CN116978062A - Vehicle safety seat rotation control method, system, device and storage medium - Google Patents
Vehicle safety seat rotation control method, system, device and storage medium Download PDFInfo
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- B60N—SEATS SPECIALLY ADAPTED FOR VEHICLES; VEHICLE PASSENGER ACCOMMODATION NOT OTHERWISE PROVIDED FOR
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- B60N2/02—Seats specially adapted for vehicles; Arrangement or mounting of seats in vehicles the seat or part thereof being movable, e.g. adjustable
- B60N2/0224—Non-manual adjustments, e.g. with electrical operation
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- B60N2/42—Seats specially adapted for vehicles; Arrangement or mounting of seats in vehicles for particular purposes or particular vehicles the seat constructed to protect the occupant from the effect of abnormal g-forces, e.g. crash or safety seats
- B60N2/427—Seats or parts thereof displaced during a crash
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Abstract
The application discloses a vehicle safety seat rotation control method, a system, a device and a medium, comprising the following steps: acquiring first body weight information of a target passenger on the safety seat, and acquiring front image information and side image information of the target passenger; extracting a first human body contour and a second human body contour of a target passenger; acquiring a first face image of a target passenger according to the first human body outline, and determining first gender information and first age information of the target passenger according to the first face image; inputting the first body weight information, the first gender information, the first age information, the first human body contour and the second human body contour into a pre-trained passenger body type recognition model to obtain a first body type of a target passenger; and determining corresponding rotation angle parameters according to the first body type, and performing rotation control on the safety seat according to the rotation angle parameters when the collision signal is detected by the vehicle. The application improves the accuracy of the rotation control of the safety seat and can be widely applied to the technical field of vehicle control.
Description
Technical Field
The application relates to the technical field of vehicle control, in particular to a vehicle safety seat rotation control method, a system, a device and a storage medium.
Background
With the development of intelligent networking of automobiles, vehicle monitoring and control technologies are becoming more and more intelligent. In the prior art, in order to reduce injuries to passengers by rotating a seat in a collision, the types and lengths of seat belts used are often used to acquire the body shape of a user corresponding to the seat, so that the seat is rotated correspondingly in the collision to reduce injuries. The method can only roughly judge the body type of the user, and cannot accurately acquire the body type of the user, so that the obtained seat rotation angle is not accurate enough, the accuracy of the rotation control of the safety seat is influenced, the collision protection effect of the safety seat on passengers is influenced, and the riding safety and riding experience of the user are also influenced.
Disclosure of Invention
The present application aims to solve at least one of the technical problems existing in the prior art to a certain extent.
Therefore, an object of an embodiment of the present application is to provide a vehicle safety seat rotation control method that improves accuracy of safety seat rotation control, thereby improving riding safety and riding experience of a user.
It is another object of an embodiment of the present application to provide a vehicle safety seat rotation control system.
In order to achieve the technical purpose, the technical scheme adopted by the embodiment of the application comprises the following steps:
in a first aspect, an embodiment of the present application provides a rotation control method for a vehicle safety seat, including the steps of:
acquiring first body weight information of a target passenger on a safety seat, and acquiring front image information and side image information of the target passenger;
extracting a first human body contour of the target passenger according to the front image information, and extracting a second human body contour of the target passenger according to the side image information;
acquiring a first face image of the target passenger according to the first human body contour, and determining first gender information and first age information of the target passenger according to the first face image;
inputting the first body weight information, the first gender information, the first age information, the first human body contour and the second human body contour into a pre-trained passenger body type recognition model to obtain a first body type of the target passenger;
and determining corresponding rotation angle parameters according to the first body type, and performing rotation control on the safety seat according to the rotation angle parameters when the collision signal is detected by the vehicle.
Further, in one embodiment of the present application, the step of acquiring first body weight information of a target occupant on a safety seat and acquiring front side image information and side image information of the target occupant specifically includes:
acquiring first pressure information through a first pressure sensor arranged on a seat of the safety seat, acquiring second pressure information through a second pressure sensor arranged on a chair back of the safety seat, and acquiring third pressure information through a third pressure sensor arranged on a foot pad in front of the safety seat;
determining first body weight information of the target occupant according to the first pressure information, the second pressure information and the third pressure information;
acquiring front image information of the target passenger through a first image pickup device arranged in front of the safety seat;
side image information of the target occupant is acquired by a second image pickup device provided inside the door.
Further, in one embodiment of the present application, the step of extracting the first body contour of the target occupant according to the front image information and extracting the second body contour of the target occupant according to the side image information specifically includes:
and performing edge detection on the front image information to obtain a first human body contour of the target passenger, and performing edge detection on the side image information to obtain a second human body contour of the target passenger.
Further, in one embodiment of the present application, the step of acquiring a first face image of the target occupant according to the first human body contour, and determining first gender information and first age information of the target occupant according to the first face image specifically includes:
determining a head area according to the first human body outline, and extracting a first face image of the target passenger from the front image information according to the head area;
and inputting the first face image into a preset gender and age identification model to obtain first gender information and first age information of the target passenger.
Further, in one embodiment of the present application, the vehicle safety seat rotation control method further includes a step of training the occupant body type recognition model in advance, which specifically includes:
acquiring a plurality of preset passenger body type sample data, and determining a body type label corresponding to each passenger body type sample data, wherein the passenger body type sample data comprises second body weight information, second gender information, second age information, third human body contour and fourth human body contour of a tested passenger;
constructing a training data set according to the occupant body type sample data and the corresponding body type class labels;
and inputting the training data set into a convolutional neural network constructed in advance for training to obtain the trained passenger body type recognition model.
Further, in one embodiment of the present application, the step of inputting the training data set into a convolutional neural network constructed in advance to perform training, and obtaining a trained model of identifying the body shape of the occupant specifically includes:
inputting the training data set into the convolutional neural network, and identifying to obtain an occupant body type identification result;
determining a loss value of the convolutional neural network according to the occupant body type recognition result and the body type class label;
updating model parameters of the convolutional neural network through a back propagation algorithm according to the loss value, and returning to the step of inputting the training data set into the convolutional neural network;
and stopping training when the loss value reaches a preset first threshold value or the iteration number reaches a preset second threshold value, and obtaining the trained passenger body type recognition model.
Further, in an embodiment of the present application, the determining a corresponding rotation angle parameter according to the first body type category, when the vehicle detects the collision signal, the step of performing rotation control on the safety seat according to the rotation angle parameter specifically includes:
acquiring a preset safety seat rotation strategy pool, wherein the safety seat rotation strategy pool comprises a plurality of first mapping relations, and the first mapping relations are mapping relations between pre-constructed body type categories and rotation angles;
matching the first body type category in the safety seat rotation strategy pool to obtain a corresponding rotation angle parameter;
and when the vehicle detects a collision signal, the vehicle body controller is used for carrying out rotation control on the safety seat according to the rotation angle parameter.
In a second aspect, an embodiment of the present application provides a vehicle safety seat rotation control system including:
the information acquisition module is used for acquiring first body weight information of a target passenger on the safety seat and acquiring front image information and side image information of the target passenger;
the human body contour extraction module is used for extracting a first human body contour of the target passenger according to the front image information and extracting a second human body contour of the target passenger according to the side image information;
the sex and age determining module is used for acquiring a first face image of the target passenger according to the first human body outline and determining first sex information and first age information of the target passenger according to the first face image;
the body type identification module is used for inputting the first body weight information, the first gender information, the first age information, the first human body outline and the second human body outline into a pre-trained passenger body type identification model to obtain a first body type of the target passenger;
and the parameter determining and rotating control module is used for determining corresponding rotating angle parameters according to the first body type category, and carrying out rotating control on the safety seat according to the rotating angle parameters when the collision signal is detected by the vehicle.
In a third aspect, an embodiment of the present application provides a vehicle safety seat rotation control device, including:
at least one processor;
at least one memory for storing at least one program;
the at least one program, when executed by the at least one processor, causes the at least one processor to implement a vehicle safety seat rotation control method as described above.
In a fourth aspect, an embodiment of the present application also provides a computer-readable storage medium in which a processor-executable program is stored, which when executed by a processor, is for performing a vehicle safety seat rotation control method as described above.
The advantages and benefits of the application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the application.
According to the embodiment of the application, first body weight information of a target passenger on a safety seat is acquired, front image information and side image information of the target passenger are acquired, a first human body contour of the target passenger is extracted according to the front image information, a second human body contour of the target passenger is extracted according to the side image information, a first face image of the target passenger is acquired according to the first human body contour, first gender information and first age information of the target passenger are determined according to the first face image, the first body weight information, the first gender information, the first age information, the first human body contour and the second human body contour are input into a pre-trained passenger body type recognition model, a first body type category of the target passenger is obtained, corresponding rotation angle parameters are determined according to the first body type category, and when a collision signal is detected by a vehicle, rotation control is performed on the safety seat according to the rotation angle parameters. According to the embodiment of the application, the first body weight information, the front image information and the side image information of the target passenger are acquired, the first body contour and the second body contour of the side of the target passenger are extracted, the face image is acquired based on the first body contour, the first gender information and the first age information of the target passenger are acquired by recognition, the first body weight information, the first gender information, the first age information, the first body contour and the second body contour are further input into a pre-trained passenger body type recognition model, the first body type of the target passenger is acquired by recognition, so that the corresponding rotation angle parameters can be determined according to the first body type, and the rotation control is performed on the safety seat according to the rotation angle parameters when the vehicle detects collision signals, so that the accuracy of the rotation control of the safety seat is improved, the collision protection effect of the safety seat on the passenger is improved, and the riding safety and riding experience of the user are also improved.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following description will refer to the drawings that are needed in the embodiments of the present application, and it should be understood that the drawings in the following description are only for convenience and clarity to describe some embodiments in the technical solutions of the present application, and other drawings may be obtained according to these drawings without any inventive effort for those skilled in the art.
FIG. 1 is a flow chart of steps of a method for controlling rotation of a safety seat for a vehicle according to an embodiment of the present application;
FIG. 2 is a block diagram of a vehicle safety seat rotation control system according to an embodiment of the present application;
fig. 3 is a block diagram of a rotation control device for a vehicle safety seat according to an embodiment of the present application.
Detailed Description
Embodiments of the present application are described in detail below, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to like or similar elements or elements having like or similar functions throughout. The embodiments described below by referring to the drawings are illustrative only and are not to be construed as limiting the application. The step numbers in the following embodiments are set for convenience of illustration only, and the order between the steps is not limited in any way, and the execution order of the steps in the embodiments may be adaptively adjusted according to the understanding of those skilled in the art.
In the description of the present application, the plurality means two or more, and if the description is made to the first and second for the purpose of distinguishing technical features, it should not be construed as indicating or implying relative importance or implicitly indicating the number of the indicated technical features or implicitly indicating the precedence of the indicated technical features. Furthermore, unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art.
Referring to fig. 1, an embodiment of the present application provides a rotation control method for a vehicle safety seat, which specifically includes the following steps:
s101, acquiring first body weight information of a target passenger on the safety seat, and acquiring front image information and side image information of the target passenger.
Specifically, in the embodiment of the application, the first body weight information of the target passenger is obtained through the pressure sensor, and the front image information and the side image information of the target passenger are obtained through the image pickup device, so that it can be understood that the front image information can reflect the body form of the target passenger from the front, and the side image information can reflect the body form of the target passenger from the side.
Further as an alternative embodiment, the step of acquiring first body weight information of the target occupant on the safety seat and acquiring front side image information and side image information of the target occupant specifically includes:
s1011, acquiring first pressure information through a first pressure sensor arranged on a seat of the safety seat, acquiring second pressure information through a second pressure sensor arranged on a seat back of the safety seat, and acquiring third pressure information through a third pressure sensor arranged on a foot pad in front of the safety seat;
s1012, determining first body weight information of the target passenger according to the first pressure information, the second pressure information and the third pressure information;
s1013, acquiring front image information of a target passenger through a first image pickup device arranged in front of the safety seat;
s1014, side image information of the target occupant is acquired by a second image pickup device provided inside the door.
Specifically, when a target passenger is on the safety seat, the weight of the target passenger is borne by the three parts of the seat, the chair back and the foot pad, and the first pressure sensor, the second pressure sensor and the third pressure sensor are respectively arranged on the seat, the chair back and the foot pad, so that the first pressure information, the second pressure information and the third pressure information can be correspondingly acquired, and the stress analysis can be carried out on the target passenger by combining the angle parameters of the seat, the chair back and the foot pad, so that the first weight information of the target passenger is obtained. Further, front image information of the target occupant is acquired by a first image pickup device provided in front of the safety seat, and side image information of the target occupant is acquired by a second image pickup device provided inside the door.
S102, extracting a first human body contour of the target passenger according to the front image information, and extracting a second human body contour of the target passenger according to the side image information.
Further as an optional embodiment, the step of extracting a first human body contour of the target occupant according to the front image information and extracting a second human body contour of the target occupant according to the side image information specifically includes:
and performing edge detection on the front image information to obtain a first human body contour of the target passenger, and performing edge detection on the side image information to obtain a second human body contour of the target passenger.
Specifically, the embodiment of the application performs edge detection on the front image information and the side image information to obtain the first human body contour of the front face and the second human body contour of the side of the target passenger.
In some alternative embodiments, an infrared image of the target occupant may be acquired, thereby improving accuracy of human contour detection.
In some alternative embodiments, the front image information may be compared with the background image information of the front of the safety seat to obtain a first human contour of the foreground, and the side image information may be compared with the background image information of the side of the safety seat to obtain a second human contour of the foreground.
S103, acquiring a first face image of the target passenger according to the first human body outline, and determining first gender information and first age information of the target passenger according to the first face image.
Further as an optional embodiment, the step of acquiring a first face image of the target occupant according to the first human body contour, and determining the first gender information and the first age information of the target occupant according to the first face image specifically includes:
s1031, determining a head area according to the first human body contour, and extracting a first face image of a target passenger from front image information according to the head area;
s1032, inputting the first face image into a preset gender and age identification model to obtain first gender information and first age information of the target passenger.
Specifically, after the first human body contour is determined, the top area of the first human body contour is determined to be a head area, a first face image of the target passenger is extracted from the front image information based on the head area, and then the first face image is input into a preset gender and age identification model to obtain first gender information and first age information of the target passenger. It should be noted that, the gender age identification model in the embodiment of the present application adopts the neural network model existing in the prior art, and the embodiment of the present application is not described herein.
S104, inputting the first body weight information, the first gender information, the first age information, the first human body contour and the second human body contour into a pre-trained passenger body type recognition model to obtain a first body type of the target passenger.
Specifically, the passenger body type recognition model of the embodiment of the application is obtained through convolutional neural network training, and the obtained first body weight information, first gender information, first age information, first human body contour and second human body contour are input into the passenger body type recognition model, so that a corresponding passenger body type recognition result, namely, a first body type category of a target passenger can be obtained.
Further as an alternative embodiment, the vehicle safety seat rotation control method further includes a step of training an occupant body type recognition model in advance, which specifically includes:
s201, acquiring a plurality of preset passenger body type sample data, and determining a body type label corresponding to each passenger body type sample data, wherein the passenger body type sample data comprises second body weight information, second gender information, second age information, third body contour and fourth body contour of a tested passenger;
s202, constructing a training data set according to the passenger body type sample data and the corresponding body type labels;
s203, inputting the training data set into a convolutional neural network constructed in advance for training, and obtaining a trained passenger body type recognition model.
Specifically, when the training data set is constructed, second body weight information, second gender information, second age information, third body contour and fourth body contour of the test occupant (the third body contour and the fourth body contour are respectively a front body contour and a side body contour of the test occupant) are acquired, and meanwhile, body type category labels corresponding to the body type sample data of the respective occupants are determined based on manual labeling, and are used for indicating body type categories corresponding to the body type sample data of the respective occupants, including but not limited to body type thin, body type micro thin, body type standard, body type micro fat and body type fat. And generating a training data set according to the passenger body type sample data and the corresponding body type label. It can be understood that more gears can be more specifically set for the division of body types, and the embodiment of the application does not limit the division of specific body types.
Further as an optional implementation manner, the step of inputting the training data set into a pre-constructed convolutional neural network to perform training to obtain a trained occupant body shape recognition model specifically includes:
s2031, inputting a training data set into a convolutional neural network, and identifying to obtain an occupant body type identification result;
s2032, determining a loss value of the convolutional neural network according to the occupant body type recognition result and the body type label;
s2033, updating model parameters of the convolutional neural network through a back propagation algorithm according to the loss value, and returning to the step of inputting the training data set into the convolutional neural network;
and S2034, stopping training when the loss value reaches a preset first threshold value or the iteration number reaches a preset second threshold value, and obtaining a trained passenger body type recognition model.
Specifically, after data in the training data set is input into the initialized convolutional neural network model, a recognition result output by the model, namely, a passenger body type recognition result, can be obtained, and the accuracy of model prediction can be evaluated according to the passenger body type recognition result and the label information, so that parameters of the model are updated. For the model of identifying the body shape of the passenger, the accuracy of the model prediction result can be measured by a Loss Function (Loss Function), wherein the Loss Function is defined on single training data and is used for measuring the prediction error of one training data, and particularly determining the Loss value of the training data through the label of the single training data and the prediction result of the model on the training data. In actual training, one training data set has a lot of training data, so that a Cost Function (Cost Function) is generally adopted to measure the overall error of the training data set, and the Cost Function is defined on the whole training data set and is used for calculating the average value of the prediction errors of all the training data, so that the prediction effect of the model can be better measured. For a general machine learning model, based on the cost function, a regular term for measuring the complexity of the model can be used as a training objective function, and based on the objective function, the loss value of the whole training data set can be obtained. There are many kinds of common loss functions, such as 0-1 loss function, square loss function, absolute loss function, logarithmic loss function, cross entropy loss function, etc., which can be used as the loss function of the machine learning model, and will not be described in detail herein. In the embodiment of the application, one loss function can be selected to determine the loss value of training. Based on the trained loss value, updating the parameters of the model by adopting a back propagation algorithm, and iterating for several rounds to obtain the trained passenger body type recognition model. Specifically, the number of iteration rounds may be preset, or training may be considered complete when the test set meets the accuracy requirements.
S105, determining corresponding rotation angle parameters according to the first body type, and performing rotation control on the safety seat according to the rotation angle parameters when the collision signal is detected by the vehicle.
Further as an optional embodiment, determining a corresponding rotation angle parameter according to the first body type category, and when the vehicle detects a collision signal, performing rotation control on the safety seat according to the rotation angle parameter, which specifically includes:
s1051, acquiring a preset safety seat rotation strategy pool, wherein the safety seat rotation strategy pool comprises a plurality of first mapping relations, and the first mapping relations are mapping relations between pre-constructed body type categories and rotation angles;
s1052, matching the rotation angle parameters in the safety seat rotation strategy pool according to the first body type to obtain corresponding rotation angle parameters;
s1053, when the vehicle detects collision signals, the vehicle body controller is used for carrying out rotation control on the safety seat according to the rotation angle parameters.
Specifically, in the embodiment of the application, a safety seat rotation strategy pool is constructed in advance, mapping relations between different rotation angles and body types are preset, index matching is performed in the safety seat rotation strategy pool according to the identified first body type, and corresponding rotation angle parameters can be obtained and stored. When the vehicle detects a collision signal, the vehicle body controller is used for carrying out rotation control on the safety seat based on the rotation angle parameter, so that the damage of collision to a target passenger can be reduced to the greatest extent.
The method steps of the embodiments of the present application are described above. It may be appreciated that, in the embodiment of the present application, the first body weight information, the front image information and the side image information of the target occupant are obtained, the first body contour of the front face and the second body contour of the side face of the target occupant are extracted, the face image is obtained based on the first body contour, the first gender information and the first age information of the target occupant are obtained by recognition, and then the first body weight information, the first gender information, the first age information, the first body contour and the second body contour are input into the pre-trained occupant body type recognition model, so that the first body type category of the target occupant is obtained by recognition, thereby determining the corresponding rotation angle parameter according to the first body type category, and performing rotation control on the safety seat according to the rotation angle parameter when the vehicle detects the collision signal, thereby improving the accuracy of rotation control of the safety seat, thereby improving the collision protection effect of the safety seat on the occupant, and improving the riding safety and riding experience of the user.
Referring to fig. 2, an embodiment of the present application provides a vehicle safety seat rotation control system including:
the information acquisition module is used for acquiring first body weight information of a target passenger on the safety seat and acquiring front image information and side image information of the target passenger;
the human body contour extraction module is used for extracting a first human body contour of the target passenger according to the front image information and extracting a second human body contour of the target passenger according to the side image information;
the sex and age determining module is used for acquiring a first face image of the target passenger according to the first human body outline and determining first sex information and first age information of the target passenger according to the first face image;
the body type recognition module is used for inputting the first body weight information, the first gender information, the first age information, the first human body outline and the second human body outline into a pre-trained passenger body type recognition model to obtain a first body type of a target passenger;
and the parameter determining and rotating control module is used for determining corresponding rotating angle parameters according to the first body type category, and performing rotating control on the safety seat according to the rotating angle parameters when the collision signal is detected by the vehicle.
The content in the method embodiment is applicable to the system embodiment, the functions specifically realized by the system embodiment are the same as those of the method embodiment, and the achieved beneficial effects are the same as those of the method embodiment.
Referring to fig. 3, an embodiment of the present application provides a rotation control device for a vehicle safety seat, including:
at least one processor;
at least one memory for storing at least one program;
the at least one program, when executed by the at least one processor, causes the at least one processor to implement the above-described vehicle safety seat rotation control method.
The content in the method embodiment is applicable to the embodiment of the device, and the functions specifically realized by the embodiment of the device are the same as those of the method embodiment, and the obtained beneficial effects are the same as those of the method embodiment.
The embodiment of the application also provides a computer-readable storage medium in which a processor-executable program is stored, which when executed by a processor, is for executing the above-described vehicle safety seat rotation control method.
The computer readable storage medium of the embodiment of the application can execute the method for controlling the rotation of the safety seat of the vehicle, which is provided by the embodiment of the method of the application, and can execute the steps of the embodiment of the method in any combination, thereby having the corresponding functions and beneficial effects of the method.
Embodiments of the present application also disclose a computer program product or computer program comprising computer instructions stored in a computer readable storage medium. The computer instructions may be read from a computer-readable storage medium by a processor of a computer device, and executed by the processor, to cause the computer device to perform the method shown in fig. 1.
In some alternative embodiments, the functions/acts noted in the block diagrams may occur out of the order noted in the operational illustrations. For example, two blocks shown in succession may in fact be executed substantially concurrently or the blocks may sometimes be executed in the reverse order, depending upon the functionality/acts involved. Furthermore, the embodiments presented and described in the flowcharts of the present application are provided by way of example in order to provide a more thorough understanding of the technology. The disclosed methods are not limited to the operations and logic flows presented herein. Alternative embodiments are contemplated in which the order of various operations is changed, and in which sub-operations described as part of a larger operation are performed independently.
Furthermore, while the present application has been described in the context of functional modules, it should be appreciated that, unless otherwise indicated, one or more of the functions and/or features described above may be integrated in a single physical device and/or software module or one or more of the functions and/or features may be implemented in separate physical devices or software modules. It will also be appreciated that a detailed discussion of the actual implementation of each module is not necessary to an understanding of the present application. Rather, the actual implementation of the various functional modules in the apparatus disclosed herein will be apparent to those skilled in the art from consideration of their attributes, functions and internal relationships. Accordingly, one of ordinary skill in the art can implement the application as set forth in the claims without undue experimentation. It is also to be understood that the specific concepts disclosed are merely illustrative and are not intended to be limiting upon the scope of the application, which is to be defined in the appended claims and their full scope of equivalents.
The above functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on this understanding, the technical solution of the present application may be embodied in essence or a part contributing to the prior art or a part of the technical solution in the form of a software product stored in a storage medium, including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the above-described method of the various embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
Logic and/or steps represented in the flowcharts or otherwise described herein, e.g., a ordered listing of executable instructions for implementing logical functions, can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For the purposes of this description, a "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device.
More specific examples (a non-exhaustive list) of the computer-readable medium would include the following: an electrical connection (electronic device) having one or more wires, a portable computer diskette (magnetic device), a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium upon which the program described above is printed, as the program described above may be electronically captured, via, for instance, optical scanning of the paper or other medium, then compiled, interpreted, or otherwise processed in a suitable manner, if necessary, and then stored in a computer memory.
It is to be understood that portions of the present application may be implemented in hardware, software, firmware, or a combination thereof. In the above-described embodiments, the various steps or methods may be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, may be implemented using any one or combination of the following techniques, as is well known in the art: discrete logic circuits having logic gates for implementing logic functions on data signals, application specific integrated circuits having suitable combinational logic gates, programmable Gate Arrays (PGAs), field Programmable Gate Arrays (FPGAs), and the like.
In the foregoing description of the present specification, reference has been made to the terms "one embodiment/example", "another embodiment/example", "certain embodiments/examples", and the like, means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the application. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiments or examples. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
While embodiments of the present application have been shown and described, it will be understood by those of ordinary skill in the art that: many changes, modifications, substitutions and variations may be made to the embodiments without departing from the spirit and principles of the application, the scope of which is defined by the claims and their equivalents.
While the preferred embodiment of the present application has been described in detail, the present application is not limited to the above embodiments, and various equivalent modifications and substitutions can be made by those skilled in the art without departing from the spirit of the present application, and these equivalent modifications and substitutions are intended to be included in the scope of the present application as defined in the appended claims.
Claims (10)
1. A vehicle safety seat rotation control method characterized by comprising the steps of:
acquiring first body weight information of a target passenger on a safety seat, and acquiring front image information and side image information of the target passenger;
extracting a first human body contour of the target passenger according to the front image information, and extracting a second human body contour of the target passenger according to the side image information;
acquiring a first face image of the target passenger according to the first human body contour, and determining first gender information and first age information of the target passenger according to the first face image;
inputting the first body weight information, the first gender information, the first age information, the first human body contour and the second human body contour into a pre-trained passenger body type recognition model to obtain a first body type of the target passenger;
and determining corresponding rotation angle parameters according to the first body type, and performing rotation control on the safety seat according to the rotation angle parameters when the collision signal is detected by the vehicle.
2. The vehicle safety seat rotation control method according to claim 1, characterized in that the step of acquiring first body weight information of a target occupant on the safety seat and acquiring front side image information and side image information of the target occupant specifically includes:
acquiring first pressure information through a first pressure sensor arranged on a seat of the safety seat, acquiring second pressure information through a second pressure sensor arranged on a chair back of the safety seat, and acquiring third pressure information through a third pressure sensor arranged on a foot pad in front of the safety seat;
determining first body weight information of the target occupant according to the first pressure information, the second pressure information and the third pressure information;
acquiring front image information of the target passenger through a first image pickup device arranged in front of the safety seat;
side image information of the target occupant is acquired by a second image pickup device provided inside the door.
3. The vehicle safety seat rotation control method according to claim 1, wherein the step of extracting a first human body contour of the target occupant from the front image information and extracting a second human body contour of the target occupant from the side image information is specifically:
and performing edge detection on the front image information to obtain a first human body contour of the target passenger, and performing edge detection on the side image information to obtain a second human body contour of the target passenger.
4. The vehicle safety seat rotation control method according to claim 1, characterized in that the step of acquiring a first face image of the target occupant from the first human body contour and determining first sex information and first age information of the target occupant from the first face image specifically comprises:
determining a head area according to the first human body outline, and extracting a first face image of the target passenger from the front image information according to the head area;
and inputting the first face image into a preset gender and age identification model to obtain first gender information and first age information of the target passenger.
5. The vehicle safety seat rotation control method according to claim 1, characterized in that the vehicle safety seat rotation control method further comprises a step of training the occupant body type recognition model in advance, specifically comprising:
acquiring a plurality of preset passenger body type sample data, and determining a body type label corresponding to each passenger body type sample data, wherein the passenger body type sample data comprises second body weight information, second gender information, second age information, third human body contour and fourth human body contour of a tested passenger;
constructing a training data set according to the occupant body type sample data and the corresponding body type class labels;
and inputting the training data set into a convolutional neural network constructed in advance for training to obtain the trained passenger body type recognition model.
6. The method according to claim 5, wherein the step of inputting the training data set into a convolutional neural network constructed in advance to train and obtain the trained model for identifying the occupant body shape comprises the steps of:
inputting the training data set into the convolutional neural network, and identifying to obtain an occupant body type identification result;
determining a loss value of the convolutional neural network according to the occupant body type recognition result and the body type class label;
updating model parameters of the convolutional neural network through a back propagation algorithm according to the loss value, and returning to the step of inputting the training data set into the convolutional neural network;
and stopping training when the loss value reaches a preset first threshold value or the iteration number reaches a preset second threshold value, and obtaining the trained passenger body type recognition model.
7. The method according to any one of claims 1 to 6, characterized in that the step of determining a corresponding rotation angle parameter according to the first body type, and performing rotation control on the safety seat according to the rotation angle parameter when the vehicle detects a collision signal, specifically comprises:
acquiring a preset safety seat rotation strategy pool, wherein the safety seat rotation strategy pool comprises a plurality of first mapping relations, and the first mapping relations are mapping relations between pre-constructed body type categories and rotation angles;
matching the first body type category in the safety seat rotation strategy pool to obtain a corresponding rotation angle parameter;
and when the vehicle detects a collision signal, the vehicle body controller is used for carrying out rotation control on the safety seat according to the rotation angle parameter.
8. A vehicle safety seat rotation control system, characterized by comprising:
the information acquisition module is used for acquiring first body weight information of a target passenger on the safety seat and acquiring front image information and side image information of the target passenger;
the human body contour extraction module is used for extracting a first human body contour of the target passenger according to the front image information and extracting a second human body contour of the target passenger according to the side image information;
the sex and age determining module is used for acquiring a first face image of the target passenger according to the first human body outline and determining first sex information and first age information of the target passenger according to the first face image;
the body type identification module is used for inputting the first body weight information, the first gender information, the first age information, the first human body outline and the second human body outline into a pre-trained passenger body type identification model to obtain a first body type of the target passenger;
and the parameter determining and rotating control module is used for determining corresponding rotating angle parameters according to the first body type category, and carrying out rotating control on the safety seat according to the rotating angle parameters when the collision signal is detected by the vehicle.
9. A vehicle safety seat rotation control device characterized by comprising:
at least one processor;
at least one memory for storing at least one program;
when the at least one program is executed by the at least one processor, the at least one processor is caused to implement a vehicle safety seat rotation control method as claimed in any one of claims 1 to 7.
10. A computer-readable storage medium in which a processor-executable program is stored, characterized in that the processor-executable program is for performing a vehicle safety seat rotation control method according to any one of claims 1 to 7 when executed by a processor.
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| CN202310953631.4A CN116978062A (en) | 2023-07-31 | 2023-07-31 | Vehicle safety seat rotation control method, system, device and storage medium |
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| CN202310953631.4A CN116978062A (en) | 2023-07-31 | 2023-07-31 | Vehicle safety seat rotation control method, system, device and storage medium |
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117173181A (en) * | 2023-11-03 | 2023-12-05 | 沈阳金杯李尔汽车座椅有限公司 | Seat delivery verification method and system based on image decomposition |
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117173181A (en) * | 2023-11-03 | 2023-12-05 | 沈阳金杯李尔汽车座椅有限公司 | Seat delivery verification method and system based on image decomposition |
| CN117173181B (en) * | 2023-11-03 | 2024-01-26 | 沈阳金杯李尔汽车座椅有限公司 | Seat delivery verification method and system based on image decomposition |
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