Seat control method and device

Abstract

The invention relates to the technical field of vehicle control, and discloses a seat control method and device, and the method comprises the steps: obtaining the related data of a vehicle; wherein the related data comprises road surface data, vehicle vibration data and vehicle pose data; determining a seat control result of the vehicle according to the related data of the vehicle; wherein the seat control result indicates a target displacement amount of a seat in a target direction, the target direction is at least one of multiple directions, and the multiple directions comprise the X direction, the Y direction and the Z direction; and according to the seat control result of the vehicle, the seat of the vehicle is controlled to move by the target displacement in the target direction.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of vehicle control, in particular to a seat control method and device. BACKGROUND

[0002] During the driving process of a vehicle, the driver often encounters rough road surfaces. When the vehicle passes through such road surfaces, the vehicle will vibrate, causing the driver to vibrate and affecting the comfort of the driver.

[0003] Therefore, how to improve the comfort of the driver becomes a technical problem to be solved. SUMMARY

[0004] Therefore, the present application provides a seat control method, which comprises: acquiring relevant data of a vehicle; wherein the relevant data comprises road surface data, vehicle vibration data and vehicle pose data; determining a seat control result of the vehicle according to the relevant data of the vehicle; wherein the seat control result indicates a target displacement amount of the seat in a target direction, and the target direction is at least one of a plurality of directions, and the plurality of directions comprise: X direction, Y direction and Z direction; and controlling the seat of the vehicle to move the target displacement amount along the target direction according to the seat control result of the vehicle.

[0005] The seat control method provided by the present application reflects the road conditions where the vehicle is currently located and the state of the vehicle itself by acquiring the road surface data, the vehicle vibration data and the vehicle pose data, determines the target displacement amount of the seat in the target direction (at least one of the X direction, the Y direction and the Z direction) according to the road conditions where the vehicle is currently located and the state of the vehicle itself, and offsets or reduces the impact caused by the vehicle vibration in advance through the active movement of the seat, so that the body of the driver remains relatively stable during the driving process of the vehicle, effectively reduces the body shaking caused by the vehicle vibration, and further improves the comfort of the driver.

[0006] In one possible implementation, determining the seat control result of the vehicle according to the relevant data of the vehicle comprises: determining the seat control result of the vehicle according to the relevant data of the vehicle by using a neural network model.

[0007] The seat control method provided by the present application can effectively fuse multi-source data from different sensors. Different types of data may have different dimensions, distributions and characteristics, and traditional data processing methods may encounter difficulties when fusing these data. However, the neural network model can automatically learn the weight relationship between different data through its internal weight adjustment mechanism, organically fuse the multi-source data, and provide more comprehensive and accurate information for determining the seat control result.

[0008] In a possible implementation, the road surface data of the vehicle is acquired by: scanning the road in front of the vehicle by using a laser radar configured on the vehicle to obtain the road surface data; and / or collecting image information of the road in front of the vehicle by using a camera configured on the vehicle; and processing the image information of the road in front of the vehicle to obtain the road surface data.

[0009] The seat control method provided in the application can accurately calculate the distance from the target object by emitting a laser beam and measuring the time of reflected light, and can generate high-precision three-dimensional point cloud data to accurately obtain the subtle features such as the ups and downs, slopes and potholes of the road in front of the vehicle, thereby providing accurate road surface information for seat control.

[0010] In addition, compared with the laser radar, the camera generally has a lower cost. The configuration of the camera on the vehicle can acquire the road surface data at a relatively low cost, thereby reducing the overall vehicle manufacturing cost. Meanwhile, with the continuous development of the camera technology, the performance of the camera is continuously improved, and the camera can meet the road surface data acquisition requirement under a certain accuracy requirement.

[0011] In a possible implementation, the related data further includes: vehicle speed data; and the seat control result of the vehicle is determined according to the related data of the vehicle, including: determining the vehicle vibration result according to the vehicle vibration data and the road surface data; when the vehicle vibration result indicates that the vibration of the vehicle is related to the road surface data, determining the vehicle bumping level according to the road surface data, the vehicle speed data and the vehicle vibration data; and determining the seat control result of the vehicle according to the vehicle bumping level and the vehicle pose data.

[0012] The seat control method provided in the application can more accurately determine whether the vibration is caused by the road surface condition by combining the vehicle vibration data and the road surface data to determine the vehicle vibration result. When it is confirmed that the vibration is related to the road surface, the influence of the same road surface condition on the vehicle and the driver and passenger under different vehicle speeds is considered, the vehicle bumping level is further determined by comprehensively considering the road surface data, the vehicle speed data and the vehicle vibration data, and then the seat control result of the vehicle is determined according to the vehicle bumping level and the vehicle pose data, thereby effectively reducing the body shaking caused by the vehicle bumping, and further improving the comfort of the driver and passenger.

[0013] In a second aspect, the application provides a seat control device, including: an acquisition module configured to acquire related data of a vehicle; wherein the related data includes road surface data, vehicle vibration data and vehicle pose data; a determination module configured to determine a seat control result of the vehicle according to the related data of the vehicle; wherein the seat control result indicates a target displacement amount of a seat in a target direction, and the target direction is at least one of a plurality of directions, and the plurality of directions include an X direction, a Y direction and a Z direction; and a control module configured to control the seat of the vehicle to move by the target displacement amount in the target direction according to the seat control result of the vehicle.

[0014] In a possible implementation, the determining module comprises a first determining unit configured to determine the seat control result of the vehicle according to the relevant data of the vehicle by using the neural network model.

[0015] In a possible implementation, the relevant data further comprises vehicle speed data, and the determining module comprises a second determining unit configured to determine a vehicle vibration result according to the vehicle vibration data and the road surface data, a third determining unit configured to determine a vehicle bumping level according to the road surface data, the vehicle speed data and the vehicle vibration data when the vehicle vibration result indicates that the vibration of the vehicle is related to the road surface data, and a fourth determining unit configured to determine the seat control result of the vehicle according to the vehicle bumping level and the vehicle pose data.

[0016] In a third aspect, the present application provides a computer device, comprising a memory and a processor, the memory and the processor are connected with each other in communication, the memory stores computer instructions, and the processor executes the computer instructions to perform the seat control method of the first aspect or any of the corresponding embodiments thereof.

[0017] In a fourth aspect, the present application provides a computer readable storage medium, which stores computer instructions, and the computer instructions are used to make a computer execute the seat control method of the first aspect or any of the corresponding embodiments thereof.

[0018] In a fifth aspect, the present application provides a computer program product, which comprises computer instructions, and the computer instructions are used to make a computer execute the seat control method of the first aspect or any of the corresponding embodiments thereof. BRIEF DESCRIPTION OF DRAWINGS

[0019] In order to more clearly illustrate the specific embodiments of the present application or the technical solutions in the prior art, the drawings needed in the specific embodiments or the prior art description will be briefly introduced as follows. Obviously, the drawings in the following description are some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0020] Figure 1 is a structural schematic diagram of the seat control system provided by the embodiment of the present application;

[0021] Figure 2 is a flowchart of the seat control method according to the embodiment of the present application;

[0022] Figure 3 is a structural block diagram of the seat control device according to the embodiment of the present application;

[0023] Figure 4is a schematic diagram of a hardware structure of a computer device according to an embodiment of the present application. DETAILED DESCRIPTION

[0024] To make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are some but not all of the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by a person of ordinary skill in the art without creative work fall within the protection scope of the present application.

[0025] Please refer to Figure 1 , Figure 1 is a schematic diagram of a structure of a seat control system according to an embodiment of the present application.

[0026] As shown in Figure 1 , the seat control system comprises a driving computer, a road surface detection system, a vibration sensor, a pose detection system, a seat controller and a seat.

[0027] The road surface detection system, the vibration sensor, the pose detection system and the seat controller can be communicatively connected with the driving computer respectively.

[0028] The road surface detection system can detect road surface data in front of the vehicle, the vibration sensor can detect vehicle vibration data of the vehicle, and the pose detection system can detect vehicle pose data of the vehicle. The driving computer can receive the road surface data, the vehicle vibration data and the vehicle pose data, and then determine a seat control result of the seat according to the road surface data, the vehicle vibration data and the vehicle pose data. The seat control result indicates a target displacement amount of the seat in a target direction, and the target direction is at least one of a plurality of directions, and the plurality of directions comprise an X direction, a Y direction and a Z direction.

[0029] After the driving computer determines the seat control result of the seat, the seat controller can be controlled according to the seat control result, so that the seat controller controls the seat to move the target displacement amount along the target direction.

[0030] According to the embodiments of the present application, a seat control method embodiment is provided. It should be noted that the steps shown in the flowchart of the drawings can be executed in a computer system such as a set of computer executable instructions, and although the logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0031] A seat control method is provided in the present embodiment, which can be used for the driving computer described above, Figure 2 is a flowchart of a seat control method according to an embodiment of the present application, as Figure 2As shown, the flow includes the following steps:

[0032] In step S201, relevant data of the vehicle is acquired; wherein the relevant data includes road surface data, vehicle vibration data, and vehicle pose data.

[0033] The road surface data can indicate information of the road surface condition on which the vehicle travels. The road surface data can include road surface flatness (such as whether there are pits and bumps), roughness (such as the difference between asphalt pavement and gravel pavement), slope (uphill, downhill, or horizontal road surface), and the like.

[0034] The vehicle vibration data can indicate vibration-related information of the vehicle during travel due to factors such as uneven road surface, engine operation, tire friction with the ground, and the like.

[0035] The vehicle pose data can indicate position and attitude information of the vehicle in a three-dimensional space, including the position of the vehicle in the X direction (usually the front-to-back direction of the vehicle), the Y direction (the left-to-right direction), and the Z direction (the up-to-down direction), as well as attitude information such as the pitch angle (rotation angle around the X axis), the roll angle (rotation angle around the Y axis), and the yaw angle (rotation angle around the Z axis) of the vehicle.

[0036] It should be noted that the X direction can be the length direction of the vehicle, the Y direction can be the width direction of the vehicle, and the Z direction can be the vertical direction.

[0037] Specifically, various sensors installed on the vehicle are used to collect road surface data, vehicle vibration data, and vehicle pose data in real time. These data are the basis for determining the seat control result subsequently, and accurate acquisition of these data is crucial for achieving precise seat control.

[0038] As an example, a laser radar installed on the front or around the vehicle can emit a laser beam and measure the reflection time, and obtain three-dimensional profile information of the road surface by analyzing the reflection signal, so as to determine the flatness and slope of the road surface, and the like.

[0039] As an example, a vehicle-mounted camera is used to shoot road surface images, and image processing algorithms are used to identify features such as pits and bumps on the road surface, and thus obtain road surface condition information.

[0040] As an example, an acceleration sensor is installed at a key position such as the chassis or body of the vehicle to measure the acceleration changes of the vehicle in the X direction, the Y direction, and the Z direction. By integrating and analyzing the acceleration signal, information such as the vibration frequency and vibration amplitude of the vehicle can be obtained.

[0041] As an example, multiple vibration sensors are installed on the vehicle to detect the vehicle vibration data of the vehicle through the multiple vibration sensors.

[0042] As an example, the acceleration, angular velocity, and magnetic field intensity of the vehicle are measured by an inertial measurement unit. By fusing and processing these data, the attitude information of the vehicle, such as the pitch angle, roll angle, and yaw angle, and the position change of the vehicle in three-dimensional space can be calculated in real time.

[0043] In step S202, the seat control result of the vehicle is determined according to the relevant data of the vehicle. The seat control result indicates the target displacement amount of the seat in the target direction, and the target direction is at least one of the multiple directions, including the X direction, the Y direction, and the Z direction.

[0044] The seat control result indicates the target displacement amount of the seat in the target direction. The target direction is at least one of the multiple directions, including the X direction, the Y direction, and the Z direction, and the target displacement amount can indicate the numerical value of the movement in the target direction.

[0045] Based on the various data obtained in step S201, specific algorithms or models are used for analysis and processing. Considering the road surface condition, vehicle vibration condition, and current pose of the vehicle, the target displacement amount of the seat in one or more directions of the X direction, Y direction, and Z direction is calculated, and the seat control result is determined.

[0046] As an example, a series of rules are pre-set, and according to the combination of different road surface data, vehicle vibration data, and vehicle pose data, the corresponding seat control result is determined. For example, when the road flatness is poor and the vehicle vibration amplitude is large, if the vehicle is in a pitch state, the target displacement amount of the seat in the Z direction is increased to provide better cushioning and support.

[0047] As an example, a large amount of actual driving data is collected, including the best control result of the seat under different road conditions, vehicle speed, vehicle vibration, and pose, as training samples. These samples are used to train machine learning models such as neural networks, decision trees, etc. In actual application, the real-time acquired vehicle related data is input into the trained model, and the model outputs the target displacement amount of the seat in each direction, thereby determining the seat control result.

[0048] In step S203, the seat of the vehicle is controlled to move the target displacement amount in the target direction according to the seat control result of the vehicle.

[0049] The seat control result determined in step S202 is converted into actual seat movement instructions, which are executed through the seat control system of the vehicle (such as an electric seat adjustment mechanism). The seat moves according to the specified target direction and target displacement amount, achieving dynamic adjustment of the seat position.

[0050] As an example, the seat control system controls the motor in the corresponding direction to rotate according to the target displacement amount determined in step S202, so as to realize the accurate movement of the seat. For example, if the target displacement amount is to move 50 mm forward in the X direction, the control system drives the motor in the front part of the seat to move the seat forward by a specified distance.

[0051] The seat control method provided by the present application reflects the current road conditions and the state of the vehicle by obtaining the road data, vehicle vibration data and vehicle pose data, determines the target displacement amount of the seat in the target direction (at least one of the X direction, the Y direction and the Z direction) according to the current road conditions and the state of the vehicle, and offsets or reduces the impact of the vehicle bumping in advance through the active movement of the seat, so as to keep the body of the driver and passenger relatively stable during the driving of the vehicle, effectively reduce the body shaking caused by the vehicle bumping, and further improve the comfort of the driver and passenger.

[0052] In one possible implementation, the above step S201 includes:

[0053] Step a1, using a neural network model, determining the seat control result of the vehicle according to the related data of the vehicle.

[0054] The neural network model can be a computational model simulating the structure and working principle of human brain neurons, which is composed of an input layer, a hidden layer and an output layer. The input layer receives external data, the hidden layer abstractly represents and learns the input information through multi-layer neuron connection and weight adjustment, and the output layer converts the processing result into executable instructions. It has learning and training ability, can recognize patterns and make decisions, and is widely used in intelligent cockpit in fields such as speech recognition, image processing and natural language processing, so as to realize more intelligent human-computer interaction and vehicle control. The neural network model can be a convolutional neural network or a recurrent neural network, and is not limited here.

[0055] In the present embodiment, the neural network model can be used as a model for outputting the seat control result of the vehicle. After determining the related data of the vehicle, the neural network model can be used to determine the seat control result of the vehicle according to the related data of the vehicle. The related data of the vehicle can be the input of the neural network model.

[0056] As an example,

[0057] The collected image data is preprocessed, such as adjusting image size, normalization and other operations, to meet the input requirements of the convolutional neural network. At the same time, other vehicle-related data is standardized to ensure that the data is within the same scale range. Design a suitable convolutional neural network architecture, including the number and parameters of convolutional layers, pooling layers and fully connected layers. For example, a network containing multiple convolutional layers and pooling layers can be constructed to extract high-level features of the image, and then these features are spliced with vehicle vibration data, pose data, etc. to input to the fully connected layer for further processing. A large amount of labeled data is used to train the convolutional neural network model, and the weights and parameters of the model are adjusted to accurately predict the seat control result. During training, methods such as cross-validation can be used to evaluate the performance of the model to prevent overfitting. Real-time vehicle-related data is input into the trained convolutional neural network model, and the convolutional neural network model outputs the target displacement of the seat in the target direction, i.e. determines the seat control result.

[0058] For example, in an intelligent connected vehicle, the road surface image in front of the vehicle is collected by the vehicle-mounted camera, and the vehicle vibration data and pose data are obtained. The convolutional neural network model is used to extract features from the road surface image, such as road flatness, obstacles, etc. information, combined with vehicle vibration data and pose data, through the trained convolutional neural network model to predict the target displacement of the seat in the Z direction, to adapt to different road conditions and improve the driving comfort.

[0059] The seat control method provided by the present application can effectively fuse multi-source data from different sensors. Different types of data may have different dimensions, distributions and characteristics, and traditional data processing methods may encounter difficulties in fusing these data. The neural network model can automatically learn the weight relationship between different data through its internal weight adjustment mechanism, organically fuse multi-source data, and provide more comprehensive and accurate information for determining the seat control result.

[0060] In one possible implementation, the above method further comprises:

[0061] Step b1, using the laser radar configured by the vehicle to scan the road in front of the vehicle to obtain road surface data.

[0062] The vehicle can be configured with at least one laser radar, and the road in front of the vehicle is scanned by the laser radar to obtain road surface data.

[0063] As an example, the laser radar transmitter emits near-infrared laser pulses to the front of the vehicle, and the pulses propagate at the speed of light and irradiate the road surface or obstacle surface. The reflected laser is captured by the radar receiver, and by measuring the time difference between laser emission and reception, combined with the speed of light, the distance between the target object and the vehicle is calculated.

[0064] As an example, a laser radar performs a 360° scan or a directional scan on the front environment through mechanical rotation (such as a mechanical laser radar) or optical scanning (such as a solid-state laser radar), continuously obtaining a large number of distance data points. Each data point contains three-dimensional coordinates (x, y, z) and reflection intensity information, which together constitute a point cloud map reflecting the shape of the road surface and obstacles. The point cloud map can intuitively present features such as road boundaries, potholes, and obstacles.

[0065] The seat control method provided by the application can accurately calculate the distance from the target object by emitting a laser beam and measuring the time of reflected light, and can generate high-precision three-dimensional point cloud data to accurately obtain the subtle features such as the ups and downs, slopes, and potholes of the front road, thereby providing accurate road surface information for seat control.

[0066] In one possible implementation, the above method further comprises:

[0067] Step b2, acquiring image information of the front road of the vehicle by using a camera configured on the vehicle.

[0068] Step b3, processing the image information of the front road of the vehicle to obtain road surface data.

[0069] The camera configured on the vehicle can be a monocular camera, a multi-view camera, an infrared camera, etc., which is not specifically limited here. The image information of the front road can indicate a continuous video stream or image frame data containing lane lines, traffic signs, pedestrians, other vehicles, and road surface conditions, which are collected in real time by the camera.

[0070] The original image is preprocessed, feature extracted, and structurally analyzed to generate road surface data.

[0071] As an example, Gaussian filtering or median filtering is used to eliminate random noise in the image. The image is converted to a bird's eye view to eliminate perspective distortion, facilitating lane line detection (such as the warpPerspective function of OpenCV). The frequency domain characteristics (such as Fourier transform) of the road surface texture in the image are analyzed to evaluate the road surface roughness.

[0072] In one possible implementation, when the front road of the vehicle is scanned by using the laser radar configured on the vehicle, the road surface data obtained and the road surface data obtained by using the camera configured on the vehicle can be compared, when the comparison results of the two are different, an error can be fed back, and when the comparison results of the two are the same, the road surface data can be sent.

[0073] Compared with a laser radar, the camera has a generally lower cost. The camera arranged on the vehicle can obtain road surface data at a relatively low cost, thereby reducing the manufacturing cost of the vehicle. Meanwhile, with the continuous development of the camera technology, the performance of the camera is continuously improved, and the camera can meet the demand for collecting road surface data under a certain precision requirement.

[0074] In one possible implementation, the relevant data further includes vehicle speed data; and the step S202 includes:

[0075] In step S2021, a vehicle vibration result is determined according to the vehicle vibration data and the road surface data.

[0076] The vehicle vibration result can indicate whether the vibration of the vehicle is mainly caused by the road unevenness by analyzing the correlation between the vehicle vibration data and the road surface data.

[0077] Specifically, whether the vibration is caused by the road unevenness is determined by analyzing the spatiotemporal correlation between the vehicle vibration signal and the road surface feature.

[0078] As an example, a classification model (such as a random forest model) is trained, and the vibration feature (such as RMS acceleration and peak frequency) and the road surface feature (such as the flatness index) are input into the classification model, and a vibration cause label (road surface / non-road surface) is output.

[0079] In step S2022, when the vehicle vibration result indicates that the vibration of the vehicle is related to the road surface data, a vehicle bumping level is determined according to the road surface data, the vehicle speed data and the vehicle vibration data.

[0080] The bumping level can be a level (such as a “slight” level, a “moderate” level and a “severe” level) determined according to the vehicle vibration intensity, the frequency and the road surface feature.

[0081] As an example, the bumping level of the vehicle can be determined by using the following formula.

[0082] I = w1 x D + w2 x V + w3 x A; wherein D is the road surface data, V is the vehicle speed, A is the vehicle vibration data, w1 is a first weight coefficient corresponding to the road surface data, w2 is a second weight coefficient corresponding to the vehicle speed, w3 is a third weight coefficient corresponding to the vehicle vibration data, and I is the bumping level of the vehicle.

[0083] It should be noted that the first weight coefficient, the second weight coefficient and the third weight coefficient can be obtained by querying a pre-set relationship table.

[0084] In step S2023, a seat control result of the vehicle is determined according to the bumping level of the vehicle and vehicle pose data.

[0085] The seat control result of the vehicle is determined according to the bumping level of the vehicle and the vehicle posture data.

[0086] As an example, when the bumping is slight, the current state of the seat can be maintained, and only the damping coefficient is fine-tuned (for example, the hardness is reduced by 10%). When the bumping is moderate, the seat active damping can be activated, the seat height is raised by 5-10 mm, and the lateral support is increased. When the bumping is severe, the seat height is quickly lowered and the seat belt pretensioner is locked.

[0087] As an example, a vehicle-seat dynamics model is established to take the bumping level and the posture data as inputs, and to optimize the seat adjustment trajectory (such as height and inclination angle) to minimize the vertical acceleration of the occupant.

[0088] As an example, the bumping level of the vehicle is determined according to a preset correspondence relationship. The preset correspondence relationship is a correspondence relationship between target data and the bumping level of the vehicle, and the target data includes road surface data, vehicle speed data, and vehicle vibration data. The road surface data can reflect the flatness, undulation, and pothole conditions of the road, the vehicle speed data reflects the speed of the vehicle, and the vehicle vibration data directly reflects the vibration intensity of the vehicle during driving. The preset correspondence relationship comprehensively considers these key factors, avoids the one-sidedness of determining the bumping level based on a single data, accurately determines the bumping level of the vehicle, effectively reduces the body shaking caused by the bumping of the vehicle, and improves the comfort of the driver and the passenger.

[0089] The seat control method provided by the application determines the vehicle vibration result by combining the vehicle vibration data and the road surface data, can more accurately determine whether the vibration is caused by the road surface condition. When it is confirmed that the vibration is related to the road surface, the influence of the same road surface condition on the vehicle and the driver and the passenger at different speeds is considered, the bumping level is further determined by comprehensively considering the road surface data, the vehicle speed data, and the vehicle vibration data, the seat control result of the vehicle is determined according to the bumping level of the vehicle and the vehicle posture data, the body shaking caused by the bumping of the vehicle is effectively reduced, and the comfort of the driver and the passenger is improved.

[0090] In the embodiment, a seat control device is also provided, which is used to implement the above-mentioned embodiments and preferred embodiments, and will not be described again. As used below, the term "module" can be a combination of software and / or hardware that implements a predetermined function. Although the device described in the following embodiments is preferably implemented in software, hardware, or a combination of software and hardware is also possible and contemplated.

[0091] The embodiment provides a seat control device, which is used to implement the above-mentioned embodiments and preferred embodiments, and will not be described again. As used below, the term "module" can be a combination of software and / or hardware that implements a predetermined function. Although the device described in the following embodiments is preferably implemented in software, hardware, or a combination of software and hardware is also possible and contemplated. Figure 3As shown, it includes: an acquisition module 301, used to obtain relevant data of the vehicle; wherein the relevant data includes road surface data, vehicle vibration data and vehicle posture data; a determination module 302, used to determine the seat control result of the vehicle according to the relevant data of the vehicle; wherein the seat control result indicates the target displacement of the seat in the target direction, and the target direction is at least one of multiple directions, and the multiple directions include: X direction, Y direction and Z direction; a control module 303, used to control the vehicle seat to move the target displacement along the target direction according to the vehicle seat control result.

[0092] The further functional description of each of the above modules and units is the same as that of the above corresponding embodiments and will not be repeated here.

[0093] The seat control device in this embodiment is presented in the form of a functional unit, where the functional unit refers to an ASIC (Application Specific Integrated Circuit) circuit, a processor and memory that executes one or more software or fixed programs, and / or other devices that can provide the above functions.

[0094] The embodiment of the present invention also provides a computer device having the above Figure 3 Seat controls shown.

[0095] See also Figure 4 , Figure 4 is a structural diagram of a computer device provided by an optional embodiment of the present invention, such as Figure 4 As shown, the computer device includes: one or more processors 10, memory 20, and interfaces for connecting various components, including high-speed interfaces and low-speed interfaces. Various components utilize different buses to communicate with each other and can be installed on a common mainboard or installed in other ways as needed. The processor can process the instructions executed in the computer device, including instructions stored in the memory or on the memory to display the graphical information of the GUI on an external input / output device (such as, a display device coupled to the interface). In some optional embodiments, if necessary, multiple processors and / or multiple buses can be used together with multiple memories and multiple memories. Equally, multiple computer devices can be connected, and each device provides part of the necessary operations (for example, as a server array, a group of blade servers, or a multi-processor system). Figure 4 A processor 10 is taken as an example.

[0096] The processor 10 can be a central processor, a network processor, or a combination thereof. The processor 10 can further include hardware chips. The hardware chips can be application specific integrated circuits, programmable logic devices, or a combination thereof. The programmable logic devices can be complex programmable logic devices, field programmable logic gate arrays, general array logic, or any combination thereof.

[0097] The memory 20 stores instructions executable by the at least one processor 10 to cause the at least one processor 10 to perform the methods illustrated by the above embodiments.

[0098] The memory 20 can include a program storage area and a data storage area. The program storage area can store an operating system, application programs required by at least one function, and the like. The data storage area can store data created according to the use of the computer device, and the like. In addition, the memory 20 can include a high-speed random access memory, and can further include a non-transitory memory such as at least one disk storage device, a flash memory device, or other non-transitory solid state storage device. In some alternative embodiments, the memory 20 can optionally include a memory disposed remotely from the processor 10, which can be connected to the computer device through a network. Examples of the network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and a combination thereof.

[0099] The memory 20 can include a volatile memory such as a random access memory, and can also include a non-volatile memory such as a flash memory, a hard disk, or a solid state disk. The memory 20 can further include a combination of the above-mentioned types of memories.

[0100] The computer device further includes a communication interface 30 for communication of the computer device with other devices or communication networks.

[0101] The embodiments of the present application also provide a computer readable storage medium. The above-mentioned methods according to the embodiments of the present application can be implemented in hardware, firmware, or recorded in a storage medium, or stored in a remote storage medium or a non-transitory machine readable storage medium and downloaded to a local storage medium through network downloading of computer code, so that the methods described herein can be processed by such software on a storage medium using a general purpose computer, a special purpose processor, or programmable or special purpose hardware. The storage medium can be a magnetic disk, an optical disk, a read-only memory, a random access memory, a flash memory, a hard disk, or a solid state disk, etc. Further, the storage medium can further include a combination of the above-mentioned types of memories. It can be understood that the computer, the processor, the microprocessor controller, or the programmable hardware includes a storage component that can store or receive software or computer code, which, when accessed and executed by the computer, the processor, or the hardware, implements the methods illustrated by the above embodiments.

[0102] Part of the present application can be applied as a computer program product, for example, computer program instructions, when executed by a computer, through the operation of the computer, can invoke or provide the method and / or technical solutions according to the present application. Those skilled in the art should understand that the form of computer program instructions in computer readable medium includes but is not limited to source files, executable files, installation package files, etc., and accordingly, the way of computer program instructions executed by computer includes but is not limited to: the computer directly executes the instructions, or the computer compiles the instructions and then executes the corresponding compiled program, or the computer reads and executes the instructions, or the computer reads and installs the instructions and then executes the corresponding installed program. Here, the computer readable medium can be any available computer readable storage medium or communication medium accessible to the computer.

[0103] Although the embodiments of the present application are described in conjunction with the drawings, various modifications and changes can be made by those skilled in the art without departing from the spirit and scope of the present application, and such modifications and changes fall within the scope defined by the appended claims.

Claims

1. A seat control method, characterized in that: The method comprises: Acquiring relevant data of the vehicle; wherein the relevant data includes road surface data, vehicle vibration data, and vehicle posture data; Determining a seat control result of the vehicle based on the relevant data of the vehicle; wherein the seat control result indicates a target displacement of the seat in a target direction, the target direction being at least one of a plurality of directions, the plurality of directions including: an X direction, a Y direction, and a Z direction; According to the seat control result of the vehicle, the seat of the vehicle is controlled to move along the target direction by the target displacement amount.

2. The seat control method according to claim 1, characterized in that: Determining a seat control result of the vehicle according to relevant data of the vehicle includes: A neural network model is used to determine a seat control result of the vehicle according to relevant data of the vehicle.

3. The seat control method according to claim 1, characterized in that: The relevant data also includes: vehicle speed data; and determining the seat control result of the vehicle according to the relevant data of the vehicle, including: determining a vehicle vibration result based on the vehicle vibration data and the road surface data; When the vehicle vibration result indicates that the vibration of the vehicle is correlated with the road surface data, determining a bumpiness level of the vehicle based on the road surface data, the vehicle speed data, and the vehicle vibration data; A seat control result of the vehicle is determined according to the bump level of the vehicle and the vehicle posture data.

4. The seat control method according to claim 1, characterized in that: Obtain vehicle road data, including: Use the laser radar configured on the vehicle to scan the road ahead of the vehicle to obtain road surface data; and / or; Using the camera configured on the vehicle to collect image information of the road ahead of the vehicle; Image information of the road ahead of the vehicle is processed to obtain road surface data.

5. A seat control device, characterized in that: The device comprises: An acquisition module, configured to acquire relevant data of the vehicle; wherein the relevant data includes road surface data, vehicle vibration data, and vehicle posture data; a determination module, configured to determine a seat control result of the vehicle based on the relevant data of the vehicle; wherein the seat control result indicates a target displacement of the seat in a target direction, the target direction being at least one of a plurality of directions, the plurality of directions including: an X direction, a Y direction, and a Z direction; A control module is used to control the seat of the vehicle to move the target displacement along the target direction according to the seat control result of the vehicle.

6. The seat control device according to claim 5, characterized in that: The modules to be determined include: The first determining unit is configured to determine a seat control result of the vehicle according to relevant data of the vehicle by using a neural network model.

7. The seat control device according to claim 5, characterized in that: The relevant data also includes: vehicle speed data; and the determination module includes: a second determining unit, configured to determine a vehicle vibration result based on the vehicle vibration data and the road surface data; a third determining unit, configured to determine a bumpiness level of the vehicle based on the road surface data, the vehicle speed data, and the vehicle vibration data when the vehicle vibration result indicates that the vibration of the vehicle is correlated with the road surface data; A fourth determining unit is configured to determine a seat control result of the vehicle according to the bumpiness level of the vehicle and the vehicle posture data.

8. A computer device, characterized in that: include: A memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the seat control method according to any one of claims 1 to 4 by executing the computer instructions.

9. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a computer to execute the seat control method according to any one of claims 1 to 4.

10. A computer program product, characterized in that The method comprises computer instructions for causing a computer to execute the seat control method according to any one of claims 1 to 4.

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