Pedestrian protection test leg shape posture monitoring method and device

Abstract

The invention relates to the technical field of vehicle collision, in particular to a pedestrian protection test leg type posture monitoring method and device, and the method comprises the steps: collecting point cloud information from the launching moment of a leg type impactor to the collision moment of the leg type impactor and a test vehicle; based on the point cloud information, determining a first contact moment of the leg type impactor and the test vehicle, and extracting contact point cloud information corresponding to the first contact moment; based on the contact point cloud information, the height between the bottom end of the leg-shaped impactor and the ground datum plane and the attitude angle of the leg-shaped impactor are calculated. Therefore, the problems that accurate judgment on the collision moment in the collision process of the leg-shaped impactor and the vehicle is not involved, and accurate analysis and evaluation on collision details are difficult to achieve in the prior art are solved.

Description

Technical Field

[0001] This application relates to the field of vehicle collision technology, and in particular to a method and device for monitoring the leg posture of pedestrians in protection tests. Background Technology

[0002] In related technologies, based on the internationally accepted structural characteristics of the upper body of a frontal collision dummy, the structural components of each part of the pedestrian protection dummy can be designed and manufactured separately using computer digital modeling or physical object manufacturing methods, and then integrated into an overall impact structure. Alternatively, a pressure model can be built on the bumper cross-section model, and after obtaining the maximum deformation through collision simulation, a leg-shaped impactor model can be arranged, and the positional relationship data can be extracted to determine whether the bumper's pedestrian protection performance is qualified.

[0003] However, the relevant technologies do not address the precise determination of the moment of collision during the collision between the leg impactor and the vehicle, making it difficult to accurately analyze and evaluate the details of the collision, which urgently needs improvement. Summary of the Invention

[0004] This application provides a method and device for monitoring the leg posture in pedestrian protection tests, in order to solve the problems in related technologies, such as the lack of accurate determination of the collision moment during the collision between the leg impactor and the vehicle, and the difficulty in accurately analyzing and evaluating the collision details.

[0005] The first aspect of this application provides a method for monitoring the leg posture in a pedestrian protection test, comprising the following steps: collecting point cloud information from the launch time of the leg impactor to the collision time between the leg impactor and the test vehicle; determining the first contact time between the leg impactor and the test vehicle based on the point cloud information, and extracting the contact point cloud information corresponding to the first contact time; and calculating the height between the bottom of the leg impactor and the ground reference plane and the posture angle of the leg impactor based on the contact point cloud information.

[0006] Optionally, in one embodiment of this application, the step of collecting point cloud information from the launch time of the leg impactor to the collision time between the leg impactor and the test vehicle includes: detecting whether the point cloud information meets a preset condition; if the point cloud information does not meet the preset condition, processing the point cloud information until processed point cloud information that meets the preset condition is obtained.

[0007] Optionally, in one embodiment of this application, determining the first contact time between the leg impactor and the test vehicle based on the point cloud information includes: determining multiple frames of point cloud information corresponding to the point cloud information; slicing the multiple frames of point cloud information along the longitudinal plane of the test vehicle through the center direction of the leg impactor to obtain a first circumscribed rectangle of the leg impactor and a second circumscribed rectangle of the test vehicle; determining a first support point within the first circumscribed rectangle and a second support point within the second circumscribed rectangle; constructing a Minkowski difference set based on the first support point and the second support point; constructing a simplex based on the support points in the Minkowski difference set; and determining the first contact time based on the simplex.

[0008] Optionally, in one embodiment of this application, determining the first contact time based on the simplex includes: obtaining the target support point of the simplex; determining the target direction from the target support point to the origin of the coordinate system based on the target support point; determining the iteration direction of the simplex based on the target direction; determining the contact result between the leg-shaped impactor and the test vehicle based on the iteration direction; and determining the first contact time based on the contact result.

[0009] Optionally, in one embodiment of this application, calculating the height between the bottom of the leg impactor and the ground reference plane and the attitude angle of the leg impactor based on the contact point cloud information includes: determining the ground reference plane based on the point cloud information; determining a first height of the ground reference plane in the target coordinate system and a second height of the bottom of the leg impactor in the target coordinate system based on the ground reference plane; and calculating the height between the bottom of the leg impactor and the ground reference plane based on the first height and the second height.

[0010] Optionally, in one embodiment of this application, calculating the height between the bottom of the leg impactor and the ground reference plane and the attitude angle of the leg impactor based on the contact point cloud information includes: determining a first direction vector of the leg impactor based on the contact point cloud information; obtaining a second direction vector of the target coordinate system; and calculating the attitude angle based on the first direction vector and the second direction vector.

[0011] A second aspect of this application provides a monitoring device for the leg posture in a pedestrian protection test, comprising: a data acquisition module for acquiring point cloud information from the launch time of the leg impactor to the collision time between the leg impactor and the test vehicle; a determination module for determining, based on the point cloud information, the first contact time between the leg impactor and the test vehicle, and extracting the contact point cloud information corresponding to the first contact time; and a calculation module for calculating, based on the contact point cloud information, the height between the bottom of the leg impactor and the ground reference plane and the posture angle of the leg impactor.

[0012] Optionally, in one embodiment of this application, the acquisition module includes: a detection unit for detecting whether the point cloud information meets a preset condition; and a processing unit for processing the point cloud information if the point cloud information does not meet the preset condition, until processed point cloud information that meets the preset condition is obtained.

[0013] Optionally, in one embodiment of this application, the determining module includes: a first determining unit, configured to determine multi-frame point cloud information corresponding to the point cloud information based on the point cloud information; a generating unit, configured to slice the multi-frame point cloud information along the longitudinal plane of the test vehicle through the center direction of the leg-shaped impactor to obtain a first circumscribed rectangle of the leg-shaped impactor and a second circumscribed rectangle of the test vehicle; a second determining unit, configured to determine a first support point within the first circumscribed rectangle and a second support point within the second circumscribed rectangle; a first constructing unit, configured to construct a Minkowski difference set based on the first support point and the second support point; a second constructing unit, configured to construct a simplex based on the support points in the Minkowski difference set; and a third determining unit, configured to determine the first contact time based on the simplex.

[0014] Optionally, in one embodiment of this application, the third determining element includes: an acquisition subunit for acquiring the target support point of the simplex; a first determining subunit for determining the target direction from the target support point to the origin of the coordinate system based on the target support point; a second determining subunit for determining the iteration direction of the simplex based on the target direction; a third determining subunit for determining the contact result between the leg-shaped impactor and the test vehicle based on the iteration direction; and a fourth determining subunit for determining the first contact time based on the contact result.

[0015] Optionally, in one embodiment of this application, the calculation module includes: a fourth determining unit, configured to determine the ground reference plane based on the point cloud information; a fifth determining unit, configured to determine a first height of the ground reference plane in the target coordinate system and a second height of the bottom end of the leg-shaped impactor in the target coordinate system based on the ground reference plane; and a first calculation unit, configured to calculate the height between the bottom end of the leg-shaped impactor and the ground reference plane based on the first height and the second height.

[0016] Optionally, in one embodiment of this application, the calculation module includes: a sixth determining unit, configured to determine a first direction vector of the leg-shaped impactor based on the contact point cloud information; an acquiring unit, configured to acquire a second direction vector of the target coordinate system; and a second calculation unit, configured to calculate the attitude angle based on the first direction vector and the second direction vector.

[0017] A third aspect of this application provides a vehicle, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the method for monitoring leg posture in pedestrian protection tests as described in the above embodiments.

[0018] A fourth aspect of this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method for monitoring leg posture in pedestrian protection experiments.

[0019] A fifth aspect of this application provides a computer program product, including a computer program that, when executed, implements the above-described method for monitoring leg posture in pedestrian protection testing.

[0020] This application embodiment can determine the first contact moment between the leg impactor and the test vehicle based on point cloud information collected from the launch time of the leg impactor to the moment of collision between the leg impactor and the test vehicle, and extract the corresponding contact point cloud information to calculate the height between the bottom of the leg impactor and the ground reference plane, as well as the attitude angle of the leg impactor. By using the point cloud information, the height and angle between the leg impactor and the ground reference plane at the first contact moment can be accurately determined, improving efficiency, automatically completing real-time monitoring of the leg's attitude, and improving the accuracy of collision detail analysis and evaluation. Therefore, it solves the problems in related technologies that do not address the accurate determination of the collision moment during the collision between the leg impactor and the vehicle, making it difficult to achieve accurate analysis and evaluation of collision details.

[0021] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0022] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein:

[0023] Figure 1 This is a schematic diagram of the structure of a real-time monitoring system for leg posture in a pedestrian protection test according to an embodiment of this application; Figure 2 This is a block diagram illustrating radius filtering according to one embodiment of this application; Figure 3 A flowchart of the detection process for BEV (Bird's Eye View) according to an embodiment of this application; Figure 4 This is a schematic diagram showing the height between a leg-shaped impactor and a ground reference plane according to one embodiment of this application; Figure 5 This is a schematic diagram of the posture angle of a leg-shaped impactor according to an embodiment of this application; Figure 6 This is a flowchart of a method for monitoring leg posture in pedestrian protection testing according to an embodiment of this application; Figure 7 This is a rough schematic diagram illustrating the determination of the first contact moment based on the GJK algorithm according to an embodiment of this application; Figure 8 A flowchart illustrating the working principle of a method for monitoring leg posture in pedestrian protection testing according to an embodiment of this application; Figure 9 A detailed schematic diagram illustrating the determination of the first contact moment based on the GJK (Gilbert-Johnson-Keerthi) algorithm according to an embodiment of this application; Figure 10 This is a block diagram of a pedestrian protection test leg posture monitoring device provided according to an embodiment of this application; Figure 11 This is a structural schematic diagram of a vehicle provided according to an embodiment of this application. Detailed Implementation

[0024] The embodiments of this application are described in detail below. Examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain this application, and should not be construed as limiting this application.

[0025] Before introducing the method for monitoring leg posture in pedestrian protection tests proposed in the embodiments of this application, we will first introduce a real-time monitoring system for leg posture in pedestrian protection tests involved in the embodiments of this application.

[0026] Specifically, Figure 1 This is a schematic diagram of the structure of a real-time monitoring system for leg posture in a pedestrian protection test according to an embodiment of this application.

[0027] like Figure 1 As shown, the real-time leg posture monitoring system for this pedestrian protection test includes a test vehicle 101, a leg impactor 102, a lidar 103, a processor 104, and a display 105.

[0028] In this embodiment of the application, a lidar 103 is installed between the leg impactor 102 and the test vehicle 101, and at a position perpendicular to the longitudinal plane of the test vehicle 101. The lidar 103 is used to collect multi-frame point cloud information from the moment the leg impactor 102 is launched to the moment the leg impactor 102 collides with the test vehicle 101. In addition, one end of the lidar 103 is electrically connected to one end of the processor 104 and is driven and controlled by the processor 104. The other end of the processor 104 is connected to the display 105. The display 105 is used to detect the leg movement trajectory, the posture angle at the first contact moment, and the height between the bottom of the leg impactor 102 and the ground reference plane at the first contact moment in real time.

[0029] For example, in this embodiment of the application, the firing time of the leg-shaped impactor 102 can be used as the start time. The moment of collision between the leg-shaped impactor 102 and the test vehicle 101 is taken as the end moment. And thus record and store Inside, the point cloud information received by the lidar 103.

[0030] Furthermore, in this embodiment, radius filtering is first used to remove noise points from the point cloud information. Then, pass-through filtering is used to obtain the point cloud information and elevation difference information within the range, filtering out points that are not within the range of values ​​in the x, y, and z dimensions of the lidar 103 coordinate system, thus narrowing the detection range of the point cloud. Finally, the YOLO3D (You Only Look Once Three Dimensional) algorithm is used for detection, thereby obtaining effective multi-frame point cloud information of the test vehicle 101, the leg-shaped impactor 102, and the ground, and thus obtaining processed point cloud information that meets preset conditions. The preset conditions can be set by those skilled in the art according to actual conditions, and this application does not impose specific limitations.

[0031] Radius filtering is a method for removing outliers. Its operation involves assuming that each point has at least several neighboring points within its radius, and that the number of points within the radius is greater than or equal to a set threshold. If the number of points within the radius is less than the set threshold, the corresponding point is removed. For example... Figure 2 As shown, the radius of each point is set to d, and the square, triangular, and pentagonal points in the figure are examined respectively: if the threshold is set to 1, the square points will be eliminated; if the threshold is set to 2, the square and pentagonal points will be eliminated. In this embodiment, d can be set to 0.2 and the threshold can be set to 16. The specific settings can be made by those skilled in the art according to the actual situation, and this application does not impose specific limitations.

[0032] The YOLO3D algorithm is based on the YOLO single-stage real-time detection architecture and is optimized for the sparsity of point cloud information and 3D geometric characteristics. It outputs 3D oriented bounding boxes with heading angles using BEV projection and end-to-end regression.

[0033] The testing process for BEVs is as follows: Figure 3 As shown, it consists of three parts: BEV generator, network backbone, and detection head.

[0034] The BEV generator converts point cloud information into BEV feature maps. These BEV feature maps are generated by discretizing the point cloud information in the xy-coordinate plane and then projecting it. During the discretization operation, a resolution parameter needs to be explicitly set. Specifically, a point cloud spatial range is predefined, with dimensions of length × width × height. All point cloud information within this specific spatial range will be mapped to a pixel in the BEV feature map after discretization.

[0035] The network backbone is based on VoxelNet (VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection). In the data preprocessing stage, PointPillars (a column-based point cloud object detection model) simplifies the representation of voxels in VoxelNet, transforming the original three-dimensional voxel structure into a two-dimensional columnar structure. In terms of feature extraction, PointPillars only applies a single voxel feature encoding layer to process the data. In terms of the main network structure, PointPillars abandons the 3D convolution operation in VoxelNet and uses 2D convolution to construct the main network, thereby extracting feature information from the BEV feature map.

[0036] The detection head includes two tasks: target classification and target localization. BEV can not only represent point cloud information in the form of images, but also identify the spatial relationships of obstacles in the three-dimensional world. Therefore, BEV-based detection can be compared with image detection methods: the target classification task is the same as the target classification task in image detection methods, but the localization task is different from that in image detection methods, point cloud localization requires a rotated bounding box.

[0037] Furthermore, in this embodiment, the point cloud can be traversed across multiple frames using the GJK algorithm until the first contact moment between the leg impactor 102 and the test vehicle 101 is determined, and the point cloud information corresponding to the first contact moment is returned. Then, based on the point cloud information corresponding to the first contact moment, this embodiment can use the least squares method to fit a ground reference plane, thereby calculating the height between the bottom of the leg impactor 102 and the ground reference plane, and further calculating the height of the leg impactor 102 from the ground reference plane. , , The attitude angle at the first contact moment is calculated by multiplying the three directional quantities with the x, y, and z directions of the lidar.

[0038] It should be noted that, in this application embodiment, pedestrian protection testing is an important test in vehicle passive safety, generally speaking. Pedestrian protection testing includes adult head shape performance testing, child head shape performance testing, leg shape performance testing, etc., and this application does not impose specific limitations. Among them, leg shape performance testing is further divided into Flex-PLI and aPLI leg shape tests. Flex-PLI and aPLI leg shape tests include sensors that measure bending moment and elongation to evaluate the injury to the pedestrian's legs during a collision. During the test, the regulations stipulate that the height between the bottom of the Flex-PLI leg impactor and the ground reference plane at the first contact moment of the collision between the Flex-PLI leg impactor and the test vehicle is 75mm, as shown in the schematic diagram. Figure 4 As shown; the height between the bottom of the aPLI leg impactor and the ground reference plane at the first contact moment of the collision between the leg impactor and the test vehicle is 25mm, the allowable height deviation is ±10mm, and the attitude angle error in the lateral plane, horizontal plane, and longitudinal plane is ±2°, as illustrated in the diagram. Figure 5 As shown.

[0039] The following describes a method and apparatus for monitoring leg posture in pedestrian protection tests according to embodiments of this application, with reference to the accompanying drawings. Addressing the problem mentioned in the background art regarding the lack of precise determination of the collision moment during a collision between a leg impactor and a vehicle, making it difficult to accurately analyze and evaluate collision details, this application provides a method for monitoring leg posture in pedestrian protection tests. In this method, based on point cloud information collected from the launch time of the leg impactor to the moment of collision between the leg impactor and the test vehicle, the first contact moment between the leg impactor and the test vehicle can be determined, and the corresponding contact point cloud information can be extracted. This allows for the calculation of the height between the bottom of the leg impactor and the ground reference plane, as well as the posture angle of the leg impactor. By utilizing the point cloud information, the height and angle between the leg impactor and the ground reference plane at the first contact moment can be accurately determined, improving efficiency and automatically completing real-time monitoring of leg posture, thus enhancing the accuracy of collision detail analysis and evaluation. Therefore, this solves the problems in related technologies, such as the lack of precise determination of the collision moment during a collision between a leg impactor and a vehicle, making it difficult to accurately analyze and evaluate collision details.

[0040] Specifically, Figure 6 This is a flowchart of a method for monitoring leg posture in a pedestrian protection test according to an embodiment of this application.

[0041] like Figure 6 As shown, the method for monitoring leg posture in this pedestrian protection experiment includes the following steps: In step S601, point cloud information is collected from the launch time of the leg impactor to the collision time between the leg impactor and the test vehicle.

[0042] It is understood that, in the embodiments of this application, the leg impactor is a collision testing device that simulates the lower limbs of a pedestrian. It is typically composed of a rigid structure (representing bones), foam material (representing muscles), and a rubber outer layer (representing skin). It can replace the lower limbs of a real pedestrian in vehicle collision tests to assess the potential damage to the legs of pedestrians caused by vehicle bumpers, hoods, and other components. The leg impactor may include, but is not limited to, the Flex-PLI leg impactor and the aPLI leg impactor; this application does not impose specific limitations.

[0043] Point cloud information can be understood as a set of spatial points obtained through three-dimensional scanning technology (such as lidar, structured light, etc., which are not specifically limited in this application). It may include, but is not limited to, ground point cloud information, test vehicle point cloud information, and leg impactor point cloud information, which are not specifically limited in this application.

[0044] The launch moment can be understood as the moment when the leg-shaped impactor is released from the launching device (such as a hydraulic propulsion system); the collision moment can be understood as the moment when the maximum impact force, maximum deformation, or specific collision conditions (such as a speed change threshold) are reached in the collision event between the leg-shaped impactor and the test vehicle. This application does not impose any specific limitations.

[0045] In some embodiments, the launch time of the leg impactor can be taken as the start time of point cloud information acquisition, and the collision time when the leg impactor collides with the test vehicle can be taken as the end time of point cloud information acquisition, thereby obtaining point cloud information from the start time to the end time.

[0046] For example, in combination Figure 1 As shown, in this embodiment of the application, point cloud information can be collected by lidar during the complete time period from the moment the leg impactor is launched to the moment the leg impactor collides with the test vehicle.

[0047] Optionally, in one embodiment of this application, collecting point cloud information from the launch time of the leg impactor to the collision time between the leg impactor and the test vehicle includes: detecting whether the point cloud information meets preset conditions; if the point cloud information does not meet the preset conditions, processing the point cloud information until processed point cloud information that meets the preset conditions is obtained.

[0048] In some embodiments, the present application embodiments may first detect whether the point cloud information meets preset conditions, and if the preset conditions are not met, start the point cloud information processing flow and repeat the processing until the processed point cloud information that meets the preset conditions is obtained.

[0049] For example, in combination Figure 1 As shown, in this embodiment, radius filtering can be used to remove noise points in the point cloud information, and then pass-through filtering can be used to obtain point cloud information and elevation difference information within the range. Points that are not within the range of values ​​in the x, y, and z dimensions under the lidar coordinate system are filtered out, thus narrowing the range of the detected point cloud. Finally, the YOLO3D network is used for detection to identify the effective multi-frame point cloud information of the test vehicle, the leg impactor, and the ground, thereby obtaining the processed point cloud information that meets the preset conditions.

[0050] In step S602, based on the point cloud information, the first contact time between the leg-shaped impactor and the test vehicle is determined, and the contact point cloud information corresponding to the first contact time is extracted.

[0051] It is understood that, in the embodiments of this application, the first contact moment can be understood as the time point at which the leg-shaped impactor first physically contacts the test vehicle.

[0052] In some embodiments, this application can first determine the first contact moment between the leg impactor and the test vehicle based on point cloud information, and extract the contact point cloud information corresponding to the first contact moment. The contact point cloud information must meet the requirements of the regulations regarding the height and attitude angle between the bottom of the leg impactor and the ground reference plane at the first contact moment.

[0053] For example, in this application embodiment, the first contact time can be determined by iterating through the point cloud information using the GJK algorithm based on the point cloud information, and the contact point cloud information corresponding to the first contact time between the leg-shaped impactor and the test vehicle can be extracted.

[0054] Optionally, in one embodiment of this application, determining the first contact time between the leg-shaped impactor and the test vehicle based on point cloud information includes: determining multiple frames of point cloud information corresponding to the point cloud information; slicing the multiple frames of point cloud information along the longitudinal plane of the test vehicle through the center of the leg-shaped impactor to obtain a first circumscribed rectangle of the leg-shaped impactor and a second circumscribed rectangle of the test vehicle; determining a first support point within the first circumscribed rectangle and a second support point within the second circumscribed rectangle; constructing a Minkowski difference set based on the first and second support points; constructing a simplex based on the support points in the Minkowski difference set; and determining the first contact time based on the simplex.

[0055] It is understood that the embodiments of this application can determine the first contact time based on the GJK algorithm, the content of which is as follows: Figure 7 As shown, it includes: Step S701: Output the point cloud information frame by frame to generate multiple frames of point cloud information.

[0056] Step S702: Slice the frame point cloud information.

[0057] In this embodiment, the frame point cloud information can be traversed, and the test vehicle point cloud information and the leg impactor point cloud information in the first frame point cloud information can be sliced ​​along the longitudinal plane of the test vehicle through the center of the leg impactor to form the first circumscribed rectangle of the test vehicle and the second circumscribed rectangle of the leg impactor in the longitudinal plane of the test.

[0058] Step S703: Given the initial direction of motion, determine the first support point and the second support point.

[0059] The initial direction of motion can be understood as the line connecting the centers of the first and second circumscribed rectangles pointing in the direction of the test vehicle.

[0060] Furthermore, in this embodiment, the support points of the first circumscribed rectangle in the initial motion direction are calculated, and the initial motion direction is reversed to calculate the support points of the second circumscribed rectangle in the corresponding motion direction.

[0061] Step S704: Construct the Minkowski difference set based on the first support point and the second support point.

[0062] Among them, the Minkowski difference set uses polygons. All points, minus the polygon A set of points obtained from all points in the set can be, but is not limited to, represented as: , In the GJK algorithm, it is not necessary to obtain the complete Minkowski difference polygon; only a difference polygon that includes the origin needs to be calculated. Furthermore, the point cloud slices in this embodiment are in two-dimensional space; therefore, only a triangle is needed, and such a difference polygon is called a simplex.

[0063] Furthermore, in this embodiment, the area method can be used to determine whether a point is inside a triangle. That is, the point is connected to the three vertices of the triangle to form three new triangles. The areas of the three new triangles are equal when the point is inside the triangle and greater than the area of ​​the original triangle when the point is outside the triangle. The formula for calculating the area of ​​the triangle can be Heron's formula. The specific formula can be set by those skilled in the art according to the actual situation. This application does not impose any specific restrictions.

[0064] Step S705: Construct a simplex based on the support points in the Minkowski difference set.

[0065] In the GJK algorithm, the simplex can be understood as a polytope in k-dimensional space, which is a convex hull composed of k+1 vertices. In one-dimensional space, the simplex refers to a line segment; in two-dimensional space, it refers to a triangle or tetrahedron. The points within the simplex are called support points.

[0066] Step S706: Determine the first contact time based on the simplex.

[0067] Optionally, in one embodiment of this application, determining the first contact moment based on the simplex includes: acquiring the target support point of the simplex; determining the target direction from the target support point to the origin of the coordinate system based on the target support point; determining the iteration direction of the simplex based on the target direction; determining the contact result between the leg-shaped impactor and the test vehicle based on the iteration direction; and determining the first contact moment based on the contact result.

[0068] It is understood that, in the embodiments of this application, the target support point can be understood as the point on the Minkowski difference set that is farthest from the origin along a specific direction (such as the current iteration direction or its opposite direction). It can be calculated by the support function, such as the first support point, the second support point, and the third support point, etc. This application does not impose any specific restrictions.

[0069] The support function first calculates the point corresponding to the maximum value of the vector dot product, and then uses this point as the farthest vertex on the corresponding direction vector, thus determining the farthest vertex of the polygon in a certain direction. For example, for vectors... , The formula for calculating the dot product of vectors can be, but is not limited to, as follows: .

[0070] The iteration direction can be understood as the direction vector used to query the next support point of the Minkowski difference set in each iteration. In general, it is determined based on the geometric properties of the simplex.

[0071] The contact result is used to indicate whether two objects are in contact, the contact location, and the depth of penetration. It may include, but is not limited to, no contact: the Minkowski difference set does not contain the origin, and the two objects have not collided; and contact: the Minkowski difference set contains the origin, and further calculations are required.

[0072] In some embodiments, the content of determining the first contact moment based on the simplex in this application embodiment is as follows: First, the first support point in the simplex can be used as the target support point, and the target direction can be determined based on the direction of the first support point toward the origin of the coordinate system. The target direction is then used as the direction of the next iteration. Based on the iteration direction, the support points of the first circumscribed rectangle and the second circumscribed rectangle in the iteration direction are calculated again through the support function. Then, the Minkowski difference is performed to obtain the second support point of the simplex, which is then placed in the simplex.

[0073] Furthermore, this embodiment of the application can determine whether the leg-shaped impactor is in contact with the test vehicle. Specifically, this embodiment of the application can draw a straight line perpendicular to the iteration direction through the origin. If the line cannot separate the two sides, it means that the simplex cannot contain the origin, and the contact result can be directly determined as no contact. The next multi-frame point cloud information is then traversed. If the line can separate the first support point and the second support point to both sides, it means that the simplex can cross the origin. Based on the normal vector of the line connecting the first support point and the second support point toward the origin, the target direction is determined, and this target direction is used as the direction for the next iteration. Then, the support function is used to obtain the two support points of the first and second bounding rectangles respectively, and the process is performed. The Minkowski difference is used to obtain the third support point of the simplex. Further, in this embodiment, it is determined whether the origin is inside the simplex. If it is not inside the simplex, the contact result is determined to be non-contact, and the next multi-frame point cloud information is traversed. Otherwise, the third support point is placed in the simplex, and the following steps are performed: it is determined whether the triangle formed by the three vertices in the current simplex contains the origin. If it does, the contact result is determined to be non-contact, and the current frame point cloud information is returned. Otherwise, all vertices are traversed. If the new vertex already exists in the current simplex, the contact result is determined to be non-contact. Otherwise, a new simplex is constructed with the new vertex, and it is re-determined whether the triangle formed by the three vertices in the current simplex contains the origin.

[0074] In step S603, based on the contact point cloud information, the height between the bottom of the leg impactor and the ground reference plane and the attitude angle of the leg impactor are calculated.

[0075] It is understood that in the embodiments of this application, height can be understood as the vertical distance from the bottom of the leg impactor to the ground reference plane, reflecting the height of the leg impactor above the ground at the first contact moment; attitude angle can be understood as the spatial attitude of the leg impactor relative to the ground reference plane, usually described by rotation angle (such as pitch angle, yaw angle, roll angle) or direction vector.

[0076] In some embodiments, this application can calculate the vertical height of the bottom of the leg impactor relative to the ground reference plane based on the acquired contact point cloud information, and calculate the attitude angle of the leg impactor in space.

[0077] Optionally, in one embodiment of this application, calculating the height between the bottom of the leg impactor and the ground reference plane and the attitude angle of the leg impactor based on contact point cloud information includes: determining the ground reference plane based on point cloud information; determining a first height of the ground reference plane in the target coordinate system and a second height of the bottom of the leg impactor in the target coordinate system based on the ground reference plane; and calculating the height between the bottom of the leg impactor and the ground reference plane based on the first height and the second height.

[0078] In some embodiments, this application first uses point cloud information and least squares method to fit a ground reference surface. The main contents include: for Given a point cloud dataset, the equation for the fitted plane is defined as follows: The constraints are: To obtain the best-fit plane, the sum of the squared distances from the k nearest neighbors of a point to that plane must be minimized, i.e., satisfying: ,in, It is any point in the point cloud data The distance to this plane, where, Therefore, in this embodiment of the application, the equation is solved by singular value decomposition matrix. , , , Four parameters make The minimum value is then used to fit the ground reference surface.

[0079] Furthermore, in this embodiment, the first height coordinate of the ground reference plane in the lidar coordinate system is calculated based on the ground reference plane. And project the point cloud of the current frame's leg-shaped impactor onto the lidar coordinate system. On the axis, the second height coordinates of the bottom end of the leg-shaped impactor in the lidar coordinate system are obtained. Then, based on the first and second height coordinates, the height h between the bottom of the leg impactor and the ground reference plane is calculated. The calculation formula can be, but is not limited to, the following: , It should be noted that, in the embodiments of this application, the target coordinate system can be understood as the lidar coordinate system, or it can be other coordinate systems. The specific settings can be made by those skilled in the art according to the actual situation, and this application does not impose any specific restrictions.

[0080] Optionally, in one embodiment of this application, calculating the height between the bottom of the leg impactor and the ground reference plane and the attitude angle of the leg impactor based on the contact point cloud information includes: determining a first direction vector of the leg impactor based on the contact point cloud information; obtaining a second direction vector of the target coordinate system; and calculating the attitude angle based on the first direction vector and the second direction vector.

[0081] It is understood that, in the embodiments of this application, the first direction vector of the leg-shaped impactor may include, but is not limited to, an axial direction vector, which represents the direction vector along the long axis of the leg-shaped impactor and reflects the front-back extension direction of the leg-shaped impactor; a width direction vector, which represents the direction vector along the width direction of the leg-shaped impactor and reflects the left-right width of the leg-shaped impactor; and a thickness direction vector, which represents the direction vector along the thickness direction of the leg-shaped impactor and reflects the front-back thickness of the leg-shaped impactor, etc., and this application does not impose specific limitations.

[0082] The second direction vector of the target coordinate system may include, but is not limited to, a lateral direction vector, which represents a horizontal vector in the target coordinate system along the front-back direction of the test vehicle or ground reference plane (such as the x-axis direction); a longitudinal direction vector, which represents a horizontal vector in the target coordinate system along the left-right direction of the vehicle or ground reference plane (such as the y-axis direction); and a vertical direction vector, which represents a direction vector in the target coordinate system perpendicular to the ground reference plane (such as the z-axis direction).

[0083] In some embodiments, this application can determine the first direction vector of the leg-shaped impactor based on contact point cloud information, while simultaneously obtaining the second direction vector of the target coordinate system, and then calculate the attitude angle based on the first and second direction vectors.

[0084] For example, in embodiments of this application, principal component analysis can be used to obtain the axial direction vector of the leg-shaped impactor. Using the right-hand rule, we can obtain the direction vectors for the other two directions, such as the width direction vector. Thickness direction vector And obtain the three direction vectors of the lidar coordinate system, such as the horizontal direction vector. Vertical direction vector Vertical direction vector Furthermore, in this embodiment, based on the first and second direction vectors, the angles between the vectors in the three directions are calculated to obtain the corresponding attitude angles, which may include, but are not limited to, the following: , , .

[0085] , , , in, This indicates the angle between the axial direction of the impactor and the lateral direction of the lidar, reflecting the attitude deviation of the impactor in the lateral direction of the lidar. This indicates the angle between the width direction of the impactor and the longitudinal direction of the lidar, reflecting the attitude deviation of the impactor in the longitudinal direction of the lidar. This indicates the angle between the thickness direction of the impactor and the vertical direction of the lidar, reflecting the attitude deviation of the impactor in the vertical direction of the lidar.

[0086] The working principle of the pedestrian protection test leg posture monitoring method proposed in this application will be introduced below with reference to a specific embodiment.

[0087] in, Figure 8 This is a flowchart illustrating the working principle of a method for monitoring leg posture in pedestrian protection testing according to an embodiment of this application.

[0088] like Figure 8 As shown, the method for monitoring leg posture in this pedestrian protection experiment can be divided into three parts: The first part includes preprocessing and point cloud detection.

[0089] The embodiments of this application can be combined with Figure 1 As shown, point cloud information is collected using lidar during the complete time period from the moment the leg impactor is launched to the moment the leg impactor collides with the test vehicle. When the point cloud information does not meet the preset conditions, the point cloud information is preprocessed, such as removing noise points, narrowing the detection range, and identifying valid point cloud information. This application does not impose specific limitations, thereby obtaining processed point cloud information that meets the preset conditions.

[0090] The second part includes multi-frame point cloud information and GJK.

[0091] In this embodiment, point cloud information can be output frame by frame to generate multiple frames of point cloud information, and the first contact time can be determined based on the GJK algorithm. The process is as follows: Figure 9 As shown, it mainly includes: Step S901: Generate multi-frame point cloud information.

[0092] Step S902: Slice the point cloud information.

[0093] Step S903: Given the initial direction of motion.

[0094] The initial direction of motion can be understood as the line connecting the centers of the first and second circumscribed rectangles pointing in the direction of the test vehicle.

[0095] Step S904: Solve for the support points in the current initial motion direction.

[0096] In this embodiment, the support points of the first circumscribed rectangle in the initial motion direction are calculated, and the support points of the second circumscribed rectangle in the corresponding motion direction are calculated by reversing the initial motion direction.

[0097] Step S905: Take the target direction toward the origin as the direction for the next iteration.

[0098] In this embodiment of the application, the first support point in the simplex can be used as the target support point, and the target direction can be determined based on the direction of the first support point toward the origin of the coordinate system, and the target direction can be used as the direction of the next iteration.

[0099] Step S906: Solve for the next support point according to the iteration direction.

[0100] In this embodiment, the support points of the first and second circumscribed rectangles in the iteration direction can be recalculated using the support function based on the iteration direction, and then the Minkowski difference can be calculated to obtain the second support point of the simplex, which is then placed into the simplex.

[0101] Step S907: Based on the point cloud information of the current frame, determine whether the line connecting the two support points crosses the origin of the coordinate system.

[0102] In this embodiment, based on the current frame point cloud information, it can determine whether the line connecting the two support points crosses the origin, that is, whether the leg-shaped impactor is in contact with the test vehicle. Specifically, this embodiment can draw a straight line perpendicular to the iteration direction through the origin. If the line cannot separate the two sides, it means that the simplex cannot contain the origin, and the contact result can be directly determined as no contact. Step S901 is executed to traverse the next multi-frame point cloud information. If the line can separate the first support point and the second support point to both sides, it means that the simplex can cross the origin, and step S908 is executed.

[0103] Step S908: Determine the direction of the next iteration.

[0104] In this embodiment, the target direction can be determined based on the normal vector of the line connecting the first support point and the second support point toward the origin of the coordinate system, and this target direction can be used as the direction for the next iteration.

[0105] Step S909: Generate the third support point.

[0106] In this embodiment, the support function can be used to obtain two support points of the first and second circumscribed rectangles respectively, and the Minkowski difference can be performed to obtain the third support point of the simplex.

[0107] Step S910: Determine whether the origin of the coordinate system is inside the simplex.

[0108] In this embodiment of the application, if the origin of the coordinate system is not within the simplex, the contact result is determined to be no contact, and step S911 is executed; otherwise, step S913 is executed.

[0109] Step S911: Traverse all vertices.

[0110] Step S912: Determine whether the current vertex has been traversed.

[0111] If the process has already been traversed, proceed to step S901; otherwise, proceed to step S910.

[0112] Step S913: The leg-shaped impactor and the test vehicle are in contact.

[0113] The third part includes least-squares fitting of the plane and calculation of attitude angles and heights.

[0114] In this embodiment, the least squares method can be used to fit the ground reference surface, and based on the ground reference surface, the first height coordinate of the ground reference surface in the lidar coordinate system can be calculated. And project the point cloud of the current frame's leg-shaped impactor onto the lidar coordinate system. On the axis, the second height coordinates of the bottom end of the leg-shaped impactor in the lidar coordinate system are obtained. Then, based on the first and second height coordinates, the height h between the bottom of the leg-shaped impactor and the ground reference plane is calculated.

[0115] Furthermore, the embodiments of this application can utilize principal component analysis to obtain the axial direction vector of the leg-shaped impactor. Using the right-hand rule, we can obtain the direction vectors for the other two directions, such as the width direction vector. Thickness direction vector And obtain the three direction vectors of the lidar coordinate system, such as the horizontal direction vector. Vertical direction vector Vertical direction vector Furthermore, in this embodiment, based on the first and second direction vectors, the angles between the vectors in the three directions are calculated to obtain the corresponding attitude angles, which may include, but are not limited to, the following: , , .

[0116] The method for monitoring leg posture in pedestrian protection tests proposed in this application can determine the first contact moment between the leg impactor and the test vehicle based on point cloud information collected from the launch time of the leg impactor to the moment of collision between the leg impactor and the test vehicle. The corresponding contact point cloud information is then extracted to calculate the height between the bottom of the leg impactor and the ground reference plane, as well as the posture angle of the leg impactor. By using the point cloud information, the height and angle between the leg impactor and the ground reference plane at the first contact moment can be accurately determined, improving efficiency and automatically completing real-time monitoring of leg posture, thus enhancing the accuracy of collision detail analysis and evaluation. This solves the problems in related technologies, such as the lack of accurate determination of the collision moment during the collision between the leg impactor and the vehicle, making it difficult to accurately analyze and evaluate collision details.

[0117] Next, referring to the accompanying drawings, a monitoring device for leg posture in pedestrian protection testing according to an embodiment of this application is described.

[0118] Figure 10 This is a block diagram of a pedestrian protection test leg posture monitoring device provided according to an embodiment of this application.

[0119] like Figure 10 As shown, the monitoring device 10 for the leg posture of the pedestrian protection test includes: a data acquisition module 100, a determination module 200, and a calculation module 300.

[0120] The acquisition module 100 is used to acquire point cloud information from the launch time of the leg impactor to the collision time between the leg impactor and the test vehicle.

[0121] The determination module 200 is used to determine the first contact time between the leg-shaped impactor and the test vehicle based on point cloud information, and to extract the contact point cloud information corresponding to the first contact time.

[0122] The calculation module 300 is used to calculate the height between the bottom of the leg impactor and the ground reference plane and the attitude angle of the leg impactor based on the contact point cloud information.

[0123] Optionally, in one embodiment of this application, the acquisition module 100 includes a detection unit and a processing unit.

[0124] The detection unit is used to detect whether the point cloud information meets the preset conditions.

[0125] The processing unit is used to process the point cloud information if the point cloud information does not meet the preset conditions, until the processed point cloud information that meets the preset conditions is obtained.

[0126] Optionally, in one embodiment of this application, the determining module 200 includes: a first determining unit, a generating unit, a second determining unit, a first constructing unit, a second constructing unit, and a third determining unit.

[0127] The first determining unit is used to determine the multi-frame point cloud information corresponding to the point cloud information based on the point cloud information.

[0128] The generation unit is used to slice the multi-frame point cloud information along the longitudinal plane of the test vehicle through the center of the leg-shaped impactor to obtain the first circumscribed rectangle of the leg-shaped impactor and the second circumscribed rectangle of the test vehicle.

[0129] The second determining unit is used to determine the first support point within the first circumscribed rectangle and the second support point within the second circumscribed rectangle.

[0130] The first building unit is used to construct the Minkowski difference set based on the first support point and the second support point.

[0131] The second building unit is used to construct the simplex based on the support points in the Minkowski difference set.

[0132] The third determining unit is used to determine the first contact time based on the simplex.

[0133] Optionally, in one embodiment of this application, the third determining unit includes: an acquisition subunit, a first determining subunit, a second determining subunit, a third determining subunit, and a fourth determining subunit.

[0134] Among them, the acquisition sub-unit is used to acquire the target support points of the simplex.

[0135] The first determining sub-unit is used to determine the target direction from the target support point to the origin of the coordinate system based on the target support point.

[0136] The second determining sub-unit is used to determine the iteration direction of the simplex based on the target direction.

[0137] The third determining sub-unit is used to determine the contact results between the leg-shaped impactor and the test vehicle based on the iteration direction.

[0138] The fourth determining sub-unit is used to determine the first contact moment based on the contact result.

[0139] Optionally, in one embodiment of this application, the calculation module 300 includes: a fourth determining unit, a fifth determining unit, and a first calculation unit.

[0140] The fourth determining unit is used to determine the ground reference surface based on point cloud information.

[0141] The fifth determining unit is used to determine the first height of the ground reference plane in the target coordinate system and the second height of the bottom of the leg-shaped impactor in the target coordinate system based on the ground reference plane.

[0142] The first calculation unit is used to calculate the height between the bottom of the leg-shaped impactor and the ground reference plane based on the first height and the second height.

[0143] Optionally, in one embodiment of this application, the calculation module 300 includes: a sixth determining unit, an acquisition unit, and a second calculation unit.

[0144] The sixth determining unit is used to determine the first direction vector of the leg-shaped impactor based on the contact point cloud information.

[0145] The acquisition unit is used to acquire the second direction vector of the target coordinate system.

[0146] The second calculation unit is used to calculate the attitude angle based on the first direction vector and the second direction vector.

[0147] It should be noted that the explanation of the aforementioned embodiment of the method for monitoring the leg posture of pedestrian protection test also applies to the monitoring device for the leg posture of pedestrian protection test in this embodiment, and will not be repeated here.

[0148] The pedestrian protection leg posture monitoring device proposed in this application can determine the first contact moment between the leg impactor and the test vehicle based on point cloud information collected from the launch time of the leg impactor to the collision time between the leg impactor and the test vehicle. It also extracts the corresponding contact point cloud information to calculate the height between the bottom of the leg impactor and the ground reference plane, as well as the posture angle of the leg impactor. By using the point cloud information, the device accurately determines the height and angle between the leg impactor and the ground reference plane at the first contact moment, improving efficiency and automatically completing real-time monitoring of the leg posture, thus enhancing the accuracy of collision detail analysis and evaluation. This solves the problems in related technologies, such as the lack of accurate determination of the collision moment during the collision between the leg impactor and the vehicle, making it difficult to accurately analyze and evaluate collision details.

[0149] Figure 11 This is a schematic diagram of the structure of a vehicle according to an embodiment of this application. The vehicle may include: The memory 1101, the processor 1102, and the computer program stored on the memory 1101 and executable on the processor 1102.

[0150] When the processor 1102 executes the program, it implements the method for monitoring the leg posture of pedestrian protection tests provided in the above embodiments.

[0151] Furthermore, the vehicle also includes: Communication interface 1103 is used for communication between memory 1101 and processor 1102.

[0152] The memory 1101 is used to store computer programs that can run on the processor 1102.

[0153] The memory 1101 may include high-speed RAM memory, and may also include non-volatile memory, such as at least one disk storage.

[0154] If the memory 1101, processor 1102, and communication interface 1103 are implemented independently, then the communication interface 1103, memory 1101, and processor 1102 can be interconnected via a bus to complete communication between them. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. The bus can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 11 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0155] Optionally, in a specific implementation, if the memory 1101, processor 1102, and communication interface 1103 are integrated on a single chip, then the memory 1101, processor 1102, and communication interface 1103 can communicate with each other through an internal interface.

[0156] The processor 1102 may be a central processing unit (CPU), an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of this application.

[0157] This application also provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the above-described method for monitoring leg posture in pedestrian protection experiments.

[0158] This application also provides a computer program product, including a computer program that, when executed, implements the above-described method for monitoring leg posture in pedestrian protection experiments.

[0159] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0160] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "N" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0161] Any process or method described in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or N executable instructions for implementing custom logic functions or processes, and the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as should be understood by those skilled in the art to which embodiments of this application pertain.

[0162] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include: an electrical connection having one or more wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). In addition, computer-readable media can even be paper or other suitable media on which programs can be printed, because programs can be obtained electronically by optically scanning paper or other media, followed by editing, interpreting or otherwise processing as necessary, and then stored in computer memory.

[0163] It should be understood that the various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, the N steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. If implemented in hardware, as in another embodiment, it can be implemented using any one or more of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0164] Those skilled in the art will understand that all or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.

[0165] Furthermore, the functional units in the various embodiments of this application can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into a module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0166] The storage medium mentioned above can be a read-only memory, a disk, or an optical disk, etc. Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of this application.

Claims

1. A method for monitoring leg posture in pedestrian protection experiments, characterized in that, Includes the following steps: Point cloud information was collected from the launch time of the leg impactor to the moment of collision between the leg impactor and the test vehicle; Based on the point cloud information, the first contact time between the leg-shaped impactor and the test vehicle is determined, and the contact point cloud information corresponding to the first contact time is extracted. Based on the contact point cloud information, the height between the bottom of the leg-shaped impactor and the ground reference plane and the attitude angle of the leg-shaped impactor are calculated.

2. The method according to claim 1, characterized in that, The point cloud information collected from the launch time of the leg impactor to the collision time between the leg impactor and the test vehicle includes: Detect whether the point cloud information meets preset conditions; If the point cloud information does not meet the preset conditions, the point cloud information is processed until processed point cloud information that meets the preset conditions is obtained.

3. The method according to claim 1, characterized in that, Determining the first contact moment between the leg-shaped impactor and the test vehicle based on the point cloud information includes: Based on the point cloud information, determine the multi-frame point cloud information corresponding to the point cloud information; The multi-frame point cloud information is sliced ​​along the longitudinal plane of the test vehicle through the center of the leg impactor to obtain the first circumscribed rectangle of the leg impactor and the second circumscribed rectangle of the test vehicle. Determine the first support point within the first bounding rectangle and the second support point within the second bounding rectangle; Based on the first support point and the second support point, construct the Minkowski difference set; Based on the support points in the Minkowski difference set, construct a simplex; The first contact time is determined based on the simplex.

4. The method according to claim 3, characterized in that, Determining the first contact time based on the simplex includes: Obtain the target support points of the simplex; Based on the target support point, determine the target direction from the target support point to the origin of the coordinate system; Based on the target direction, determine the iteration direction of the simplex; Based on the iterative direction, the contact result between the leg-shaped impactor and the test vehicle is determined; Based on the contact results, the first contact time is determined.

5. The method according to claim 1, characterized in that, The calculation of the height between the bottom of the leg impactor and the ground reference plane and the attitude angle of the leg impactor based on the contact point cloud information includes: Based on the point cloud information, the ground reference surface is determined; Based on the ground reference plane, determine the first height of the ground reference plane in the target coordinate system and the second height of the bottom of the leg-shaped impactor in the target coordinate system; Based on the first height and the second height, the height between the bottom of the leg-shaped impactor and the ground reference plane is calculated.

6. The method according to claim 5, characterized in that, The calculation of the height between the bottom of the leg impactor and the ground reference plane and the attitude angle of the leg impactor based on the contact point cloud information includes: Based on the contact point cloud information, the first direction vector of the leg-shaped impactor is determined; Obtain the second direction vector of the target coordinate system; The attitude angle is calculated based on the first direction vector and the second direction vector.

7. A monitoring device for leg posture in pedestrian protection experiments, characterized in that, include: The acquisition module is used to acquire point cloud information from the launch time of the leg impactor to the collision time between the leg impactor and the test vehicle; The determination module is used to determine the first contact time between the leg-shaped impactor and the test vehicle based on the point cloud information, and to extract the contact point cloud information corresponding to the first contact time. The calculation module is used to calculate the height between the bottom of the leg impactor and the ground reference plane and the attitude angle of the leg impactor based on the contact point cloud information.

8. A vehicle, characterized in that, include: The system includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the method for monitoring leg posture in pedestrian protection testing as described in any one of claims 1-6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, The program is executed by the processor to implement the method for monitoring leg posture in pedestrian protection tests as described in any one of claims 1-6.

10. A computer program product, characterized in that, Includes a computer program, which, when executed, is used to implement the method for monitoring leg posture in pedestrian protection tests as described in any one of claims 1-6.

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