WO2020078461A1 - 车辆座椅智能调节方法和装置、车辆、电子设备、介质 - Google Patents

车辆座椅智能调节方法和装置、车辆、电子设备、介质 Download PDF

Info

Publication number
WO2020078461A1
WO2020078461A1 PCT/CN2019/111928 CN2019111928W WO2020078461A1 WO 2020078461 A1 WO2020078461 A1 WO 2020078461A1 CN 2019111928 W CN2019111928 W CN 2019111928W WO 2020078461 A1 WO2020078461 A1 WO 2020078461A1
Authority
WO
WIPO (PCT)
Prior art keywords
seat
information
adjustment
face
passenger
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Ceased
Application number
PCT/CN2019/111928
Other languages
English (en)
French (fr)
Chinese (zh)
Inventor
易成名
梁冠华
韦阳
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Shanghai Sensetime Intelligent Technology Co Ltd
Original Assignee
Shanghai Sensetime Intelligent Technology Co Ltd
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Shanghai Sensetime Intelligent Technology Co Ltd filed Critical Shanghai Sensetime Intelligent Technology Co Ltd
Priority to EP19872875.0A priority Critical patent/EP3798048A4/en
Priority to JP2020530515A priority patent/JP2021504236A/ja
Priority to SG11202009429PA priority patent/SG11202009429PA/en
Priority to KR1020207014696A priority patent/KR20200071118A/ko
Publication of WO2020078461A1 publication Critical patent/WO2020078461A1/zh
Priority to US16/882,860 priority patent/US20200282867A1/en
Anticipated expiration legal-status Critical
Ceased legal-status Critical Current

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; 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
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F3/00—Input arrangements for transferring data to be processed into a form capable of being handled by the computer; Output arrangements for transferring data from processing unit to output unit, e.g. interface arrangements
    • G06F3/01—Input arrangements or combined input and output arrangements for interaction between user and computer
    • G06F3/011—Arrangements for interaction with the human body, e.g. for user immersion in virtual reality
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60R—VEHICLES, VEHICLE FITTINGS, OR VEHICLE PARTS, NOT OTHERWISE PROVIDED FOR
    • B60R16/00—Electric or fluid circuits specially adapted for vehicles and not otherwise provided for; Arrangement of elements of electric or fluid circuits specially adapted for vehicles and not otherwise provided for
    • B60R16/02—Electric or fluid circuits specially adapted for vehicles and not otherwise provided for; Arrangement of elements of electric or fluid circuits specially adapted for vehicles and not otherwise provided for electric constitutive elements
    • B60R16/037—Electric or fluid circuits specially adapted for vehicles and not otherwise provided for; Arrangement of elements of electric or fluid circuits specially adapted for vehicles and not otherwise provided for electric constitutive elements for occupant comfort, e.g. for automatic adjustment of appliances according to personal settings, e.g. seats, mirrors, steering wheel
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60N—SEATS SPECIALLY ADAPTED FOR VEHICLES; VEHICLE PASSENGER ACCOMMODATION NOT OTHERWISE PROVIDED FOR
    • B60N2/00—Seats specially adapted for vehicles; Arrangement or mounting of seats in vehicles
    • B60N2/002—Seats provided with an occupancy detection means mounted therein or thereon
    • B60N2/0021—Seats provided with an occupancy detection means mounted therein or thereon characterised by the type of sensor or measurement
    • B60N2/0024—Seats provided with an occupancy detection means mounted therein or thereon characterised by the type of sensor or measurement for identifying, categorising or investigation of the occupant or object on the seat
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60N—SEATS SPECIALLY ADAPTED FOR VEHICLES; VEHICLE PASSENGER ACCOMMODATION NOT OTHERWISE PROVIDED FOR
    • B60N2/00—Seats specially adapted for vehicles; Arrangement or mounting of seats in vehicles
    • B60N2/02—Seats specially adapted for vehicles; Arrangement or mounting of seats in vehicles the seat or part thereof being movable, e.g. adjustable
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60N—SEATS SPECIALLY ADAPTED FOR VEHICLES; VEHICLE PASSENGER ACCOMMODATION NOT OTHERWISE PROVIDED FOR
    • B60N2/00—Seats specially adapted for vehicles; Arrangement or mounting of seats in vehicles
    • B60N2/02—Seats specially adapted for vehicles; Arrangement or mounting of seats in vehicles the seat or part thereof being movable, e.g. adjustable
    • B60N2/0224—Non-manual adjustments, e.g. with electrical operation
    • B60N2/0244—Non-manual adjustments, e.g. with electrical operation with logic circuits
    • B60N2/0268—Non-manual adjustments, e.g. with electrical operation with logic circuits using sensors or detectors for adapting the seat or seat part, e.g. to the position of an occupant
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60N—SEATS SPECIALLY ADAPTED FOR VEHICLES; VEHICLE PASSENGER ACCOMMODATION NOT OTHERWISE PROVIDED FOR
    • B60N2/00—Seats specially adapted for vehicles; Arrangement or mounting of seats in vehicles
    • B60N2/02—Seats specially adapted for vehicles; Arrangement or mounting of seats in vehicles the seat or part thereof being movable, e.g. adjustable
    • B60N2/0224—Non-manual adjustments, e.g. with electrical operation
    • B60N2/0244—Non-manual adjustments, e.g. with electrical operation with logic circuits
    • B60N2/0273—Non-manual adjustments, e.g. with electrical operation with logic circuits taking into account user data, e.g. knee height or physical state
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60N—SEATS SPECIALLY ADAPTED FOR VEHICLES; VEHICLE PASSENGER ACCOMMODATION NOT OTHERWISE PROVIDED FOR
    • B60N2/00—Seats specially adapted for vehicles; Arrangement or mounting of seats in vehicles
    • B60N2/02—Seats specially adapted for vehicles; Arrangement or mounting of seats in vehicles the seat or part thereof being movable, e.g. adjustable
    • B60N2/0224—Non-manual adjustments, e.g. with electrical operation
    • B60N2/0244—Non-manual adjustments, e.g. with electrical operation with logic circuits
    • B60N2/0277—Non-manual adjustments, e.g. with electrical operation with logic circuits characterised by the calculation method or calculation flow chart of sensor data for adjusting the seat
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60R—VEHICLES, VEHICLE FITTINGS, OR VEHICLE PARTS, NOT OTHERWISE PROVIDED FOR
    • B60R11/00—Arrangements for holding or mounting articles, not otherwise provided for
    • B60R11/04—Mounting of cameras operative during drive; Arrangement of controls thereof relative to the vehicle
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/045—Combinations of networks
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/0464—Convolutional networks [CNN, ConvNet]
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/40—Extraction of image or video features
    • G06V10/44—Local feature extraction by analysis of parts of the pattern, e.g. by detecting edges, contours, loops, corners, strokes or intersections; Connectivity analysis, e.g. of connected components
    • G06V10/443—Local feature extraction by analysis of parts of the pattern, e.g. by detecting edges, contours, loops, corners, strokes or intersections; Connectivity analysis, e.g. of connected components by matching or filtering
    • G06V10/449—Biologically inspired filters, e.g. difference of Gaussians [DoG] or Gabor filters
    • G06V10/451—Biologically inspired filters, e.g. difference of Gaussians [DoG] or Gabor filters with interaction between the filter responses, e.g. cortical complex cells
    • G06V10/454—Integrating the filters into a hierarchical structure, e.g. convolutional neural networks [CNN]
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
    • G06V10/82—Arrangements for image or video recognition or understanding using pattern recognition or machine learning using neural networks
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; 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
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; 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
    • G06V40/161—Detection; Localisation; Normalisation
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; 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
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; 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
    • G06V40/171—Local features and components; Facial parts ; Occluding parts, e.g. glasses; Geometrical relationships
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; 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
    • G06V40/172—Classification, e.g. identification
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60N—SEATS SPECIALLY ADAPTED FOR VEHICLES; VEHICLE PASSENGER ACCOMMODATION NOT OTHERWISE PROVIDED FOR
    • B60N2/00—Seats specially adapted for vehicles; Arrangement or mounting of seats in vehicles
    • B60N2/002—Seats provided with an occupancy detection means mounted therein or thereon
    • B60N2/0021—Seats provided with an occupancy detection means mounted therein or thereon characterised by the type of sensor or measurement
    • B60N2/0022—Seats provided with an occupancy detection means mounted therein or thereon characterised by the type of sensor or measurement for sensing anthropometric parameters, e.g. heart rate or body temperature
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60N—SEATS SPECIALLY ADAPTED FOR VEHICLES; VEHICLE PASSENGER ACCOMMODATION NOT OTHERWISE PROVIDED FOR
    • B60N2210/00—Sensor types, e.g. for passenger detection systems or for controlling seats
    • B60N2210/10—Field detection presence sensors
    • B60N2210/16—Electromagnetic waves
    • B60N2210/22—Optical; Photoelectric; Lidar [Light Detection and Ranging]
    • B60N2210/24—Cameras
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60N—SEATS SPECIALLY ADAPTED FOR VEHICLES; VEHICLE PASSENGER ACCOMMODATION NOT OTHERWISE PROVIDED FOR
    • B60N2220/00—Computerised treatment of data for controlling of seats
    • B60N2220/20—Computerised treatment of data for controlling of seats using a deterministic algorithm
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60N—SEATS SPECIALLY ADAPTED FOR VEHICLES; VEHICLE PASSENGER ACCOMMODATION NOT OTHERWISE PROVIDED FOR
    • B60N2220/00—Computerised treatment of data for controlling of seats
    • B60N2220/30—Computerised treatment of data for controlling of seats using a neural network
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods

Definitions

  • This application relates to intelligent driving technology, in particular to a method and device for intelligently adjusting a vehicle seat, a vehicle, an electronic device, and a medium.
  • the first thing the driver does before driving is usually to adjust the seat after getting on the vehicle, which includes height, front-to-rear distance and seat back tilt angle, which also relates to the distance between the leg and the brake, throttle, clutch, etc. Comfortable and strong maneuverability is appropriate; proper seat position can ensure the driver's correct sitting posture to reduce injuries.
  • the embodiments of the present application provide a technical solution for intelligent adjustment of a vehicle seat.
  • a method for intelligently adjusting a vehicle seat includes:
  • the adjustment information is output, or the seat is adjusted based on the adjustment information.
  • a vehicle seat intelligent adjustment device includes:
  • Personnel detection unit used to detect the image of the occupants of the vehicle to obtain the detection results
  • a related information determining unit configured to determine the body shape related information and / or face height information of the passenger based on the detection result
  • An adjustment information determining unit configured to determine the adjustment information of the seat of the occupant based on the body shape-related information and / or the face height information;
  • the information processing unit is configured to output the adjustment information or adjust the seat based on the adjustment information.
  • a vehicle including: the vehicle seat intelligent adjustment device as described in any one of the above.
  • an electronic device including a processor, and the processor includes the vehicle seat intelligent adjustment device according to any one of the above.
  • an electronic device includes: a memory for storing executable instructions;
  • a processor for communicating with the memory to execute the executable instructions to complete any of the above vehicle seat intelligent adjustment methods.
  • a computer storage medium for storing computer readable instructions, and when the instructions are executed, the operation of the vehicle seat intelligent adjustment method described above is performed.
  • a computer program product including computer readable code, and when the computer readable code runs on a device, a processor in the device executes to implement any of the above An instruction of the method for intelligently adjusting a vehicle seat.
  • the image of the occupant of the vehicle is detected to obtain a detection result; the body shape-related information of the occupant is determined according to the detection result And / or face height information; determine the adjustment information of the seat of the occupant based on body-related information and / or face height information; output adjustment information, or adjust the seat based on the adjustment information.
  • the embodiments of the present application can realize the targeted intelligent adjustment of the seats occupied by the occupants of the vehicle based on the image detection technology, that is, the body shape-related information and / or face height information of the occupants can be obtained through image detection, and based on the body shape
  • the related information and / or face height information can more accurately determine the seat adjustment information.
  • the seat adjusted according to the seat adjustment information provides a more suitable sitting posture for the occupant and improves the comfort of the occupant.
  • FIG. 1 is a schematic flowchart of a method for intelligently adjusting a vehicle seat provided by an embodiment of the present application.
  • FIG. 2 is another schematic flowchart of a method for intelligently adjusting a vehicle seat provided by an embodiment of the present application.
  • Fig. 3 is a reference diagram of the position of the vehicle coordinate system and the camera coordinate system.
  • FIG. 4 is a schematic result diagram of the translation of the camera coordinate system space point to the vehicle-machine coordinate system.
  • FIG. 5 is a schematic diagram of the seat adjustment simplified camera coordinate system and the vehicle coordinate system.
  • FIG. 6 is a schematic diagram of the coordinate point (x 1 , z 1 ) in the camera coordinate system rotating to the coordinate point (x 0 , z 0 ) in the vehicle coordinate system.
  • FIG. 7 is another schematic flowchart of a method for intelligently adjusting a vehicle seat provided by an embodiment of the present application.
  • FIG. 8 is a schematic flowchart of another part of the method for intelligently adjusting a vehicle seat provided by an embodiment of the present application.
  • FIG. 9 is a schematic flowchart of still another part of the method for intelligently adjusting a vehicle seat provided by an embodiment of the present application.
  • FIG. 10 is a schematic structural diagram of a vehicle seat intelligent adjustment device provided by an embodiment of the present application.
  • FIG. 11 is a schematic structural diagram of an electronic device suitable for implementing a terminal device or a server according to an embodiment of the present application.
  • the embodiments of the present application can be applied to a computer system / server, which can operate together with many other general-purpose or special-purpose computing system environments or configurations.
  • Examples of well-known computing systems, environments, and / or configurations suitable for use with computer systems / servers include, but are not limited to: in-vehicle devices, personal computer systems, server computer systems, thin clients, thick clients, handheld or laptop Equipment, microprocessor-based systems, set-top boxes, programmable consumer electronics, network personal computers, small computer systems, mainframe computer systems, and distributed cloud computing technology environments that include any of the above systems, etc.
  • the computer system / server may be described in the general context of computer system executable instructions (such as program modules) executed by the computer system.
  • program modules may include routines, programs, target programs, components, logic, data structures, etc., which perform specific tasks or implement specific abstract data types.
  • the computer system / server can be implemented in a distributed cloud computing environment. In a distributed cloud computing environment, tasks are performed by remote processing devices linked through a communication network. In a distributed cloud computing environment, program modules may be located on local or remote computing system storage media including storage devices.
  • FIG. 1 is a schematic flowchart of a method for intelligently adjusting a vehicle seat provided by an embodiment of the present application.
  • the method may be executed by any electronic device, such as a vehicle-mounted device, a terminal device, a server, a mobile device, etc., which is not limited in the embodiments of the present application.
  • the method in this embodiment includes:
  • Step 110 Detect the image of the occupant of the vehicle to obtain the detection result.
  • the occupants may include drivers and / or passengers, and images are collected for the occupants entering the vehicle to detect the collected images of the occupants.
  • the detection may be implemented based on a neural network or other methods.
  • the neural networks in the embodiments of the present application may be a multi-layer neural network (ie, deep neural network), wherein the neural network may be a multi-layer convolutional neural network, for example, LeNet, AlexNet , GoogLeNet, VGG, ResNet and other arbitrary neural network models.
  • Each neural network may use a neural network of the same type and structure, or a neural network of a different type and / or structure. The embodiments of the present application do not limit this.
  • this step 110 may be executed by the processor invoking the corresponding instruction stored in the memory, or may be executed by the human detection unit 101 executed by the processor.
  • Step 120 Determine the body shape-related information and / or face height information of the occupant according to the detection result.
  • Body type-related information may include, but is not limited to: race, gender, and other information related to passengers riding on the vehicle (such as: fatness and thinness, leg length information, skeleton size information , Hand length information, etc.) influential information.
  • detecting face reference points based on key point detection network to obtain face reference points to determine face height information neural network based on attribute detection performing attribute detection on images of passengers to determine body type related information, or
  • the body shape-related information can be determined through the results of human body or face detection, or the body shape-related information can be obtained directly by classification neural network detection, for example: through the gender recognized by the face, the skeleton size information of the rider can be obtained, female The skeleton of is usually smaller, while the skeleton of men is usually larger.
  • this step 120 may be executed by the processor invoking the corresponding instruction stored in the memory, or may be executed by the related information determining unit 102 executed by the processor.
  • Step 130 based on the body shape-related information and / or face height information, determine the seat adjustment information of the passenger.
  • the comfortable sitting posture of the human body is not only related to the height of the sitting posture, but also to the body shape.
  • the embodiments of the present application determine the seat position by obtaining information about the driver ’s body shape and / or face height Adjustment information.
  • this step 130 may be executed by the processor invoking the corresponding instruction stored in the memory, or may be executed by the adjustment information determining unit 103 executed by the processor.
  • Step 140 output adjustment information, or adjust the seat based on the adjustment information.
  • the adjustment information of the seat may be relative information between the desired state of the seat corresponding to the occupant and the current state of the current seat.
  • the current state of the seat may be the vehicle's factory position or any other position (eg, The seat status set for the previous driver, etc.), the current status of the seat can be known from the seat setting history, and the seat adjustment information can be obtained according to the current status of the seat; or, the seat adjustment information can also correspond to The desired state of the seat of the occupant, the desired state of the seat corresponds to the occupant's final adjustment of the seat to the target value of each adjustment parameter, where the adjustment reference may include but is not limited to at least one of the following: the up and down position of the seat, The left-right position of the seat, the front-rear position of the seat, the reclining angle of the backrest chair, the reclining angle of the seat cushion, etc.
  • this step 140 may be executed by the processor invoking the corresponding instruction stored in the memory, or may be executed by the information processing unit 104 executed by the processor.
  • the image of the occupant is detected to obtain a detection result; the body shape-related information and / or face height information of the occupant is determined according to the detection result; Information and / or face height information, determine the adjustment information of the seat of the occupant; output the adjustment information, or adjust the seat based on the adjustment information.
  • the embodiments of the present application can realize the personalized intelligent adjustment of the seats occupied by the occupants of the vehicle based on the image detection technology, that is, the body shape-related information and / or face height information of the occupants can be obtained through image detection, and based on the present Implementation of image detection to obtain the occupant ’s body shape-related information and / or face height information, based on the body shape-related information and / or face height information, the seat adjustment information can be determined more accurately, adjusted according to the seat adjustment information
  • the seat provides a more suitable sitting position for the occupants, improves the comfort of the occupants, and realizes the targeted intelligent adjustment of the seat.
  • the detection result includes the coordinates of the face reference point; the method of this embodiment includes:
  • Step 210 Perform face reference point detection on the image of the occupant to obtain the coordinates of the face reference point of the occupant in the camera coordinate system.
  • the face reference point can be any point on the face, it can be a key point on the face, or it can be another position on the face. Because the vehicle's driving process, the vision of the passenger has an important role; For the driver, ensuring the driver ’s binocular height during driving can improve driving safety. Therefore, the face reference point can be set to a point related to the eyes, for example: at least one key point to determine the position of the eyes, Or the position of the eyebrows, etc .; the embodiments of the present application do not limit the number and position of specific face reference points, as long as the height of the face can be determined.
  • the face reference point includes at least one face key point and / or at least one other face position point;
  • step 210 includes: performing face reference point detection on the image of the occupant to obtain the occupant's on-camera The coordinates of at least one face key point in the coordinate system;
  • the position of key points on the face can be determined through the neural network, for example: one or more of 21 key points on the face, 106 key points on the face, and 240 key points on the face, through different networks
  • the number of obtained key points is different, which can include the key points of facial features, or can also include the key points of the face contour.
  • the key points are different in density and density, which leads to the difference in the number of key points obtained; one of the obtained key points Or, as multiple reference points for the face, only different parts need to be selected according to the situation.
  • the embodiments of the present application do not limit the position and number of key points of the face.
  • the reference point may also be other position points of the face on the face image determined based on the detection results of the key points of the face.
  • These other position points of the face may not be key points, that is, any position points on the face, However, its position can be determined according to the key points of the face, for example, the position of the eyebrow center can be determined based on the key points of the eyes and the key points of the eyebrows.
  • Step 220 Convert the coordinates of the reference point of the human face from the camera coordinate system to the vehicle coordinate system.
  • the face reference point since the face reference point is obtained from the image collected by the camera, the face reference point corresponds to the camera coordinate system, and when determining the seat information, it needs to be determined under the vehicle-machine coordinate system. Therefore, The face reference point needs to be converted from the camera coordinate system to the vehicle coordinate system.
  • the coordinate system conversion method commonly used in the prior art may be used to convert the coordinates of the eyebrow position from the camera coordinate system to the vehicle coordinate system, for example:
  • FIG. 3 is a reference map of the position of the vehicle coordinate system and the camera coordinate system, Among them, in the vehicle coordinate system: the y axis is the front wheel axle, the x axis is parallel to the upper left edge, and the z axis is perpendicular to the ground and downward.
  • FIG. 4 is a schematic result diagram of the translation of the camera coordinate system space point to the vehicle-machine coordinate system. As shown in Figure 4: The origin Oc of the camera coordinate system is translated to the origin O of the vehicle coordinate system.
  • Oc is (Xwc, Ywc, Zwc) in the vehicle coordinate system, and Oc is (0,0,0) in the camera coordinate system, and it is translated to the origin O (0,0,0) of the vehicle coordinate system.
  • FIG. 5 is a schematic diagram of the seat adjustment simplified camera coordinate system and the vehicle coordinate system.
  • the X axis in the vehicle coordinate system is not adjusted, then the coordinate points in the camera coordinate system are converted to the vehicle coordinate system and simplified to the two-dimensional coordinate system.
  • FIG. 6 is a schematic diagram of the coordinate point (x 1 , z 1 ) in the camera coordinate system rotating to the coordinate point (x 0 , z 0 ) in the vehicle coordinate system.
  • the coordinate point of the driver's head is (y 1 , z 1 ) detected under the camera coordinate system, and after the rotation angle ⁇ , that is, the installation angle of the camera, the coordinate point (x 0 , z 0 ).
  • the coordinate point under the camera coordinate system can be rotated and translated to the coordinates under the vehicle coordinate system as follows:
  • Step 230 Determine the face height information of the passenger based on the coordinates of the face reference point in the vehicle-machine coordinate system.
  • the face height information of the occupant in the vehicle can be determined, that is, the relative positional relationship between the face height and the seat can be determined, and the desired seat state information corresponding to the face height information can be obtained.
  • Step 240 based on the face height information, determine the adjustment information of the passenger's seat.
  • Step 250 output adjustment information, or adjust the seat based on the adjustment information.
  • the embodiment of the present application determines seat adjustment information through face height information, provides individual seat adjustment information for different occupants, more in line with the individual needs of the occupants, and improves the comfort of riding.
  • FIG. 7 is another schematic flowchart of a method for intelligently adjusting a vehicle seat provided by an embodiment of the present application.
  • the detection result includes body shape-related information.
  • the body shape-related information includes race information and / or gender information; the method in this embodiment includes:
  • Step 710 Input the image of the occupant into the neural network for attribute detection to perform attribute detection to obtain the attribute detection result output by the neural network.
  • an attribute detection is implemented by a neural network, and the attribute detection result includes race information and / or gender information of the passenger; optionally, the neural network may be a classification network including at least one branch, In the case of including one branch, classify racial information or gender information, and in the case of including two branches, classify racial information and gender information; determine the racial classification and gender classification of passengers.
  • Step 720 based on the attribute detection result, obtain the race information and / or gender information of the passenger corresponding to the image.
  • Step 730 based on the body shape-related information, determine the adjustment information of the passenger's seat.
  • the comfortable sitting posture of the human body is not only related to the sitting posture height, but also to the body shape.
  • the embodiments of the present application determine the seat adjustment information by obtaining information about the driver's body shape.
  • Step 740 output adjustment information, or adjust the seat based on the adjustment information.
  • FIG. 8 is a schematic flowchart of another part of the method for intelligently adjusting a vehicle seat provided by an embodiment of the present application. As shown in FIG. 8, step 130 in the foregoing embodiment may include:
  • Step 802 Obtain a preset seat adjustment conversion relationship related to body shape and / or face height.
  • Step 804 based on the body shape-related information and / or face height information, and the seat adjustment conversion relationship, determine the desired state of the seat corresponding to the passenger.
  • the seat adjustment conversion relationship may include, but is not limited to, a conversion formula or a correspondence table, etc.
  • the conversion formula may input body shape and / or face height into the formula to obtain data corresponding to the desired state of the seat, and the correspondence table may directly The data corresponding to the desired state of the seat is obtained based on a look-up table of body shape and / or face height.
  • the corresponding relationship table may be obtained through big data statistics, or obtained in other ways. The embodiment of the present application does not limit the specific way of obtaining the corresponding relationship table.
  • step 806 the adjustment information of the seat is determined based on the desired state of the seat, or the adjustment information of the seat is determined based on the desired state of the seat and the current state of the seat.
  • the adjustment information of the seat is determined directly based on the desired state of the seat without considering the relative information. How to adjust the seat is directly based on the motor without calculating the relative state information;
  • the current state of the seat can be the vehicle's factory position or any other position (for example, the previous The seat state set by the driver, etc.), the current state of the seat can be known from the seat setting history, and the adjustment information of the seat can be obtained according to the current state of the seat.
  • the desired state of the seat is also different, and multiple sets of corresponding formulas can be combined for different genders and races, such as: For the yellow race + male, for a seat adjustment formula, each dimension of the eyebrow coordinates (x, y, z) and backrest adjustment angle entered in each formula corresponds to a cubic unary function, for example:
  • the final calculated seat expectation state (x out , y out ) can be determined based on the coordinates of the eyebrow coordinates in the x-axis, y-axis, and z-axis directions , Z out , angle out ) through the final motor adjustment allocation formula to obtain the adjustment amount of 4 motors;
  • x out represents the front and rear position information of the seat
  • y out represents the seat cushion tilt angle information
  • z out represents the up and down of the seat Position information
  • angle out represents information about the reclining angle of the backrest chair;
  • a 4 , b 4 , c 4 , and d 4 are constants obtained through many experiments.
  • the final calculated seat expectation state (x out , y out , z out , angle out ) may also be determined based on the coordinates of the eyebrow coordinates in the z-axis direction (ie, the height of the eyebrows). Based on the following formula:
  • x out represents the front and rear position information of the seat
  • y out represents the seat cushion tilt angle information
  • z out represents the seat up and down position information
  • angle out represents the backrest seat tilt angle information
  • step 130 in the foregoing embodiment may include:
  • Step 901 Obtain a preset first seat adjustment conversion relationship related to face height.
  • the seat adjustment conversion relationship may include, but is not limited to, a conversion formula or a correspondence table, etc.
  • the conversion formula may input the face height into the formula to obtain data corresponding to the desired state of the seat, and the correspondence table may be directly based on the face height Look up the table to obtain the data corresponding to the desired state of the seat.
  • the correspondence table can be obtained through big data statistics, or obtained in other ways. This embodiment does not limit the specific way to obtain the correspondence table.
  • Step 902 based on the face height information and the first seat adjustment conversion relationship, determine a desired state of the first seat corresponding to the driver.
  • Step 903 Obtain a preset second seat adjustment conversion relationship related to body shape-related information.
  • the embodying related information corresponds to the second seat adjustment conversion relationship, which is different from the first seat adjustment conversion relationship, and its form may include but is not limited to a conversion formula or correspondence
  • the relationship table and the like can determine the second seat desired state through the second seat adjustment conversion relationship in combination with the body shape-related information and the first seat desired state.
  • Step 904 Determine the second seat desired state based on the body type-related information, the second seat adjustment conversion relationship, and the first seat desired state.
  • Step 905 the adjustment information of the seat is determined based on the desired state of the second seat, or the adjustment information of the seat is determined based on the desired state of the second seat and the current state of the seat.
  • the seat adjustment information is determined by combining body shape-related information and face height information, where the number of classifications obtained by combining race and gender in the body shape-related information is limited, and only needs to be determined as a combination, for example: male + yellow race , Applicable to all passengers under this category, personalization is insufficient, but the information is easy to obtain; while the face height information is more personalized, the adjustment information corresponding to different passengers may be different, so this embodiment has achieved General information combined with personalized information improves the accuracy of the determined adjustment information.
  • the method of the embodiment of the present application further includes: establishing and storing a mapping relationship between seat adjustment information corresponding to the passenger and identity information of the passenger.
  • the mapping relationship between the seat adjustment information corresponding to the passenger and the identity information of the passenger is stored.
  • the corresponding seat adjustment information can be obtained directly through the mapping relationship based on the identity information of the passenger; the efficiency of intelligent adjustment of the vehicle seat is improved.
  • the seat adjustment information may include, but is not limited to, at least one of the following information: the target value of the seat adjustment parameter, the up and down adjustment amount of the seat, the front and rear adjustment amount of the seat, the backrest Adjustment amount of chair tilt angle, adjustment amount of seat cushion tilt angle, etc.
  • the seat needs to be adjusted in multiple directions, in addition to the up-down, front-rear, left-right adjustments usually included, but also includes the adjustment of the reclining angle of the backrest chair and the adjustment of the reclining angle the amount.
  • the adjustment information can be the output adjustment amount, more generally, it can also be a direct target adjustment state, such as: directly outputting the target value of each adjustment parameter to which the seat is finally adjusted, up, down, left, right, and so on, and how to adjust to achieve
  • the target value can be processed by a motor or other equipment.
  • adjusting the seat based on the adjustment information of the seat includes at least one of the following operations:
  • the tilt angle of the seat cushion is adjusted based on the adjustment amount of the tilt angle of the seat cushion.
  • the adjustment amount can be sent to the in-vehicle controller.
  • the controller controls the motor or other equipment that adjusts the seat.
  • the seat can be adjusted in different directions through multiple motors to achieve Intelligent adjustment of the seat.
  • the embodiments of the present application further include:
  • the occupant In response to the identity authentication being passed, the occupant has the seat adjustment authority.
  • the passengers are authenticated. Only the passengers who have passed the authentication have the seat adjustment authority, which can improve the safety of the vehicle.
  • the person who has passed the authentication can be the person who has traveled in the vehicle, or in advance
  • the identity authentication process may be implemented based on face feature matching or other methods.
  • the embodiment of the present application does not limit the specific method of identity authentication.
  • identity authentication based on the image includes:
  • the passenger has the seat adjustment authority, including:
  • the passenger In response to the existence of a pre-stored facial feature that matches the facial feature, the passenger has the seat adjustment authority through identity authentication.
  • identity authentication is implemented through face feature matching.
  • face recognition is performed on the image, and the face features of the passenger can be obtained through a convolutional neural network.
  • the authentication process is based on the face features and at least one stored person.
  • Face features are matched, optionally, whether the match is determined by judging the similarity between the face features and the pre-stored face features (for example, the similarity between the two features can be determined based on the distance between the two features) ,
  • the obtained similarity between the face feature of the rider and a pre-stored face feature reaches a set threshold (the value of the threshold can be set according to the actual situation), indicating that the rider and the stored person
  • the passenger has the seat adjustment authority.
  • the passenger has seat adjustment authority, including:
  • the database In response to the identity authentication being passed, it is determined whether the identity information of the passenger is included in the database; the database stores the identity information of at least one passenger and the desired state of the seat;
  • the passenger In response to the database not including the passenger's identity information, the passenger is allowed to make seat adjustments.
  • It may also include, in response to the database including the identity information of the occupant, invoking the stored desired state of the seat to adjust the seat for the occupant.
  • the database includes at least one pre-stored face feature and at least one seat position information, and each pre-stored face feature corresponds to one seat position information.
  • a database can be established for the occupants, and the pre-stored facial features and corresponding seat status information of the occupants can be stored in the database; when the occupants enter the vehicle, if the occupants are recognized in the database Pre-existing face features already exist (the recognition process can be based on face matching, when it is determined that the collected face features match the pre-stored face features, it means that the driver has known seat adjustment information), based on the matching pre-existing face features
  • the corresponding seat status information can realize the personalized adjustment of the seat of the passenger; among them, the information stored in the database needs to be set when the passenger first uses the vehicle.
  • the database When a passenger enters the vehicle for the first time, the database When there are no pre-stored facial features matching the driver ’s facial features, the occupant is allowed to adjust the seat.
  • the method before step 110, the method further includes:
  • the image of the occupant is collected by at least one camera installed inside the vehicle.
  • the face image of the occupant is collected by a camera provided in front of the vehicle seat, and when the face image is collected by the camera, the position of the camera is used as the origin of the camera coordinate system.
  • the camera includes but is not limited to At least one of the following: an infrared camera, an RGB camera, a TOF camera, a binocular camera, etc.
  • the embodiment of the present application does not limit which camera is specifically used.
  • Each camera has its own characteristics. For example, when collecting face images through an infrared (IR) camera or a dual-pass camera, the fill light device can be turned on when the light is insufficient, so that the captured face images are clearer and easy to identify and process.
  • IR infrared
  • collecting the image of the occupant through at least one camera provided inside the vehicle includes:
  • the image of the occupant is collected by at least one camera and fill light device provided inside the vehicle.
  • the judgment error when the image of the occupant is collected, the judgment error may be caused due to insufficient light in the car, for example: the ethnographic information is misjudged due to insufficient light, etc .;
  • a supplementary light device is added to improve the recognizability of the collected image.
  • the supplementary light device may be a supplementary light lamp or the like.
  • FIG. 10 is a schematic structural diagram of a vehicle seat intelligent adjustment device provided by an embodiment of the present application.
  • the device of this embodiment can be used to implement the above-mentioned embodiments of the method for intelligently adjusting vehicle seats of the present application. As shown in FIG. 10, the device of this embodiment includes:
  • the person detection unit 101 is used to detect the image of the occupant of the vehicle to obtain the detection result.
  • the related information determining unit 102 is configured to determine body shape related information and / or face height information of the occupant according to the detection result.
  • the adjustment information determining unit 103 is configured to determine the adjustment information of the seat of the occupant based on the body shape-related information and / or face height information.
  • the information processing unit 104 is used to output adjustment information or adjust the seat based on the adjustment information.
  • the embodiments of the present application can realize the targeted intelligent adjustment of the seat taken by the occupant of the vehicle based on the image detection technology, that is, obtained through image detection Seat-related information and / or face height information of passengers, and seat adjustment information can be determined more accurately based on body-related information and / or face height information, and seats adjusted according to seat adjustment information are passengers It provides a more suitable sitting posture, improves the comfort of riders, and realizes intelligent adjustment of the seat.
  • the detection result includes the coordinates of the face reference point
  • the person detection unit is used to detect the face reference point of the image of the occupant, and obtain the coordinates of the face reference point of the occupant in the camera coordinate system;
  • the relevant information determination unit is used to convert the coordinates of the face reference point from the camera coordinate system to the vehicle-machine coordinate system when determining the face height information of the occupant according to the detection result; based on the face reference point coordinates under the vehicle-machine coordinate system Determine the face height information of the passenger.
  • the embodiment of the present application determines seat adjustment information through face height information, provides individual seat adjustment information for different occupants, more in line with the individual needs of the occupants, and improves the comfort of riding.
  • the face reference point includes at least one face key point and / or at least one other face position point;
  • the related information determination unit is used for detecting the face reference point coordinates of the passenger in the camera coordinate system when performing face reference point detection on the image of the passenger, and acquiring the face reference point of the passenger's image to obtain the passenger At least one face key point coordinate in the camera coordinate system; and / or, determining at least one face other position point based on the at least one face key point coordinate.
  • the body type-related information includes racial information and / or gender information
  • the person detection unit is used to input the image of the passenger to the neural network used for attribute detection to perform attribute detection, and obtain the attribute detection result output by the neural network;
  • the related information determining unit determines the body shape related information of the passenger according to the detection result, it is used to obtain the race information and / or gender information of the passenger corresponding to the image based on the attribute detection result.
  • the adjustment information determining unit is used to obtain a preset seat adjustment conversion relationship related to body shape and / or face height; based on body shape related information and / or face height information, And the seat adjustment conversion relationship, determine the desired seat state corresponding to the occupant; determine the seat adjustment information based on the seat expectation state, or determine the seat adjustment information based on the seat expectation state and the seat current state.
  • the adjustment information of the seat is determined directly based on the desired state of the seat without considering the relative information. How to adjust the seat is directly based on the motor without calculating the relative state information;
  • the current state of the seat can be the vehicle's factory position or any other position (such as the previous driver)
  • the seat status set by the operator), the current status of the seat can be known from the seat setting history, and the adjustment information of the seat can be obtained according to the current status of the seat.
  • the adjustment information determining unit is configured to obtain a preset first seat adjustment conversion relationship related to face height; based on the face height information and the first seat adjustment conversion relationship, Determine the first desired seat state corresponding to the driver; obtain a preset second seat adjustment conversion relationship related to body shape related information; based on the body shape related information, the second seat adjustment conversion relationship and the first seat desired state, The second seat desired state is determined; the seat adjustment information is determined based on the second seat desired state, or the seat adjustment information is determined based on the second seat desired state and the seat current state.
  • the seat adjustment information is determined by combining body shape-related information and face height information, where the number of classifications obtained by combining race and gender in the body shape-related information is limited, and only needs to be determined as a combination, for example: male + yellow race , Applicable to all passengers under this category, personalization is insufficient, but the information is easy to obtain; while the face height information is more personalized, the adjustment information corresponding to different passengers may be different, so this embodiment has achieved General information combined with personalized information improves the accuracy of the determined adjustment information.
  • the apparatus of the embodiments of the present application further includes:
  • the relationship storage unit is used to establish and store a mapping relationship between seat adjustment information corresponding to the occupant and identity information of the occupant.
  • the mapping relationship between the seat adjustment information corresponding to the passenger and the identity information of the passenger is stored.
  • the corresponding seat adjustment information can be obtained directly through the mapping relationship based on the identity information of the passenger; the efficiency of intelligent adjustment of the vehicle seat is improved.
  • the seat adjustment information includes at least one of the following information: the target value of the seat adjustment parameter, the up and down adjustment amount of the seat, the front and rear adjustment amount of the seat, and the left and right adjustment of the seat The amount, the adjustment amount of the reclining angle of the backrest chair, the adjustment amount of the reclining angle of the seat cushion, etc.
  • the seat needs to be adjusted in multiple directions, in addition to the up-down, front-rear, left-right adjustments usually included, but also includes the adjustment of the reclining angle of the backrest chair and the adjustment of the reclining angle the amount.
  • the adjustment information can be the output adjustment amount, more generally, it can also be a direct target adjustment state, such as: directly outputting the target value of each adjustment parameter to which the seat is finally adjusted, up, down, left, right, and so on, and how to adjust to achieve
  • the target value can be processed by a motor or other equipment.
  • the information processing unit performs at least one of the following operations when adjusting the seat based on the adjustment information of the seat:
  • the tilt angle of the seat cushion is adjusted based on the adjustment amount of the tilt angle of the seat cushion.
  • the apparatus of the embodiments of the present application further includes:
  • Identity authentication unit for identity authentication based on images
  • the authority acquisition unit is used for responding to the identity authentication, and the occupant has the seat adjustment authority.
  • the passengers are authenticated. Only the passengers who have passed the authentication have the seat adjustment authority, which can improve the safety of the vehicle.
  • the person who has passed the authentication can be the person who has traveled in the vehicle, or in advance
  • the identity authentication process may be implemented based on face feature matching or other methods.
  • the embodiment of the present application does not limit the specific method of identity authentication.
  • the identity authentication unit is used to perform face recognition based on the image to obtain the facial features of the occupant; match the facial features with at least one pre-stored facial feature with authority;
  • the authority acquisition unit is configured to respond to the existence of pre-stored facial features matching the facial features, pass identity authentication, and the occupant has seat adjustment authority.
  • the authority acquisition unit is configured to determine whether the database includes the identity information of the passenger in response to the identity authentication being passed; the database stores the identity information of at least one passenger and the desired state of the seat; the database includes at least one pre-stored face Feature and at least one seat position information, each pre-stored face feature corresponds to one seat position information; in response to the database does not include the passenger's identity information, allowing the passenger to adjust the seat.
  • the apparatus of the embodiments of the present application further includes:
  • the image acquisition unit is used for acquiring images of the occupants through at least one camera provided inside the vehicle.
  • the face image of the occupant is collected by a camera provided in front of the vehicle seat, and when the face image is collected by the camera, the position of the camera is used as the origin of the camera coordinate system.
  • the camera includes but is not limited to At least one of the following: an infrared camera, an RGB camera, a TOF camera, a binocular camera, etc.
  • the embodiment of the present application does not limit which camera is specifically used.
  • Each camera has its own characteristics. For example, when collecting face images through an infrared (IR) camera or a dual-pass camera, the fill light device can be turned on when the light is insufficient, so that the captured face images are clearer and easy to identify and process.
  • IR infrared
  • the image acquisition unit is configured to acquire images of the occupants through at least one camera and fill light device provided inside the vehicle.
  • a vehicle including the vehicle seat intelligent adjustment device provided in any one of the foregoing embodiments.
  • an electronic device including a processor, the processor including the vehicle seat intelligent adjustment device provided in any one of the foregoing embodiments.
  • an electronic device includes: a memory for storing executable instructions;
  • a processor for communicating with the memory to execute the executable instruction to complete the operation of the vehicle seat intelligent adjustment method provided by any one of the above embodiments.
  • a computer storage medium for storing computer readable instructions, which are executed to perform the operation of the vehicle seat intelligent adjustment method provided in any of the above embodiments when the instructions are executed .
  • the neural network in each embodiment of the present application may be a multi-layer neural network (ie, deep neural network), for example, a multi-layer convolutional neural network, for example, any neural network such as LeNet, AlexNet, GoogLeNet, VGG, ResNet, etc. Network model.
  • Each neural network can use neural networks of the same type and structure, or neural networks of different types and structures. The embodiments of the present application do not limit this.
  • An embodiment of the present application also provides an electronic device, which may be, for example, a mobile terminal, a personal computer (PC), a tablet computer, or a server. 11, which shows a schematic structural diagram of an electronic device 1100 suitable for implementing the terminal device or server of the embodiment of the present application:
  • the electronic device 1100 includes one or more processors and a communication part Etc.
  • the one or more processors are, for example, one or more central processing units (CPUs) 1101, and / or one or more special-purpose processors.
  • the special-purpose processors may serve as the acceleration unit 1113, which may include but not limited to images
  • the processor can be loaded into the random access memory according to the executable instructions stored in the read only memory (ROM) 1102 or from the storage section 1108 (RAM) 1103 executable instructions to perform various appropriate actions and processes.
  • the communication part 1112 may include but is not limited to a network card, and the network card may include but not limited to an IB (Infiniband) network card.
  • the processor may communicate with the read-only memory 1102 and / or the random access memory 1103 to execute executable instructions, connect to the communication part 1112 through the bus 1104, and communicate with other target devices via the communication part 1112, thereby completing the embodiment of the present application
  • the operation corresponding to any of the methods, for example, detecting the image of the occupant of the vehicle to obtain the detection result; determining the body type-related information and / or face height information of the passenger based on the detection result; based on the body-type related information and / or The face height information determines the adjustment information of the seat of the occupant; the adjustment information is output, or the seat is adjusted based on the adjustment information.
  • RAM 1103 various programs and data necessary for device operation can also be stored.
  • the CPU 1101, ROM 1102, and RAM 1103 are connected to each other through a bus 1104.
  • ROM1102 is an optional module.
  • the RAM 1103 stores executable instructions, or writes executable instructions to the ROM 1102 at runtime, and the executable instructions cause the central processing unit 1101 to perform operations corresponding to the above communication method.
  • An input / output (I / O) interface 1105 is also connected to the bus 1104.
  • the communication unit 1112 may be integratedly installed, or may be configured to have multiple sub-modules (for example, multiple IB network cards), and be on the bus link.
  • the following components are connected to the I / O interface 1105: an input section 1106 including a keyboard, a mouse, etc .; an output section 1107 including a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker; a storage section 1108 including a hard disk, etc. ; And a communication section 1109 including a network interface card such as a LAN card, a modem, etc. The communication section 1109 performs communication processing via a network such as the Internet.
  • the driver 1110 is also connected to the I / O interface 1105 as necessary.
  • a removable medium 1111 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 1110 as necessary, so that the computer program read out therefrom is installed into the storage portion 1108 as needed.
  • FIG. 11 is only an optional implementation method.
  • the number and types of the components in FIG. 11 can be selected, deleted, added, or replaced according to actual needs; Separate settings or integrated settings can also be used for the setting of different functional components.
  • the acceleration unit 1113 and the CPU 1101 can be set separately or the acceleration unit 1113 can be integrated on the CPU 1101.
  • the communication unit can be set separately or integrated on the CPU 1101. Or on the acceleration unit 1113, etc.
  • embodiments of the present application include a computer program product including a computer program tangibly contained on a machine-readable medium, the computer program containing program code for performing the method shown in the flowchart, the program code may include a corresponding Execute the instructions corresponding to the method steps provided in the embodiments of the present application, for example, detect the image of the occupant of the vehicle to obtain the detection result; determine the body shape related information and / or face height information of the occupant according to the detection result; based on the body type Information and / or face height information, determine the adjustment information of the seat of the occupant; output the adjustment information, or adjust the seat based on the adjustment information.
  • the computer program may be downloaded and installed from the network through the communication section 1109, and / or installed from the removable medium 1111.
  • the computer program is executed by the central processing unit (CPU) 1101, the operation of the above-mentioned functions defined in the method of the present application is performed.
  • the method and apparatus of the present application may be implemented in many ways.
  • the method and apparatus of the present application may be implemented by software, hardware, firmware, or any combination of software, hardware, and firmware.
  • the above sequence of steps for the method is for illustration only, and the steps of the method of the present application are not limited to the sequence specifically described above unless specifically stated otherwise.
  • the present application may also be implemented as programs recorded in a recording medium, and these programs include machine-readable instructions for implementing the method according to the present application.
  • the present application also covers a recording medium storing a program for executing the method according to the present application.

Landscapes

  • Engineering & Computer Science (AREA)
  • Theoretical Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Health & Medical Sciences (AREA)
  • Mechanical Engineering (AREA)
  • General Physics & Mathematics (AREA)
  • General Health & Medical Sciences (AREA)
  • Transportation (AREA)
  • Aviation & Aerospace Engineering (AREA)
  • Multimedia (AREA)
  • Oral & Maxillofacial Surgery (AREA)
  • Evolutionary Computation (AREA)
  • Human Computer Interaction (AREA)
  • Artificial Intelligence (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Biomedical Technology (AREA)
  • Software Systems (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Molecular Biology (AREA)
  • Computing Systems (AREA)
  • General Engineering & Computer Science (AREA)
  • Mathematical Physics (AREA)
  • Biophysics (AREA)
  • Data Mining & Analysis (AREA)
  • Computational Linguistics (AREA)
  • Biodiversity & Conservation Biology (AREA)
  • Databases & Information Systems (AREA)
  • Medical Informatics (AREA)
  • Seats For Vehicles (AREA)
PCT/CN2019/111928 2018-10-19 2019-10-18 车辆座椅智能调节方法和装置、车辆、电子设备、介质 Ceased WO2020078461A1 (zh)

Priority Applications (5)

Application Number Priority Date Filing Date Title
EP19872875.0A EP3798048A4 (en) 2018-10-19 2019-10-18 METHOD AND DEVICE FOR INTELLIGENT VEHICLE AND VEHICLE SEAT ADJUSTMENT, ELECTRONIC DEVICE AND BRACKET
JP2020530515A JP2021504236A (ja) 2018-10-19 2019-10-18 車両シート知能化調節方法及び装置、車両、電子機器並びに媒体
SG11202009429PA SG11202009429PA (en) 2018-10-19 2019-10-18 Method and device for intelligent adjustment of vehicle seat, and vehicle, electronic device, and medium
KR1020207014696A KR20200071118A (ko) 2018-10-19 2019-10-18 차량 좌석의 스마트 조절 방법 및 장치, 차량, 전자 기기, 매체
US16/882,860 US20200282867A1 (en) 2018-10-19 2020-05-26 Method and apparatus for intelligent adjustment of vehicle seat, vehicle, electronic device, and medium

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
CN201811224906.6 2018-10-19
CN201811224906.6A CN111071113A (zh) 2018-10-19 2018-10-19 车辆座椅智能调节方法和装置、车辆、电子设备、介质

Related Child Applications (1)

Application Number Title Priority Date Filing Date
US16/882,860 Continuation US20200282867A1 (en) 2018-10-19 2020-05-26 Method and apparatus for intelligent adjustment of vehicle seat, vehicle, electronic device, and medium

Publications (1)

Publication Number Publication Date
WO2020078461A1 true WO2020078461A1 (zh) 2020-04-23

Family

ID=70283340

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/CN2019/111928 Ceased WO2020078461A1 (zh) 2018-10-19 2019-10-18 车辆座椅智能调节方法和装置、车辆、电子设备、介质

Country Status (7)

Country Link
US (1) US20200282867A1 (https=)
EP (1) EP3798048A4 (https=)
JP (1) JP2021504236A (https=)
KR (1) KR20200071118A (https=)
CN (1) CN111071113A (https=)
SG (1) SG11202009429PA (https=)
WO (1) WO2020078461A1 (https=)

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114459623A (zh) * 2022-04-13 2022-05-10 河北化工医药职业技术学院 人脸识别体温检测装置角度调节控制方法及装置
CN119568046A (zh) * 2024-12-24 2025-03-07 长城汽车股份有限公司 一种调节车辆的方法、车辆和存储介质

Families Citing this family (31)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP7415469B2 (ja) * 2019-11-15 2024-01-17 株式会社アイシン 体格推定装置および姿勢推定装置
SG10202002676SA (en) * 2020-03-23 2021-10-28 Nec Asia Pacific Pte Ltd Method and apparatus for determining a contextual threshold
CN111605442A (zh) * 2020-05-25 2020-09-01 北京汽车集团越野车有限公司 一种汽车座椅调节方法、装置、汽车和可读存储介质
CN111845258A (zh) * 2020-06-16 2020-10-30 宁波吉利汽车研究开发有限公司 一种车内设备调控方法、装置、终端及存储介质
CN111881886A (zh) * 2020-08-21 2020-11-03 董秀园 基于姿态识别的智能座椅控制方法及装置
CN114312498B (zh) * 2020-09-29 2023-03-24 阿波罗智联(北京)科技有限公司 座椅调节装置、方法、设备、车辆座椅及车辆
US11807181B2 (en) * 2020-10-27 2023-11-07 GM Global Technology Operations LLC Vision-based airbag enablement
CN112506353A (zh) * 2021-01-08 2021-03-16 蔚来汽车科技(安徽)有限公司 车辆交互系统、方法、存储介质和车辆
CN115209596A (zh) * 2021-04-08 2022-10-18 佛吉亚歌乐电子(厦门)有限公司 照明装置的亮度控制方法、装置、设备、存储介质及系统
CN113043922A (zh) * 2021-04-25 2021-06-29 武汉驰必得科技有限公司 基于用户特征识别和数据分析的电动汽车驾驶座椅智能调控方法、系统、设备和存储介质
CN113139474A (zh) * 2021-04-26 2021-07-20 中汽研软件测评(天津)有限公司 生物识别技术和数据驱动下的汽车座舱智能适应模型算法
KR20220149053A (ko) 2021-04-30 2022-11-08 주식회사 알고리고 시트 포지션 자동화를 위한 인공신경망 장치 및 방법
CN113511115A (zh) * 2021-05-28 2021-10-19 南昌智能新能源汽车研究院 一种基于双目视觉的汽车座椅姿态智能控制方法
CN113183835A (zh) * 2021-05-31 2021-07-30 广汽本田汽车有限公司 坐姿控制方法、装置、座椅及计算机可读存储介质
JP7729072B2 (ja) 2021-06-02 2025-08-26 マツダ株式会社 運転姿勢設定装置および該方法
CN113665512A (zh) * 2021-08-31 2021-11-19 东风汽车集团股份有限公司 车辆个性化调整系统
KR20230055627A (ko) * 2021-10-19 2023-04-26 (주)케이시크 연결좌석 개인화 시스템 및 방법
CN114789701A (zh) * 2021-11-26 2022-07-26 合众新能源汽车有限公司 一种座椅调节控制方法及系统
CN114590180A (zh) * 2022-02-12 2022-06-07 青岛理工大学 车辆
CN114475371B (zh) * 2022-02-23 2023-02-28 东风汽车集团股份有限公司 一种越野车前后排座椅智能调节系统及控制方法
CN116788179A (zh) * 2022-03-15 2023-09-22 采埃孚汽车科技(张家港)有限公司 车辆内饰系统、用于调整内饰部件的方法、设备及介质
CN114851921B (zh) * 2022-05-11 2023-09-12 广州小鹏汽车科技有限公司 一种乘坐空间的调整方法、装置、电子设备及存储介质
CN117048443A (zh) * 2022-05-12 2023-11-14 北京罗克维尔斯科技有限公司 车辆的调整方法及装置、车辆、电子设备和存储介质
CN114919471A (zh) * 2022-05-30 2022-08-19 重庆长安汽车股份有限公司 汽车座椅自适应调节方法、装置、电子设备及存储介质
CN115284976B (zh) * 2022-08-10 2023-09-12 东风柳州汽车有限公司 车辆座椅自动调节方法、装置、设备及存储介质
CN116118654A (zh) * 2022-12-29 2023-05-16 成都赛力斯科技有限公司 车辆座舱参数调节系统、方法以及智能车辆
CN116331071A (zh) * 2023-05-04 2023-06-27 上汽通用汽车有限公司 车辆座椅调节方法和系统、以及存储介质
CN117325724A (zh) * 2023-10-10 2024-01-02 重庆长安汽车股份有限公司 一种座椅调节方法、装置、车辆、电子设备及存储介质
CN117315726B (zh) * 2023-11-30 2024-03-22 武汉未来幻影科技有限公司 一种驾驶员坐姿高低识别方法、装置以及处理设备
US20250342609A1 (en) * 2024-05-06 2025-11-06 DISTANCE TECHNOLOGIES Oy On-the-fly recalibration of tracking camera
CN119348513A (zh) * 2024-10-17 2025-01-24 中国第一汽车股份有限公司 座椅调控方法、装置、车载娱乐系统和存储介质

Citations (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JPH0769107A (ja) * 1993-09-06 1995-03-14 Mitsubishi Motors Corp ドライビングポジション制御装置
JP2005238968A (ja) * 2004-02-26 2005-09-08 Toyota Motor Corp 車載用健康診断装置および車載用健康診断方法
JP2006290074A (ja) * 2005-04-07 2006-10-26 Nissan Motor Co Ltd 車両用シート装置および運転姿勢制御方法
CN102963274A (zh) * 2012-10-25 2013-03-13 浙江吉利汽车研究院有限公司杭州分公司 一种自适应汽车座椅系统及座椅调节方法
JP2014201174A (ja) * 2013-04-03 2014-10-27 本田技研工業株式会社 車両制御システム
CN105109365A (zh) * 2015-09-18 2015-12-02 宇龙计算机通信科技(深圳)有限公司 一种调整汽车座位参数的方法及装置
CN106183901A (zh) * 2016-08-11 2016-12-07 戴姆勒股份公司 智能座椅及包括该智能座椅的车辆
CN106627261A (zh) * 2016-11-08 2017-05-10 广州大学 一种基于人脸识别的汽车座椅自主记忆系统和方法
CN108657029A (zh) * 2018-05-17 2018-10-16 华南理工大学 一种基于肢体长度预测的汽车驾驶席座椅智能调节系统及方法

Family Cites Families (23)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
DE102004013598A1 (de) * 2004-03-19 2005-10-06 Robert Bosch Gmbh Vorrichtung zur Einstellung von Sitzkomponenten
DE102004015047A1 (de) * 2004-03-26 2005-10-13 Bayerische Motoren Werke Ag Einstellverfahren für einen Kraftfahrzeug-Sitz
JP4737216B2 (ja) * 2008-03-31 2011-07-27 マツダ株式会社 車両用シート装置
TWI440573B (zh) * 2011-06-23 2014-06-11 Altek Corp 多模組辨識系統及其控制方法
US8872640B2 (en) * 2011-07-05 2014-10-28 Saudi Arabian Oil Company Systems, computer medium and computer-implemented methods for monitoring health and ergonomic status of drivers of vehicles
JP2014073712A (ja) * 2012-10-03 2014-04-24 Nissan Motor Co Ltd 運動量測定装置および運動量測定方法
KR101442641B1 (ko) * 2012-12-27 2014-09-19 전자부품연구원 차량용 좌석 및 미러 조정 장치 및 방법
CN103707781B (zh) * 2013-12-26 2016-02-24 苏州清研微视电子科技有限公司 基于距离图像的驾驶员座椅位置自动调整系统
CN104691461A (zh) * 2015-03-25 2015-06-10 乐视致新电子科技（天津）有限公司 一种汽车座椅调整方法、装置和系统
US9925892B2 (en) * 2015-03-26 2018-03-27 Faurecia Automotive Seating, Llc Adjustable vehicle seat
US9919670B2 (en) * 2015-06-17 2018-03-20 Ford Global Technologies, Llc System and method for occupant height
CN105599642B (zh) * 2015-12-18 2018-02-02 小米科技有限责任公司 座椅自动调节方法、装置及终端
CN106143370B (zh) * 2016-08-24 2018-08-03 东风汽车公司 座椅智能调节装置及方法
JP6807951B2 (ja) * 2016-12-20 2021-01-06 三菱電機株式会社 画像認証装置、画像認証方法および自動車
CN108248460A (zh) * 2016-12-29 2018-07-06 乐视汽车（北京）有限公司 用于调整汽车座椅位置的方法、系统和汽车
CN106956620B (zh) * 2017-03-10 2019-06-11 湖北文理学院 驾驶员坐姿自动调整系统及方法
FR3067309B1 (fr) * 2017-06-09 2020-02-07 STMicroelectronics (Grand Ouest) SAS Procede et systeme de reglage automatique d'au moins un equipement d'un vehicule automobile
CN107813732A (zh) * 2017-09-20 2018-03-20 众泰新能源汽车有限公司 车辆自动调节座椅及其调节方法
CN107901792B (zh) * 2017-10-31 2020-09-22 深圳创维汽车智能有限公司 汽车座椅调节方法、装置及计算机可读存储介质
US11465631B2 (en) * 2017-12-08 2022-10-11 Tesla, Inc. Personalization system and method for a vehicle based on spatial locations of occupants' body portions
US10850693B1 (en) * 2018-04-05 2020-12-01 Ambarella International Lp Determining comfort settings in vehicles using computer vision
US11491895B2 (en) * 2018-04-27 2022-11-08 Ts Tech Co., Ltd. ECU device, vehicle seat, system for estimating lower limb length of seated person, and attachment structure for sitting height detection sensor
JP7047689B2 (ja) * 2018-09-20 2022-04-05 株式会社デンソー 疲労軽減装置、疲労軽減方法、及び制御プログラム

Patent Citations (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JPH0769107A (ja) * 1993-09-06 1995-03-14 Mitsubishi Motors Corp ドライビングポジション制御装置
JP2005238968A (ja) * 2004-02-26 2005-09-08 Toyota Motor Corp 車載用健康診断装置および車載用健康診断方法
JP2006290074A (ja) * 2005-04-07 2006-10-26 Nissan Motor Co Ltd 車両用シート装置および運転姿勢制御方法
CN102963274A (zh) * 2012-10-25 2013-03-13 浙江吉利汽车研究院有限公司杭州分公司 一种自适应汽车座椅系统及座椅调节方法
JP2014201174A (ja) * 2013-04-03 2014-10-27 本田技研工業株式会社 車両制御システム
CN105109365A (zh) * 2015-09-18 2015-12-02 宇龙计算机通信科技(深圳)有限公司 一种调整汽车座位参数的方法及装置
CN106183901A (zh) * 2016-08-11 2016-12-07 戴姆勒股份公司 智能座椅及包括该智能座椅的车辆
CN106627261A (zh) * 2016-11-08 2017-05-10 广州大学 一种基于人脸识别的汽车座椅自主记忆系统和方法
CN108657029A (zh) * 2018-05-17 2018-10-16 华南理工大学 一种基于肢体长度预测的汽车驾驶席座椅智能调节系统及方法

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
See also references of EP3798048A4

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114459623A (zh) * 2022-04-13 2022-05-10 河北化工医药职业技术学院 人脸识别体温检测装置角度调节控制方法及装置
CN119568046A (zh) * 2024-12-24 2025-03-07 长城汽车股份有限公司 一种调节车辆的方法、车辆和存储介质

Also Published As

Publication number Publication date
CN111071113A (zh) 2020-04-28
EP3798048A1 (en) 2021-03-31
US20200282867A1 (en) 2020-09-10
KR20200071118A (ko) 2020-06-18
EP3798048A4 (en) 2021-08-04
JP2021504236A (ja) 2021-02-15
SG11202009429PA (en) 2020-10-29

Similar Documents

Publication Publication Date Title
WO2020078461A1 (zh) 车辆座椅智能调节方法和装置、车辆、电子设备、介质
KR102391380B1 (ko) 운전 환경의 스마트 조정 및 운전자 등록 방법 및 장치, 차량, 기기
CN113335146B (zh) 自动调节驾驶员相关车载设备的调节方法、装置和系统
JP2021504214A5 (https=)
US12091020B2 (en) Method and system for driver posture monitoring
JP2021504236A5 (https=)
WO2022027895A1 (zh) 异常坐姿识别方法、装置、电子设备、存储介质及程序
CN113632098A (zh) 人脸图像处理方法、装置和车辆
CN116194342A (zh) 用于分析车辆内部的计算机实现的方法
CN111867466A (zh) 体格估计装置及体格估计方法
CN114248666A (zh) 基于人脸识别的座椅调整方法、装置、介质及设备
CN111854620A (zh) 基于单目相机的实际瞳距测定方法、装置以及设备
CN114889542A (zh) 一种基于驾驶员监测与识别的座舱协同控制系统和方法
CN117863987A (zh) 车辆座椅的调节方法、装置、电子设备及存储介质
CN118894023A (zh) 基于驾乘人员特征的汽车驾驶座椅调节系统与方法
JP2018060466A (ja) 画像処理装置、検知装置、学習装置、画像処理方法、および画像処理プログラム
CN117373002A (zh) 乘坐乘客身高估计
CN120552694A (zh) 座椅调节方法及装置、电子设备、存储介质
CN118457379A (zh) 车辆智能座椅的调节方法、装置、设备及存储介质
CN119239447A (zh) 基于坐姿的后视镜自动调节方法、装置、电子设备及介质
CN118494498A (zh) 驾驶用户身份识别方法、装置、汽车控制器和存储介质
CN116563827A (zh) 一种视线估计方法、系统及电子设备
CN117944530A (zh) 座椅调节方法、装置、电子设备、存储介质及车辆
CN115147816B (zh) 视线区域检测方法、系统、设备、介质和车辆
US20260044207A1 (en) Gaussian-based method for gaze zone detection

Legal Events

Date Code Title Description
ENP Entry into the national phase

Ref document number: 20207014696

Country of ref document: KR

Kind code of ref document: A

Ref document number: 2020530515

Country of ref document: JP

Kind code of ref document: A

121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 19872875

Country of ref document: EP

Kind code of ref document: A1

WWE Wipo information: entry into national phase

Ref document number: 19872875.0

Country of ref document: EP

ENP Entry into the national phase

Ref document number: 2019872875

Country of ref document: EP

Effective date: 20201221

NENP Non-entry into the national phase

Ref country code: DE