weather conditions, providing basic data for subsequent grid division and height calculation. The millimeter wave radar point cloud data can be specifically 4D millimeter wave radar point cloud data.

[0034] Step 120: dividing the target road surface area into a plurality of grid units.

[0035] In this step, the target road surface area can be divided into regular grid units according to a fixed side length, and each grid unit serves as an independent height statistical unit.

[0036] Step 130: based on the point cloud data, the height information of the point cloud in each grid unit is counted, and based on the height information, the representative height of each grid unit is determined.

[0037] In this step, only one height value (representative height) is retained for each grid unit, which not only compresses the data but also reflects the overall road surface undulation, which is used for subsequent tire contact area sampling and suspension control.

[0038] Step 140: extracting the tire contact area from the target road surface area, and determining the road surface height of the current driving position of the vehicle based on the representative height of the grid unit in the tire contact area.

[0039] By intercepting the part of the area actually contacted by the tire and the road surface from the target road surface area, and then using the representative height of each grid unit in the area to determine the road surface height true value of the current position of the vehicle, the error introduced by the rut outside the invalid area is avoided, and a centimeter-level height reference is provided for suspension control.

[0040] The embodiment of the present application solves the problems of low road surface height perception accuracy and poor reliability in extreme weather conditions by using the point cloud data of laser radar and millimeter wave radar. Laser radar provides high-precision and high-density point cloud data, while millimeter wave radar can still work stably in extreme weather environment. The fusion of the data of the two can generate high-precision and dense road surface point cloud data in any weather conditions, thereby ensuring the all-weather working ability of the perception system. In addition, the representative height of the grid unit in the tire contact area is used as the road surface height true value of the current position of the vehicle, which provides more accurate road surface height data and helps to establish a more accurate vehicle dynamics model, which is very important for vehicle stability control, anti-rollover and other safety functions.

[0041] Figure 2A flow chart of another embodiment of the road surface elevation detection method of the present application is shown, which can be performed by a domain controller in the vehicle, for example, a chassis domain controller. As shown in Figure 2 The method comprises the following steps:

[0042] Step 210: Obtain point cloud data of the target road surface area, which contains laser radar point cloud data and millimeter wave radar point cloud data.

[0043] Wherein, the laser radar and the 4D millimeter wave radar can be arranged in the front area of the vehicle, and the original perception data of both are not in point cloud format, which needs to be converted into point cloud data through specific analysis processing and invalid point filtering operation. Due to the different working principles of laser radar and 4D millimeter wave radar, the methods of original data analysis are also different. Whether it is laser radar or 4D millimeter wave radar, in the original data analysis process, the information of each point needs to be processed, and finally the valid point set is output.

[0044] In an optional manner, before obtaining the point cloud data of the target road surface area, the road surface elevation detection method of the present application can further comprise the following steps:

[0045] Based on the suspension control requirement, set the longitudinal length of the target road surface area;

[0046] Based on the current vehicle speed, the sampling frequency of the laser radar and the millimeter wave radar, set the transverse length of the target road surface area.

[0047] In this embodiment, according to the suspension control requirement, the detection length of the target road surface area in the longitudinal direction (driving direction) of the vehicle is set as L1, specifically, since the response range of the suspension system to the road surface elevation change is usually limited within 15m, L1 is taken as ≤15m; based on the real-time driving speed v of the vehicle and the sampling frequency f of the laser radar / 4D millimeter wave radar, the detection width L2 of the target road surface area in the transverse direction (perpendicular to the driving direction) of the vehicle is calculated and determined.

[0048] It should be noted that although the effective measurement distance of the laser radar and the 4D millimeter wave radar usually exceeds 200m, this method ensures that the point cloud data actually processed is distributed within the interval range of longitudinal ≤L1 and transverse ≤L2 through installation parameter optimization (such as adjusting the sensor pitch angle, transverse offset, etc.) and data interception strategy.

[0049] Taking a typical working condition as an example: when the driving speed v of the vehicle is 120km / h (about 33.3m / s) and the sampling frequency f of the laser radar is 10Hz, the driving distance of the vehicle within a single frame data acquisition period is 3.33m, at this time, the transverse detection width L2 can be set as 3.5m on the left and right (total width 7m), which is approximately equal to a standard lane width, which can not only meet the suspension control requirement, but also avoid invalid data redundancy.

[0050] Step 220: Divide the target road surface area into a plurality of grid cells.

[0051] In an optional manner, the target road surface area is divided into a plurality of grid cells, which can specifically include the following steps:

[0052] According to the current computing power of the vehicle-mounted computing platform, the side length of the grid cell is determined;

[0053] According to the determined side length, the target road surface area is divided into a plurality of grid cells.

[0054] For a target road surface area (longitudinal length ≤ 15m, transverse width ≤ 7m), the origin of the target road surface area can use the origin of the UTM coordinate system or a self-defined origin.

[0055] The target road surface area is divided into a plurality of grid cells, and the value of the grid cell side length δ needs to be dynamically determined in combination with the computing power configuration of the vehicle-mounted computing platform. Specifically:

[0056] When the vehicle-mounted computing platform is equipped with a high-performance computing module such as a GPU, δ can be configured to be on the order of 5cm;

[0057] When only relying on a conventional computing unit such as a CPU, δ can be configured to be on the order of 10cm (such as 0.1m);

[0058] The value of the grid cell side length can also be adjusted to other appropriate values (such as 2.5cm, 15cm, etc.) according to actual needs, but it needs to be ensured that the grid density meets the suspension control accuracy requirements.

[0059] Grid cell representation method: each grid cell takes the coordinate value of its center point as the identification of the grid cell; the side length of the grid cell (usually a fixed value δ) is used to define the spatial coverage of the grid cell in the target road surface area, that is, each grid cell actually represents a square / rectangular area with the center coordinate as the center and the side length δ.

[0060] Step 230: Based on the point cloud data, the elevation information of the point cloud in each grid cell is counted, and based on the elevation information, the representative elevation of each grid cell is determined.

[0061] In this step, based on the obtained point cloud data, the elevation of each grid cell in the target area is counted, the elevation value of all point clouds in each grid cell is counted, the elevation value set of the grid cell is formed, and according to the preset statistical rule (such as arithmetic mean, median or weighted average, etc.), the representative elevation value of each grid cell is determined, which is used to represent the overall elevation characteristics of the road surface in the grid area.

[0062] Wherein, before the point cloud data mapping is performed, a vehicle position and sensor coordinate conversion processing needs to be performed, specifically, a position parameter of the vehicle in a target road surface region coordinate system can be determined first, the position parameter takes the rear wheel center point as the spatial position reference point of the vehicle, and is denoted as p0=(x0, y0, z0); an extrinsic parameter matrix of the laser radar / 4D millimeter wave radar relative to the vehicle coordinate system is determined, the extrinsic parameter matrix includes a rotation matrix R=[R00, R01, R02; R10, R11, R12; R20, R21, R22] and a translation vector t=(Δx, Δy, Δz), and the extrinsic parameter matrix has been calibrated before the vehicle is manufactured. Based on the rigid body transformation principle, a position parameter p1 of the radar in the target road surface region coordinate system is calculated, and the calculation formula is p1=R·p0+t. Then, point cloud positioning processing is performed, specifically, for each point cloud, the original coordinates of the point cloud in the radar coordinate system are p2=(x2, y2, z2), and through coordinate transformation, the position p of the point cloud in the target road surface region coordinate system is calculated, and the calculation formula is:

[0063] p=(R00·x0+R01·y0+R02·z0+Δx+x2, R10·x0+R11·y0+R12·z0+Δy+y2, R20·x0+R21·y0+R22·z0+Δz+z2).

[0064] According to the (x, y) component values of the point cloud coordinates p, the specific grid unit to which the point cloud belongs is determined through a spatial grid indexing algorithm, thereby providing a data basis for subsequent spatial correlation analysis of the height values. Through the above steps, accurate mapping of the point cloud data and the spatial grid can be realized.

[0065] In an optional manner, the height information of the point cloud in each grid unit is counted based on the point cloud data, and the representative height of each grid unit is determined based on the height information, which can specifically include the following steps:

[0066] For each grid unit, the height values of all point clouds falling in the current frame and the historical frames are determined, and the representative height value of the grid unit is calculated based on the height values of all point clouds in the grid unit by using a preset representative height algorithm.

[0067] In the embodiment, for any grid unit in the target road surface region, all point cloud data falling in the grid unit in the current frame and the historical frames are traversed, the z coordinate values of the point clouds are extracted to form a height value set, and the representative height value of the grid unit is calculated by using a preset representative height algorithm for the height value set of the grid unit, for example, the arithmetic mean value can be calculated as the representative height value. Wherein, the point cloud attribution determination is based on the (x, y) coordinates.

[0068] The calculation of the representative elevation value needs to consider a multi-source error compensation mechanism: due to the differences in the incidence angle of the laser radar, the spatial distribution dispersion of the point cloud and the hardware noise of the sensor, there are inherent deviations in the z coordinate value, and the arithmetic mean can effectively suppress random errors and improve the robustness of the elevation estimate. When new point cloud data falls into a grid cell, real-time trigger of the representative elevation value update calculation ensures the synchronization of the elevation data with the real-time road conditions.

[0069] In a typical implementation scenario, if a grid cell falls into n point clouds in a single frame, the coordinates are p1(x1, y1, z1), p2(x2, y2, z2)…p n (x n ,y n ,z n ), then the representative elevation value of the grid cell is updated to z_rep=(z1+z2+…+z n ) / n; when subsequent frame data continues to fall, the arithmetic mean is updated using an incremental calculation method to avoid the consumption of computing power caused by repeated calculation of the full amount of data.

[0070] Step 240: determining an anchor point in the target road surface area, the anchor point representing the projection position of the center of the rear wheel of the vehicle in the target road surface area.

[0071] Step 250: determining the instantaneous ground contact envelope of the tire outer contour on the road surface based on the anchor point, the current steering angle and the tire parameters, and taking the area surrounded by the envelope as the tire contact area, the tire parameters including at least one of the tire width, the load and the air pressure.

[0072] The tire contact area reflects the range of the actual contact area between the tire and the road surface.

[0073] The real-time point cloud data streams of the laser radar and the 4D millimeter wave radar are used to continuously update the road surface elevation data of each grid cell in the target road surface area.

[0074] In the embodiment of the application, the position of the anchor point (the center of the rear wheel of the vehicle) in the grid coordinate system can be output by the vehicle positioning module. When the vehicle positioning module cannot directly output the anchor point position data, a multi-sensor fusion algorithm can be used to perform dead reckoning based on the measurement values of the wheel speed meter, the IMU and the GPS to output the position coordinates of the center of the rear wheel of the vehicle in the grid coordinate system in real time. Then, the accurate position coordinates of the center of the tire in the grid coordinate system can be calculated according to the fixed offset of the center of the tire relative to the center of the rear wheel (pre-stored through vehicle parameter calibration), and the instantaneous ground contact envelope area of the tire outer contour in the grid coordinate system is determined in combination with the real-time steering angle and the tire parameters.

[0075] Step 260: determining the road surface elevation of the current driving position of the vehicle based on the representative elevation of the grid cells in the tire contact area.

[0076] Referring to Figure 3 As shown in FIG. 1, which is a schematic diagram of a tire contact area provided by the present application, each grid cell is denoted by "n+x", and the red area in the figure represents the area of the tire in contact with the ground, i.e., the tire contact area. As can be seen from the figure, the set of grid cells that are crushed is {n+2, n+3, n+5, n+6, n+7, n+8, n+9, n+10, n+11}. For the grid cells in the crushed area, the representative elevation value (such as the arithmetic mean, maximum value, minimum value, etc.) thereof is extracted as an input parameter of the active suspension control system, so as to realize real-time sensing of the road elevation and precise regulation of the suspension system.

[0077] Based on the representative elevation value of each grid cell in the tire contact area, the road elevation at the current driving position of the vehicle is determined, i.e., the road elevation value at the current trajectory point in the driving trajectory of the vehicle.

[0078] In an alternative manner, the road elevation detection method of the present application can further include the following steps:

[0079] Obtaining the future driving trajectory of the vehicle;

[0080] Extracting a plurality of trajectory points from the future driving trajectory of the vehicle, and determining the road elevation value corresponding to each trajectory point;

[0081] Based on the road elevation value corresponding to each trajectory point, a first reference elevation value is calculated using a preset reference elevation algorithm;

[0082] Differencing the road elevation value of each trajectory point from the first reference elevation value to obtain trajectory bump sequence data representing the degree of trajectory bumping.

[0083] In the embodiment, the future driving trajectory of the vehicle can be predicted based on the steering wheel angle sensor data and the vehicle dynamics model, and a spatial path sequence containing a plurality of discrete trajectory points can be generated. The trajectory point density can be dynamically adjusted according to the downstream demand accuracy, the on-board computing power limit and the actual road condition complexity. Through a spatial mapping algorithm, the coordinates of each trajectory point are converted to the grid coordinate system of the target road surface area, and the representative elevation value of the corresponding grid cell is extracted to form a trajectory point elevation sequence. A preset reference elevation algorithm is used to calculate the reference elevation value (hereinafter referred to as the first reference elevation value) of the trajectory point elevation sequence, which is used as a reference reference elevation. For example, the arithmetic mean value can be calculated as the first reference elevation value. For each trajectory point, the difference between its elevation value and the first reference elevation value is calculated to generate a trajectory bump sequence data. Through a curve fitting algorithm, the discrete bump sequence data is fitted into a continuous change curve to enhance the recognition degree of the bump feature. In a typical embodiment, when the trajectory point spacing is set to 10 cm, 150 trajectory points on the future 15 m driving path of the vehicle can be extracted, the elevation values of the points are calculated and the deviations from the reference elevation value are calculated to form a complete bump degree quantization description.

[0084] Referring to Figure 4 , a schematic diagram of the trajectory point elevation sequence provided by the present application is shown. As shown in the figure, the road surface elevation value of the current trajectory point of the vehicle is , and it is expected that the vehicle will reach a certain position after driving a distance, and the road surface elevation value at this position is . Similarly, the road surface elevation values at other positions are calculated , . These road surface elevation values can be estimated in advance, so that the trajectory bump degree can be predicted. The arithmetic mean value of these trajectory point elevation values is calculated , and the difference between the road surface elevation value of each trajectory point and the arithmetic mean value is calculated , …, . These differences can represent the bump degree of the predicted trajectory.

[0085] In an optional manner, in the case of a target road surface area being a lane area, the road surface elevation detection method of the present application can further include the following steps:

[0086] Based on the representative elevation values of each grid cell in the lane area, a second reference elevation value of the lane area is calculated using a preset reference elevation algorithm;

[0087] The representative elevation value of each grid cell is subtracted from the second reference elevation value to obtain lane bump sequence data representing the lane bump degree.

[0088] In the present embodiment, in the case that the target road surface region is a lane region, based on the real-time point cloud data stream of the lidar / 4D millimeter wave radar, the representative elevation value of each grid cell in the lane region is continuously updated, the representative elevation values of all grid cells in the lane region are calculated, for example, arithmetic average operation is performed, the reference elevation value of the region (for the sake of distinction, referred to as the second reference elevation value here) is obtained, the difference between the representative elevation value of each grid cell and the second reference elevation value is calculated, and the lane bump sequence data is generated. The difference value represents the road surface fluctuation feature, and the positive and negative difference values represent the convex region and the concave region respectively. Optionally, the lane bump map is constructed by three-dimensional visualization technology: taking the plane coordinates (X, Y) of the lane region as the XY plane, taking the difference value as the Z axis direction, and using color gradient or height mapping to present the road surface fluctuation state, wherein the positive Z axis direction corresponds to the convex region (such as the red system), and the negative Z axis direction corresponds to the concave region (such as the blue system). During the vehicle driving process, the lane bump sequence data can be continuously updated and the bump map can be refreshed in real time with the dynamic expansion of the radar scanning range, and finally a complete lane bump state holographic atlas is formed.

[0089] Referring to Figure 5 The flowchart of the road surface elevation detection method provided by the present application is shown in FIG. 1. The original data collected by the lidar and the 4D millimeter wave radar are converted into point cloud data. Based on the current vehicle speed, the sampling frequency of the lidar and the millimeter wave radar, the range of the target road surface region is set, the point cloud data within the range is extracted, the grid cell where the point cloud is located is determined based on the point cloud xy value, for each grid cell in the target road surface region, the arithmetic average value of all point cloud z values in the grid cell is calculated, the arithmetic average value is taken as the representative elevation value of the grid cell, and the road surface elevation of the current driving position of the vehicle is determined. Further, based on the steering wheel angle sensor data and the vehicle dynamics model, the future driving trajectory of the vehicle can be predicted, a plurality of trajectory points are extracted from the future driving trajectory of the vehicle, the coordinates of each trajectory point are converted to the grid coordinate system of the target road surface region, the representative elevation value of the corresponding grid cell is extracted, and the trajectory point elevation sequence is formed. The arithmetic average value of the trajectory point elevation sequence is calculated as the reference reference elevation, and the difference between the elevation value of each trajectory point and the reference elevation is calculated to generate the trajectory bump sequence data.

[0090] The embodiment of the present application solves the problem of low road elevation perception accuracy and poor reliability in extreme weather conditions by using point cloud data of laser radar and millimeter wave radar. Laser radar provides high-precision and high-density point cloud data, while millimeter wave radar can still work stably in extreme weather environment. By fusing the data of the two, high-precision and dense road point cloud data can be generated in any weather conditions, thereby ensuring the all-weather working ability of the perception system. In addition, the representative elevation of the grid unit in the tire contact area is used as the true value of the road elevation of the current position of the vehicle, which provides more accurate road elevation data and helps to establish a more accurate vehicle dynamics model, which is very important for vehicle stability control, rollover prevention and other safety functions.

[0091] Figure 6 The structure diagram of the embodiment of the road elevation detection device of the present application is shown. As shown in the figure, the device 600 comprises an acquisition module 610, a division module 620, a first determination module 630 and a second determination module 640. Figure 6

[0092] The acquisition module is used to acquire point cloud data of a target road area, and the point cloud data comprises laser radar point cloud data and millimeter wave radar point cloud data.

[0093] The division module is used to divide the target road area into a plurality of grid units.

[0094] The first determination module is used to count the elevation information of the point cloud in each grid unit based on the point cloud data, and determine the representative elevation of each grid unit based on the elevation information.

[0095] The second determination module is used to extract a tire contact area from the target road area, and determine the road elevation of the current driving position of the vehicle based on the representative elevation of the grid unit in the tire contact area.

[0096] In an optional manner, the second determination module is specifically used for:

[0097] determining an anchor point in the target road area, the anchor point representing the projection position of the center of the rear wheel of the vehicle in the target road area;

[0098] determining the instantaneous ground contact envelope of the tire contour on the road surface based on the anchor point, the current steering angle and the tire parameters, and taking the area surrounded by the envelope as the tire contact area, the tire parameters comprising at least one of the tire width, the load and the air pressure.

[0099] In an optional manner, the road elevation detection device of the present application is specifically further used for:

[0100] setting the longitudinal length of the target road area based on the suspension control requirement;

[0101] ​Set the lateral length of the target road surface region based on the current vehicle speed, the sampling frequency of the laser radar and the millimeter wave radar.

[0102] In an optional mode, the dividing module is specifically configured to:

[0103] Determine the side length of the grid cell according to the current computing power of the vehicle-mounted computing platform.

[0104] Subdivide the target road surface region into a plurality of grid cells according to the determined side length.

[0105] In an optional mode, the first determining module is specifically configured to:

[0106] For each grid cell, determine the height values of all point clouds falling in the current frame and the historical frame, and calculate the representative height value of the grid cell by using a preset representative height algorithm based on the height values of all point clouds in the grid cell.

[0107] In an optional mode, the road surface height detection device is specifically further configured to:

[0108] Obtain the future driving trajectory of the vehicle.

[0109] Extract a plurality of trajectory points from the future driving trajectory of the vehicle, and determine the road surface height values corresponding to the trajectory points.

[0110] Calculate a first reference height value by using a preset reference height algorithm based on the road surface height values corresponding to the trajectory points.

[0111] Determine the trajectory bump sequence data representing the trajectory bump degree by subtracting the first reference height value from the road surface height values of the trajectory points.

[0112] In an optional mode, in the case where the target road surface region is a lane region, the road surface height detection device is specifically further configured to:

[0113] Calculate a second reference height value of the lane region by using a preset reference height algorithm based on the representative height values of the grid cells in the lane region.

[0114] Determine the lane bump sequence data representing the lane bump degree by subtracting the second reference height value from the representative height values of the grid cells.

[0115] The embodiment of the present application solves the problem of low accuracy and poor reliability of road surface elevation perception in extreme weather conditions by using point cloud data of laser radar and millimeter wave radar. The laser radar provides high-precision and high-density point cloud data, and the millimeter wave radar can still work stably in extreme weather environment. The fusion of the data of the two can generate high-precision and dense road surface point cloud data in any weather condition, thereby ensuring the all-weather working ability of the perception system. In addition, the representative elevation of the grid unit in the tire contact area is used as the true value of the road surface elevation of the current position of the vehicle, which provides more accurate road surface elevation data and helps to establish a more accurate vehicle dynamics model, which is very important for vehicle stability control, rollover prevention and other safety functions.

[0116] Figure 7 The structure diagram of the embodiment of the road surface elevation detection device of the present application is shown, and the specific embodiment of the present application does not limit the specific implementation of the road surface elevation detection device.

[0117] As Figure 7 shown, the road surface elevation detection device can include a processor 702, a communications interface 704, a memory 706, and a communications bus 708.

[0118] The processor 702, the communications interface 704, and the memory 706 communicate with each other through the communications bus 708. The communications interface 704 is used to communicate with network elements such as clients or other servers. The processor 702 is used to execute the program 710, and can specifically execute the related steps in the above-mentioned road surface elevation detection method embodiment.

[0119] Specifically, the program 710 can include program code, which includes computer executable instructions.

[0120] The processor 702 can be a central processing unit CPU, or an application specific integrated circuit ASIC, or one or more integrated circuits configured to implement the embodiments of the present application. The one or more processors included in the road surface elevation detection device can be the same type of processor, such as one or more CPUs; or can be different types of processors, such as one or more CPUs and one or more ASICs.

[0121] The memory 706 is used to store the program 710. The memory 706 can include a high-speed RAM memory, and can also include a non-volatile memory, such as at least one disk memory.

[0122] The program 710 can be specifically invoked by the processor 702 to enable the road elevation detection device to perform the following operations:

[0123] Obtain point cloud data of a target road area, the point cloud data comprising laser radar point cloud data and millimeter wave radar point cloud data;

[0124] Divide the target road area into a plurality of grid cells;

[0125] Based on the point cloud data, count the elevation information of the point cloud in each grid cell, and based on the elevation information, determine the representative elevation of each grid cell;

[0126] Extract a tire contact area from the target road area, and based on the representative elevation of the grid cell in the tire contact area, determine the road elevation at the current driving position of the vehicle.

[0127] In an optional manner, the program 710 is invoked by the processor 702 to enable the road elevation detection device to perform the following operations:

[0128] Determine an anchor point in the target road area, the anchor point representing the projection position of the center of the rear wheel of the vehicle in the target road area;

[0129] Based on the anchor point, the current steering angle, and tire parameters, determine the instantaneous ground contact envelope of the tire contour on the road surface, and take the area surrounded by the envelope as the tire contact area, the tire parameters including at least one of tire width, load, and air pressure.

[0130] In an optional manner, the program 710 is invoked by the processor 702 to enable the road elevation detection device to perform the following operations:

[0131] Based on the suspension control requirement, set the longitudinal length of the target road area;

[0132] Based on the current vehicle speed, the sampling frequency of the laser radar, and the sampling frequency of the millimeter wave radar, set the lateral length of the target road area.

[0133] In an optional manner, the program 710 is invoked by the processor 702 to enable the road elevation detection device to perform the following operations:

[0134] Determine the edge length of the grid cell according to the current computing power of the vehicle-mounted computing platform;

[0135] According to the determined edge length, divide the target road area into a plurality of grid cells.

[0136] In an optional manner, the program 710 is invoked by the processor 702 to enable the road elevation detection device to perform the following operations:

[0137] For each grid cell, the height values of all point clouds falling in the current frame and the historical frames are determined, and a preset representative height algorithm is used to calculate a representative height value of the grid cell based on the height values of all point clouds in the grid cell.

[0138] In an alternative way, the program 710 is invoked by the processor 702 to make the road elevation detection device perform the following operations:

[0139] Obtaining a future driving trajectory of the vehicle;

[0140] Extracting a plurality of trajectory points from the future driving trajectory of the vehicle, and determining the road elevation values corresponding to the trajectory points;

[0141] Based on the road elevation values corresponding to the trajectory points, a preset reference height algorithm is used to calculate a first reference height value;

[0142] The road elevation values of the trajectory points are subtracted from the first reference height value to obtain trajectory bump sequence data representing the degree of trajectory bump.

[0143] In an alternative way, in the case that the target road surface area is a lane area, the program 710 is invoked by the processor 702 to make the road elevation detection device perform the following operations:

[0144] Based on the representative height values of each grid cell in the lane area, a preset reference height algorithm is used to calculate a second reference height value of the lane area;

[0145] The representative height values of each grid cell are subtracted from the second reference height value to obtain lane bump sequence data representing the degree of lane bump.

[0146] The embodiment of the present application solves the problem of low road elevation perception accuracy and poor reliability in extreme weather conditions by using point cloud data of laser radar and millimeter wave radar. Laser radar provides high-precision and high-density point cloud data, while millimeter wave radar can still work stably in extreme weather environment. The fusion of the data of the two can generate high-precision and dense road point cloud data in any weather conditions, thereby ensuring the all-weather working ability of the perception system. In addition, the representative height of the grid cell in the tire contact area is used as the true value of the road elevation at the current position of the vehicle, which provides more accurate road elevation data and helps to establish more accurate vehicle dynamics.

[0147] Figure 8 The structure schematic diagram of the embodiment of the vehicle of the present application is shown. As shown in the figure, the vehicle 800 comprises a sensor, one or more processors and a communication interface; Figure 8

[0148] The sensor is used to collect point cloud data;

[0149] ​The processor is configured to execute the steps in the road surface height detection method.

[0150] The embodiment of the present application solves the problems of low accuracy and poor reliability of road surface height perception in extreme weather conditions by using point cloud data of laser radar and millimeter wave radar. Laser radar provides high-precision and high-density point cloud data, while millimeter wave radar can still work stably in extreme weather environment. By fusing the data of the two, high-precision and dense road surface point cloud data can be generated in any weather condition, thereby ensuring the all-weather working ability of the perception system. In addition, the representative height of the grid unit in the tire contact area is used as the true value of the road surface height of the current position of the vehicle, which provides more accurate road surface height data and helps to establish more accurate vehicle dynamics.

[0151] The embodiment of the present application provides a computer readable storage medium, and the storage medium stores at least one executable instruction. When the executable instruction runs on a road surface height detection device / apparatus, the road surface height detection device / apparatus executes the road surface height detection method in any method embodiment.

[0152] The executable instruction can be specifically used to make the road surface height detection device / apparatus execute the following operations:

[0153] Obtain point cloud data of a target road surface area, the point cloud data including laser radar point cloud data and millimeter wave radar point cloud data;

[0154] Divide the target road surface area into a plurality of grid units;

[0155] Based on the point cloud data, count the height information of the point cloud in each grid unit, and based on the height information, determine the representative height of each grid unit;

[0156] Extract a tire contact area from the target road surface area, and based on the representative height of the grid unit in the tire contact area, determine the road surface height of the current driving position of the vehicle.

[0157] In an optional manner, the executable instruction makes the road surface height detection device / apparatus execute the following operations:

[0158] Determine an anchor point in the target road surface area, the anchor point representing the projection position of the center of the rear wheel of the vehicle in the target road surface area;

[0159] Based on the anchor point, the current steering angle and the tire parameters, determine the instantaneous ground contact envelope of the tire contour on the road surface, and take the area surrounded by the envelope as the tire contact area, the tire parameters including at least one of the tire width, the load and the air pressure.

[0160] In an optional manner, the executable instruction makes the road surface height detection device / apparatus execute the following operations:

[0161] based on the suspension control requirement, set a longitudinal length of the target road surface region;

[0162] based on the current vehicle speed, sampling frequency of the laser radar and the millimeter wave radar, set a lateral length of the target road surface region.

[0163] In an optional manner, the executable instructions cause the road surface elevation detection device / apparatus to perform the following operations:

[0164] determine the edge length of the grid cell according to the current computing power of the vehicle-mounted computing platform;

[0165] subdivide the target road surface region into a plurality of grid cells according to the determined edge length.

[0166] In an optional manner, the executable instructions cause the road surface elevation detection device / apparatus to perform the following operations:

[0167] for each grid cell, determine the height values of all point clouds falling in the current frame and the historical frame, and calculate a representative elevation value of the grid cell based on the height values of all point clouds in the grid cell using a preset representative elevation algorithm.

[0168] In an optional manner, the executable instructions cause the road surface elevation detection device / apparatus to perform the following operations:

[0169] obtain a future driving trajectory of the vehicle;

[0170] extract a plurality of trajectory points from the future driving trajectory of the vehicle, and determine the road surface elevation values corresponding to the trajectory points;

[0171] calculate a first reference elevation value based on the road surface elevation values corresponding to the trajectory points using a preset reference elevation algorithm;

[0172] obtain trajectory bump sequence data representing the degree of trajectory bumping by subtracting the first reference elevation value from the road surface elevation values of the trajectory points.

[0173] In an optional manner, in the case where the target road surface region is a lane region, the executable instructions cause the road surface elevation detection device / apparatus to perform the following operations:

[0174] calculate a second reference elevation value of the lane region based on the representative elevation values of each grid cell in the lane region using a preset reference elevation algorithm;

[0175] obtain lane bump sequence data representing the degree of lane bumping by subtracting the second reference elevation value from the representative elevation values of each grid cell.

[0176] The embodiment of the present application solves the problem of low road elevation perception accuracy and poor reliability in extreme weather conditions by using point cloud data of laser radar and millimeter wave radar. Laser radar provides high-precision and high-density point cloud data, while millimeter wave radar can still work stably in extreme weather environment. The fusion of data of the two can generate high-precision and dense road point cloud data in any weather conditions, thereby ensuring the all-weather working ability of the perception system. In addition, the representative elevation of the grid unit in the tire contact area is used as the true value of the road elevation of the current position of the vehicle, which provides more accurate road elevation data and helps to establish more accurate vehicle dynamics.

[0177] The algorithms and displays presented herein are not inherently related to any particular computer, virtual system, or other apparatus. Furthermore, the embodiments of the present application are not described with reference to any particular programming language.

[0178] In the specification provided herein, a large number of specific details are described. However, it can be understood that the embodiments of the present application can be practiced without these specific details. Similarly, in order to simplify the present application and help understand one or more of the various inventive aspects, in the above description of the exemplary embodiments of the present application, various features of the embodiments of the present application are sometimes grouped together into a single embodiment, figure, or description thereof. Among them, the claims following the detailed description are hereby expressly incorporated into the detailed description, wherein each claim itself is a separate embodiment of the present application.

[0179] Those skilled in the art can understand that the modules in the device in the embodiments can be adaptively changed and arranged in one or more devices different from the embodiments. The modules or units or components in the embodiments can be combined into one module or unit or component, and in addition, they can be divided into multiple sub-modules or sub-units or sub-components. Except that at least some of such features and / or processes or units are mutually exclusive.

[0180] It should be noted that the above-mentioned embodiments illustrate rather than limit the application, and that those skilled in the art will be able to design many alternative embodiments without departing from the scope of the appended claims. In the claims, any reference signs placed between parentheses shall not be construed as limiting the claim. The word 'comprising' does not exclude the presence of elements or steps other than those listed in a claim. The word 'a' or 'an' preceding an element does not exclude the presence of a plurality of such elements. The application can be implemented by means of both hardware and software, and any combination thereof. In a unitary claim, several devices, apparatuses or means can be listed, comprising means for carrying out a certain task. The use of the term'means' in a claim is intended to refer to a combination of devices, apparatuses or means for carrying out a task. The word 'first','second', 'third', etc. do not imply any order. The use of these terms is to be construed as an indication of particular embodiments. Steps in the above-described embodiments, unless otherwise specified, are not to be construed as necessarily limiting the order in which the steps are performed.

Claims

1. A method of detecting the elevation of a road surface, characterized by, The method comprises: acquiring point cloud data of a target road surface area, the point cloud data comprising laser radar point cloud data and millimeter wave radar point cloud data; dividing the target road surface area into a plurality of grid cells; based on the point cloud data, counting the height information of the point cloud in each grid cell, and based on the height information, determining the representative height of each grid cell; extracting a tire contact area from the target road surface area, and based on the representative height of the grid cell in the tire contact area, determining the road surface height of the current driving position of the vehicle.

2. The method of claim 1, wherein, The extraction of the tire contact area from the target road surface area comprises: determining an anchor point in the target road surface area, the anchor point representing the projected position of the center of the rear wheel of the vehicle in the target road surface area; based on the anchor point, the current steering angle and the tire parameters, determining the instantaneous ground contact envelope of the tire contour on the road surface, and taking the area surrounded by the envelope as the tire contact area, the tire parameters including at least one of the tire width, the load and the air pressure.

3. The method according to claim 1 or 2, characterized in that, Before acquiring the point cloud data of the target road surface area, the method further comprises: based on the suspension control requirement, setting the longitudinal length of the target road surface area; based on the current vehicle speed, the sampling frequency of the laser radar and the millimeter wave radar, setting the transverse length of the target road surface area.

4. The method according to claim 1 or 2, characterized in that, The division of the target road surface area into a plurality of grid cells comprises: determining the edge length of the grid cell according to the current computing power of the vehicle-mounted computing platform; according to the determined edge length, dividing the target road surface area into a plurality of grid cells.

5. The method according to claim 1 or 2, characterized in that, The counting of the height information of the point cloud in each grid cell based on the point cloud data, and the determination of the representative height of each grid cell based on the height information, comprises: for each grid cell, determining the height value of all the point clouds falling in the current frame and the historical frames, and based on the height value of all the point clouds in the grid cell, calculating the representative height value of the grid cell using a preset representative height algorithm.

6. The method of claim 1 or 2, wherein, The method further comprises: acquiring the future driving trajectory of the vehicle; extracting a plurality of trajectory points from the future driving trajectory of the vehicle, and determining the road surface height value corresponding to each trajectory point; based on the road surface height value corresponding to each trajectory point, calculating a first reference height value using a preset reference height algorithm; differencing the road surface height value of each trajectory point from the first reference height value to obtain trajectory bump sequence data representing the degree of trajectory bump.

7. The method of claim 1, wherein, In the case where the target road surface area is a lane area, the method further comprises: based on the representative height value of each grid cell in the lane area, calculating a second reference height value of the lane area using a preset reference height algorithm; differencing the representative height value of each grid cell from the second reference height value to obtain lane bump sequence data representing the degree of lane bump.

8. A road surface elevation detecting device characterized by comprising: The device comprises: an acquisition module for acquiring point cloud data of a target road surface area, the point cloud data comprising laser radar point cloud data and millimeter wave radar point cloud data; a division module for dividing the target road surface area into a plurality of grid cells; The first determining module is configured to count the elevation information of the point cloud in each grid unit based on the point cloud data, and determine the representative elevation of each grid unit based on the elevation information. The second determining module is configured to extract a tire contact area from the target road surface area, and determine the road surface elevation of the current driving position of the vehicle based on the representative elevation of the grid unit in the tire contact area.

9. A road elevation detecting apparatus characterized by comprising: The method comprises: A processor, a memory, a communication interface, and a communication bus, wherein the processor, the memory, and the communication interface communicate with each other through the communication bus; The memory is configured to store at least one executable instruction, and the executable instruction causes the processor to perform the operations of the road surface elevation detection method according to any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The storage medium stores at least one executable instruction, and the executable instruction causes the road surface elevation detection device / apparatus to perform the operations of the road surface elevation detection method according to any one of claims 1-7 when the road surface elevation detection device / apparatus runs.

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