Vehicle seat control method and system, computer equipment and storage medium

Abstract

The invention provides a vehicle seat control method and system, computer equipment and a storage medium, and the method comprises the steps: extracting road surface features according to target point cloud data, and recognizing a pit region in a road surface based on the road surface features; and the pit area is associated with the vehicle driving state, and damping control is conducted on the vehicle seat according to the association result. According to the method, the pit area is associated with the vehicle driving state to perform damping control on the vehicle seat, so that the vehicle seat damping system can adapt to complex road conditions such as pits, and the adaptability and effect of seat damping are improved; in addition, the vibration impact of the vehicle seat can be effectively reduced, so that passengers can obtain more stable riding experience under complex road conditions. According to the method, the vehicle seat is subjected to damping control, so that the influence of road impact on passengers is reduced, the stability of vehicle control and the safety of the passengers are improved, the fatigue and discomfort of a driver and the passengers in long-distance driving are reduced, and the driving comfort is improved.

Description

Technical Field

[0001] The present application relates to the field of seat technology, and in particular to a vehicle seat control method, system, computer equipment and storage medium. Background Art

[0002] Traditional seat shock absorption systems mainly rely on mechanical springs and damping structures to reduce the vibration transmitted to passengers during driving through passive shock absorption. However, when dealing with complex road conditions (such as potholes, speed bumps, etc.), the traditional seat shock absorption system has limited shock absorption effect and it is difficult to achieve active response and adaptive adjustment to vibration. Summary of the invention

[0003] In view of the above-mentioned shortcomings of the prior art, the purpose of the present application is to provide a vehicle seat control method, system, computer device and storage medium to solve the technical problem that the traditional seat shock absorption system has limited shock absorption effect under complex road conditions.

[0004] To achieve the above objectives and other related objectives, the present application provides a vehicle seat control method, comprising the following steps:

[0005] Acquire target point cloud data, wherein the target point cloud data includes road surface point cloud data;

[0006] Extracting road surface features according to the target point cloud data, and identifying pothole areas in the road surface based on the road surface features, wherein the road surface features include at least one of road surface height features, road surface curvature features, and road surface slope features;

[0007] The pit area is associated with the vehicle driving state, and the vehicle seat is subjected to shock absorption control according to the association result; wherein the vehicle driving state includes at least one of the vehicle speed, the vehicle position, and the vehicle steering angle.

[0008] In one embodiment of the present application, if the vehicle seat includes a magnetorheological seat, the process of performing shock absorption control on the vehicle seat according to the correlation result includes:

[0009] Calculating a target current for adjusting a damping response of a magnetorheological shock absorber according to an association result corresponding to the pit area and the vehicle driving state, and adjusting the magnetorheological shock absorber based on the target current to obtain a damping force fed back by the magnetorheological shock absorber;

[0010] The vibration amplitude of the seat acceleration sensor based on the damping force feedback is read, and when the vibration amplitude is not within a preset amplitude range, the damping force is updated until the vibration amplitude corresponding to the updated damping force is within the preset amplitude range.

[0011] In one embodiment of the present application, the process of extracting road features according to target point cloud data includes:

[0012] Performing three-dimensional space surface fitting on the coordinates of each point cloud in the road surface point cloud data; including:

[0013]

[0014] Wherein, (x, y) represents the coordinates of each point cloud in the road surface point cloud data, z represents the height of the fitting surface, a0, ..., a5 represent the fitting parameters, represents the fitting error;

[0015] Calculating a height difference of a fitted surface based on a maximum height value of the fitted surface and a minimum height value of the fitted surface, and using the height difference of the fitted surface as a road surface height feature; comprising:

[0016] Δz=z1-z2;

[0017] In the formula, Δz represents the height difference of the fitting surface, z1 represents the maximum height value of the fitting surface, and z2 represents the minimum height value of the fitting surface;

[0018] Calculating the curvature of the fitted surface and using the curvature of the fitted surface as a road curvature feature; comprising:

[0019]

[0020] In the formula, κ represents the curvature of the fitting surface;

[0021] Calculating the inclination based on the gradient of the fitted surface, and using the inclination as a road surface slope feature; comprising:

[0022]

[0023] In the formula, Represents the gradient of the fitting surface in the x and y directions;

[0024] Slope indicates the inclination.

[0025] In one embodiment of the present application, the process of identifying a pothole area in a road surface based on the road surface features includes:

[0026] Calculate the road surface point P i (x i ,y i ,z i ) The residual value of the fitting surface corresponding to the road surface characteristics; including:

[0027]

[0028] In the formula, Represents a point (x i ,y i ) corresponds to the height value of the fitted surface, d i Represents road point P i (x i ,y i ,z i ) a residual value of a fitting surface corresponding to the road surface feature;

[0029] The residual values ​​of all road points are compared with the preset threshold T th Compare and take the residual value greater than the preset threshold T th The road surface points are marked as abnormal points;

[0030] All marked abnormal points are spatially clustered, and each clustering result is used as a pothole area to obtain the pothole area in the road surface.

[0031] In one embodiment of the present application, the method further includes:

[0032] Calculate the center position (x) of the pit area based on the point cloud data of the pit area center ,y center );include:

[0033]

[0034] In the formula, n represents the number of point clouds in the pit area;

[0035] According to the lowest point P of the pit area min (x min ,y min ,z min ), calculate the depth d of the pit area pothole ;include:

[0036]

[0037] In the formula, Represents a point (x min ,y min ) corresponds to the height of the fitted surface, z min Represents a point (x min ,y min )'s actual height.

[0038] In one embodiment of the present application, after obtaining the pothole area in the road surface, the method further includes:

[0039] Establish the local surface equation of the pit area; including: z(x,y)=a0+a1x+a2y+a3x 2 +a4xy+a5y 2 ;

[0040] The local curvature of the pit area is calculated by the local surface equation of the pit area; including:

[0041]

[0042] Where Curvature represents the local curvature of the pit area, f x ,f y represents the surface partial derivative of the pit area, f xx ,f yy ,f xy represents the second-order partial derivative of the pit area.

[0043] In one embodiment of the present application, the process of associating the pit area with the vehicle driving state includes:

[0044] Get the vehicle position (x) at time k k ,y k ), the vehicle direction angle θ at time k k , the vehicle speed v at time k k , the vehicle steering angle δ at time k k , the pit position d at time k pothole,k and the pit depth h at time k pothole,k ;

[0045] Based on the vehicle position (x k ,y k ), the vehicle direction angle θ at time k k , the vehicle speed v at time k k , the vehicle steering angle δ at time k k , the pit position d at time k pothole,k and the pit depth h at time k pothole,k , define the state vector x k ;include:

[0046]

[0047] The state vector x k Inputting into a pre-determined or real-time determined observation function to associate the pothole area with the vehicle driving state; including:

[0048] z k =h(x k )+v k ;

[0049]

[0050] Where h() is the observation function, v k is the measurement noise.

[0051] The present application also provides a vehicle seat control system, the system comprising:

[0052] A data acquisition module, used to acquire target point cloud data, wherein the target point cloud data includes road surface point cloud data;

[0053] A pothole recognition module, used to extract road surface features according to the target point cloud data, and recognize pothole areas in the road surface based on the road surface features, wherein the road surface features include at least one of road surface height features, road surface curvature features, and road surface slope features;

[0054] The shock absorption control module is used to associate the pit area with the vehicle driving state and perform shock absorption control on the vehicle seat according to the association result; wherein the vehicle driving state includes at least one of the vehicle speed, the vehicle position, and the vehicle steering angle.

[0055] The present application also provides a computer device, including a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of any one of the above-mentioned vehicle seat control methods.

[0056] The present application also provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the steps of any one of the above-mentioned vehicle seat control methods are implemented.

[0057] As described above, the present application provides a vehicle seat control method, system, computer device and storage medium, which have the following beneficial effects: by acquiring target point cloud data, then extracting road surface features according to the target point cloud data, and identifying the pothole area in the road surface based on the road surface features, finally associating the pothole area with the vehicle driving state, and performing shock absorption control on the vehicle seat according to the association result. It can be seen that the present application performs shock absorption control on the vehicle seat by associating the pothole area with the vehicle driving state, which not only enables the vehicle seat shock absorption system to adapt to complex road conditions such as potholes, and improves the adaptability and effect of seat shock absorption; but also effectively reduces the vibration impact of the vehicle seat, so that passengers can get a smoother riding experience under complex road conditions. At the same time, the present application reduces the impact of road surface impact on passengers by performing shock absorption control on the vehicle seat, improves the stability of vehicle control and the safety of passengers, reduces the fatigue and discomfort of drivers and passengers during long-distance driving, and improves driving comfort. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] Figure 1 A schematic flow chart of a vehicle seat control method provided in an embodiment of the present application;

[0059] Figure 2 A schematic diagram of a sensing process for controlling a vehicle seat provided in an embodiment of the present application;

[0060] Figure 3 A schematic diagram of a decision control process for controlling a vehicle seat provided in an embodiment of the present application;

[0061] Figure 4 A schematic diagram of the hardware structure of a vehicle seat control system provided in an embodiment of the present application;

[0062] Figure 5 The figure is a schematic diagram of the hardware structure of a computer device suitable for implementing one or more embodiments of the present application. DETAILED DESCRIPTION

[0063] The following describes the embodiments of the present application through specific examples, and those skilled in the art can easily understand other advantages and effects of the present application from the contents disclosed in this specification. The present application can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present application. It should be noted that the following embodiments and features in the embodiments can be combined with each other without conflict.

[0064] It should be noted that the illustrations provided in this embodiment only illustrate the basic concept of the present application in a schematic manner, and the figures only show the components related to the present application rather than being drawn according to the number, shape and size of the components in actual implementation. In actual implementation, the type, quantity and proportion of each component may be changed arbitrarily, and the component layout may also be more complicated.

[0065] Figure 1 FIG. 2 shows a flow chart of a vehicle seat control method provided by an embodiment of the present application. Specifically, in an exemplary embodiment, Figure 1 As shown, this embodiment provides a vehicle seat control method, the method comprising the following steps:

[0066] S110, obtaining target point cloud data, the target point cloud data includes road surface point cloud data. In some embodiments, the process of obtaining road surface point cloud data can be to sense and identify the three-dimensional spatial data on the road surface through the laser radar pre-configured on the vehicle, and obtain the laser radar three-dimensional point cloud data; pre-process the laser radar three-dimensional point cloud data, and perform point cloud segmentation based on the pre-processing result to obtain the road surface point cloud data; wherein the pre-processing includes but is not limited to denoising, filtering, downsampling, etc. Specifically, as an example, the three-dimensional spatial data on the road surface can be sensed and identified in real time by the laser radar, and the slight changes in the road surface can be captured, and the three-dimensional spatial point cloud data composed of multiple three-dimensional coordinate points can be output, and each of the multiple three-dimensional coordinate points represents a certain position point on the road surface. The collected three-dimensional spatial point cloud data is denoised, filtered and down-sampled. Denoising is to remove environmental interference or error data, filtering can remove unnecessary long-distance points, and down-sampling can reduce the amount of calculation. Clear and sparse point cloud data is output to retain important road surface features. Then the road surface points are separated from the three-dimensional spatial point cloud data. Since the three-dimensional point cloud data contains points from different objects, the road surface point segmentation algorithm RANSAC algorithm is needed to identify and extract the road surface point cloud data and output the road surface point cloud data, where the road surface point cloud data contains the road surface information on which the vehicle is traveling. Randomly select the minimum number of points from the road surface point cloud data for plane fitting. For plane fitting, select three points for fitting. For the plane equation of the three-dimensional point cloud data; including: Ax+By+Cz+D=0, where A, B, C, D are plane parameters, which can be estimated from the selected three points by the least squares method. The distance formula from a point to a plane is: Wherein, (x0, y0, z0) is the coordinate of any point cloud in the three-dimensional space point cloud data. In addition, the judgment of the inner point and the outer point is: if Dostamce≤ò, the point is an inner point, otherwise it is an outer point, and ∈ is the set distance threshold. In some embodiments, the vehicle can be a fuel vehicle or a new energy vehicle.

[0067] S120, extracting road surface features according to the target point cloud data, and identifying pothole areas in the road surface based on the road surface features, where the road surface features include at least one of road surface height features, road surface curvature features, and road surface slope features;

[0068] S130, associating the pothole area with the vehicle driving state, and performing shock absorption control on the vehicle seat according to the association result; wherein the vehicle driving state includes at least one of the vehicle speed, the vehicle position, and the vehicle steering angle.

[0069] In one embodiment of the present application, if the vehicle seat includes a magnetorheological seat, the process of performing shock absorption control on the vehicle seat according to the association result includes: calculating a target current for adjusting the damping response of the magnetorheological shock absorber according to the association result corresponding to the pit area and the vehicle driving state, and adjusting the magnetorheological shock absorber based on the target current to obtain the damping force fed back by the magnetorheological shock absorber; reading the vibration amplitude of the seat acceleration sensor based on the damping force feedback, and updating the damping force when the vibration amplitude is not within the preset amplitude range, until the vibration amplitude corresponding to the updated damping force is within the preset amplitude range. In some embodiments, the process of performing shock absorption control on the magnetorheological seat according to the association result corresponding to the pit area and the vehicle driving state can be: adjusting the vibration amplitude according to the vehicle speed v k and pit depth h pothole,k Generate the corresponding target current I target , to adjust the damping response of the magnetorheological shock absorber; including: I target =K v v k +K h h pothole,k , where K v is the vehicle speed control gain, K h The target current I target Applied to the control of magnetorheological shock absorbers to adjust the shock absorption effect of the seat in real time. The magnetorheological shock absorber adjusts its internal damping characteristics according to the input current to provide a damping force corresponding to the target current; including: F actual =Map(I target ), where F actual It is the damping force generated by the magnetorheological shock absorber according to the target current. Map(I) is the mapping relationship between the current and the output force. Map(I) can be obtained through experiments. Finally, the vibration amplitude is fed back by the seat acceleration sensor to determine whether the vibration amplitude is within the allowable range. If not, the magnetorheological shock absorber damping force is updated until the vibration amplitude corresponding to the updated damping force is within the preset amplitude range. Therefore, this method can identify potholes in advance and adjust the damping in real time, which can effectively reduce vibration shock and enable passengers to have a smoother riding experience under complex road conditions. At the same time, through the identification of potholes and the precise adjustment of damping, the frequency and amplitude of seat vibration are significantly reduced, thereby reducing the fatigue caused by long-term driving. Moreover, the rapid response capability of the magnetorheological shock absorption system enables the seat to be adjusted to the optimal shock absorption state in a very short time, more effectively alleviating the impact of road unevenness on passengers.

[0070] In one embodiment of the present application, the process of extracting road features according to target point cloud data includes:

[0071] Perform three-dimensional space surface fitting on the coordinates of each point cloud in the road surface point cloud data; including: In the formula, (x, y) represents the coordinates of each point cloud in the road point cloud data, z represents the height of the fitting surface, a0,...,a5 represent the fitting parameters, represents the fitting error;

[0072] Based on the maximum height value and the minimum height value of the fitted surface, the height difference of the fitted surface is calculated, and the height difference of the fitted surface is used as the road surface height feature to measure the road surface undulation; including: Δz=z1-z2; where Δz represents the height difference of the fitted surface, z1 represents the maximum height value of the fitted surface, and z2 represents the minimum height value of the fitted surface;

[0073] Calculate the curvature of the fitted surface and use the curvature of the fitted surface as the curvature feature of the road surface; including: In the formula, κ represents the curvature of the fitting surface;

[0074] The inclination is calculated based on the gradient of the fitted surface and is used as the road slope feature; including: In the formula, It represents the gradient of the fitted surface in the x and y directions, indicating the changes in the road surface in these directions; Slope represents the inclination.

[0075] It can be seen that in the process of extracting road features based on the target point cloud data, the road point cloud can be accurately modeled using surface fitting to extract road surface features such as ups and downs, pothole locations, and depth. Therefore, the shape and changes of the road surface can be analyzed by fitting the surface using the fitting method, and road surface features such as ups and downs, curvature, and slope can be output, especially the road surface features of abnormal terrain areas such as potholes can be accurately extracted.

[0076] In one embodiment of the present application, the process of identifying a pothole area in a road surface based on road surface features includes:

[0077] Calculate the road surface point P i (x i ,y i ,z i ) The residual value of the fitting surface corresponding to the road surface characteristics; including: In the formula, Represents a point (x i ,y i ) corresponds to the height value of the fitted surface, d i Represents road point P i (x i ,y i ,z i ) The residual value of the fitting surface corresponding to the road surface characteristics;

[0078] The residual values ​​of all road points are compared with the preset threshold T thCompare and take the residual value greater than the preset threshold T th The road surface points are marked as abnormal points; all the marked abnormal points are spatially clustered, and each clustering result is regarded as a pothole area to obtain the pothole area in the road surface. Among them, the preset threshold T th It can be used to distinguish smooth areas from pits and convex areas. i The difference i If d is greater than the threshold, the point is considered to be located in the pit or convex area, that is, d i >T th The points with large difference values ​​are marked as outliers. Based on the results of surface difference detection, all points with difference values ​​greater than the threshold are marked, that is, the point set that may be a pit or a convex area. For pits, these points are usually located in the pit area because their height is less than the height of the fitted surface. Points with large difference values ​​and heights less than the fitted surface are marked as pits. When performing spatial clustering on all marked outliers, the K-means clustering algorithm can be used to divide the detected outliers into different areas. Through spatial clustering, different pit areas are identified, and each area represents an independent pit.

[0079] Specifically, the process of using the K-means clustering algorithm to divide the detected outliers into different regions can be:

[0080] (1) Initialize cluster centers.

[0081] Select k initial cluster centers (also called centroids). These initial points can be randomly selected, and the point set is X = {x1, x2, ..., x n}, where x i are the coordinates of the road point.

[0082] (2) Calculate the distance from each point to the cluster center.

[0083] For each road point x, calculate its distance from each cluster center c j The Euclidean distance is usually used: Among them, x i =(x i1 ,x i2 ,…,x ip ) represents the coordinates of the road point, c j =(c j1 ,c j2 ,…,c jp ) are the coordinates of the cluster center.

[0084] (3) Assign each point to the nearest cluster center.

[0085] For each point x i Assigned to the cluster center c closest to itj The corresponding cluster C j , that is, C j =x i :d(x i ,c j )≤d(x i ,c l )l≠j.

[0086] (4) Update cluster center.

[0087] Calculate the new center of each cluster, that is, take the average coordinates of all points in each cluster as the new cluster center; including: Among them, |C j | is cluster C j The number of points in .

[0088] Repeat steps (2) to (4) until the cluster center no longer changes significantly or the maximum number of iterations is reached. The usual convergence condition is that the change in the cluster center is less than the set threshold ∈, in, represents the Euclidean distance between the new and old cluster centers.

[0089] The detected outliers are divided into different regions by using the K-means clustering algorithm to identify different pit areas.

[0090] In one embodiment of the present application, the following may further be included: calculating the center position (x) of the pit area according to the point cloud data of the pit area center ,y center );include: In the formula, n represents the number of point clouds in the pit area;

[0091] According to the lowest point P of the pit area min (x min ,y min ,z min ), calculate the depth d of the pit area pothole ;include: In the formula, Represents a point (x min ,y min ) corresponds to the height of the fitted surface, z min Represents a point (x min ,y min ) actual height. That is, the depth of the pit refers to the difference between the height of the lowest point in the pit area and the height of the corresponding point on the fitting surface. At the same time, in order to further analyze the severity of the pit, the maximum depth of the area can also be calculated, and the depths of all points in the area are selected and the maximum value is taken.

[0092] In one embodiment of the present application, after obtaining the pothole area in the road surface, the method may further include: establishing a local surface equation of the pothole area, that is, the surface fitting of the pothole area may adopt quadratic surface fitting to obtain the local surface equation of the pothole area; z(x,y)=a0+a1x+a2y+a3x 2 +a4xy+a5y 2 ;

[0093] The local curvature of the pit area is calculated by the local surface equation of the pit area; including: Where Curvature represents the local curvature of the pit area, f x ,f y represents the surface partial derivative of the pit area, f xx ,f yy ,f xy represents the second-order partial derivative of the pit area.

[0094] In one embodiment of the present application, the process of associating the pit area with the vehicle driving state includes: obtaining the vehicle position (x k ,y k ), the vehicle direction angle θ at time k k , the vehicle speed v at time k k , the vehicle steering angle δ at time k k , the pit position d at time k pothole,k and the pit depth h at time k pothole,k ;

[0095] Based on the vehicle position (x k ,y k ), the vehicle direction angle θ at time k k , the vehicle speed v at time k k , the vehicle steering angle δ at time k k , the pit position d at time k pothole,k and the pit depth h at time k pothole,k , define the state vector x k ;include:

[0096] The state vector x k Input into the observation function determined in advance or in real time to associate the pothole area with the vehicle driving state; including: k =h(x k )+v k ; Where h() is the observation function, v k is the measurement noise.

[0097] In addition, the vehicle dynamics model can be used to describe the state evolution and thus predict the vehicle's driving state; including: k+1 =f(x k ,u k )+w k , where f(·) is the nonlinear state transfer function, u k is the control input (such as acceleration, steering angular velocity), w k is the process noise. It can be specifically expressed as:

[0098]

[0099] Where L is the vehicle wheelbase, a k is the acceleration, ω k is the steering angular velocity, d pothole,k -v k The relative distance between the Δt pit and the vehicle decreases as the vehicle travels.

[0100] At the same time, when defining the state vector x k After that, we can also calculate the state vector x k Extended Kalman filter processing is performed to obtain the real-time state vector containing pothole information and vehicle driving status. The specific steps of extended Kalman filtering are as follows:

[0101] ① Prediction step

[0102] State prediction: Use the state transition equation to predict the state at the next moment; including:

[0103] Covariance prediction: predict the state covariance matrix P k∣k-1 ;include: in, is the Jacobian matrix of the state transfer matrix, Q k is the process noise covariance matrix.

[0104] ②Update steps

[0105] Kalman gain calculation: Calculate the Kalman gain; including: in, is the Jacobian matrix of the observation model, R k is the measurement noise covariance matrix.

[0106] State update: Correct the predicted state using observed data; includes:

[0107] Covariance Update: Update the state covariance matrix; including: P k =(IK k H k )k∣k-1 .

[0108] After completing the state estimation of the extended Kalman filter, the real-time state vector containing the pothole information and the vehicle driving state is obtained.

[0109] In another exemplary embodiment of the present application, the embodiment further provides a seat control method, which can be applied to a magnetorheological seat. The seat control method includes a perception recognition process and a decision control process. Figure 2 As shown in Figure 3 As shown. Figure 2 In the process, the laser radar can emit a laser beam and receive reflected light to obtain the three-dimensional space point cloud data of the surrounding environment. After identifying the pothole area through the acquired three-dimensional space point cloud data, the shock absorption system adjusts the shock absorption characteristics of the seat in advance to achieve pre-adjustment. The laser radar identifies the position, depth, shape and other characteristics of the pothole by real-time scanning and analyzing the road surface, and outputs the spatial coordinates, depth, shape, curvature and other information of the pothole, as well as parameters related to the current driving state of the vehicle (such as speed, acceleration, etc.). Synchronize the identification data of the pothole with the real-time driving state of the vehicle (such as speed, driving direction, acceleration, seat load, etc.). Data is transmitted through the CAN (Controller Area Network, CAN for short) bus of the vehicle control system, and finally the comprehensive data of the pothole characteristics and the vehicle driving state are integrated to perform forward-looking calculations of the shock absorption control, and calculate the shock absorption characteristics required when the pothole is about to be reached, such as damping value, response time, etc. In the process, the laser radar can emit a laser beam and receive reflected light to obtain the reflected light. Figure 3 In the process, the pothole information identified by the lidar can be fused with the data collected by other sensors (such as accelerometers, GPS, IMU, etc.), combined with the real-time driving status of the vehicle (such as speed, steering angle, etc.) to generate a dynamic shock absorption adjustment strategy. For example, a fusion algorithm (extended Kalman filter) is used to combine the pothole information with the real-time driving status of the vehicle (such as speed, steering angle, etc.) for comprehensive analysis to form a complete environment and state model to generate a dynamic magnetorheological seat shock absorption control strategy. Then, a comprehensive model integrating pothole information and vehicle status data is used to provide real-time feedback for the shock absorption system and guide the adjustment of the magnetorheological seat. Finally, the pothole identification and modeling results are transmitted to the magnetorheological seat shock absorption control system in real time, thereby achieving optimal comfort and shock absorption effects. Among them, Figure 2 and Figure 3 The processes involved in the laser radar data collection, point cloud data preprocessing, road surface point cloud data separation, road surface feature extraction, pothole area detection and identification, and seat shock absorption control can be referred to the other embodiments mentioned above, and will not be repeated in this embodiment.

[0110] In summary, the present application provides a vehicle seat control method, which can obtain target point cloud data, then extract road features according to the target point cloud data, and identify the pit area in the road based on the road features, and finally associate the pit area with the vehicle driving state, and perform shock absorption control on the vehicle seat according to the association result. It can be seen that the present method performs shock absorption control on the vehicle seat by associating the pit area with the vehicle driving state, which can not only make the vehicle seat shock absorption system adapt to complex road conditions such as pits, and improve the adaptability and effect of seat shock absorption; but also can effectively reduce the vibration impact of the vehicle seat, so that passengers can get a smoother riding experience under complex road conditions. At the same time, the present method reduces the impact of road impact on passengers by performing shock absorption control on the vehicle seat, improves the stability of vehicle control and the safety of passengers, reduces the fatigue and discomfort of drivers and passengers in long-distance driving, and improves driving comfort. In addition, the present method can identify pits in advance and adjust the damping in real time, which can effectively reduce the vibration impact, so that passengers can get a smoother riding experience under complex road conditions. At the same time, through the identification of pits and precise adjustment of damping, the frequency and amplitude of seat vibration are significantly reduced, thereby reducing the fatigue caused by long-term driving. Moreover, the rapid response capability of the magnetorheological shock absorption system enables the seat to be adjusted to the optimal shock absorption state in a very short time, more effectively alleviating the impact of road unevenness on passengers. And by optimizing the control strategy of the magnetorheological shock absorption system, the energy consumption and component wear caused by frequent adjustments are reduced, the service life of the magnetorheological shock absorption system is extended, and efficient energy utilization is guaranteed. Therefore, this method adopts a combination of the pre-judgment mode and the real-time feedback mode. This dual-mode control ensures that in the event of failure or misjudgment of the pre-judgment, precise adjustments can still be made through real-time feedback, thereby taking into account the accuracy and fault tolerance of the shock absorption system response.

[0111] In another exemplary embodiment of the present application, Figure 4 As shown, this embodiment also provides a vehicle seat control system, including:

[0112] The data acquisition module 410 is used to obtain target point cloud data, which includes road point cloud data. In some embodiments, the process of obtaining road point cloud data can be to sense and identify the three-dimensional spatial data on the road surface through the laser radar pre-configured on the vehicle to obtain the laser radar three-dimensional point cloud data; pre-process the laser radar three-dimensional point cloud data, and perform point cloud segmentation based on the pre-processing result to obtain the road point cloud data; wherein the pre-processing includes but is not limited to denoising, filtering, downsampling, etc. Specifically, as an example, the three-dimensional spatial data on the road surface can be sensed and identified in real time by the laser radar, and the slight changes in the road surface can be captured, and the three-dimensional spatial point cloud data composed of multiple three-dimensional coordinate points can be output, and each of the multiple three-dimensional coordinate points represents a certain position point on the road surface. The collected three-dimensional spatial point cloud data is denoised, filtered and down-sampled. Denoising is to remove environmental interference or error data, filtering can remove unnecessary long-distance points, and down-sampling can reduce the amount of calculation. Clear and sparse point cloud data is output to retain important road features. Then the road surface points are separated from the three-dimensional spatial point cloud data. Since the three-dimensional point cloud data contains points from different objects, the road surface point segmentation algorithm RANSAC algorithm is needed to identify and extract the road surface point cloud data and output the road surface point cloud data, where the road surface point cloud data contains the road surface information on which the vehicle is traveling. Randomly select the minimum number of points from the road surface point cloud data for plane fitting. For plane fitting, select three points for fitting. For the plane equation of the three-dimensional point cloud data; including: Ax+By+Cz+D=0, where A, B, C, D are plane parameters, which can be estimated from the selected three points by the least squares method. The distance formula from a point to a plane is: Among them, (x0, y0, z0) are the coordinates of any point cloud in the three-dimensional space point cloud data. In addition, the judgment of the inner point and the outer point is: if Distance≤ò, the point is an inner point, otherwise it is an outer point, and ∈ is the set distance threshold. Among them, in some embodiments, the vehicle can be a fuel vehicle or a new energy vehicle.

[0113] A pothole recognition module 420 is used to extract road surface features according to the target point cloud data, and recognize pothole areas in the road surface based on the road surface features, where the road surface features include at least one of road surface height features, road surface curvature features, and road surface slope features;

[0114] The shock absorption control module 430 is used to associate the pothole area with the vehicle driving state and perform shock absorption control on the vehicle seat according to the association result; wherein the vehicle driving state includes at least one of the vehicle speed, the vehicle position, and the vehicle steering angle.

[0115] In one embodiment of the present application, if the vehicle seat includes a magnetorheological seat, the process of performing shock absorption control on the vehicle seat according to the association result includes: calculating a target current for adjusting the damping response of the magnetorheological shock absorber according to the association result corresponding to the pit area and the vehicle driving state, and adjusting the magnetorheological shock absorber based on the target current to obtain the damping force fed back by the magnetorheological shock absorber; reading the vibration amplitude of the seat acceleration sensor based on the damping force feedback, and updating the damping force when the vibration amplitude is not within the preset amplitude range, until the vibration amplitude corresponding to the updated damping force is within the preset amplitude range. In some embodiments, the process of performing shock absorption control on the magnetorheological seat according to the association result corresponding to the pit area and the vehicle driving state can be: adjusting the vibration amplitude according to the vehicle speed v k and pit depth h pothole,k Generate the corresponding target current I target , to adjust the damping response of the magnetorheological shock absorber; including: I target =K v v k +K h h pothole,k , where K v is the vehicle speed control gain, K h The target current I target Applied to the control of magnetorheological shock absorbers to adjust the shock absorption effect of the seat in real time. The magnetorheological shock absorber adjusts its internal damping characteristics according to the input current to provide a damping force corresponding to the target current; including: F actual =Map(I target ), where F actual It is the damping force generated by the magnetorheological shock absorber according to the target current. Map (I) is the mapping relationship between the current and the output force. Map (I) can be obtained through experiments. Finally, the vibration amplitude is fed back by the seat acceleration sensor to determine whether the vibration amplitude is within the allowable range. If not, the magnetorheological shock absorber damping force is updated until the vibration amplitude corresponding to the updated damping force is within the preset amplitude range. Therefore, this method can identify potholes in advance and adjust the damping in real time, which can effectively reduce vibration shock and enable passengers to have a smoother riding experience under complex road conditions. At the same time, through the identification of potholes and the precise adjustment of damping, the frequency and amplitude of seat vibration are significantly reduced, thereby reducing the fatigue caused by long-term driving. Moreover, the rapid response capability of the magnetorheological shock absorption system enables the seat to be adjusted to the optimal shock absorption state in a very short time, more effectively alleviating the impact of road unevenness on passengers.

[0116] In one embodiment of the present application, the process of extracting road features according to target point cloud data includes:

[0117] Perform three-dimensional space surface fitting on the coordinates of each point cloud in the road surface point cloud data; including: In the formula, (x, y) represents the coordinates of each point cloud in the road point cloud data, z represents the height of the fitting surface, a0,...,a5 represent the fitting parameters, represents the fitting error;

[0118] Based on the maximum height value and the minimum height value of the fitted surface, the height difference of the fitted surface is calculated, and the height difference of the fitted surface is used as the road surface height feature to measure the road surface undulation; including: Δz=z1-z2; where Δz represents the height difference of the fitted surface, z1 represents the maximum height value of the fitted surface, and z2 represents the minimum height value of the fitted surface;

[0119] Calculate the curvature of the fitted surface and use the curvature of the fitted surface as the curvature feature of the road surface; including: In the formula, κ represents the curvature of the fitting surface;

[0120] The inclination is calculated based on the gradient of the fitted surface and is used as the road slope feature; including: In the formula, It represents the gradient of the fitted surface in the x and y directions, indicating the changes in the road surface in these directions; Slope represents the inclination.

[0121] It can be seen that in the process of extracting road features based on the target point cloud data, the road point cloud can be accurately modeled using surface fitting to extract road surface features such as ups and downs, pothole locations, and depth. Therefore, the shape and changes of the road surface can be analyzed by fitting the surface using the fitting method, and road surface features such as ups and downs, curvature, and slope can be output, especially the road surface features of abnormal terrain areas such as potholes can be accurately extracted.

[0122] In one embodiment of the present application, the process of identifying a pothole area in a road surface based on road surface features includes:

[0123] Calculate the road surface point P i (x i ,y i ,z i ) The residual value of the fitting surface corresponding to the road surface characteristics; including: In the formula, Represents a point (x i ,y i ) corresponds to the height value of the fitted surface, d i Represents road point P i (x i ,y i ,z i ) The residual value of the fitting surface corresponding to the road surface characteristics;

[0124] The residual values ​​of all road points are compared with the preset threshold T thCompare and take the residual value greater than the preset threshold T th The road surface points are marked as abnormal points; all the marked abnormal points are spatially clustered, and each clustering result is regarded as a pothole area to obtain the pothole area in the road surface. Among them, the preset threshold T th It can be used to distinguish smooth areas from pits and convex areas. i The difference i If d is greater than the threshold, the point is considered to be located in the pit or convex area, that is, d i >T th The points with large difference values ​​are marked as outliers. Based on the results of surface difference detection, all points with difference values ​​greater than the threshold are marked, that is, the point set that may be a pit or a convex area. For pits, these points are usually located in the pit area because their height is less than the height of the fitted surface. Points with large difference values ​​and heights less than the fitted surface are marked as pits. When performing spatial clustering on all marked outliers, the K-means clustering algorithm can be used to divide the detected outliers into different areas. Through spatial clustering, different pit areas are identified, and each area represents an independent pit.

[0125] Specifically, the process of using the K-means clustering algorithm to divide the detected outliers into different regions can be:

[0126] (1) Initialize cluster centers.

[0127] Select k initial cluster centers (also called centroids). These initial points can be randomly selected, and the point set is X = {x1, x2, ..., x n}, where x i are the coordinates of the road point.

[0128] (2) Calculate the distance from each point to the cluster center.

[0129] For each road point x, calculate its distance from each cluster center c j The Euclidean distance is usually used: Among them, x i =(x i1 ,x ii2 ,…,x ip ) represents the coordinates of the road point, c j =(c j1 ,c j2 ,…,c jp ) are the coordinates of the cluster center.

[0130] (3) Assign each point to the nearest cluster center.

[0131] For each point x i Assigned to the cluster center c closest to itj The corresponding cluster C j , that is, C j =x i :d(x i ,c j )≤d(x i ,c l )l≠j.

[0132] (4) Update cluster center.

[0133] Calculate the new center of each cluster, that is, take the average coordinates of all points in each cluster as the new cluster center; including: Among them, |C j | is cluster C j The number of points in .

[0134] Repeat steps (2) to (4) until the cluster center no longer changes significantly or the maximum number of iterations is reached. The usual convergence condition is that the change in the cluster center is less than the set threshold ∈, in, represents the Euclidean distance between the new and old cluster centers.

[0135] The detected outliers are divided into different regions by using the K-means clustering algorithm to identify different pit areas.

[0136] In one embodiment of the present application, the following may further be included: calculating the center position (x) of the pit area according to the point cloud data of the pit area center ,y center );include: In the formula, n represents the number of point clouds in the pit area;

[0137] According to the lowest point P of the pit area min (x min ,y min ,z min ), calculate the depth d of the pit area pothole ;include: In the formula, Represents a point (x min ,y min ) corresponds to the height of the fitted surface, z min Represents a point (x min ,y min ) actual height. That is, the depth of the pit refers to the difference between the height of the lowest point in the pit area and the height of the corresponding point on the fitting surface. At the same time, in order to further analyze the severity of the pit, the maximum depth of the area can also be calculated, and the depths of all points in the area are selected and the maximum value is taken.

[0138] In one embodiment of the present application, after obtaining the pothole area in the road surface, the method may further include: establishing a local surface equation of the pothole area, that is, the surface fitting of the pothole area may adopt quadratic surface fitting to obtain the local surface equation of the pothole area; z(x,y)=a0+a1x+a2y+a3x 2 +a4xy+a5y 2 ;

[0139] The local curvature of the pit area is calculated by the local surface equation of the pit area; including: Where Curvature represents the local curvature of the pit area, f x ,f y represents the surface partial derivative of the pit area, f xx ,f yy ,f xy represents the second-order partial derivative of the pit area.

[0140] In one embodiment of the present application, the process of associating the pit area with the vehicle driving state includes: obtaining the vehicle position (x k ,y k ), the vehicle direction angle θ at time k k , the vehicle speed v at time k k , the vehicle steering angle δ at time k k , the pit position d at time k pothole,k and the pit depth h at time k pothole,k ;

[0141] Based on the vehicle position (x k ,y k ), the vehicle direction angle θ at time k k , the vehicle speed v at time k k , the vehicle steering angle δ at time k k , the pit position d at time k pothole,k and the pit depth h at time k pothole,k , define the state vector x k ;include:

[0142] The state vector x k Input into the observation function determined in advance or in real time to associate the pothole area with the vehicle driving state; including: Where h() is the observation function, v k is the measurement noise.

[0143] In addition, the vehicle dynamics model can be used to describe the state evolution and thus predict the vehicle's driving state; including: k+1 =f(x k ,uk )+w k , where f(·) is the nonlinear state transfer function, u k is the control input (such as acceleration, steering angular velocity), w k is the process noise. It can be specifically expressed as:

[0144]

[0145] Where L is the vehicle wheelbase, a k is the acceleration, ω k is the steering angular velocity, d pothole,k -v k The relative distance between the Δt pit and the vehicle decreases as the vehicle travels.

[0146] At the same time, when defining the state vector x k After that, we can also calculate the state vector x k Extended Kalman filter processing is performed to obtain the real-time state vector containing pothole information and vehicle driving status. The specific steps of extended Kalman filtering are as follows:

[0147] ① Prediction step

[0148] State prediction: Use the state transition equation to predict the state at the next moment; including:

[0149] Covariance prediction: predict the state covariance matrix P k∣k-1 ;include: in, is the Jacobian matrix of the state transfer matrix, Q k is the process noise covariance matrix.

[0150] ②Update steps

[0151] Kalman gain calculation: Calculate the Kalman gain; including: in, is the Jacobian matrix of the observation model, R k is the measurement noise covariance matrix.

[0152] State update: Correct the predicted state using observed data; includes:

[0153] Covariance Update: Update the state covariance matrix; including: P k =(IK k H k ) k∣k-1 .

[0154] After completing the state estimation of the extended Kalman filter, the real-time state vector containing the pothole information and the vehicle driving state is obtained.

[0155] In another exemplary embodiment of the present application, the embodiment further provides a seat control system that can be applied to a magnetorheological seat. The seat control system includes a sensing and recognition process and a decision control process. Figure 2 As shown in Figure 3 As shown. Figure 2 In the process, the laser radar can emit a laser beam and receive reflected light to obtain the three-dimensional space point cloud data of the surrounding environment. After identifying the pothole area through the acquired three-dimensional space point cloud data, the shock absorption system adjusts the shock absorption characteristics of the seat in advance to achieve pre-adjustment. The laser radar identifies the position, depth, shape and other characteristics of the pothole by real-time scanning and analyzing the road surface, and outputs the spatial coordinates, depth, shape, curvature and other information of the pothole, as well as parameters related to the current driving state of the vehicle (such as speed, acceleration, etc.). Synchronize the identification data of the pothole with the real-time driving state of the vehicle (such as speed, driving direction, acceleration, seat load, etc.). Data is transmitted through the CAN (Controller Area Network, CAN for short) bus of the vehicle control system, and finally the comprehensive data of the pothole characteristics and the vehicle driving state are integrated to perform forward-looking calculations of the shock absorption control, and calculate the shock absorption characteristics required when the pothole is about to be reached, such as damping value, response time, etc. In the process, the laser radar can emit a laser beam and receive reflected light to obtain the reflected light. Figure 3 In the process, the pothole information identified by the lidar can be fused with the data collected by other sensors (such as accelerometers, GPS, IMU, etc.), combined with the real-time driving status of the vehicle (such as speed, steering angle, etc.) to generate a dynamic shock absorption adjustment strategy. For example, a fusion algorithm (extended Kalman filter) is used to combine the pothole information with the real-time driving status of the vehicle (such as speed, steering angle, etc.) for comprehensive analysis to form a complete environment and state model to generate a dynamic magnetorheological seat shock absorption control strategy. Then, a comprehensive model integrating pothole information and vehicle status data is used to provide real-time feedback for the shock absorption system and guide the adjustment of the magnetorheological seat. Finally, the pothole identification and modeling results are transmitted to the magnetorheological seat shock absorption control system in real time, thereby achieving optimal comfort and shock absorption effects. Among them, Figure 2 and Figure 3 The processes involved in the laser radar data collection, point cloud data preprocessing, road surface point cloud data separation, road surface feature extraction, pothole area detection and identification, and seat shock absorption control can be referred to the other embodiments mentioned above, and will not be repeated in this embodiment.

[0156] In summary, the present application provides a vehicle seat control system, which can obtain target point cloud data, then extract road features according to the target point cloud data, and identify the pit area in the road based on the road features, and finally associate the pit area with the vehicle driving state, and perform shock absorption control on the vehicle seat according to the association result. It can be seen that the system performs shock absorption control on the vehicle seat by associating the pit area with the vehicle driving state, which can not only make the vehicle seat shock absorption system adapt to complex road conditions such as pits, and improve the adaptability and effect of seat shock absorption; but also can effectively reduce the vibration impact of the vehicle seat, so that passengers can get a smoother riding experience under complex road conditions. At the same time, by performing shock absorption control on the vehicle seat, the system reduces the impact of road impact on passengers, improves the stability of vehicle control and the safety of passengers, reduces the fatigue and discomfort of drivers and passengers during long-distance driving, and improves driving comfort. In addition, the system can identify pits in advance and adjust the damping in real time, which can effectively reduce the vibration impact, so that passengers can get a smoother riding experience under complex road conditions. At the same time, through the identification of potholes and precise adjustment of damping, the frequency and amplitude of seat vibration are significantly reduced, thereby reducing the fatigue caused by long-term driving. Moreover, the rapid response capability of the magnetorheological shock absorption system enables the seat to be adjusted to the optimal shock absorption state in a very short time, more effectively alleviating the impact of road unevenness on passengers. And by optimizing the control strategy of the magnetorheological shock absorption system, the energy consumption and component wear caused by frequent adjustments are reduced, the service life of the magnetorheological shock absorption system is extended, and efficient energy utilization is guaranteed. Therefore, this system adopts a combination of pre-judgment mode and real-time feedback mode. This dual-mode control ensures that in the event of pre-judgment failure or misjudgment, precise adjustments can still be made through real-time feedback, thereby taking into account the accuracy and fault tolerance of the shock absorption system response.

[0157] It should be noted that the vehicle seat control system provided in the above embodiment and the vehicle seat control method provided in the above embodiment belong to the same concept, wherein the specific manner in which the vehicle seat control method performs operations has been described in detail in the above method embodiment, and will not be repeated here. In actual applications, the vehicle seat control system provided in the above embodiment can allocate the above functions to different functional modules as needed, that is, divide the internal structure of the vehicle seat control system into different functional modules, and then implement all or part of the functions of the corresponding functional modules through the vehicle seat control method described in the above embodiment, and this is not specifically limited here.

[0158] The present application also provides a computer device, which may include a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program so that the computer device executes Figure 1 The steps of the vehicle seat control method. Figure 5 FIG. 1 is a schematic diagram showing a structure of a computer device 1000. Figure 5 As shown, the computer device 1000 includes: a processor 1010 , a memory 1020 , a power supply 1030 , a display unit 1040 , and an input unit 1060 .

[0159] The processor 1010 is the control center of the computer device 1000. It uses various interfaces and lines to connect various components, and executes various functions of the computer device 1000 by running or executing computer programs / instructions stored in the memory 1020, thereby monitoring the computer device 1000 as a whole. In the embodiment of the present application, when the processor 1010 calls the computer program stored in the memory 1020, the following is executed: Figure 1 The steps of the vehicle seat control method. Optionally, the processor 1010 may include one or more processing units; preferably, the processor 1010 may integrate an application processor and a modem processor, wherein the application processor mainly processes the operating system, user interface and application, etc., and the modem processor mainly processes wireless communication. In some embodiments, the processor and the memory may be implemented on a single chip, and in some embodiments, they may also be implemented separately on separate chips.

[0160] The memory 1020 may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system, various applications, etc.; the data storage area may store instruction data, etc. created according to the use of the computer device 1000. In addition, the memory 1020 may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one disk storage device, a flash memory device, or other non-volatile solid-state storage devices, etc.

[0161] The computer device 1000 also includes a power source 1030 (such as a battery) for supplying power to various components. The power source can be logically connected to the processor 1010 through a power management system, so that the power management system can manage functions such as charging, discharging, and power consumption.

[0162] The display unit 1040 may be used to display information input by the user or information provided to the user and various menus of the computer device 1000, and in the embodiment of the present application, is mainly used to display the display interface of each application in the computer device 1000 and objects such as text and pictures displayed in the display interface. The display unit 1040 may include a display panel 1050. The display panel 1050 may be configured in the form of a liquid crystal display (LCD), an organic light-emitting diode (OLED), or the like.

[0163] The input unit 1060 may be used to receive information such as numbers or characters input by the user. The input unit 1060 may include a touch panel 1070 and other input devices 1080. The touch panel 1070, also known as a touch screen, may collect touch operations of the user on or near it (such as operations of the user using a finger, a stylus, or any other suitable object or accessory on or near the touch panel 1070).

[0164] Specifically, the touch panel 1070 can detect the user's touch operation, detect the signal brought by the touch operation, convert these signals into touch point coordinates, send them to the processor 1010, and receive and execute the command sent by the processor 1010. In addition, the touch panel 1070 can be implemented in various types such as resistive, capacitive, infrared and surface acoustic wave. Other input devices 1080 can include but are not limited to one or more of a physical keyboard, function keys (such as volume control keys, power on / off keys, etc.), a trackball, a mouse, a joystick, etc.

[0165] Of course, the touch panel 1070 can cover the display panel 1050. When the touch panel 1070 detects a touch operation on or near it, it is transmitted to the processor 1010 to determine the type of touch event, and then the processor 1010 provides a corresponding visual output on the display panel 1050 according to the type of touch event. Figure 5 In the embodiment, the touch panel 1070 and the display panel 1050 are two independent components to implement the input and output functions of the computer device 1000, but in some embodiments, the touch panel 1070 and the display panel 1050 can be integrated to implement the input and output functions of the computer device 1000.

[0166] The computer device 1000 may further include one or more sensors, such as a pressure sensor, a gravity acceleration sensor, a proximity light sensor, etc. Of course, according to the needs of specific applications, the computer device 1000 may also include other components such as a camera.

[0167] The embodiment of the present application also provides a computer-readable storage medium, in which a computer program / instruction is stored. When the computer program / instruction is executed by a processor, the above-mentioned device can perform the above-mentioned Figure 1 The steps of the vehicle seat control method.

[0168] It can be understood by those skilled in the art that Figure 5This is only an example of a computer device and does not constitute a limitation on the device. The device may include more or fewer components than shown in the figure, or combine certain components, or different components. For the convenience of description, the above parts are divided into modules (or units) according to their functions and described separately. Of course, when implementing this application, the functions of each module (or unit) can be implemented in the same or multiple software or hardware.

[0169] Those skilled in the art should understand that the present application may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code. The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be applied to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the functions in the process. Figure 1 A process or multiple processes and / or boxes Figure 1 These computer program instructions can also be stored in a computer-readable memory that can guide a computer or other programmable data processing device to work in a specific way, so that the instructions stored in the computer-readable memory produce a product including an instruction device, which implements the functions specified in the process. Figure 1 A process or multiple processes and / or boxes Figure 1 These computer program instructions can also be loaded onto a computer or other programmable data processing device, so that a series of operation steps are executed on the computer or other programmable device to produce a computer-implemented process, so that the instructions executed on the computer or other programmable device provide for implementing the process in the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0170] It should be noted that the above embodiments collect, store, use, process, transmit, provide, disclose, delete, and other related data (such as three-dimensional space point cloud data, road surface point cloud data, etc.) with or with the consent of the user. For example, the three-dimensional space point cloud data and road surface point cloud data are authorized with the knowledge and consent of the user; or the user actively provides it after reading the relevant instructions, or the user actively authorizes / provides / uploads it when using some or all of the functions described in the above embodiments, or other methods / channels are obtained with or with the consent of the user.

[0171] The above embodiments are merely illustrative of the principles and effects of the present application and are not intended to limit the present application. Anyone familiar with the technology may modify or change the above embodiments without violating the spirit and scope of the present application. Therefore, all equivalent modifications or changes made by a person of ordinary skill in the art without departing from the spirit and technical ideas disclosed in the present application shall still be covered by the claims of the present application.

Claims

1. A vehicle seat control method, characterized in that: The method comprises the following steps: Acquire target point cloud data, wherein the target point cloud data includes road surface point cloud data; Extracting road surface features according to the target point cloud data, and identifying pothole areas in the road surface based on the road surface features, wherein the road surface features include at least one of road surface height features, road surface curvature features, and road surface slope features; The pit area is associated with the vehicle driving state, and the vehicle seat is subjected to shock absorption control according to the association result; wherein the vehicle driving state includes at least one of the vehicle speed, the vehicle position, and the vehicle steering angle.

2. The vehicle seat control method according to claim 1, characterized in that: If the vehicle seat includes a magnetorheological seat, the process of performing shock absorption control on the vehicle seat according to the correlation result includes: Calculating a target current for adjusting a damping response of a magnetorheological shock absorber according to an association result corresponding to the pit area and the vehicle driving state, and adjusting the magnetorheological shock absorber based on the target current to obtain a damping force fed back by the magnetorheological shock absorber; The vibration amplitude of the seat acceleration sensor based on the damping force feedback is read, and when the vibration amplitude is not within a preset amplitude range, the damping force is updated until the vibration amplitude corresponding to the updated damping force is within the preset amplitude range.

3. The vehicle seat control method according to claim 1 or 2, characterized in that: The process of extracting road features based on target point cloud data includes: Performing three-dimensional space surface fitting on the coordinates of each point cloud in the road surface point cloud data; including: Wherein, (x, y) represents the coordinates of each point cloud in the road surface point cloud data, z represents the height of the fitting surface, a0, ..., a5 represent the fitting parameters, represents the fitting error; Calculating a height difference of a fitted surface based on a maximum height value of the fitted surface and a minimum height value of the fitted surface, and using the height difference of the fitted surface as a road surface height feature; comprising: Δz=z1-z2; In the formula, Δz represents the height difference of the fitting surface, z1 represents the maximum height value of the fitting surface, and z2 represents the minimum height value of the fitting surface; Calculating the curvature of the fitted surface and using the curvature of the fitted surface as a road curvature feature; comprising: In the formula, κ represents the curvature of the fitting surface; Calculating the inclination based on the gradient of the fitted surface, and using the inclination as a road surface slope feature; comprising: In the formula, Represents the gradient of the fitted surface in the x and y directions; Slope indicates the inclination.

4. The vehicle seat control method according to claim 3, characterized in that: The process of identifying a pothole area in a road surface based on the road surface features includes: Calculate the road surface point P i (x i ,y i ,z i ) The residual value of the fitting surface corresponding to the road surface characteristics; including: In the formula, Indicates the height value of the fitting surface corresponding to the point (xi, yi), d i Residual value representing the fitting surface corresponding to the road surface point Pi (xi, yi, zi) and the road surface feature; The residual values ​​of all road points are compared with the preset threshold T th Compare and take the residual value greater than the preset threshold T th The road surface points are marked as abnormal points; All marked abnormal points are spatially clustered, and each clustering result is used as a pothole area to obtain the pothole area in the road surface.

5. The vehicle seat control method according to claim 4, characterized in that: The method further comprises: Calculate the center position (x) of the pit area based on the point cloud data of the pit area center ,y center );include: In the formula, n represents the number of point clouds in the pit area; According to the lowest point Pmin(xmin,ymin,zmin) of the pit area, calculate the depth d of the pit area pothole ;include: In the formula, Represents a point (x min ,y min ) corresponds to the height of the fitted surface, z min Represents a point (x min ,y min )'s actual height.

6. The vehicle seat control method according to claim 4 or 5, characterized in that: After obtaining the pit area in the road surface, the method further includes: Establish the local surface equation of the pit area; including: z(x,y)=a0+a1x+a2y+a3x 2 +a4xy+a5y 2 ; The local curvature of the pit area is calculated by the local surface equation of the pit area; including: Where Curvature represents the local curvature of the pit area, f x ,f y represents the surface partial derivative of the pit area, f xx ,f yy ,f xy represents the second-order partial derivative of the pit area.

7. The vehicle seat control method according to claim 1 or 2, characterized in that: The process of associating the pit area with the vehicle driving state includes: Get the vehicle position (x) at time k k ,y k ), the vehicle direction angle θ at time k k , the vehicle speed v at time k k , the vehicle steering angle δ at time k k , the pit position d at time k pothole,k and the pit depth h at time k pothole,k ; Based on the vehicle position (x k ,y k ), the vehicle direction angle θ at time k k , the vehicle speed v at time k k , the vehicle steering angle δ at time k k , the pit position d at time k pothole,k and the pit depth h at time k pothole,k , define the state vector x k ;include: The state vector x k Inputting into a pre-determined or real-time determined observation function to associate the pothole area with the vehicle driving state; including: z k =h(x k )+v k ; Where h() is the observation function, v k is the measurement noise.

8. A vehicle seat control system, characterized in that: The system comprises: A data acquisition module, used to acquire target point cloud data, wherein the target point cloud data includes road surface point cloud data; A pothole recognition module, used to extract road surface features according to the target point cloud data, and recognize pothole areas in the road surface based on the road surface features, wherein the road surface features include at least one of road surface height features, road surface curvature features, and road surface slope features; The shock absorption control module is used to associate the pit area with the vehicle driving state and perform shock absorption control on the vehicle seat according to the association result; wherein the vehicle driving state includes at least one of the vehicle speed, the vehicle position, and the vehicle steering angle.

9. A computer device, characterized in that: The invention comprises a memory, a processor and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the vehicle seat control method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: A computer program is stored thereon, and when the computer program is executed by a processor, the steps of the vehicle seat control method described in any one of claims 1 to 7 are implemented.

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