CN115520196A - Method and device for determining weight of driver, electronic equipment and storage medium - Google Patents

Method and device for determining weight of driver, electronic equipment and storage medium Download PDF

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Publication number
CN115520196A
CN115520196A CN202211138518.2A CN202211138518A CN115520196A CN 115520196 A CN115520196 A CN 115520196A CN 202211138518 A CN202211138518 A CN 202211138518A CN 115520196 A CN115520196 A CN 115520196A
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China
Prior art keywords
driver
determining
seat
target
weight
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CN202211138518.2A
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CN115520196B (en
Inventor
钱文国
贾勇
詹灯辉
黄燊
巫辉燕
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Guangzhou Automobile Group Co Ltd
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Guangzhou Automobile Group Co Ltd
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    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
    • B60W40/08—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models related to drivers or passengers
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V20/00—Scenes; Scene-specific elements
    • G06V20/50—Context or environment of the image
    • G06V20/59—Context or environment of the image inside of a vehicle, e.g. relating to seat occupancy, driver state or inner lighting conditions
    • G06V20/593—Recognising seat occupancy
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
    • G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
    • G06V40/16—Human faces, e.g. facial parts, sketches or expressions
    • G06V40/168—Feature extraction; Face representation
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
    • B60W40/08—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models related to drivers or passengers
    • B60W2040/0809—Driver authorisation; Driver identical check
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
    • B60W40/08—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models related to drivers or passengers
    • B60W2040/0872—Driver physiology

Abstract

The application provides a method and a device for determining the weight of a driver, an electronic device and a storage medium, wherein the method comprises the following steps: acquiring an image of a driving seat, and determining whether a driver exists in the driving seat according to the image; if the driver exists in the driving position, identifying the identity of the driver according to the image, and determining the identity of the driver; determining target proportionality coefficients corresponding to the drivers based on the identities of the drivers, wherein the target proportionality coefficients corresponding to different drivers are different; obtaining sample weight data of the driver; determining the weight of the driver according to the target proportionality coefficient and the sample weight data. According to the method and the device, the weights of different drivers are determined according to different target proportionality coefficients corresponding to the different drivers, and the accuracy of the determined weights of the drivers is improved.

Description

Method and device for determining weight of driver, electronic equipment and storage medium
Technical Field
The present application relates to the field of automotive electronics, and in particular, to a method and an apparatus for determining a weight of a driver, an electronic device, and a storage medium.
Background
With the increasing use demand of automobiles, the requirements of people on intelligent functions in automobiles are higher and higher. Meanwhile, due to the fact that the life rhythm is accelerated, people can easily ignore the self health condition. At present, the body temperature, the fatigue state and the like of a driver are monitored by installing a health monitoring module on an automobile, the weight of the driver cannot be accurately detected, and the problem of how to improve the accuracy of the determination of the weight of the driver becomes urgent to solve.
Disclosure of Invention
In view of the above, embodiments of the present application provide a method and an apparatus for determining a weight of a driver, an electronic device, and a storage medium to improve the above problem.
According to an aspect of an embodiment of the present application, there is provided a method for determining a weight of a driver, the method including: acquiring an image of a driving seat, and determining whether a driver exists in the driving seat according to the image; if the driver exists in the driving position, identifying the identity of the driver according to the image, and determining the identity of the driver; determining target proportionality coefficients corresponding to the drivers based on the identities of the drivers, wherein the target proportionality coefficients corresponding to different drivers are different; obtaining sample weight data of the driver; determining the weight of the driver according to the target proportionality coefficient and the sample weight data.
According to an aspect of an embodiment of the present application, there is provided a weight determination device for a driver, the device including: the image acquisition module is used for acquiring an image of a driving seat and determining whether a driver exists in the driving seat according to the image; the identity recognition module is used for carrying out identity recognition on the driver according to the image and determining the identity of the driver if the driver exists in the driving position; the target proportionality coefficient determining module is used for determining the target proportionality coefficients corresponding to the drivers based on the identities of the drivers, wherein the target proportionality coefficients corresponding to different drivers are different; the sample weight data acquisition module is used for acquiring sample weight data of the driver; and the weight determining module is used for determining the weight of the driver according to the target proportionality coefficient and the sample weight data.
According to an aspect of an embodiment of the present application, there is provided an electronic device including: a processor; a memory having computer readable instructions stored thereon which, when executed by the processor, implement a method of weight determination for a driver as described above.
According to an aspect of embodiments of the present application, there is provided a computer-readable storage medium having stored thereon computer-readable instructions which, when executed by a processor, implement the method for determining the weight of a driver as described above.
According to the scheme of the application, whether a driver exists in a driving position is determined through an image of the driving position, when the driver exists, the identity of the driver is identified based on the image of the driving position, the identity of the driver is determined, due to the fact that target proportionality coefficients corresponding to different drivers are different, a target proportionality coefficient corresponding to the current driver is determined according to the identity of the driver, and finally the weight of the current driver is determined according to the obtained sample weight data and the target proportionality coefficient of the driver. The method and the device can determine the weight of the driver according to different target proportion coefficients corresponding to different drivers, and accuracy of determining the weight of the driver is improved.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention, as claimed.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and together with the description, serve to explain the principles of the application. It is obvious that the drawings in the following description are only some embodiments of the application, and that for a person skilled in the art, other drawings can be derived from them without inventive effort.
Fig. 1 is a schematic diagram illustrating a scenario applicable to the present application according to an embodiment of the present application.
Fig. 2 is a flowchart illustrating a method for determining a weight of a driver according to an embodiment of the present application.
Fig. 3 is a flowchart illustrating specific steps of step 240 according to an embodiment of the present application.
Fig. 4 is a flowchart illustrating specific steps of step 250 according to an embodiment of the present application.
Fig. 5 is a flowchart illustrating a method of determining a weight of a driver according to another embodiment of the present application.
Fig. 6 is a flowchart illustrating specific steps of step 340 according to an embodiment of the present application.
Fig. 7 is a flowchart illustrating specific steps prior to step 330 according to an embodiment of the present application.
Fig. 8 is a flowchart illustrating a method of determining a weight of a driver according to still another embodiment of the present application.
Fig. 9 is a block diagram of a driver's weight determination device according to an embodiment of the present application.
Fig. 10 is a hardware block diagram of an electronic device according to an embodiment of the present application.
While specific embodiments of the invention have been illustrated and described in detail in the foregoing drawings, the drawings and detailed description are not intended to limit the scope of the inventive concept in any way, but rather to illustrate it by a person skilled in the art with the aid of specific embodiments.
Detailed Description
Example embodiments will now be described more fully with reference to the accompanying drawings. Example embodiments may, however, be embodied in many different forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of example embodiments to those skilled in the art.
It should be noted that the terms "first," "second," and the like in the description and claims of the present invention and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the invention described herein are capable of operation in sequences other than those illustrated or described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
Fig. 1 is a schematic view of a scenario applicable to the present application according to an embodiment of the present application. As shown in fig. 1, the scenario includes a vehicle 110 and an electronic device 120. The vehicle 110 includes, among other things, a driver monitoring system 111, a seat control module 112, an end-of-vehicle host 113, and a remote communication module 114. Optionally, a pressure sensor may be included in the seat control module 112.
As shown in fig. 1, the driver monitoring system 111 monitors the driver at the driving seat and confirms the identity of the driver at the driving seat, the image of the driving seat acquired by the driver monitoring system 111 may be sent to the electronic device 120 through the remote communication module 114, the electronic device 120 identifies the identity of the driver at the driving seat, and sends the identification result to the remote communication module 114; the vehicle-end host 113 may also perform identification based on the image of the driving position acquired by the driver monitoring system 111. The vehicle-end host machine 113 determines whether to control the seat control module 112 to adjust the seat of the driving seat according to the recognition result, if the recognition result indicates that the current driver is an authenticated person, the seat information corresponding to the driver can be obtained in the vehicle-end host machine 113, the seat control module 112 is controlled to adjust the seat of the driving seat according to the seat information, meanwhile, the target proportion coefficient corresponding to the driver is determined according to the seat information, then, the sample weight data of the driver is obtained through the seat control module 112, and finally, the weight of the driver is determined according to the target proportion coefficient and the sample weight data of the driver. After determining the weight, the weight of the driver may be transmitted to the electronic device 120 through the remote communication module 114. The electronic device 120 receives the weight of the driver sent by the remote communication module 114, compares the weight threshold corresponding to the driver with the weight, and if the weight exceeds the weight threshold, performs a weight health abnormality prompt and stores the weight.
Referring to fig. 2, fig. 2 illustrates a method for determining a weight of a driver according to an embodiment of the present application, and in a specific embodiment, the method for determining a weight of a driver may be applied to the device 600 for determining a weight of a driver illustrated in fig. 9 and an electronic device 700 (fig. 10) equipped with the device 600 for determining a weight of a driver. The specific flow of the embodiment will be described below, and it is understood that the method may be executed by an electronic device with computing processing capability, such as a desktop computer, a notebook computer, a vehicle-mounted terminal, a vehicle-mounted large screen, and other terminal devices, and may also be executed interactively by a processing system including a server and a terminal. As will be described in detail with respect to the flow shown in fig. 2, the method for determining the weight of the driver may specifically include the following steps:
step 210, acquiring an image of a driving seat, and determining whether a driver exists in the driving seat according to the image.
As one mode, a Driver Monitoring System (DMS) may be installed in the vehicle, and may be used to identify the identity of the driver and also to determine the driving state of the driver during driving (i.e., whether the driver is in a fatigue state, etc.). In an embodiment of the present application, it is determined whether a driver is currently present based on an image of a driving seat acquired by a driver monitoring system.
Alternatively, an Occupant Monitoring System (OMS) may be installed in the vehicle to identify the persons in the primary and secondary driving spaces,
alternatively, the driver monitoring system or the passenger monitoring system may be mounted at the position of the rear view mirror of the cab, thereby facilitating identification of the person in the driving position.
And step 220, if a driver exists in the driving position, identifying the identity of the driver according to the image, and determining the identity of the driver.
As one mode, when the driver monitoring system detects that a driver is present in the driver seat, the driver monitoring system compares the acquired image with a pre-stored image of the driver (i.e., an authenticated person), or extracts face information in the acquired image, and then compares the extracted face information with pre-stored face information of the driver, thereby identifying the identity of the driver. Optionally, the driver monitoring system may be used to identify facial information of at least two different authorized persons. If the driver corresponding to the image is determined to be the authentication personnel according to the comparison between the acquired image and the pre-stored image of the authentication personnel, the identity of the driver is determined to be the owner of the vehicle; and if the driver corresponding to the image is determined not to be the authenticated person according to the comparison between the acquired image and the pre-stored image of the authenticated person, determining that the driver is the temporary driver.
And 230, determining target proportionality coefficients corresponding to the drivers based on the identities of the drivers, wherein the target proportionality coefficients corresponding to different drivers are different.
The target scale factor refers to a scale factor determined according to the identity of the driver for calculating the weight of the driver. As a mode, if the identity of the driver is an authenticated person, the scaling factor stored in association with the information of the driver may be directly obtained, optionally, the target scaling factors corresponding to different authenticated persons may be set or modified by the user, the target scaling factors corresponding to the authenticated persons may be the same or different, and may be set according to actual conditions, where no specific limitation is made here.
Alternatively, if the driver is identified as a temporary driver, the scaling factor preset when the vehicle is shipped from the factory may be directly obtained, and the scaling factor may be used as the target scaling factor for the temporary driver.
As yet another approach, the determination of the target scaling factor is also associated with a seat of the driving seat. The gravity center of the driver on the seat is different due to the difference of the backrest of the seat and the front and back positions of the seat, and when the gravity center of the driver is different, the weight information of the driver detected by the pressure sensor for detecting the weight of the driver on the seat is not available, so that the weight of the driver which is finally confirmed is different. In order to ensure the accuracy of the weight of the driver, different proportionality coefficients can be correspondingly set according to the backrest of the seat and the front and back positions of the seat, and then the target proportionality coefficients can be obtained by combining the proportionality coefficients preset by each certifier. For example, if the scale factor of an authenticated driver-defined setting is 1.5 and the scale factor corresponding to the back of the current seat and the fore-aft position of the seat is 1.67, then the target scale factor may be determined to be 1.585.
Step 240, sample weight data of the driver is obtained.
As one way, sample weight data of the driver of the current driving position may be acquired by a pressure sensor on the driving position. Alternatively, a sampling period and a sampling number may be set in advance, and the sample weight data of the driver is acquired according to the sampling period and the sampling number.
As one way, it may also be determined whether sample weight data of the driver is currently acquired according to the current state of the vehicle, and sample weight data of a preset number of drivers is periodically acquired according to the current state.
In some embodiments, as shown in FIG. 3, step 240 comprises:
step 241, determining the current state of the vehicle, wherein the current state comprises a static state or a driving state.
As one approach, the current state of the vehicle may be determined based on the vehicle's current speed gear. For example, if the current vehicle speed range is in neutral, the current state of the vehicle may be determined as a stationary state, and if the vehicle speed range is not in neutral, the current state of the vehicle may be determined as a traveling state.
Alternatively, it is also possible to determine whether the current state of the vehicle is the stationary state or the running state according to the current speed of the vehicle.
And 242, if the current state is the static state, acquiring sample weight data of the driver in a preset period.
As one way, when the vehicle is in a stationary state, sample weight data of a preset number of drivers is acquired for a preset period. Alternatively, the preset period may be 200ms, and the preset number may be 20 periods of sample weight data of the driver, where each period may obtain 15 groups of sample weight data of the driver at different times. The preset period and the preset number may be set according to actual needs, and are not particularly limited herein.
And 243, if the current state is the driving state, acquiring sample weight data of the driver in the preset period when the current driving information of the vehicle meets a sampling condition.
As one mode, when the vehicle is in a running state, the accuracy of the sample weight data of the driver detected by the pressure sensor under the seat is high only when the vehicle is in a steady driving state, and when the vehicle is currently in the running state, it is required to determine whether the vehicle is currently in the steady driving state, that is, it is required to determine whether the current running information of the vehicle meets the sampling condition, and the sample weight data of the driver is obtained when the current running information of the vehicle meets the sampling condition.
Alternatively, the current travel information of the vehicle may include a current acceleration of the vehicle, a steering wheel steering angular velocity, a current heading angle, and the like. Optionally, the sampling condition may be that the absolute value of the current acceleration of the vehicle is smaller than an acceleration threshold, the steering angular velocity of the steering wheel is smaller than an angular velocity threshold, or the turning angle of the steering wheel is smaller than an angle threshold, and the current heading angle is smaller than a heading angle threshold, wherein the acceleration threshold, the angular velocity threshold, and the heading angle threshold may be set according to actual needs, and are not specifically limited herein. For example, the sampling condition may be that the absolute value of the current acceleration of the vehicle is smaller than1.5m/s 2 And the steering angular velocity of the steering wheel is less than 5rad/s.
When the current running information of the vehicle meets the sampling condition, the current running state of the vehicle can be determined, and at the moment, the sample weight data of the driver can be periodically acquired, so that the accuracy of the determined weight of the driver is ensured.
And step 250, determining the weight of the driver according to the target proportionality coefficient and the sample weight data.
As one mode, a mean value calculation may be performed according to the sample weight data to obtain a weight mean value, and then the obtained weight mean value is multiplied by the target proportion coefficient to obtain the weight of the driver.
Alternatively, after the weight of the driver is determined, the weight of the driver may be checked, the determined weight of the driver may be subtracted from the actual weight input by the user, and if the weight difference is in the difference interval, the determined weight may be valid and stored.
As still another mode, when it is determined that the weight of the driver is effective, the weight of the driver is stored in association with the face information of the driver. Optionally, the weight may be stored in a local database of the vehicle, or the weight may be stored in an electronic device communicatively connected to the vehicle through the T-Box, or may be stored in a cloud server corresponding to a client for detecting the weight of the driver.
In some embodiments, as shown in FIG. 4, step 250 comprises:
and 251, denoising the sample weight data to obtain reference sample weight data.
As a mode, denoising the sample weight data may be to remove the maximum value and the minimum value in the sample data, or to screen an abnormal value in the sample weight data. For example, a sample weight threshold may be preset, and sample weight data greater than the sample weight threshold may be filtered, and optionally, the sample weight threshold may be set according to actual conditions, and is not specifically limited herein.
As another mode, a sample weight difference threshold may be further set, a timestamp corresponding to each sample weight data may be obtained, that is, the time for obtaining each sample weight data may be obtained, a sample weight difference value may be obtained by subtracting two consecutive sample weight data according to the timestamp corresponding to each sample weight data, then it may be determined whether each sample weight difference value is greater than the sample weight difference threshold, sample weight data having a sample weight difference value greater than the sample weight difference threshold may be determined, then abnormal sample weight data may be determined according to the two sample weight data and the sample difference value adjacent thereto, and the abnormal sample weight data may be filtered out, so as to obtain reference sample weight data.
Step 252, determining the average of the reference sample weight data to obtain the weight average of the driver.
As one mode, the sample weight data may be obtained according to a preset period, for example, 15 groups of sample weight data are obtained according to 200ms of each group, and 3 periods are obtained, so that there are 3 groups of corresponding reference sample weight data, the reference sample weight data of each group is subjected to mean value calculation, and then the mean values of the 3 groups are subjected to mean value calculation, so as to obtain the weight mean value of the driver.
And 253, determining the weight of the driver according to the target proportion coefficient and the weight average value.
As one mode, the target proportion coefficient is multiplied by the weight average value, thereby obtaining the weight of the driver. For example, if the target scaling factor is K, the weight average is E, and the weight of the driver is G, G = K × E.
In the embodiment of the application, whether a driver exists in a driving position is determined through an image of the driving position, when the driver exists, the identity of the driver is identified based on the image of the driving position, the identity of the driver is determined, target proportionality coefficients corresponding to different drivers are different, so that the target proportionality coefficient corresponding to the current driver is determined according to the identity of the driver, and finally the weight of the current driver is determined according to the obtained sample weight data and the target proportionality coefficients of the driver. The method and the device can determine the weight of the driver according to different target proportion coefficients corresponding to different drivers, and accuracy of determining the weight of the driver is improved.
Referring to fig. 5, fig. 5 illustrates a method for determining a weight of a driver according to an embodiment of the present application, and in a specific embodiment, the method for determining a weight of a driver may be applied to the device 600 for determining a weight of a driver illustrated in fig. 9 and the electronic device 700 (fig. 10) equipped with the device 600 for determining a weight of a driver. The specific flow of the embodiment will be described below, and it is understood that the method may be executed by an electronic device with computing processing capability, such as a desktop computer, a notebook computer, a vehicle-mounted terminal, a vehicle-mounted large screen, and other terminal devices, and may also be executed interactively by a processing system including a server and a terminal. As will be described in detail with respect to the flow shown in fig. 5, the method for determining the weight of the driver may specifically include the following steps:
step 310, acquiring an image of a driving seat, and determining whether a driver exists in the driving seat according to the image.
And step 320, if a driver exists in the driving seat, identifying the identity of the driver according to the image, and determining the identity of the driver.
For the detailed description of steps 310 to 320, please refer to steps 210 to 220, which are not described herein.
Step 330, determining whether the driver is an authenticated person based on the identity of the driver.
As one mode, the authenticated person is a person whose user has face information stored in advance. Optionally, facial information of at least two persons may be stored in the vehicle.
As one mode, the face information may be extracted and integrated according to the acquired image of the driving seat, the extracted face information may be compared with the pre-stored face information, and it may be determined whether there is pre-stored face information whose face information similarity is greater than a similarity threshold, that is, whether the driver of the current driving seat is an authenticated person.
Step 340, if the driver is determined to be the authenticated person, determining a target proportionality coefficient corresponding to the driver based on a first mode.
As one mode, the first mode may be to determine the target scaling factor of the driver according to the scaling factor set corresponding to each certificated person.
As another mode, the first mode can be that the target proportionality coefficient corresponding to the driver is determined according to the current state of the vehicle and the preset proportionality coefficient of the driver.
In some embodiments, as shown in FIG. 6, step 340 includes:
step 341, determining whether the duration of the driver at the driving position is greater than a duration threshold.
As one way, when the driver only moves the vehicle, the weight of the driver does not need to be detected, and in order to avoid waste of resources and reduce the calculation pressure of the vehicle, in the case of driving the vehicle for a short time, whether the weight of the driver needs to be detected currently is determined by judging the time length of the driver at the driving position. Alternatively, the method may include continuously acquiring images of the driving position according to a driver monitoring system in the vehicle, determining a time period during which the driver is located in the driving position according to the images, and comparing the time period with a time threshold value to determine whether the weight of the driver needs to be detected currently. The duration threshold may be set according to actual needs, and is not specifically limited herein.
Step 341, if the duration is greater than the duration threshold, determining the current state of the vehicle.
As one mode, when the obtained time length of the driver at the driving seat is greater than the time threshold, it can only be determined that there is a driver at the current driving seat, but the weight of the driver can also be detected if the driver has a rest at the driving seat.
And 342, determining a target proportionality coefficient corresponding to the driver based on the current state.
When a driver rests at a driving position, the vehicle is in a static state, and the seat of the driving position is adjusted according to the habit or hobby of the driver during resting, at the moment, the driver is positioned behind the gravity center of the driving position; when the driver drives the vehicle, the vehicle is in a driving state, the seat is adjusted according to the habit or the preference of the driver when driving conveniently, and the driver is positioned behind the gravity center of the driving position. Therefore, when the current state of the vehicle is different, the sample weight data of the driver detected by the pressure sensor is different, and in order to ensure the accuracy of the weight of the driver, when the current state of the vehicle is in a static state or a driving state, the corresponding proportionality coefficient is correspondingly set, so that the target proportionality coefficient of the driver can be determined according to the current state of the vehicle.
In some embodiments, the current state includes a stationary state and a driving state; step 342 includes: if the current state is the static state, determining that the seat of the driving position is in a leisure mode, and determining the target proportionality coefficient according to the position information of the seat in the leisure mode; and if the current state is the driving state, determining that the seat of the driving position is in a driving mode, and determining the proportional target coefficient according to the position information of the seat in the driving mode.
As a mode, when the seat is in the leisure mode, the driver takes a rest or plays an entertainment activity such as a mobile phone at the driving position, and at this time, the proportionality coefficient of the seat in the leisure mode, which is pre-stored by the driver, can be obtained, and the proportionality coefficient is used as the target proportionality coefficient of the driver; when the driver is controlling the running of the vehicle when the seat is in the driving mode, the proportionality coefficient that the driver has previously stored that the seat is in the driving mode may be acquired as the target proportionality coefficient for the driver.
And 350, if the driver is determined to be a non-authenticated person, determining a target proportionality coefficient corresponding to the driver based on a second mode.
As one mode, when it is determined that the current driver seat is a non-authenticated person, i.e., the current driver seat is a temporary driver, at this time, the determination may be made according to the second mode of determination of the target scaling factor for the temporary driver. This second approach works well for any non-certified person. Optionally, the second method may be to directly obtain a scaling factor set by the user for the non-certified person, and use the scaling factor as the non-certified person to obtain the target scaling factor.
In some embodiments, step 350 includes: and if the driver is a non-authenticated person, determining the seat information of the driving seat and an initial proportionality coefficient corresponding to the seat information, and taking the initial proportionality coefficient as the target proportionality coefficient.
The seat information may include an angle between the seat back and the seat cushion, a length of a slide rail of the seat, and the like.
As one mode, before the vehicle leaves the factory, different initial proportionality coefficients are set for seat information corresponding to different positions obtained from a seat in a driving seat. For example, when the seat back is at 90 ° and the slide rail of the seat is at an extension of 5cm, the corresponding initial scaling factor is 1.5. Therefore, the initial scaling factor corresponding to the current seat information can be determined according to the seat information of the driving position.
As another way, an initial scaling factor variation value corresponding to the variation setting of the seat information may be obtained, and an initial scaling may be set for the initial seat information corresponding to the initial position of the seat, so as to determine the initial scaling factor corresponding to the current seat information according to the current seat information. For example, each time the seat gets a back increase of 5 °, its corresponding initial scaling factor increases by 0.25; for every 1cm of extension of the seat slide, the corresponding initial proportionality coefficient is reduced by 0.13, and the initial seat information may be when the seat back is at 90 ° and the seat slide is at 0cm of extension. The initial seat information, the initial scaling factor change value corresponding to the seat information obtained change setting, and the like may be determined according to actual needs, which is not specifically limited herein.
And step 360, obtaining sample weight data of the driver.
Step 370, determining the weight of the driver according to the target scale factor and the sample weight data.
For the detailed description of steps 360-370, refer to steps 240-250, which are not repeated herein.
In this embodiment, the weight determination of the authenticated person and the weight determination of the non-authenticated person are distinguished by setting different target proportionality coefficients for the authenticated person and the non-authenticated person, so that the user can conveniently monitor the weight data of the authenticated person in real time, and the accuracy of the weight determination of the authenticated user is improved.
In some embodiments, as shown in fig. 7, prior to step 330, the method further comprises:
step 410, determining whether the driver is an authenticated person according to the identity of the driver, and determining whether the driver drives the vehicle for the first time.
As one mode, after performing identity recognition according to the acquired image of the driving seat, when it is determined that the driver is an authenticated person, it is further required to determine whether the current driver drives the vehicle for the first time. If the driver drives the vehicle for the first time, the seat of the driving position needs to be adjusted, so that the driver can drive or have a rest conveniently. If the driver does not drive the vehicle for the first time, after the identity of the driver is recognized, the current driver is determined to be an authenticated person, historical driving information of the driver, namely seat information after the driver is adjusted when driving the vehicle or having a rest in the vehicle, can be directly obtained according to the identity of the driver, and the seat of the driving seat can be directly adjusted according to the seat information without manual adjustment by the driver.
As another mode, if it is determined that the driver is a non-authenticated person, the driver needs to perform manual adjustment without adjusting the seat of the driving seat.
Step 420, determining the current state of the vehicle and the seat information of the driving position.
As one way, when the vehicle is in a stationary state and a driving state, the driver may adjust the seat according to his or her own habits and preferences, and therefore, whether the current driver is an authorized person or not, the current state of the vehicle and the seat information of the driving seat need to be determined, so as to determine the initial scaling factor corresponding to the seat information at present, and to determine the target scaling factor according to the initial scaling factor subsequently.
And 430, determining an initial proportionality coefficient corresponding to the seat information according to the seat information of the driving seat.
As a manner, before the vehicle leaves the factory, different initial proportionality coefficients are set for seat information corresponding to different positions obtained by a seat in a driving seat of the vehicle, and the specific description may refer to the specific description of step 350, which is not described herein again.
Step 440, if the driver is an authenticated person and the driver drives the vehicle for the first time, updating the initial scaling factor according to the identity of the driver and the current state to obtain a target scaling factor.
As one mode, when the current driver is an authenticated person and drives the vehicle for the first time, the initial target scaling factor corresponding to the current state of the vehicle and the seat information of the current driving position is updated. Optionally, the updating may be performed by performing mean calculation or weighted average calculation according to a preset proportionality coefficient corresponding to the driver and an initial target proportionality coefficient corresponding to the seat information of the current driving seat, which are set by a user in a customized manner, so as to obtain a target proportionality coefficient corresponding to the driver in the current state of the vehicle. For example, if the current state of the vehicle is a driving state, the corresponding preset scaling factor of the driver is 0.68, and the initial target scaling factor corresponding to the seat information of the current driving seat is 2.13, at this time, the seat of the driving seat is in the driving mode, and the corresponding target scaling factor is 1.405; then the vehicle is in a driving mode, and the initial scale factor corresponding to the seat information of the driving position is replaced by 1.405, so that the initial scale factor is updated.
Optionally, after the target proportionality coefficient is obtained, the target proportionality coefficient is stored in association with the identity of the driver, the current state of the vehicle, and seat information of the driving seat. When the driver drives the vehicle again, the seat in the driving position is adjusted to the seat position corresponding to the stationary state of the vehicle after the identity of the driver is recognized, and the seat in the driving position is adjusted to the seat position after the vehicle is in the driving state after the distance of the vehicle movement is detected to exceed the distance threshold.
Optionally, if the driver drives the vehicle again and adjusts the seat when the vehicle is in the stationary state or the driving state, the target scaling factor is updated according to the seat position information and the adjusted seat position information.
As another mode, if the driver is a non-authenticated person, the seat in the driving position is not adjusted, the seat position after the previous driving is retained, and the target scaling factor corresponding to the seat position is set as the target scaling factor corresponding to the non-authenticated person. And if the driver adjusts the seat, determining a target proportionality coefficient according to the initial proportionality coefficient corresponding to the adjusted seat information. Optionally, after the driver of the non-authenticated person adjusts the seat, the initial scaling factor is not updated.
Optionally, at least two data sets may be preset, and the at least two data sets are used for storing weight information corresponding to the authenticated person and the unauthenticated person. The data set corresponding to the authentication personnel is in a permanent storage state and can only be deleted by a user, an automatic deleting instruction is set in the data set corresponding to the non-authentication personnel, and when the storage time length of the corresponding information reaches a time length threshold value, the automatic deleting instruction is triggered, so that the storage space is saved.
Referring to fig. 8, fig. 8 illustrates a method for determining a weight of a driver according to an embodiment of the present disclosure, and in a specific embodiment, the method for determining a weight of a driver may be applied to the device 600 for determining a weight of a driver illustrated in fig. 9 and an electronic device 700 (fig. 10) equipped with the device 600 for determining a weight of a driver. The specific flow of the embodiment will be described below, and it is understood that the method may be executed by an electronic device with computing processing capability, such as a desktop computer, a notebook computer, a vehicle-mounted terminal, a vehicle-mounted large screen, and other terminal devices, and may also be executed interactively by a processing system including a server and a terminal. As will be described in detail with respect to the flow shown in fig. 8, the method for determining the weight of the driver may specifically include the following steps:
step 510, acquiring an image of a driving seat, and determining whether a driver exists in the driving seat according to the image.
Step 520, if a driver exists in the driving position, identifying the identity of the driver according to the image, and determining the identity of the driver;
step 530, determining target proportionality coefficients corresponding to the drivers based on the identities of the drivers, wherein the target proportionality coefficients corresponding to different drivers are different;
step 540, obtaining sample weight data of the driver;
and step 550, determining the weight of the driver according to the target proportion coefficient and the sample weight data.
For the detailed description of steps 510 to 550, refer to steps 210 to 250, which are not described herein again.
And step 560, determining whether the weight of the driver exceeds a weight threshold corresponding to the driver.
As a mode, the weight threshold corresponding to each driver can be set according to the height and the health state of the driver, the weight threshold is used for indicating the weight health of the driver, and when the weight of one driver is larger than the weight threshold corresponding to the driver, the situation that the weight of the driver exceeds the standard is indicated, and the health of the driver is influenced.
And 570, if the weight of the driver exceeds the weight threshold corresponding to the driver, performing health abnormity reminding.
As a mode, the health abnormality reminding can be performed by a vehicle-mounted device large screen pop-up window reminding, for example, a character pattern with excessive weight is displayed on the vehicle-mounted device large screen, and the color and font of the character pattern can be set, so that a driver can find the character pattern conveniently. Optionally, the health abnormity can be reminded by broadcasting 'weight exceeding' through voice.
As another mode, the health abnormality reminding can be performed through an electronic device (such as a smart phone, a tablet computer, a smart wearable watch, and other electronic devices with a display function) in communication connection with the vehicle. For example, when the weight of the driver is determined to exceed the weight threshold, prompt information can be generated and sent to the electronic equipment by the T-Box in the vehicle, and the electronic equipment carries out health abnormity reminding according to the prompt information.
Optionally, the health abnormality according to the prompt information may be performed after the current driving is finished. For example, when the current gear of the vehicle is detected to be in a parking gear (P gear), the situation that the driver is distracted in the driving process due to the health abnormity reminding and accidents are caused is avoided.
In this embodiment, the health abnormality reminding is performed on the authenticated person whose weight exceeds the weight threshold, so that the user can conveniently monitor the health of the user or the family in real time, and the user can conveniently perform physical examination or exercise in time according to the weight data, so as to ensure the health of the user.
Fig. 9 is a block diagram of a driver's weight determination device according to an embodiment of the present application, and as shown in fig. 9, a control device 600 of the vehicle includes: an image acquisition module 610, an identification module 620, a target scale factor determination module 630, a sample weight data acquisition module 640, and a weight determination module 650.
The image acquisition module 610 is used for acquiring an image of a driving seat and determining whether a driver exists in the driving seat according to the image; the identity recognition module 620 is configured to, if a driver exists in the driving seat, perform identity recognition on the driver according to the image, and determine an identity of the driver; a target scaling factor determining module 630, configured to determine a target scaling factor corresponding to the driver based on the identity of the driver, where the target scaling factors corresponding to different drivers are different; a sample weight data obtaining module 640, configured to obtain sample weight data of the driver; a weight determining module 650 for determining the weight of the driver according to the target scaling factor and the sample weight data.
In some embodiments, the target scaling factor determination module 630 includes: the identity determination submodule is used for determining whether the driver is an authenticated person or not based on the identity of the driver; the first target proportionality coefficient determining submodule is used for determining a target proportionality coefficient corresponding to the driver based on a first mode if the driver is determined to be an authenticated person; or the target proportionality coefficient second determining submodule is used for determining the target proportionality coefficient corresponding to the driver based on a second mode if the driver is determined to be a non-authenticated person.
In some embodiments, the target scaling factor first determination submodule comprises: the judging unit is used for determining whether the duration of the driver at the driving position is greater than a duration threshold value; a current state determining unit, configured to determine a current state of the vehicle if the duration is greater than the duration threshold; and the target proportionality coefficient first unit is used for determining a target proportionality coefficient corresponding to the driver based on the current state.
In some embodiments, the current state includes a stationary state and a driving state; the target scale factor first unit includes: the first determining subunit is configured to determine that a seat in the driving seat is in a leisure mode if the current state is the stationary state, and determine the target scaling factor according to position information of the seat in the leisure mode; and the second determining subunit is configured to determine that the seat in the driving position is in the driving mode if the current state is the driving state, and determine the proportional target coefficient according to the position information of the seat in the driving mode.
In some embodiments, the target scaling factor second determination submodule comprises: and the second target proportionality coefficient determining unit is used for determining the seat information of the driving seat and the initial proportionality coefficient corresponding to the seat information if the driver is a non-authenticated person, and taking the initial proportionality coefficient as the target proportionality coefficient.
In some embodiments, the sample weight data acquisition module 640 comprises: the current state submodule is used for determining the current state of the vehicle, and the current state comprises a static state or a driving state; the first obtaining submodule is used for obtaining sample weight data of the driver in a preset period if the current state is the static state; and the second obtaining submodule is used for obtaining the sample weight data of the driver in the preset period when the current running information of the vehicle meets the sampling condition if the current state is the running state.
In some embodiments, the weight determination module 650 includes: the denoising submodule is used for denoising the sample weight data to obtain reference sample weight data; the weight average value determining submodule is used for determining the average of the weight data of the reference sample to obtain the weight average value of the driver; and the weight determining submodule is used for determining the weight of the driver according to the target proportion coefficient and the weight average value.
In some embodiments, the driver's weight determining apparatus 600 further comprises: the first determination module is used for determining whether the driver is an authenticated person or not according to the identity of the driver and determining whether the driver drives the vehicle for the first time or not; a second determination module for determining a current state of the vehicle and seat information of the driving seat; the initial proportionality coefficient determining module is used for determining an initial proportionality coefficient corresponding to the seat information according to the seat information of the driving seat; and the updating module is used for updating the initial proportionality coefficient according to the identity of the driver and the current state to obtain a target proportionality coefficient if the driver is an authenticated person and the driver drives the vehicle for the first time.
In some embodiments, the apparatus 600 for determining the weight of the driver further comprises: the third determination module is used for determining whether the weight of the driver exceeds a weight threshold corresponding to the driver; and the reminding module is used for carrying out health abnormity reminding if the weight of the driver exceeds the weight threshold corresponding to the driver.
According to an aspect of an embodiment of the present application, there is provided a computer program product or a computer program comprising computer instructions stored in a computer readable storage medium. The processor of the computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions to cause the computer device to perform the method of any of the above embodiments.
According to an aspect of the embodiment of the present application, there is also provided an electronic device, as shown in fig. 10, the electronic device 700 includes a processor 710 and one or more memories 720, the one or more memories 720 are used for storing program instructions executed by the processor 710, and the processor 710 implements the method for determining the weight of the driver as described above when executing the program instructions.
Further, processor 710 may include one or more processing cores. Processor 710 executes or executes instructions, programs, code sets, or instruction sets stored in memory 720 and invokes data stored in memory 720. Alternatively, the processor 710 may be implemented in hardware using at least one of Digital Signal Processing (DSP), field-Programmable Gate Array (FPGA), and Programmable Logic Array (PLA). The processor 710 may integrate one or more of a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), a modem, and the like. Wherein, the CPU mainly processes an operating system, a user interface, an application program and the like; the GPU is used for rendering and drawing display content; the modem is used to handle wireless communications. It is to be understood that the modem may be implemented by a communication chip without being integrated into the processor.
According to an aspect of the present application, there is also provided a computer-readable storage medium, which may be contained in the electronic device described in the above embodiments; or may exist separately without being assembled into the electronic device. The computer readable storage medium carries computer readable instructions which, when executed by a processor, implement the method of any of the embodiments described above.
It should be noted that the computer readable medium shown in the embodiments of the present application may be a computer readable signal medium or a computer readable storage medium or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a Read-Only Memory (ROM), an Erasable Programmable Read-Only Memory (EPROM), a flash Memory, an optical fiber, a portable Compact Disc Read-Only Memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present application, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In this application, however, a computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wired, etc., or any suitable combination of the foregoing.
The units described in the embodiments of the present application may be implemented by software, or may be implemented by hardware, and the described units may also be disposed in a processor. Wherein the names of the elements do not in some way constitute a limitation on the elements themselves.
It should be noted that although in the above detailed description several modules or units of the device for action execution are mentioned, such a division is not mandatory. Indeed, the features and functions of two or more modules or units described above may be embodied in one module or unit according to embodiments of the application. Conversely, the features and functions of one module or unit described above may be further divided into embodiments by a plurality of modules or units.
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present application. Each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. 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 involved. It will also be noted that each block of the block diagrams or flowchart illustration, and combinations of blocks in the block diagrams or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
Other embodiments of the present application will be apparent to those skilled in the art from consideration of the specification and practice of the embodiments disclosed herein. This application is intended to cover any variations, uses, or adaptations of the invention following, in general, the principles of the application and including such departures from the present disclosure as come within known or customary practice within the art to which the invention pertains.
It will be understood that the present application is not limited to the precise arrangements described above and shown in the drawings and that various modifications and changes may be made without departing from the scope thereof. The scope of the application is limited only by the appended claims.

Claims (12)

1. A method of determining a weight of a driver, the method comprising:
acquiring an image of a driving seat, and determining whether a driver exists in the driving seat according to the image;
if the driver exists in the driving position, identifying the identity of the driver according to the image, and determining the identity of the driver;
determining target proportionality coefficients corresponding to the drivers based on the identities of the drivers, wherein the target proportionality coefficients corresponding to different drivers are different;
obtaining sample weight data of the driver;
determining the weight of the driver according to the target proportionality coefficient and the sample weight data.
2. The method of claim 1, wherein determining the target scaling factor for the driver based on the identity of the driver comprises:
determining whether the driver is an authenticated person based on the identity of the driver;
if the driver is determined to be the authenticated person, determining a target proportionality coefficient corresponding to the driver based on a first mode; or
And if the driver is determined to be a non-authenticated person, determining a target proportionality coefficient corresponding to the driver based on a second mode.
3. The method of claim 2, wherein the determining the target scaling factor for the driver based on the first manner comprises:
determining whether the duration of the driver at the driving position is greater than a duration threshold;
if the duration is greater than the duration threshold, determining the current state of the vehicle;
and determining a target proportionality coefficient corresponding to the driver based on the current state.
4. The method of claim 3, wherein the current state comprises a stationary state and a driving state; the determining a target proportionality coefficient corresponding to the driver based on the current state comprises:
if the current state is the static state, determining that the seat of the driving position is in a leisure mode, and determining the target proportionality coefficient according to the position information of the seat in the leisure mode;
and if the current state is the driving state, determining that the seat of the driving position is in a driving mode, and determining the proportional target coefficient according to the position information of the seat in the driving mode.
5. The method of claim 2, wherein the determining the target scaling factor for the driver based on the second manner comprises:
and if the driver is a non-authenticated person, determining the seat information of the driving seat and an initial proportionality coefficient corresponding to the seat information, and taking the initial proportionality coefficient as the target proportionality coefficient.
6. The method of claim 1, wherein the obtaining sample weight data for the driver comprises:
determining a current state of the vehicle, the current state comprising a stationary state or a driving state;
if the current state is the static state, acquiring sample weight data of the driver in a preset period;
and if the current state is the running state, acquiring sample weight data of the driver in the preset period when the current running information of the vehicle meets a sampling condition.
7. The method of claim 1, wherein said determining the weight of the driver from the target scaling factor and the sample weight data comprises:
denoising the sample weight data to obtain reference sample weight data;
determining the average of the weight data of the reference sample to obtain the weight average of the driver;
and determining the weight of the driver according to the target proportion coefficient and the weight average value.
8. The method of claim 1, wherein prior to determining the driver's corresponding target scaling factor based on the driver's identity, the method further comprises:
determining whether the driver is an authenticated person or not according to the identity of the driver, and determining whether the driver drives the vehicle for the first time or not;
determining a current state of the vehicle and seat information of the driving seat;
determining an initial proportionality coefficient corresponding to the seat information according to the seat information of the driving seat;
and if the driver is an authenticated person and the driver drives the vehicle for the first time, updating the initial scale coefficient according to the identity of the driver and the current state to obtain a target scale coefficient.
9. The method according to any one of claims 1-8, wherein after said determining the weight of the driver from the target scaling factor and the sample weight data, the method further comprises:
determining whether the weight of the driver exceeds a weight threshold corresponding to the driver;
and if the weight of the driver exceeds the weight threshold corresponding to the driver, carrying out health abnormity reminding.
10. A weight determination device for a driver, characterized in that the device comprises:
the image acquisition module is used for acquiring an image of a driving seat and determining whether a driver exists in the driving seat according to the image;
the identity recognition module is used for recognizing the identity of the driver according to the image and determining the identity of the driver if the driver exists in the driving seat;
the target proportion coefficient determining module is used for determining a target proportion coefficient corresponding to the driver based on the identity of the driver, wherein the target proportion coefficients corresponding to different drivers are different;
the sample weight data acquisition module is used for acquiring sample weight data of the driver;
and the weight determining module is used for determining the weight of the driver according to the target proportion coefficient and the sample weight data.
11. An electronic device, characterized in that the electronic device comprises:
a processor;
a memory having computer readable instructions stored thereon which, when executed by the processor, implement the method of any of claims 1 to 9.
12. A computer-readable storage medium, having stored thereon program code that can be invoked by a processor to perform the method according to any one of claims 1 to 9.
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