CN116691505A

CN116691505A - Vehicle parameter adjusting method and device, electronic equipment and storage medium

Info

Publication number: CN116691505A
Application number: CN202310380685.6A
Authority: CN
Inventors: 陈栋; 方绍伟; 刘竹清
Original assignee: Beijing CHJ Automobile Technology Co Ltd
Current assignee: Beijing CHJ Automobile Technology Co Ltd
Priority date: 2023-04-11
Filing date: 2023-04-11
Publication date: 2023-09-05

Abstract

The present disclosure provides a vehicle parameter adjustment method. The method includes acquiring human body characteristic data of a vehicle occupant; determining a parameter adjustment signal corresponding to the human body characteristic data, wherein the parameter adjustment signal comprises at least one of a vehicle seat parameter adjustment signal and an exterior rear view mirror parameter signal; and adjusting the vehicle parameters according to the parameter adjusting signals. According to the scheme provided by the disclosure, the vehicle parameters conforming to different occupant signs are adaptively pushed according to the occupant signs, the vehicle parameter adjustment scheme and the vehicle parameter memory function are optimized, the driving fatigue is reduced, and the safety and the comfort are improved.

Description

Vehicle parameter adjusting method and device, electronic equipment and storage medium

Technical Field

The disclosure relates to the technical field of vehicles, and in particular relates to a vehicle parameter adjusting method, device and system.

Background

Intelligent vehicle parameter adjustment technology has gone through three stages of development, from manual adjustment and motor adjustment to adjustment with memory function, which plays a key role in user experience and driving safety.

Most of vehicle parameters such as automobile seats and outer rearview mirrors in the current market are realized through manual adjusting switches of passengers, adjusting time is complex, time consumption is long, and the optimal position suitable for a user can be found by multiple times of debugging, so that experience is poor. Even with the adjustment scheme with the memory function, only a shift change with low flexibility can be realized, and the vehicle with the memory function cannot well meet the demands of strange passengers when changing drivers and riding strange passengers.

Disclosure of Invention

In order to overcome the deficiencies of the prior art, the present disclosure provides a vehicle parameter adjustment method, a vehicle parameter adjustment device, an electronic apparatus, and a storage medium.

According to a first aspect of the present disclosure, there is provided a vehicle parameter adjustment method, the method comprising: acquiring human body characteristic data of vehicle personnel; determining a parameter adjustment signal corresponding to the human body characteristic data, wherein the parameter adjustment signal comprises at least one of a vehicle seat parameter adjustment signal and an exterior rear view mirror parameter signal; and adjusting the vehicle parameters according to the parameter adjusting signals.

In some embodiments of the present disclosure, acquiring human feature data of a vehicle occupant includes: performing image recognition on a vehicle occupant to obtain a recognition image; human body characteristic data is extracted from the identification image.

In some embodiments of the present disclosure, acquiring human feature data of a vehicle occupant includes: and carrying out face detection processing on the identification image to obtain human body characteristic data, wherein the human body characteristic data comprises the position information of the face pixels.

In some embodiments of the present disclosure, acquiring human feature data of a vehicle occupant includes: performing skeleton detection processing on the identification image to obtain position information of key points of the upper body; marking and correcting the position information of the key points of the upper body, and taking the corrected position information of the key points of the upper body as human body characteristic data.

In some embodiments of the present disclosure, the method further comprises: and converting the human body characteristic data in the two-dimensional space into three-dimensional human body characteristic data.

In some embodiments of the present disclosure, determining a parameter adjustment signal corresponding to the human body characteristic data includes: training the human body sign recognition model based on the human body size parameter training set; and processing the three-dimensional human body characteristic data through the human body sign recognition model to obtain the position information of the key points of the lower body, wherein the human body characteristic data is the position information of the corrected key points of the upper body.

In some embodiments of the present disclosure, determining a parameter adjustment signal corresponding to the human body characteristic data includes: determining seat parameters corresponding to the position information of the corrected upper body key points and the position information of the corrected lower body key points; and generating a vehicle seat parameter adjusting signal according to the seat parameter, wherein the vehicle seat parameter adjusting signal is used for adjusting the vehicle seat.

In some embodiments of the present disclosure, determining a parameter adjustment signal corresponding to the human body characteristic data includes: determining external rearview mirror parameters corresponding to the converted human body characteristic data, wherein the human body characteristic data is the position information of human face pixels; and generating an external rearview mirror parameter signal according to the external rearview mirror parameter.

In some embodiments of the present disclosure, the method further comprises: determining an occupant identification according to the identification image; determining whether a parameter adjustment signal corresponding to the passenger identification is stored in the vehicle; if so, the vehicle parameters are adjusted according to the parameter adjusting signals corresponding to the passenger identifications.

According to a second aspect of the present disclosure, there is provided a vehicle parameter adjustment device, the device comprising: a data acquisition unit configured to acquire human body characteristic data of a vehicle occupant; the data processing unit is used for determining a parameter adjusting signal corresponding to the human body characteristic data, wherein the parameter adjusting signal comprises at least one of a vehicle seat parameter adjusting signal and an outside rearview mirror parameter signal; and the control unit is used for adjusting the vehicle parameters according to the parameter adjusting signals.

According to a third aspect of the present disclosure, there is provided an electronic device comprising: at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the vehicle parameter adjustment method described in the first aspect of the present disclosure.

According to a fourth aspect of the present disclosure, there is provided a non-transitory computer-readable storage medium storing computer instructions for causing a computer to execute the vehicle parameter adjustment method described in the first aspect of the present disclosure.

According to a fifth aspect of the present disclosure, there is provided a computer program product comprising a computer program which, when executed by a processor, implements the vehicle parameter adjustment method described in the first aspect of the present disclosure.

According to a sixth aspect of the present disclosure, there is provided a vehicle including the vehicle parameter adjustment device described in the second aspect of the present disclosure.

It should be understood that the description in this section is not intended to represent key or critical features of the embodiments of the present application, nor is it intended to limit the scope of the present application. Other features of the present application will become apparent from the description that follows.

Drawings

The drawings are for a better understanding of the present solution and are not to be construed as limiting the present disclosure. Wherein:

fig. 1 is a schematic flow chart of a vehicle parameter adjustment method according to an embodiment of the disclosure;

fig. 2 is a schematic flow chart of a vehicle parameter adjustment method according to an embodiment of the disclosure;

fig. 3 is a schematic flow chart of a vehicle parameter adjustment method according to an embodiment of the disclosure;

fig. 4 is a schematic diagram of a human body sign recognition model according to an embodiment of the disclosure;

FIG. 5 is a schematic view of a seat parameter calculation model according to an embodiment of the present disclosure;

FIG. 6 is a model deployment and signaling roadmap provided by an embodiment of the disclosure;

fig. 7 is a schematic structural diagram of a vehicle parameter adjusting device according to an embodiment of the disclosure;

fig. 8 is a schematic block diagram of an example electronic device 800 provided by an embodiment of the disclosure.

Detailed Description

Exemplary embodiments of the present disclosure are described below in conjunction with the accompanying drawings, which include various details of the embodiments of the present disclosure to facilitate understanding, and should be considered as merely exemplary. Accordingly, one of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the present disclosure. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.

Vehicle parameter adjustment methods, apparatuses, electronic devices, and storage media of embodiments of the present disclosure are described below with reference to the accompanying drawings.

Along with the vast increase of information flow brought by intelligent networking of automobiles, the requirements of users on driving comfort and safety are continuously improved, most of automobile parameters such as automobile seats and outside rearview mirrors in the current market are realized through manual adjusting switches of passengers, adjusting time is complex, time consumption is long, and the optimal position suitable for the users can be found possibly through multiple times of debugging, so that the experience is poor. Even with the adjustment scheme with the memory function, only a shift change with low flexibility can be realized, and the vehicle with the memory function cannot well meet the demands of strange passengers when changing drivers and riding strange passengers.

The following describes a vehicle parameter adjustment scheme provided by the present disclosure with reference to the accompanying drawings. The scheme provided by the disclosure can realize automatic perception of different occupant signs, automatically recommend vehicle parameters including but not limited to seat parameters and the optimal view position of the exterior rearview mirror, and realize personalized iteration through user data accumulation.

Fig. 1 is a flowchart of a vehicle parameter adjustment method according to an embodiment of the disclosure.

As shown in fig. 1, the method is applied to a vehicle, and includes the following steps.

Step 101, acquiring human body characteristic data of a vehicle occupant.

In one embodiment of the present disclosure, an on-board communication system of a vehicle is capable of acquiring human body characteristic data of a user or an occupant according to a preset visual perception model. The human body characteristic data includes, but is not limited to, data acquired or preprocessed by image recognition of an occupant in the vehicle, including, but not limited to, recognition by a camera.

It can be understood that the human body characteristic data is obtained based on a preset visual perception model, and the preset visual perception model can perform data extraction on the identification image obtained by image identification to obtain the human body characteristic data, wherein the human body characteristic data is used for further processing.

Step 102, determining parameter adjusting signals corresponding to the human body characteristic data.

In one embodiment of the present disclosure, the parameter adjustment signal is generated after the human body characteristic data is processed based on a preset data processing model, and includes, but is not limited to, at least one of a vehicle seat parameter adjustment signal and an exterior rear view mirror parameter signal. It will be appreciated that the solution of the invention is equally applicable to the regulation of other vehicle parameters obtained from body data, and is not limited.

In one embodiment, the preset data processing model is used for further processing the human body characteristic data extracted through the preset visual perception model for generating the vehicle parameter adjusting signal.

And 103, adjusting the vehicle parameters according to the parameter adjusting signals.

In one embodiment of the present disclosure, the generated parameter adjustment signal may be a pulse signal, such as a hall number. For example, when the seat parameters need to be adjusted, the pulse number generated by the hall sensor is the position information of the seat, which can be understood that each seat position corresponds to a different pulse signal of each motor, and the hall number output by the data processing model can be used as the pulse signal to input the motor for driving the seat, so that the seat parameters can be adjusted to the required state. For another example, when the angle of the exterior mirror needs to be adjusted, the hall number output by the data processing model can be input as a pulse signal to a motor driving the exterior mirror, so that the related parameters of the exterior mirror can be adjusted. It will be appreciated that the parameter adjustment signal may be a control signal that may be used to adjust the relevant parameter of the corresponding vehicle device to the corresponding parameter value based on the calculated parameter value, without limitation in the present invention.

In summary, according to the vehicle parameter adjustment method provided by the present disclosure, human body feature data of a vehicle occupant is obtained; determining a parameter adjustment signal corresponding to the human body characteristic data, wherein the parameter adjustment signal comprises at least one of a vehicle seat parameter adjustment signal and an exterior rear view mirror parameter signal; and adjusting the vehicle parameters according to the parameter adjusting signals. According to the scheme provided by the disclosure, the vehicle parameters conforming to different occupant signs are adaptively pushed according to the occupant signs, the vehicle parameter adjustment scheme and the vehicle parameter memory function are optimized, the driving fatigue is reduced, and the safety and the comfort are improved.

Fig. 2 is a flow chart of a vehicle parameter adjustment method according to an embodiment of the disclosure. Specifically, the method comprises the following steps.

In step 201, body characteristic data of a vehicle occupant is acquired.

In one embodiment of the present disclosure, step 201 includes:

in step 2011, face detection processing is performed on the identification image to obtain human feature data, where the human feature data includes position information of face pixels.

In an embodiment of the present disclosure, the face detection process is implemented based on a face detection model. The face detection model may be a joint face detection and alignment model using a multi-tasking cascaded convolutional network (Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks, MTCNN). Specifically, the input of the model is image data, i.e., an image recognized by a camera, and the output of the model is human body feature data, such as positional information of human face pixels.

In the present disclosure, the position information of the face pixel includes a pixel position coordinate of a rectangular frame surrounding the face and a key point coordinate of the face, where the pixel position coordinate of the rectangular frame surrounding the face is specifically a center pixel coordinate of the rectangular frame and a length-width coordinate of the rectangular frame, and the key point coordinate of the face may include a pixel coordinate of an eye, a nose tip, and a mouth corner. The specific operation mode of the model is that image sampling is carried out in data transmitted back from a camera, primary face related identification is carried out on the sampled image data, then one-step screening is carried out, the result with poor effect is screened out, the result with good effect is reserved, finally final result screening is carried out, detection of eyes, nose tips and mouth corners is carried out on the screened face image, and finally the position information of the face pixels is output.

It should be noted that the position information of the face pixels includes at least the eye point position.

It can be understood that the position information of the face pixels is two-dimensional coordinates, and the two-dimensional pixel coordinate points can be converted into three-dimensional space coordinates through a two-dimensional to three-dimensional model. For example, there are two alternatives for the two-dimensional to three-dimensional model.

The first scheme adopts a three-dimensional human body posture estimation reference model, a group of two-dimensional pixel coordinates are input into the model, the two-dimensional pixel coordinates are converted into three-dimensional space coordinates by using a trained two-dimensional to three-dimensional deep learning model, and a three-dimensional space coordinate result is output.

The second scheme adopts a camera model, based on the camera imaging principle, a formula is built by using camera internal parameters (Intrinics) and camera external parameters (Extrinics), two-dimensional pixel coordinates are input, matrix calculation is carried out, and finally three-dimensional space coordinates are obtained through calculation.

In alternative embodiments of the present disclosure, the two-dimensional coordinates to three-dimensional coordinates may also be changed in other ways, without limitation.

Step 202, determining parameter adjusting signals corresponding to the human body characteristic data.

In one embodiment of the present disclosure, the human body characteristic data is processed based on a preset data processing model, where the preset data processing model includes an exterior rear view mirror parameter calculation model, and the step 202 includes:

in step 2021, the external rearview mirror parameters corresponding to the converted human body feature data are determined, where the human body feature data is the position information of the face pixels.

In some embodiments of the present disclosure, the face pixel coordinates converted into three-dimensional data are input to an exterior rearview mirror parameter calculation model, resulting in exterior rearview mirror parameters. The exterior rearview mirror parameter calculation model can be realized through a mathematical formula, for example, the model relates to the whole vehicle parameter and the light reflection principle, so that the optimal exterior rearview mirror position can be calculated through inputting face pixel coordinates.

In the embodiment of the disclosure, the facial image of the driver is acquired and identified through the camera, and the position of the eye point of the driver, namely, the position information of the face pixels is determined; and secondly, combining the whole vehicle parameters, and finding the optimal view position of the rearview mirror by using a light reflection principle.

Step 2022, generating an exterior rearview mirror parameter signal based on the exterior rearview mirror parameter.

In the present disclosure, when the XCU of the vehicle obtains the optimal position parameter of the exterior mirror through the exterior mirror parameter calculation model, the exterior mirror parameter is input into the traction control unit DCU of the vehicle, so as to generate an exterior mirror parameter signal, so as to drive the motor of the exterior mirror to act to adjust the position and angle of the exterior mirror.

Step 203, adjusting the vehicle parameters according to the parameter adjustment signal.

The relevant description of step 203 may refer to step 103 in the embodiment shown in fig. 1, and will not be described herein.

In summary, according to the vehicle parameter adjustment method provided by the present disclosure, by acquiring the position information of the face pixels of the vehicle personnel and determining the external rearview mirror parameters corresponding to the converted human body feature data, the related parameters of the external rearview mirror are adjusted, so that the external rearview mirror parameters according to different occupant signs are adaptively pushed according to the vehicle occupant signs, manual adjustment by a user is not required, the vehicle parameter memory function of a fixed gear is optimized, multi-stage adjustment is realized, the vehicle intelligence is enhanced, and the driving safety and the riding comfort are improved.

Fig. 3 is a flowchart of a vehicle parameter adjustment method according to an embodiment of the disclosure. Specifically, the method comprises the following steps.

In step 301, body characteristic data of a vehicle occupant is acquired.

In one embodiment of the present disclosure, the human feature data is processed based on a preset data processing model, wherein the preset visual perception model comprises a bone detection model, wherein step 301 comprises:

step 3011, performing skeleton detection processing on the identification image to obtain position information of key points of the upper body.

And 3012, marking and correcting the position information of the key points of the upper body, and taking the corrected position information of the key points of the upper body as human body characteristic data.

In some embodiments of the present disclosure, the bone detection process is implemented based on a bone detection model, the input of which is image data, and the output of which is two-dimensional pixel keypoint coordinates of the head, shoulders, elbows, hands, crotch, knees, feet, etc., of the human body in the identified image.

In the embodiment of the disclosure, the specific operation mode of the model is as follows: image sampling is carried out from data transmitted back by a camera, the sampled image data (namely, human body data) is input into a thermodynamic diagram regression model, key positions on an image are marked in a thermodynamic diagram-like mode, position data marked by the thermodynamic diagram are extracted and input into a coordinate correction model, the extracted two-dimensional pixel coordinates are converted into three-dimensional space coordinates, and the three-dimensional model is finely adjusted by the correction model to finally output three-dimensional space coordinate key points.

The minimum data scanned by the camera is image data of the upper body of the human body, and it is understood that the camera can also scan the lower body data, which is not limited in this disclosure.

The model is a model to be trained, and specific training data comprises image data of a human body, corresponding two-dimensional key point labeling data and three-dimensional space key point labeling data. The model training method is not limited in this disclosure.

It can be understood that the position information of the key points of the upper body is two-dimensional coordinates, and the two-dimensional pixel coordinate points can be converted into three-dimensional space coordinates through a two-dimensional to three-dimensional model. The two-dimensional coordinates to three-dimensional coordinates are described in the embodiments of fig. 1 and 2, and are not described herein.

Step 302, determining a parameter adjustment signal corresponding to the human body characteristic data.

In one embodiment of the present disclosure, the human body characteristic data is processed based on a preset data processing model, where the preset data processing model includes a human body sign recognition model, and the step 302 includes:

step 3021, training a human body sign recognition model based on the human body size parameter training set.

In step 3022, the three-dimensional human body feature data is processed through the human body sign recognition model to obtain the position information of the key point of the lower body, where the human body feature data is the corrected position information of the key point of the upper body.

In an embodiment of the present disclosure, the human body sign recognition model employs an XGBoost model, as shown in fig. 4. The model is obtained by training in advance by using human critical point data according to a human body size parameter training set in a human body database. For example, the training data of the model is key point data of the upper body of the human body, specifically, key point data of the head, shoulders, elbows and hands of the upper body, and the label data is key point data of the lower body of the human body, specifically, crotch, knees and feet. The upper body data is input into the model, and the output data of the model is constrained to perform model training, wherein the objective function is an absolute mean error function.

Those skilled in the art will appreciate that the XGboost model is an improvement over the boosting algorithm based on GBDT, and that the internal decision tree uses a regression tree. The split nodes of the regression tree fit residuals for the square loss function; for a general loss function, the fit is an approximation of the residual.

Specifically, the internal structure of the model is a tree structure, each feature in the data is used for judging by combining with a judging rule on each node in the tree through data input, which branch node needs to be carried out next is selected according to a judging result, and the like until the leaf node is finished, a result of the tree is output, and then the results of a plurality of trees are added to obtain a final result and output.

It will be appreciated that the resulting outputs are other dimensions of the human body, such as lower body keypoint coordinates.

In an embodiment of the present disclosure, the preset data processing model further includes a seat parameter calculation model, and the step 302 further includes:

step 3023, determining the seat parameters corresponding to the corrected position information of the upper body key point and the corrected position information of the lower body key point.

In step 3024, a vehicle seat parameter adjustment signal is generated based on the seat parameter, the vehicle seat parameter adjustment signal being used to adjust the vehicle seat.

In the embodiment of the disclosure, the seat parameters are determined based on the seat parameter model, and the corrected upper body key point coordinates and lower body key point coordinates are input into the seat parameter calculation model, so that an output result, namely the seat parameters, can be obtained. Fig. 5 shows a schematic diagram of a seat parameter calculation model. Specifically, the outputted seat parameters include, but are not limited to, at least one of a front-to-back position, a seat cushion angle, a back rest angle, a seat cushion height, a lumbar support amount, a foam collapse amount, wherein the foam collapse amount refers to a thickness of a human body invading foam. The seat parameter may be any seat parameter that can be adjusted on the vehicle, and is not limited in this disclosure. It can be understood that the seat parameter calculation model can obtain the most comfortable seat parameters of the human body according to the input human body parameters,

In an alternative embodiment of the present disclosure, the input parameters of the seat parameter calculation model further include vehicle model parameters, which are used to calculate the relative position of the occupant. For example, the driving postures of SUVs and saloons are different, and the most comfortable seat position under the vehicle model can be obtained through model calculation according to the input vehicle model parameter data.

In the embodiment of the present disclosure, the seat parameter calculation model is mainly calculated through regression analysis, and the related formulas can be selectively used according to actual effects, which is not limited in the present invention.

In the present disclosure, when the XCU of the vehicle obtains the optimal seat parameters through the seat parameter calculation model, the seat parameters are input into the body control module BCM of the vehicle, so as to generate a vehicle seat parameter adjustment signal to drive the seat motor to act to adjust the position of the seat and various parameters.

Step 303, adjusting a vehicle parameter according to the parameter adjustment signal.

The relevant description of step 303 may refer to step 103 in the embodiment shown in fig. 1, and will not be described herein.

In summary, according to the vehicle parameter adjustment method provided by the disclosure, the human body key point coordinates are obtained based on the preset visual perception model and are input into the preset data processing model to obtain the seat parameters, so that the relevant parameters of the seat are adjusted, the seat parameters conforming to different occupant signs are adaptively pushed according to the occupant signs of the vehicle, manual adjustment of a user is not needed, the vehicle parameter memory function of a fixed gear is optimized, multi-level adjustment is realized, the vehicle intelligence is enhanced, and the driving safety and the riding comfort are improved.

The model deployment and signaling in the embodiments described above in fig. 1 to 3 may also refer to fig. 6, and will not be described again here.

It can be appreciated that the invention can realize the storage, the retrieval and the recognition of the face information. When an occupant gets on the vehicle for the first time, face recognition is performed first and the identification of the occupant, which is an ID that uniquely identifies the occupant, such as biometric data such as an iris, is determined. Inquiring whether corresponding parameter records exist or not according to the acquired passenger representation, and if so, directly calling the vehicle parameters corresponding to the passenger according to the passenger identification and adjusting the vehicle parameters; if not, an ID is assigned to the occupant and the posture is identified for the parameter determination and adjustment process in the above embodiment.

It can be appreciated that after the vehicle adjusts the corresponding vehicle parameters according to the calculated data, the occupant can fine tune the automatically adjusted data according to the situation, and feed back the fine tuned data to the vehicle.

In other words, for similar passengers, the vehicle may push the same position parameters after the passengers get on the vehicle for the first time, but different passengers may perform individual fine tuning, and the result after fine tuning will be stored under the ID that uniquely identifies the passenger.

In addition, the automatic vehicle parameter adjusting function of the above embodiment may be manually turned on or off according to the user's need, and its switch trigger is provided on a user interface on the vehicle, such as a screen. The function can also be automatically started, the starting condition is that a sensor of the seat senses data and the vehicle is electrified, and when the starting condition is met, the vehicle starts the camera to start identification.

It will be appreciated that the seat adjustment scheme of the present disclosure is applicable to all powered seats with hall motors. In one application scenario, a camera is provided between a row of seats and a row of seats of a vehicle for identification. In addition, since each seat on the vehicle is configured with an independent occupant SBR occupancy sensor, the seat parameters at different positions in the vehicle are different, and it can be understood that one parameter is common to the primary and secondary driving seats, and one parameter is common to the left and right seats of two rows, which is not limited in this disclosure.

Corresponding to the vehicle parameter adjusting method, the present disclosure also provides a vehicle parameter adjusting device. Fig. 7 is a schematic structural diagram of a vehicle parameter adjustment device 700 according to an embodiment of the disclosure. As shown in fig. 7, the apparatus includes:

and a data acquisition unit 710 for acquiring human body characteristic data of the vehicle personnel.

The data processing unit 720 is configured to determine a parameter adjustment signal corresponding to the human body characteristic data, where the parameter adjustment signal includes at least one of a vehicle seat parameter adjustment signal and an exterior rear view mirror parameter signal.

And a control unit 730 for adjusting a vehicle parameter according to the parameter adjustment signal.

In some embodiments of the present disclosure, the apparatus 700 further comprises: an identification unit that performs image identification for a vehicle occupant to acquire an identification image; human body characteristic data is extracted from the identification image.

In some embodiments of the present disclosure, the data acquisition unit 710 is configured to: and carrying out face detection processing on the identification image to obtain human body characteristic data, wherein the human body characteristic data comprises the position information of the face pixels.

In some embodiments of the present disclosure, the data acquisition unit 710 is configured to: performing skeleton detection processing on the identification image to obtain position information of key points of the upper body; marking and correcting the position information of the key points of the upper body, and taking the corrected position information of the key points of the upper body as human body characteristic data.

In some embodiments of the present disclosure, the apparatus 700 further comprises: a data conversion unit configured to: and converting the human body characteristic data in the two-dimensional space into three-dimensional human body characteristic data.

In some embodiments of the present disclosure, the data processing unit 720 is configured to: training the human body sign recognition model based on the human body size parameter training set; and processing the three-dimensional human body characteristic data through the human body sign recognition model to obtain the position information of the key points of the lower body, wherein the human body characteristic data is the position information of the corrected key points of the upper body.

In some embodiments of the present disclosure, the data processing unit 720 is configured to: determining seat parameters corresponding to the position information of the corrected upper body key points and the position information of the corrected lower body key points; and generating a vehicle seat parameter adjusting signal according to the seat parameter, wherein the vehicle seat parameter adjusting signal is used for adjusting the vehicle seat.

In some embodiments of the present disclosure, the data processing unit 720 is configured to: determining external rearview mirror parameters corresponding to the converted human body characteristic data, wherein the human body characteristic data is the position information of human face pixels; and generating an external rearview mirror parameter signal according to the external rearview mirror parameter.

In some embodiments of the present disclosure, the apparatus 700 further comprises a query unit for determining an occupant identification from the identification image; determining whether a parameter adjustment signal corresponding to the passenger identification is stored in the vehicle; if so, the control unit 730 is configured to adjust a vehicle parameter according to the parameter adjustment signal corresponding to the occupant identification.

It should be noted that, since the apparatus embodiments of the present disclosure correspond to the method embodiments described above, the foregoing explanation of the method embodiments also applies to the apparatus of the present embodiment. Details not disclosed in the device embodiments may refer to the above method embodiments, and are not described herein.

In summary, according to the vehicle parameter adjustment method provided by the present disclosure, human body feature data of a vehicle person is obtained; determining a parameter adjustment signal corresponding to the human body characteristic data, wherein the parameter adjustment signal comprises at least one of a vehicle seat parameter adjustment signal and an exterior rear view mirror parameter signal; and adjusting the vehicle parameters according to the parameter adjusting signals. According to the scheme provided by the disclosure, the vehicle parameters conforming to different occupant signs are adaptively pushed according to the occupant signs, the vehicle parameter adjustment scheme and the vehicle parameter memory function are optimized, the driving fatigue is reduced, and the safety and the comfort are improved.

The present disclosure also provides an electronic device, a storage medium, a computer program product, and a vehicle. The vehicle includes a vehicle parameter adjustment system as described in the present disclosure.

Fig. 8 illustrates a schematic block diagram of an example electronic device 800 that may be used to implement embodiments of the present disclosure. Electronic devices are intended to represent various forms of digital computers, such as smart terminals with display screens, e.g., cell phones, tablet computers, TVs, smart car systems, laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular telephones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the disclosure described and/or claimed herein.

As shown in fig. 8, the apparatus 800 includes a computing unit 801 that can perform various appropriate actions and processes according to a computer program stored in a ROM (Read-Only Memory) 802 or a computer program loaded from a storage unit 808 into a RAM (Random Access Memory ) 803. In the RAM 803, various programs and data required for the operation of the device 800 can also be stored. The computing unit 801, the ROM 802, and the RAM 803 are connected to each other by a bus 804. An I/O (Input/Output) interface 805 is also connected to bus 804.

Various components in device 800 are connected to I/O interface 805, including: an input unit 806 such as a keyboard, mouse, etc.; an output unit 807 such as various types of displays, speakers, and the like; a storage unit 808, such as a magnetic disk, optical disk, etc.; and a communication unit 809, such as a network card, modem, wireless communication transceiver, or the like. The communication unit 809 allows the device 800 to exchange information/data with other devices via a computer network such as the internet and/or various telecommunication networks.

The computing unit 801 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of computing unit 801 include, but are not limited to, a CPU (Central Processing Unit ), GPU (Graphic Processing Units, graphics processing unit), various dedicated AI (Artificial Intelligence ) computing chips, various computing units running machine learning model algorithms, DSPs (Digital Signal Processor, digital signal processors), and any suitable processors, controllers, microcontrollers, and the like. The calculation unit 801 performs the respective methods and processes described above, such as the vehicle parameter adjustment method. For example, in some embodiments, the vehicle parameter adjustment method may be implemented as a computer software program tangibly embodied on a machine-readable medium, such as the storage unit 808. In some embodiments, part or all of the computer program may be loaded and/or installed onto device 800 via ROM 802 and/or communication unit 809. When a computer program is loaded into RAM 803 and executed by computing unit 801, one or more steps of the methods described above may be performed. Alternatively, in other embodiments, the computing unit 801 may be configured to perform the aforementioned vehicle parameter adjustment method by any other suitable means (e.g., by means of firmware).

Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuit System, FPGA (Field Programmable Gate Array ), ASIC (Application-Specific Integrated Circuit, application-specific integrated circuit), ASSP (Application Specific Standard Product, special-purpose standard product), SOC (System On Chip ), CPLD (Complex Programmable Logic Device, complex programmable logic device), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs, the one or more computer programs may be executed and/or interpreted on a programmable system including at least one programmable processor, which may be a special purpose or general-purpose programmable processor, that may receive data and instructions from, and transmit data and instructions to, a storage system, at least one input device, and at least one output device.

Program code for carrying out methods of the present disclosure may be written in any combination of one or more programming languages. These program code may be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus such that the program code, when executed by the processor or controller, causes the functions/operations specified in the flowchart and/or block diagram to be implemented. The program code may execute entirely on the machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server.

In the context of this disclosure, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. The machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, RAM, ROM, EPROM (Electrically Programmable Read-Only-Memory, erasable programmable read-Only Memory) or flash Memory, an optical fiber, a CD-ROM (Compact Disc Read-Only Memory), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having: a display device (e.g., CRT (Cathode-Ray Tube) or LCD (Liquid Crystal Display ) monitor) for displaying information to a user; and a keyboard and pointing device (e.g., a mouse or trackball) by which a user can provide input to the computer. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic input, speech input, or tactile input.

The systems and techniques described here can be implemented in a computing system that includes a background component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such background, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: LAN (Local Area Network ), WAN (Wide Area Network, wide area network), internet and blockchain networks.

The computer system may include a client and a server. The client and server are typically remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server can be a cloud server, also called a cloud computing server or a cloud host, and is a host product in a cloud computing service system, so that the defects of high management difficulty and weak service expansibility in the traditional physical hosts and VPS service ("Virtual Private Server" or simply "VPS") are overcome. The server may also be a server of a distributed system or a server that incorporates a blockchain.

It should be noted that, artificial intelligence is a subject of studying a certain thought process and intelligent behavior (such as learning, reasoning, thinking, planning, etc.) of a computer to simulate a person, and has a technology at both hardware and software level. Artificial intelligence hardware technologies generally include technologies such as sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, big data processing, and the like; the artificial intelligence software technology mainly comprises a computer vision technology, a voice recognition technology, a natural language processing technology, a machine learning/deep learning technology, a big data processing technology, a knowledge graph technology and the like.

Embodiments of the present disclosure also provide a vehicle including a screen, at least one processor, and a memory communicatively coupled to the at least one processor, the memory storing instructions executable by the at least one processor, the at least one processor being capable of performing the vehicle parameter adjustment method described in the foregoing embodiments of the present disclosure when the instructions are executed by the at least one processor.

For convenience of description, only a portion related to the present invention is shown in the drawings. Embodiments of the present disclosure and features of embodiments may be combined with each other without conflict.

It should be appreciated that the terms "system," "apparatus," "unit," and/or "module" as used in this disclosure are one method for distinguishing between different components, elements, parts, portions, or assemblies at different levels. However, if other words can achieve the same purpose, the word can be replaced by other expressions.

As used in this disclosure and in the claims, the terms "a," "an," "the," and/or "the" are not specific to a singular, but may include a plurality, unless the context clearly dictates otherwise. In general, the terms "comprises" and "comprising" merely indicate that the steps and elements are explicitly identified, and they do not constitute an exclusive list, as other steps or elements may be included in a method or apparatus. The inclusion of an element defined by the phrase "comprising one … …" does not exclude the presence of additional identical elements in a process, method, article, or apparatus that comprises an element.

Wherein, in the description of the embodiments of the present disclosure, "/" means or is meant unless otherwise indicated, e.g., a/B may represent a or B; "and/or" herein is merely an association relationship describing an association object, and means that three relationships may exist, for example, a and/or B may mean: a exists alone, A and B exist together, and B exists alone. In addition, in the description of the embodiments of the present disclosure, "a plurality" means two or more than two.

The terms "first," "second," and "second" used in this disclosure are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include one or more such feature.

A flowchart is used in this disclosure to describe the operations performed by a system according to embodiments of the present disclosure. It should be appreciated that the preceding or following operations are not necessarily performed in order precisely. Rather, the steps may be processed in reverse order or simultaneously. Also, other operations may be added to or removed from these processes, and the steps may be reordered, added, or deleted using the various forms of flow shown. For example, the steps recited in the present disclosure may be performed in parallel or sequentially or in a different order, provided that the desired results of the technical solutions of the present disclosure are achieved, and are not limited herein.

The above description is only of embodiments of the present disclosure and the description of the technical principles applied, and is not intended to limit the present disclosure. Various modifications and variations of this disclosure will be apparent to those skilled in the art. The scope of the invention in the present disclosure is not limited to the specific combination of the above technical features, but also encompasses other technical features formed by any combination of the above technical features or their equivalents without departing from the spirit of the invention. Such as those described above, are mutually substituted with the technical features having similar functions disclosed in the present disclosure (but not limited thereto).

Claims

1. A vehicle parameter adjustment method, characterized by comprising:

acquiring human body characteristic data of a vehicle passenger;

determining a parameter adjustment signal corresponding to the human body characteristic data, wherein the parameter adjustment signal comprises at least one of a vehicle seat parameter adjustment signal and an exterior rear view mirror parameter signal;

and adjusting the vehicle parameters according to the parameter adjusting signals.

2. The method of claim 1, wherein the acquiring human characteristic data of the vehicle occupant comprises:

performing image recognition on the vehicle occupant to obtain a recognition image;

and extracting the human body characteristic data from the identification image.

3. The method of claim 2, wherein the acquiring human characteristic data of the vehicle occupant comprises:

and carrying out face detection processing on the identification image to acquire the human body characteristic data, wherein the human body characteristic data comprises position information of face pixels.

4. The method of claim 2, wherein the acquiring human characteristic data of the vehicle occupant comprises:

performing skeleton detection processing on the identification image to obtain position information of key points of the upper body;

And marking and correcting the position information of the key points of the upper body, and taking the corrected position information of the key points of the upper body as the human body characteristic data.

5. A method according to claim 2 or 3, characterized in that the method further comprises:

and converting the human body characteristic data in the two-dimensional space into three-dimensional human body characteristic data.

6. The method of claim 5, wherein determining a parameter adjustment signal corresponding to the human body characteristic data comprises:

training the human body sign recognition model based on the human body size parameter training set;

and processing the three-dimensional human body characteristic data through the human body sign recognition model to obtain the position information of the key points of the lower body, wherein the human body characteristic data is the position information of the key points of the corrected upper body.

7. The method of claim 6, wherein determining a parameter adjustment signal corresponding to the human body characteristic data comprises:

determining seat parameters corresponding to the position information of the corrected upper body key points and the position information of the corrected lower body key points;

and generating the vehicle seat parameter adjusting signal according to the seat parameter, wherein the vehicle seat parameter adjusting signal is used for adjusting the vehicle seat.

8. The method of claim 5, wherein determining a parameter adjustment signal corresponding to the human body characteristic data comprises:

determining exterior rearview mirror parameters corresponding to the converted human body characteristic data, wherein the human body characteristic data is the position information of the human face pixels;

and generating the external rearview mirror parameter signal according to the external rearview mirror parameter.

9. The method according to claim 2, wherein the method further comprises:

determining an occupant identification according to the identification image;

determining whether a parameter adjustment signal corresponding to the passenger identification is stored in the vehicle;

and if so, adjusting the vehicle parameters according to the parameter adjusting signals corresponding to the passenger identifications.

10. A vehicle parameter adjustment device, characterized by comprising:

a data acquisition unit configured to acquire human body characteristic data of a vehicle occupant;

the data processing unit is used for determining a parameter adjusting signal corresponding to the human body characteristic data, wherein the parameter adjusting signal comprises at least one of a vehicle seat parameter adjusting signal and an external rearview mirror parameter signal;

and the control unit is used for adjusting the vehicle parameters according to the parameter adjusting signals.

11. An electronic device, comprising:

at least one processor; and

a memory communicatively coupled to the at least one processor; wherein,,

the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the vehicle parameter adjustment method of any one of claims 1-9.

12. A non-transitory computer-readable storage medium storing computer instructions for causing the computer to perform the vehicle parameter adjustment method according to any one of claims 1-9.

13. A computer program product, characterized in that it comprises a computer program which, when executed by a processor, implements the vehicle parameter adjustment method according to any one of claims 1-9.

14. A vehicle comprising the vehicle parameter adjustment device according to claim 10.