CN115520196B - Weight determination method and device for driver, electronic equipment and storage medium - Google Patents
Weight determination method and device for driver, electronic equipment and storage medium Download PDFInfo
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- CN115520196B CN115520196B CN202211138518.2A CN202211138518A CN115520196B CN 115520196 B CN115520196 B CN 115520196B CN 202211138518 A CN202211138518 A CN 202211138518A CN 115520196 B CN115520196 B CN 115520196B
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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
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- 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
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- 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
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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
- B60W2040/0809—Driver authorisation; Driver identical check
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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
- B60W2040/0872—Driver physiology
Abstract
The application provides a weight determining method, a weight determining device, electronic equipment and a storage medium of a driver, wherein the weight determining method comprises the following steps: acquiring an image of a driving position, and determining whether a driver exists in the driving position according to the image; if the driver exists in the driving position, carrying out identity recognition on the driver according to the image, and determining the identity of the driver; 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; acquiring sample weight data of the driver; and determining the weight of the driver according to the target proportionality coefficient and the sample weight data. According to the weight determining method and device, the weight of different drivers is determined according to different target proportion coefficients corresponding to the different drivers, and the accuracy of the determined weight of the drivers is improved.
Description
Technical Field
The present application relates to the field of automotive electronics, and more particularly, to a method and apparatus for determining a weight of a driver, an electronic device, and a storage medium.
Background
Along with the continuous increase of automobile use demands, people have also become higher and higher to intelligent function's in car. Meanwhile, due to the acceleration of life rhythm, people easily neglect the health condition of the people. At present, by installing a health monitoring module on an automobile to monitor the body temperature, fatigue state and the like of a driver, the body weight of the driver cannot be accurately detected, and how to improve the accuracy of the determination of the body weight of the driver becomes a problem to be solved urgently.
Disclosure of Invention
In view of the above, the embodiments of the present application provide a weight determining method, apparatus, electronic device and storage medium for a driver to improve the above-mentioned problems.
According to an aspect of an embodiment of the present application, there is provided a weight determining method of a driver, the method including: acquiring an image of a driving position, and determining whether a driver exists in the driving position according to the image; if the driver exists in the driving position, carrying out identity recognition on the driver according to the image, and determining the identity of the driver; 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; acquiring sample weight data of the driver; and 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 determining device for a driver, the device including: the image acquisition module is used for acquiring an image of a driving position and determining whether a driver exists in the driving position according to the image; the identity recognition module is used for carrying out identity recognition on the driver according to the image if the driver exists in the driving position, and determining the identity of the driver; 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.
According to an aspect of an embodiment of the present application, there is provided an electronic apparatus including: a processor; a memory having stored thereon computer readable instructions which, when executed by the processor, implement a weight determination method for a driver as described above.
According to an aspect of an embodiment 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 a weight determination method of a driver as described above.
In the scheme of the application, whether a driver exists on a driver seat is determined through the image of the driver seat, and when the driver exists, the identity of the driver is identified based on the image of the driver seat, the identity of the driver is determined, and as the target proportion coefficients corresponding to different drivers are different, the target proportion 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 of the driver and the target proportion coefficient. According to the application, the weight of the driver can be determined according to different target proportion coefficients corresponding to different drivers, and the accuracy of the determined 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 application as claimed.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the application and together with the description, serve to explain the principles of the application. It is evident that the drawings in the following description are only some embodiments of the present application and that other drawings may be obtained from these drawings without inventive effort for a person of ordinary skill in the art.
Fig. 1 is a schematic diagram of a scenario suitable for use in the present application, according to an embodiment of the present application.
Fig. 2 is a flowchart showing a weight determining method of a driver according to an embodiment of the present application.
FIG. 3 is a flowchart illustrating specific steps of step 240, according to one embodiment of the present application.
FIG. 4 is a flowchart illustrating specific steps of step 250 according to one embodiment of the present application.
Fig. 5 is a flowchart showing a weight determining method of a driver according to another embodiment of the present application.
FIG. 6 is a flowchart illustrating the specific steps of step 340, according to one embodiment of the present application.
FIG. 7 is a flowchart illustrating specific steps prior to step 330, according to one embodiment of the present application.
Fig. 8 is a flowchart showing a weight determining method of a driver according to still another embodiment of the present application.
Fig. 9 is a block diagram showing a weight determining apparatus for a driver according to an embodiment of the present application.
Fig. 10 is a hardware configuration diagram of an electronic device according to an embodiment of the present application.
There has been shown in the drawings, and will hereinafter be described, specific embodiments of the application with the understanding that the present disclosure is to be considered in all respects as illustrative, and not restrictive, the scope of the inventive concepts being limited to the specific embodiments shown and described.
Detailed Description
Example embodiments will now be described more fully with reference to the accompanying drawings. However, the exemplary embodiments may be embodied in many 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 the example embodiments to those skilled in the art.
It should be noted that the terms "first," "second," and the like in the description and the claims of the present application and the above figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments of the application described herein may be implemented in sequences other than those illustrated or otherwise 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 diagram of a scenario suitable for use in the present application, according to an embodiment of the present application. As shown in fig. 1, the scene includes a vehicle 110 and an electronic device 120. Among other things, vehicle 110 includes a driver monitoring system 111, a seat control module 112, an on-board mainframe 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 on the driver's seat and confirms the identity of the driver on the driver's seat, the image of the driver's 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 performs identity recognition on the driver's seat, and sends the recognition result to the remote communication module 114; the vehicle-side host 113 can also identify the driver according to the image of the driver's seat acquired by the driver monitoring system 111. The vehicle-end mainframe 113 determines whether to control the seat control module 112 to adjust the seat of the driver's seat according to the recognition result, if the recognition result indicates that the current driver is an authentication person, seat information corresponding to the driver can be obtained in the vehicle-end mainframe 113, the seat control module 112 is controlled to adjust the seat of the driver's seat according to the seat information, meanwhile, a target proportion coefficient corresponding to the driver is determined according to the seat information, then 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 sent to the electronic device 120 via the remote communication module 114. The electronic device 120 receives the weight of the driver transmitted 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, reminds of abnormal weight health and stores the weight.
Referring to fig. 2, fig. 2 shows a weight determining method of a driver according to an embodiment of the present application, and in a specific embodiment, the weight determining method of the driver may be applied to the weight determining device 600 of the driver shown in fig. 9 and the electronic apparatus 700 (fig. 10) provided with the weight determining device 600 of the driver. The specific flow of the present embodiment will be described below, and it will be understood that the method may be performed by an electronic device having computing processing capability, for example, a terminal device such as a desktop computer, a notebook computer, a vehicle-mounted terminal, or a vehicle-mounted large screen, and the method may also be interactively performed by a processing system including a server and a terminal. As will be described in detail below with respect to the flowchart shown in fig. 2, the method for determining the weight of the driver may specifically include the following steps:
step 210, an image of a driver's seat is obtained, and whether the driver exists in the driver's seat is determined according to the image.
As one way, a driver monitoring system (driver monitoring system, DMS) may be installed in the vehicle, which may be used to identify the identity of the driver, and may also be used to identify the driving state of the driver during driving (i.e., whether it is in a tired state, etc.). In an embodiment of the present application, it is determined whether the driver is currently present or not based on the image of the driver's seat acquired by the driver monitoring system.
Alternatively, an occupant monitoring system (Occupant Monitoring System, OMS) may also be installed in the vehicle for identifying the primary and secondary driver's seat personnel,
alternatively, the driver or passenger monitoring system may be mounted to the position of the rear view mirror of the cab to facilitate identification of the driver's personnel.
And 220, 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.
As one way, when the driver monitoring system detects that the driver exists in the driver seat, the driver identity is identified by comparing the acquired image with a pre-stored image of the driver (i.e., an authentication person) or extracting face information in the acquired image and then comparing the face information with the pre-stored face information of the driver. Alternatively, the driver monitoring system may be used to identify facial information of at least two different authenticated persons. If the acquired image is compared with the image of the prestored authenticatee, determining that the driver corresponding to the image is the authenticatee, and determining that the identity of the driver is the vehicle owner; if the acquired image is compared with the pre-stored image of the authenticator, and the driver corresponding to the image is determined not to be the authenticator, the identity of the driver can be determined to be a temporary driver.
And 230, 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 target proportionality coefficient refers to a proportionality coefficient for calculating the weight of the driver, which is determined according to the identity of the driver. As a way, if the identity of the driver is an authenticator, the scaling factor stored in association with the information of the driver may be directly obtained, and optionally, the target scaling factors corresponding to different authenticators may be set or modified by the user, and the target scaling factors corresponding to the authenticators may be the same or different, and may be set according to the actual situation, which is not particularly limited herein.
Alternatively, if the driver's identity is a temporary driver, the predetermined proportionality coefficient of the vehicle at the time of shipment may be directly obtained, and the proportionality coefficient may be used as the target proportionality coefficient of the temporary driver.
As yet another approach, the determination of the target scaling factor is also related to the seat of the driver's seat. Due to the difference of the backrest of the seat and the front-rear position of the seat, the center of gravity of the driver on the seat is different, and when the center of gravity 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 communicated, so that the weight of the finally confirmed driver is different. In order to ensure the accuracy of the weight of the driver, different proportional coefficients can be correspondingly set according to the backrest of the seat and the front and rear positions of the seat, and then the target proportional coefficients can be obtained by combining the proportional coefficients preset by each certification personnel. For example, if the scaling factor set by an authenticated driver is 1.5 and the scaling factor corresponding to the back of the current seat and the front-back position of the seat is 1.67, the target scaling factor may be determined to be 1.585.
Step 240, obtaining sample weight data of the driver.
As one way, sample weight data of the driver of the current driving position may be obtained by a pressure sensor on the driving position. Alternatively, a sampling period and a sampling number may be preset, and sample weight data of the driver may be obtained according to the sampling period and the sampling number.
As one way, it may also be determined whether to obtain the sample weight data of the driver at the present time according to the present state of the vehicle, and periodically obtain the sample weight data of a preset number of drivers according to the present state.
In some embodiments, as shown in fig. 3, step 240 includes:
in step 241, a current state of the vehicle is determined, including a stationary state or a driving state.
As one way, the current state of the vehicle may be determined based on the current vehicle speed gear of the vehicle. For example, if the current vehicle speed gear is in neutral, the current state of the vehicle may be determined to be a stationary state, and when the frontal vehicle speed gear of the vehicle is in non-neutral, the current state of the vehicle may be determined to be a traveling state.
As another way, it is also possible to determine whether the vehicle is stationary or in a running state at the current state of the vehicle based on the current speed of the vehicle.
And step 242, if the current state is the stationary state, acquiring sample weight data of the driver in a preset period.
As one way, a predetermined number of sample weight data of the driver is acquired during a predetermined period while the vehicle is stationary. Alternatively, the preset period may be 200ms, and the preset number may be 20 periods of sample weight data of the driver, and each period may acquire 15 sets 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.
Step 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 satisfies a sampling condition.
As one way, 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 driver's seat is high only when the vehicle is in a stationary driving state, and then it is necessary to determine whether the vehicle is currently in a stationary driving state when the vehicle is currently in a running state, that is, it is necessary to determine whether the current running information of the vehicle satisfies the sampling condition, and the sample weight data of the driver is acquired when the current running information of the vehicle satisfies 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. Alternatively, the sampling condition may be that the absolute value of the current acceleration of the vehicle is smaller than an acceleration threshold, the steering angle speed of the steering wheel is smaller than an angle speed threshold or the steering angle of the steering wheel is smaller than an angle threshold, and the current heading angle is smaller than a heading angle threshold, which are less than two, wherein the acceleration threshold, the angle speed 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 less than 1.5m/s 2 And the steering wheel steering angle speed is less than 5rad/s.
When the current running information of the vehicle meets the sampling condition, the vehicle can be determined to be in a stable running state at present, and at the moment, sample weight data of the driver can be periodically obtained, so that the accuracy of the determined weight of the driver is ensured.
Step 250, determining the weight of the driver according to the target proportionality coefficient and the sample weight data.
As one way, a mean value may be calculated from the sample weight data to obtain a weight mean value, and then the obtained weight mean value is multiplied by a target scaling factor, thereby obtaining the weight of the driver.
Alternatively, after determining the weight of the driver, the weight of the driver may be checked, the actual weight input by the user may be subtracted from the determined weight of the driver, and if the weight difference is in the difference interval, the weight may be determined to be valid and stored.
As still another way, after determining that the weight of the driver is valid, the weight of the driver is stored in association with the facial information of the driver. Alternatively, the weight can be stored in a local database of the vehicle, can be stored in an electronic device in communication connection with the vehicle through a T-Box, and can also be stored in a cloud server corresponding to a client for detecting the weight of a driver.
In some embodiments, as shown in fig. 4, step 250 includes:
and step 251, denoising the sample weight data to obtain reference sample weight data.
As one way, denoising the sample weight data may be to remove a maximum value and a minimum value in the sample weight data, or may be to screen abnormal values 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, or alternatively, the sample weight threshold may be set according to actual situations, which is not specifically limited herein.
As another way, a sample weight difference threshold may be further set, a time stamp corresponding to each sample weight data may be obtained, that is, the amount of each sample weight data is obtained, two consecutive sample weight data are subtracted according to the time stamp corresponding to each sample weight data to obtain a sample weight difference, then whether each sample weight difference is greater than the sample weight difference threshold is determined, sample weight data with a sample weight difference greater than the sample weight difference threshold is determined, then abnormal sample weight data is determined according to the two sample weight data and the adjacent sample weight difference thereof, and filtering is performed to obtain reference sample weight data.
Step 252, determining an average of the weight data of the reference sample, and obtaining a weight average of the driver.
As one way, the sample weight data may be obtained according to a preset period, for example, 15 sets of sample weight data are obtained according to 200ms time of each set, and 3 sets of corresponding reference sample weight data are obtained according to 3 periods, the average value of the reference sample weight data of each set is calculated, and then the average value of the 3 sets is calculated, so as to obtain the average value of the weight of the driver.
Step 253, determining the weight of the driver according to the target proportionality coefficient and the weight average value.
As one way, the target proportionality 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 on a driver seat is determined through an image of the driver seat, and when the driver exists, the identity of the driver is identified based on the image of the driver seat, the identity of the driver is determined, and as the target proportion coefficients corresponding to different drivers are different, the target proportion 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 acquired sample weight data of the driver and the target proportion coefficient. According to the application, the weight of the driver can be determined according to different target proportion coefficients corresponding to different drivers, and the accuracy of the determined weight of the driver is improved.
Referring to fig. 5, fig. 5 illustrates a weight determining method of a driver according to an embodiment of the present application, and in a specific embodiment, the weight determining method of the driver may be applied to the weight determining device 600 of the driver shown in fig. 9 and the electronic apparatus 700 (fig. 10) provided with the weight determining device 600 of the driver. The specific flow of the present embodiment will be described below, and it will be understood that the method may be performed by an electronic device having computing processing capability, for example, a terminal device such as a desktop computer, a notebook computer, a vehicle-mounted terminal, or a vehicle-mounted large screen, and the method may also be interactively performed by a processing system including a server and a terminal. As will be described in detail below with respect to the flowchart shown in fig. 5, the method for determining the weight of the driver may specifically include the following steps:
Step 310, an image of a driver's seat is obtained, and whether the driver exists in the driver's seat is determined according to the image.
And 320, 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.
The specific description of steps 310-320 refer to steps 210-220, which are not repeated here.
Step 330, determining whether the driver is an authenticator based on the driver's identity.
As one way, the authentication person is a person for which face information is stored in advance for the user. Alternatively, facial information of at least two persons may be stored in the vehicle.
As one way, facial information can be extracted and integrated according to the acquired image of the driving position, the extracted facial information is compared with the pre-stored facial information, whether the pre-stored facial information with the similarity larger than the similarity threshold is determined, and whether the driver of the current driving position is an authentication person can be determined.
Step 340, if it is determined that the driver is an authentication person, determining a target scaling factor corresponding to the driver based on the first mode.
As one way, the first way may be to determine the target proportionality coefficient of the driver according to the proportionality coefficient set correspondingly by each certification person.
Alternatively, the first mode may be to determine the target scaling factor corresponding to the driver by combining the current state of the vehicle with the scaling factor preset by the driver.
In some embodiments, as shown in fig. 6, step 340 includes:
step 341, determining whether the duration of the driver in the driving position is greater than a duration threshold.
As one way, when the driver is currently only moving the vehicle, the weight of the driver is not required to be detected, and in order to avoid the waste of resources and reduce the calculation pressure of the vehicle, under the condition of temporarily driving the vehicle, whether the weight of the driver is required to be detected is determined by judging the duration of the driver in the driving position. Alternatively, an image of the driver's position may be continuously acquired according to a driver monitoring system in the vehicle, a duration of the driver's position is determined according to the image, and then the duration is compared with a time threshold, so as to determine whether the weight of the driver is required to be detected currently. The time period threshold may be set according to actual needs, and is not specifically limited herein.
And 341, if the duration is greater than the duration threshold, determining the current state of the vehicle.
As one way, when the driver is located at the driving position and the time period is longer than the time threshold, it is only determined that the driver is always present at the current driving position, but the weight of the driver can be detected if the driver has a rest at the driving position, and similarly, the weight of the driver can be detected when the driver drives the vehicle.
Step 342, determining a target scaling factor corresponding to the driver based on the current state.
When the driver is at rest in the driving position, the vehicle is in a static state, and the seat of the driving position is adjusted according to the habit or preference of the driver when the driver is at rest, and at the moment, the center of gravity of the driver at the driving position is at a rear; when the driver drives the vehicle, the vehicle is in a running state, the seat is adjusted according to habit or preference of the driver when the driver is convenient to drive, and the driver is positioned behind the center of gravity 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 stationary state or a traveling state, the corresponding proportionality coefficient thereof 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 driver seat 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 proportion target coefficient according to the position information of the seat in the driving mode.
When the seat is in the leisure mode, a driver can take a rest or play a mobile phone and other entertainment and leisure activities at a driving position, and at the moment, the proportionality coefficient of the seat in the leisure mode, which is stored in advance by the driver, can be obtained and used as a target proportionality coefficient of the driver; when the seat is in the driving mode, the driver is controlling the running of the vehicle, and at this time, a pre-stored proportionality coefficient of the seat in the driving mode of the driver can be obtained and used as a target proportionality coefficient of the driver.
And step 350, if the driver is determined to be a non-authentication person, determining a target proportion coefficient corresponding to the driver based on a second mode.
As one way, when determining that the current driver is at a non-authenticated person, i.e., the current driver is at a temporary driver, the determination may be made according to a second way of determining the target proportionality coefficient for the temporary driver. This second mode is applicable to any non-authenticated person. Alternatively, the second mode may be to directly obtain the scaling factor set by the user and set for the non-authentication person, so as to obtain the target scaling factor as the non-authentication person.
In some embodiments, step 350 comprises: if the driver is a non-authentication person, determining the seat information of the driver seat and an initial proportional coefficient corresponding to the seat information, and taking the initial proportional coefficient as the target proportional 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 the seat information corresponding to different positions obtained from the seat at the driving position. For example, when the seat back is at 90 ° and the track of the seat is at 5cm extension, its corresponding initial scaling factor is 1.5. Therefore, the initial proportionality coefficient corresponding to the current seat information can be determined according to the seat information of the driving position.
Alternatively, the initial scaling factor corresponding to the seat information may be set to a value corresponding to the seat information, and an initial scaling factor corresponding to the initial position of the seat may be set to the initial seat information, 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 backrest increase of 5 °, its corresponding initial scaling factor increases by 0.25; for each 1cm extension of the seat track, the corresponding initial scaling factor is reduced by 0.13, which initial seat information may be when the seat back is at 90 ° and the seat track is at 0cm extension. The initial seat information, the initial scaling factor change value corresponding to the seat information obtained by changing the seat information, and the like can be determined according to actual needs, and are not particularly limited herein.
Step 360, obtaining sample weight data of the driver.
And step 370, determining the weight of the driver according to the target proportionality coefficient and the sample weight data.
The detailed descriptions of steps 360-370 refer to steps 240-250, which are not repeated here.
In the embodiment, the identification of the weight determination of the authenticators and the non-authenticators is realized by setting different target proportion coefficients for the authenticators and the non-authenticators, so that the real-time monitoring of the weight data of the authenticators by the user is facilitated, and the accuracy of the weight identification 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 authorized person according to the identity of the driver, and determining whether the driver drives the vehicle for the first time.
As one way, after the identification is performed according to the acquired image of the driver's seat, when it is determined that the driver is an authenticated person, it is also necessary to determine whether the current driver is driving the vehicle for the first time. If the driver is driving the vehicle for the first time, the seat of the driver's seat needs to be adjusted to facilitate the driving or rest of the driver. If the driver is not driving the vehicle for the first time, after the identity of the driver is identified, the current driver is confirmed to be an authentication person, the historical driving information of the driver can be directly obtained according to the identity of the driver, namely the driver can directly adjust the seat of the driving position according to the seat information after the driver adjusts the seat when driving the vehicle or resting in the vehicle, and the driver does not need to manually adjust again.
Alternatively, if the driver is determined to be a non-authenticated person, the driver is required to manually adjust the seat of the driver's seat without adjusting the seat.
Step 420, determining the current state of the vehicle and the seat information of the driver's seat.
As one way, since the driver adjusts the seat according to his own habits and preference when the vehicle is in a stationary state and a traveling state, whether the current driver is an authorized person or not needs to determine the current state of the vehicle and the seat information of the driving position, so as to determine the initial scaling factor currently corresponding to the seat information, and then determine the target scaling factor according to the initial scaling factor.
And 430, determining an initial proportionality coefficient corresponding to the seat information according to the seat information of the driving position.
As a way, before the vehicle leaves the factory, different initial scaling factors are set for the seat information corresponding to the different positions obtained by the seat in the driving position, and the specific description of step 350 may be referred to, and will not be repeated here.
Step 440, if the driver is an authentication 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, updating is performed according to the current state of the vehicle and the initial target proportionality coefficient corresponding to the seat information of the current driving position. Optionally, the updating may be performed by performing a mean calculation or a weighted average calculation according to a preset scaling factor corresponding to the driver and an initial target scaling factor corresponding to the seat information of the current driving position, which are set by a user in a user-defined manner, so as to obtain a target scaling factor 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 preset proportionality coefficient corresponding to the driver is 0.68, the initial target proportionality coefficient corresponding to the seat information of the current driving position is 2.13, and at this time, the seat of the driving position is in the driving mode, and the corresponding target proportionality coefficient is 1.405; and then the vehicle is in a driving mode, and the initial proportional coefficient corresponding to the seat information of the driving position is replaced by 1.405, so that the initial proportional coefficient is updated.
Optionally, after the target proportionality coefficient is obtained, the target proportionality coefficient is associated with the identity of the driver, the current state of the vehicle and the seat information of the driving position for storage. When the driver drives the vehicle again, the driver only needs to adjust the seat of the driver to the corresponding seat position when the vehicle is in a stationary state after identifying the identity of the driver, and adjust the seat of the driver to the seat position after the vehicle is in a driving state after detecting that the distance of the movement of the vehicle exceeds the distance threshold value.
Optionally, if the driver drives the vehicle again and adjusts the seat when the vehicle is in a stationary state or a driving state, the target proportionality coefficient 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 of the driver's seat is not adjusted, the seat position after the previous driving is reserved, and the target proportionality coefficient corresponding to the seat position is set as the target proportionality coefficient corresponding to the non-authenticated person. If the driver adjusts the seat, the target proportion coefficient is determined according to the adjusted seat information corresponding to the initial proportion coefficient. Optionally, the initial scaling factor is not updated after the seat is adjusted by the driver of the non-certified person.
Optionally, at least two data sets may be preset, for storing weight information corresponding to the authenticated person and the non-authenticated person. The data set corresponding to the authentication personnel is in a permanent storage state and can be deleted only by a user, and an automatic deleting instruction is set in the data set corresponding to the non-authentication personnel, and is triggered when the corresponding information storage duration reaches a duration threshold value, so that the storage space is saved.
Referring to fig. 8, fig. 8 shows a weight determining method of a driver according to an embodiment of the present application, and in a specific embodiment, the weight determining method of the driver may be applied to the weight determining device 600 of the driver shown in fig. 9 and the electronic apparatus 700 (fig. 10) provided with the weight determining device 600 of the driver. The specific flow of the present embodiment will be described below, and it will be understood that the method may be performed by an electronic device having computing processing capability, for example, a terminal device such as a desktop computer, a notebook computer, a vehicle-mounted terminal, or a vehicle-mounted large screen, and the method may also be interactively performed by a processing system including a server and a terminal. As will be described in detail below with respect to the flowchart shown in fig. 8, the method for determining the weight of the driver may specifically include the following steps:
Step 510, obtaining an image of the driver's seat, and determining whether the driver exists in the driver's seat according to the image.
Step 520, 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;
step 530, determining a target proportionality coefficient corresponding to the driver based on the identity of the driver, wherein the target proportionality coefficients corresponding to different drivers are different;
step 540, obtaining sample weight data of the driver;
step 550, determining the weight of the driver according to the target proportionality coefficient and the sample weight data.
The specific description of steps 510-550 is referred to as steps 210-250, and will not be repeated here.
Step 560, determining whether the weight of the driver exceeds the weight threshold corresponding to the driver.
As one way, a weight threshold corresponding to each driver may be set according to the height and health status of the driver, where the weight threshold is used to indicate that the weight of the driver is healthy, and when the weight of a driver is greater than the weight threshold corresponding to the driver, it indicates that the weight of the driver exceeds the standard, and the health of the driver is affected.
Step 570, if the weight of the driver exceeds the weight threshold corresponding to the driver, performing health abnormality reminding.
As a mode, the health abnormality reminding can be performed by a large screen of the vehicle, for example, a character of 'weight exceeding' is displayed on the large screen of the vehicle, and the color and the font of the character can be set, so that the driver can find the character conveniently. Optionally, the voice broadcast "weight exceeding" may also be used to alert the person of the health abnormality.
Alternatively, the health abnormality alert may be performed by an electronic device (e.g., a smart phone, a tablet computer, a smart wearable watch, etc. having a display function) or an electronic device communicatively connected to the vehicle. For example, when it is determined that the weight of the driver exceeds the weight threshold, a prompt message may be generated, the prompt message is sent to the electronic device from the T-Box in the vehicle, and the electronic device carries out the health abnormality prompt according to the prompt message.
Alternatively, the health abnormality may be performed after the current driving is finished according to the prompt information. For example, when it is detected that the current gear of the vehicle is in the parking gear (P gear), the driver is prevented from being distracted during driving due to the health abnormality alert, resulting in an accident.
In this embodiment, health abnormality reminding is performed on an authenticated person whose weight exceeds a weight threshold, so that a user can monitor the health of the user or family in real time, and the user can perform physical examination or exercise in time according to weight data, so as to ensure the health of the user.
Fig. 9 is a block diagram of a weight determining apparatus of a driver according to an embodiment of the present application, and as shown in fig. 9, a control apparatus 600 of the vehicle includes: an image acquisition module 610, an identification module 620, a target scaling factor determination module 630, a sample weight data acquisition module 640, and a weight determination module 650.
An image acquisition module 610, configured to acquire an image of a driver's seat, and determine whether the driver exists in the driver's seat according to the image; the identity recognition module 620 is configured to identify the driver according to the image if the driver exists in the driver position, and determine the identity of the driver; a target scaling factor determination 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 acquisition module 640 for acquiring sample weight data of the driver; a weight determination module 650 for determining a weight of the driver based on the target scaling factor and the sample weight data.
In some embodiments, the target scaling factor determination module 630 comprises: the identity determination submodule is used for determining whether the driver is an authentication person or not based on the identity of the driver; the first determining submodule of the target proportion coefficient is used for determining the target proportion coefficient corresponding to the driver based on a first mode if the driver is determined to be an authentication person; or the second determining submodule of the target proportion coefficient is used for determining the target proportion coefficient corresponding to the driver based on a second mode if the driver is determined to be a non-authentication person.
In some embodiments, the target scaling factor first determination submodule includes: the judging unit is used for determining whether the duration of the driver at the driving position is greater than a duration threshold value or not; the current state determining unit is used for determining the current state of the vehicle if the duration is greater than the duration threshold value; and the target proportional coefficient first unit is used for determining a target proportional 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 scaling factor first unit includes: the first determining subunit is used for determining that the seat of the driving seat is in a leisure mode if the current state is the static state, and determining the target proportionality coefficient according to the position information of the seat in the leisure mode; and the second determination subunit is used for determining that the seat of the driving position is in a driving mode if the current state is the driving state, and determining the proportion target coefficient according to the position information of the seat in the driving mode.
In some embodiments, the target scaling factor second determination submodule includes: and the target proportion coefficient second determining unit is used for determining the seat information of the driving position and the initial proportion coefficient corresponding to the seat information if the driver is a non-authentication person, and taking the initial proportion coefficient as the target proportion coefficient.
In some embodiments, the sample weight data acquisition module 640 includes: the current state sub-module is used for determining the current state of the vehicle, wherein the current state comprises a static state or a driving state; the first acquisition submodule is used for acquiring sample weight data of the driver in a preset period if the current state is the static state; and the second acquisition sub-module is used for acquiring 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 running state is the running state.
In some embodiments, the weight determination module 650 includes: the denoising sub-module 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 number of the weight data of the reference sample and obtaining the weight average value of the driver; and the weight determination 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 weight determination device 600 of the driver further comprises: the first determining module is used for determining whether the driver is an authentication person according to the identity of the driver and determining whether the driver drives the vehicle for the first time; a second determination module configured to determine a current state of the vehicle and seat information of the driving position; the initial proportion coefficient determining module is used for determining an initial proportion coefficient corresponding to the seat information according to the seat information of the driver 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 authentication person and the driver drives the vehicle for the first time.
In some embodiments, the weight determination device 600 of the driver further comprises: a third determining module, configured to determine whether a weight of the driver exceeds a weight threshold corresponding to the driver; and the reminding module is used for reminding the health abnormality if the weight of the driver exceeds the weight threshold corresponding to the driver.
According to an aspect of embodiments of the present application, there is provided a computer program product or 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 embodiments described above.
According to an aspect of the embodiment of the present application, there is further provided an electronic device, as shown in fig. 10, the electronic device 700 includes a processor 710 and one or more memories 720, and the one or more memories 720 are used to store program instructions executed by the processor 710, where the processor 710 executes the program instructions to implement the above-mentioned weight determining method of the driver.
Further, the processor 710 may include one or more processing cores. Processor 710 executes or performs 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 in at least one of digital signal processing (Digital Signal Processing, DSP), field programmable gate array (Field-Programmable Gate Array, FPGA), programmable logic array (Programmable Logic Array, PLA). The processor 710 may integrate one or a combination of several of a central processing unit (Central Processing Unit, CPU), an image processor (Graphics Processing Unit, GPU), and a modem, etc. The CPU mainly processes an operating system, a user interface, an application program and the like; the GPU is used for being responsible for rendering and drawing of display content; the modem is used to handle wireless communications. It will be appreciated that the modem may not be integrated into the processor and may be implemented solely by a single communication chip.
According to an aspect of the present application, there is also provided a computer-readable storage medium that may be contained in the electronic device described in the above-described embodiment; or may exist alone without being incorporated 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 above embodiments.
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. The computer readable storage medium can be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any 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 (Erasable Programmable Read Only Memory, EPROM), 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 context of this document, 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 the present application, however, the computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, with the computer-readable program code embodied therein. 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 of the foregoing. 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 involved 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 provided in a processor. Wherein the names of the units do not constitute a limitation of the units themselves in some cases.
It should be noted that although in the above detailed description several modules or units of a 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 in accordance with embodiments of the application. Conversely, the features and functions of one module or unit described above may be further divided into a plurality of modules or units to be embodied.
The flowcharts 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. Where 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 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 application 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 application pertains.
It is to be understood that the application is not limited to the precise arrangements and instrumentalities shown in the drawings, which have been described above, and that various modifications and changes may be effected without departing from the scope thereof. The scope of the application is limited only by the appended claims.
Claims (10)
1. A method of determining a weight of a driver, the method comprising:
acquiring an image of a driving position, and determining whether a driver exists in the driving position according to the image;
if the driver exists in the driving position, carrying out identity recognition on the driver according to the image, and determining the identity of the driver;
determining a target scaling factor corresponding to the driver based on the identity of the driver, wherein the target scaling factors corresponding to different drivers are different, and determining the target scaling factor corresponding to the driver based on the identity of the driver comprises:
Determining whether the driver is an authenticator based on the identity of the driver;
if the driver is determined to be an authentication person, determining a target proportionality coefficient corresponding to the driver based on a first mode, wherein determining the target proportionality coefficient corresponding to the driver based on the first mode 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;
determining a target proportion coefficient corresponding to the driver based on the current state; or alternatively
If the driver is determined to be a non-authentication person, determining a target proportion coefficient corresponding to the driver based on a second mode;
acquiring sample weight data of the driver;
and determining the weight of the driver according to the target proportionality coefficient and the sample weight data.
2. The method of claim 1, wherein the current state comprises a stationary state and a driving state; the determining, based on the current state, a target scaling factor corresponding to the driver includes:
if the current state is the static state, determining that the seat of the driver seat 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 target proportionality coefficient according to the position information of the seat in the driving mode.
3. The method of claim 1, wherein the determining the driver-corresponding target scaling factor based on the second manner comprises:
if the driver is a non-authentication person, determining the seat information of the driver seat and an initial proportional coefficient corresponding to the seat information, and taking the initial proportional coefficient as the target proportional coefficient.
4. The method of claim 1, wherein the obtaining the sample weight data of the driver comprises:
determining a current state of the vehicle, the current state including 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 the sampling condition.
5. 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 an average of the reference sample weight data to obtain an average of the weight of the driver;
and determining the weight of the driver according to the target proportion coefficient and the weight average value.
6. 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 authentication person according to the identity of the driver, and determining whether the driver drives the vehicle for the first time;
determining a current state of the vehicle and seat information of the driver's seat;
determining an initial proportionality coefficient corresponding to the seat information according to the seat information of the driving position;
and if the driver is an authentication person and the driver drives the vehicle for the first time, updating the initial proportionality coefficient according to the identity of the driver and the current state to obtain a target proportionality coefficient.
7. The method according to any one of claims 1-6, 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 abnormality reminding.
8. A weight determination device for a driver, the device comprising:
the image acquisition module is used for acquiring an image of a driving position and determining whether a driver exists in the driving position according to the image;
the identity recognition module is used for carrying out identity recognition on the driver according to the image if the driver exists in the driving position, and determining the identity of the driver;
the target scaling factor determining module is 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, and the target scaling factor determining module includes:
the identity determination submodule is used for determining whether the driver is an authentication person or not based on the identity of the driver;
The first determining submodule of the target proportion coefficient is used for determining the target proportion coefficient corresponding to the driver based on a first mode if the driver is determined to be an authentication person, and the first determining submodule of the target proportion coefficient 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 or not;
the current state determining unit is used for determining the current state of the vehicle if the duration is greater than the duration threshold value;
a target scaling factor first unit, configured to determine a target scaling factor corresponding to the driver based on the current state; or alternatively
The second determining submodule of the target proportion coefficient is used for determining the target proportion coefficient corresponding to the driver based on a second mode if the driver is determined to be a non-authentication person;
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.
9. An electronic device, the electronic device comprising:
a processor;
a memory having stored thereon computer readable instructions which, when executed by the processor, implement the method of any of claims 1 to 7.
10. A computer readable storage medium having stored therein program code which is callable by a processor to perform the method of any one of claims 1 to 7.
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