Seat sensor system for a vehicle

Abstract

A seat sensor system for a vehicle. A computer-implemented method, when executed by data processing hardware, causes the data processing hardware to perform operations. The operations include receiving sensor data associated with a vehicle seat from multiple seat sensors, determining that the sensor data exceeds an activation threshold for a seat sensor application, and activating the seat sensor application based on the sensor data exceeding the activation threshold. The operations also include aggregating the sensor data via the seat sensor application, classifying objects at the seat into object classes based on the aggregated sensor data, and generating a signal corresponding to the object via the seat sensor application based on the object classes.

Description

[0001] introduce

[0002] The information provided in this section is for the purpose of presenting the overall context of this disclosure. The work of the currently named inventors is neither expressly nor implicitly acknowledged as prior art to this disclosure, to the extent that it is described in this section and in any other way that it may not be considered prior art at the time of filing.

[0003] This disclosure generally relates to a seat sensor system for vehicles.

[0004] Vehicles are typically equipped with sensors as part of the seat assembly. These sensors can be used to detect occupants using weight-based or other contact-based sensing technologies. Sensors are often used in conjunction with seatbelt alarms to notify occupants if their seatbelts are not fastened. Traditional sensors are arranged in a daisy-chain configuration, where each sensor is arranged in parallel. As a result, these sensors do not provide a spatial mapping of their respective positions relative to the seat. This parallel configuration can lead to false activations for various objects that may be placed on the seat without the seatbelt in use. Therefore, there is a need for an improved sensor system that can provide a spatial mapping along the seat to provide an improved mapping of objects on the seat. Summary of the Invention

[0005] In some aspects, a computer-implemented method causes data processing hardware to perform operations when executed by data processing hardware. The operations include receiving sensor data associated with a vehicle seat from multiple seat sensors, determining that the sensor data exceeds an activation threshold for a seat sensor application, and activating the seat sensor application based on the sensor data exceeding the activation threshold. The operations also include aggregating the sensor data via the seat sensor application, classifying objects at the seat into object classes based on the aggregated sensor data, and generating signals corresponding to the objects via the seat sensor application based on the object classes.

[0006] In some examples, sensor data may include pressure data, spatial data, and temporal data. Optionally, classifying objects may include analyzing sensor data at a first time point and a second time point, and comparing the sensor data at the first time point with the sensor data at the second time point. In some instances, comparing the sensor data at the first time point with the sensor data at the second time point may include identifying a change in the object's position. In other examples, identifying a change in position may include detecting sensor data at the first time point via a first seat sensor and sensor data at the second time point via a second seat sensor.

[0007] Optionally, the multiple seat sensors may be linear piezoelectric sensors, and each seat sensor may be configured to independently capture sensor data. In some configurations, the multiple seat sensors may be located in one or more of the seat base, backrest, headrest, armrests, and floor space adjacent to the seat. In some instances, object classification may include identifying object classes based on sensor data, including one of the following categories: children, adults, pets, and inanimate objects. In some examples, signal generation includes issuing notifications at the vehicle's user interface.

[0008] In other aspects, the seat sensor system includes data processing hardware and memory hardware in communication with the data processing hardware. The memory hardware stores instructions that, when executed on the data processing hardware, cause the data processing hardware to perform operations. These operations include receiving sensor data associated with the vehicle's seat from a plurality of seat sensors disposed in at least one of the seat base, backrest, and headrest; determining that the sensor data exceeds an activation threshold for a seat sensor application; and activating the seat sensor application based on the sensor data exceeding the activation threshold. The operations also include aggregating the sensor data via the seat sensor application, classifying objects at the seat into object classes based on the aggregated sensor data, the object classes including one of an inanimate class and a animate class; and generating a signal corresponding to the object via the seat sensor application based on the object class.

[0009] In some examples, sensor data may include pressure data, spatial data, and temporal data. Optionally, object classification may include analyzing sensor data at a first time point and a second time point, and comparing the sensor data at the first time point with the sensor data at the second time point. In some instances, comparing the sensor data at the first time point with the sensor data at the second time point may include identifying a change in the object's position. In some configurations, identifying a change in position may include detecting sensor data at the first time point via a first seat sensor and sensor data at the second time point via a second seat sensor. Optionally, the multiple seat sensors may be linear piezoelectric sensors, and each seat sensor may be configured to capture sensor data independently. In some instances, generating a signal may include issuing a notification at the vehicle's user interface.

[0010] In another aspect, the seat sensor system for a vehicle includes data processing hardware and memory hardware in communication with the data processing hardware. The memory hardware stores instructions that, when executed on the data processing hardware, cause the data processing hardware to perform operations. These operations include receiving sensor data associated with the vehicle's seat from a plurality of seat sensors disposed in at least one of the seat base, backrest, and headrest, each seat sensor configured to independently capture sensor data; determining that the sensor data exceeds an activation threshold for a seat sensor application; activating the seat sensor application based on the sensor data exceeding the activation threshold; and aggregating the sensor data via the seat sensor application. The operations also include analyzing the sensor data at a first time point and a second time point; comparing the sensor data at the first time point with the sensor data at the second time point; classifying objects at the seat into object classes based on the aggregated sensor data, including one of an inanimate class and a animate class; and generating a signal corresponding to the object via the seat sensor application based on the object class.

[0011] In some examples, sensor data may include pressure data, spatial data, and temporal data. Optionally, comparing sensor data at a first time point with sensor data at a second time point may include identifying a change in the object's position. In some instances, identifying a change in position may include detecting sensor data at the first time point via a first seat sensor and sensor data at the second time point via a second seat sensor.

[0012] A computer-implemented method, when executed by data processing hardware, causes the data processing hardware to perform operations including: receiving sensor data associated with a vehicle seat from a plurality of seat sensors; determining that the sensor data exceeds an activation threshold for a seat sensor application; activating the seat sensor application based on the sensor data exceeding the activation threshold; aggregating the sensor data via the seat sensor application; classifying objects at the seat into object classes based on the aggregated sensor data; and generating signals corresponding to the objects via the seat sensor application based on the object classes. The sensor data includes pressure data, spatial data, and temporal data. The object classification includes analyzing sensor data at a first time point and a second time point, and comparing the sensor data at the first time point with the sensor data at the second time point. The comparison of the sensor data at the first time point with the sensor data at the second time point includes identifying a change in the object's position. Identifying a change in position includes detecting the sensor data at the first time point via a first seat sensor and detecting the sensor data at the second time point via a second seat sensor. The plurality of seat sensors are linear piezoelectric sensors, each configured to independently capture sensor data. The seat sensors, among other seat sensors, are located in one or more of the seat base, backrest, headrest, armrests, and floor space near the seat. Object classification includes identifying object classes based on sensor data, with these classes including children, adults, pets, and inanimate objects. Signal generation includes issuing notifications at the vehicle's user interface.

[0013] A seat sensor system includes: data processing hardware; and memory hardware in communication with the data processing hardware, the memory hardware storing instructions that, when executed on the data processing hardware, cause the data processing hardware to perform operations, the operations including: receiving sensor data associated with a vehicle seat from a plurality of seat sensors, the plurality of seat sensors being disposed in at least one of a seat base, a backrest, and a headrest; determining that the sensor data exceeds an activation threshold for a seat sensor application; activating the seat sensor application based on the sensor data exceeding the activation threshold; aggregating the sensor data via the seat sensor application; classifying objects at the seat into object classes based on the aggregated sensor data, the object classes including one of an inanimate class and a animate class; and generating a signal corresponding to the object via the seat sensor application based on the object class. The sensor data includes pressure data, spatial data, and temporal data. The object classification includes analyzing sensor data at a first time point and a second time point, and comparing the sensor data at the first time point with the sensor data at the second time point. The comparison of the sensor data at the first time point with the sensor data at the second time point includes identifying changes in the object's position. The change in location is indicated by detecting sensor data at a first time point via a first seat sensor and sensor data at a second time point via a second seat sensor. The multiple seat sensors are linear piezoelectric sensors, each configured to independently capture sensor data. The signal generation includes issuing a notification at the vehicle's user interface.

[0014] A seat sensor system for a vehicle includes: data processing hardware; and memory hardware in communication with the data processing hardware, the memory hardware storing instructions that, when executed on the data processing hardware, cause the data processing hardware to perform operations, the operations including: receiving sensor data associated with a vehicle seat from a plurality of seat sensors, the plurality of seat sensors being disposed in at least one of a seat base, a backrest, and a headrest, each seat sensor being configured to independently capture sensor data; determining that the sensor data exceeds an activation threshold for a seat sensor application; activating the seat sensor application based on the sensor data exceeding the activation threshold; aggregating the sensor data via the seat sensor application; analyzing the sensor data at a first time point and a second time point; comparing the sensor data at the first time point with the sensor data at the second time point; classifying objects at the seat into object classes based on the aggregated sensor data, the object classes including one of an inanimate class and a animate class; and generating a signal corresponding to the object via the seat sensor application based on the object class. The sensor data includes pressure data, spatial data, and temporal data. The comparison of the sensor data at the first time point with the sensor data at the second time point includes identifying a change in the position of the object. The change in the marker position includes detecting sensor data at a first time point via the first seat sensor and detecting sensor data at a second time point via the second seat sensor. Attached Figure Description

[0015] The accompanying drawings described herein are for illustrative purposes only and are not intended to limit the scope of this disclosure.

[0016] Figure 1 This is a perspective view of a vehicle equipped with a seat sensor system according to this disclosure;

[0017] Figure 2 It is a partial perspective view of the interior of a vehicle having an adult occupant and a child occupant in a seat equipped with a seat sensor system according to the present disclosure;

[0018] Figure 3 This is an exemplary block diagram of a seat sensor system according to the present disclosure;

[0019] Figure 4 It is a perspective view of a seat including the seat sensor system according to this disclosure;

[0020] Figure 5 This is a perspective view of a seat sensor system according to this disclosure;

[0021] Figure 6 This is an example flowchart of the operation of the seat sensor system according to this disclosure; and

[0022] Figure 7This is an exemplary method of operating the seat sensor system according to the present disclosure.

[0023] Throughout the accompanying drawings, corresponding reference numerals indicate the relevant parts. Detailed Implementation

[0024] The example configuration will now be described more fully with reference to the accompanying drawings. The example configuration is provided so that this disclosure will be thorough and will fully communicate the scope of this disclosure to those skilled in the art. Specific details, such as examples of specific components, devices, and methods, are set forth to provide a thorough understanding of the configuration of this disclosure. It will be apparent to those skilled in the art that specific details are not required, the example configuration may be embodied in many different forms, and the specific details and example configuration should not be construed as limiting the scope of this disclosure.

[0025] The terminology used herein is for the purpose of describing specific exemplary configurations only and is not intended to be limiting. As used herein, the singular articles “a,” “an,” and “the” may also be intended to include plural forms unless the context clearly indicates otherwise. The terms “comprising,” “including,” “containing,” and “having” are inclusive and therefore specify the presence of features, steps, operations, elements, and / or components, but do not preclude the presence or addition of one or more other features, steps, operations, elements, components, and / or groups thereof. The method steps, processes, and operations described herein are not to be construed as requiring them to be performed in the specific order discussed or illustrated, unless specifically identified as such. Additional or alternative steps may be employed.

[0026] When an element or layer is described as being “on”, “joined to”, “connected to”, “attached to”, or “coupled to” another element or layer, it can be directly on, joined to, connected to, attached to, or coupled to another element or layer, or there may be intermediate elements or layers present. Conversely, when an element is described as being “directly on”, “directly joined to”, “directly connected to”, “directly attached to”, or “directly coupled to” another element or layer, there may be no intermediate elements or layers present. Other terms used to describe relationships between elements should be interpreted in the same way (e.g., “between” versus “directly between”, “adjacent” versus “directly adjacent”, etc.). As used herein, the term “and / or” includes any and all combinations of one or more associated listed items.

[0027] The terms “first,” “second,” “third,” etc., may be used herein to describe various elements, components, regions, layers, and / or sections. These elements, components, regions, layers, and / or sections should not be limited by these terms. These terms may be used only to distinguish one element, component, region, layer, or section from another. Terms such as “first,” “second,” and other numerical terms do not imply order or sequence unless the context clearly indicates otherwise. Therefore, without departing from the teachings of the example configuration, the first element, component, region, layer, or section discussed below may be referred to as the second element, component, region, layer, or section.

[0028] In this application, including the following definitions, the term "module" may be replaced by the term "circuit". The term "module" may refer to, be a part of, or include the following: application-specific integrated circuit (ASIC); digital, analog, or mixed-signal analog / digital discrete circuit; digital, analog, or mixed-signal analog / digital integrated circuit; combinational logic circuit; field-programmable gate array (FPGA); processor (shared, dedicated, or grouped) for executing code; memory (shared, dedicated, or grouped) for storing code executed by the processor; other suitable hardware components that provide the described functionality; or combinations of some or all of the above, such as in a system-on-a-chip.

[0029] The term "code" as used above can include software, firmware, and / or microcode, and can refer to programs, routines, functions, classes, and / or objects. The term "shared processor" includes a single processor that executes some or all of the code from multiple modules. The term "group processor" includes processors that, in combination with additional processors, execute some or all of the code from one or more modules. The term "shared memory" includes a single memory that stores some or all of the code from multiple modules. The term "group memory" includes memory that, in combination with additional memory, stores some or all of the code from one or more modules. The term "memory" can be a subset of the term "computer-readable medium." The term "computer-readable medium" does not include transient electrical and electromagnetic signals propagating through the medium, and therefore can be considered tangible and non-transitory memory. Non-limiting examples of non-transitory memory include tangible computer-readable media, including non-volatile memory, magnetic storage devices, and optical storage devices.

[0030] The apparatus and methods described in this application can be implemented, in whole or in part, by one or more computer programs executed by one or more processors. The computer program includes processor-executable instructions stored on at least one non-transitory tangible computer-readable medium. The computer program may also include and / or depend on stored data.

[0031] A software application (i.e., a software resource) can refer to computer software that enables a computing device to perform tasks. In some examples, a software application may be referred to as an "application," "app," or "program." Example applications include, but are not limited to, system diagnostic applications, system management applications, system maintenance applications, word processing applications, spreadsheet applications, messaging applications, media streaming applications, social networking applications, and gaming applications.

[0032] Non-transitory memory can be a physical device used to store programs (e.g., instruction sequences) or data (e.g., program state information) on a temporary or permanent basis for use by a computing device. Non-transitory memory can be volatile and / or non-volatile addressable semiconductor memory. Examples of non-volatile memory include, but are not limited to, flash memory and read-only memory (ROM) / programmable read-only memory (PROM) / erasable programmable read-only memory (EPROM) / electrically erasable programmable read-only memory (EEPROM) (e.g., commonly used for firmware, such as boot programs). Examples of volatile memory include, but are not limited to, random access memory (RAM), dynamic random access memory (DRAM), static random access memory (SRAM), phase-change memory (PCM), and disks or tapes.

[0033] These computer programs (also referred to as programs, software, software applications, or code) include machine instructions for a programmable processor and can be implemented using high-level procedural and / or object-oriented programming languages ​​and / or assembly / machine languages. As used herein, the terms “machine-readable medium” and “computer-readable medium” refer to any computer program product, non-transitory computer-readable medium, apparatus, and / or device (e.g., disk, optical disk, memory, programmable logic device (PLD)) used to provide machine instructions and / or data to a programmable processor, including machine-readable media that receive machine instructions as machine-readable signals. The term “machine-readable signal” refers to any signal used to provide machine instructions and / or data to a programmable processor.

[0034] Various implementations of the systems and techniques described herein can be implemented in digital electronic and / or optical circuit systems, integrated circuit systems, specially designed ASICs (Application-Specific Integrated Circuits), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include implementations in one or more computer programs executable and / or interpretable on a programmable system, which includes at least one programmable processor, at least one input device, and at least one output device. The at least one programmable processor can be dedicated or general-purpose and is coupled to receive data and instructions from and transfer data and instructions to the storage system.

[0035] The processes and logic described in this specification can be executed by one or more programmable processors, also known as data processing hardware, which execute one or more computer programs to perform functions by manipulating input data and generating output. The processes and logic can also be executed by a special-purpose logic circuit system, such as an FPGA (Field-Programmable Gate Array) or an ASIC (Application-Specific Integrated Circuit). Processors suitable for executing computer programs include, for example, both general-purpose and special-purpose microprocessors, and any one or more processors of any kind of digital computer. Typically, the processor receives instructions and data from read-only memory or random access memory, or both. The basic elements of a computer are a processor for executing instructions and one or more memory devices for storing instructions and data. Typically, a computer will also include one or more mass storage devices for storing data, or operatively coupled to receive data from or transfer data to one or more mass storage devices, such as a magnetic disk, magneto-optical disk, or optical disk. However, a computer does not need to have such devices. Computer-readable media suitable for storing computer program instructions and data include all forms of non-volatile memory, media, and memory devices, including, for example, semiconductor memory devices such as EPROM, EEPROM, and flash memory devices; magnetic disks such as internal hard disks or removable disks; magneto-optical disks; and CD-ROM and DVD-ROM disks. The processor and memory may be supplemented or incorporated therein by a dedicated logic circuit system.

[0036] To provide interaction with the user, one or more aspects of this disclosure can be implemented on a computer having a display device for displaying information to the user, such as a CRT (cathode ray tube), LCD (liquid crystal display) monitor, or touchscreen, and optional keyboard and pointing devices, such as a mouse or trackball, through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback, such as visual feedback, auditory feedback, or tactile feedback; and input from the user can be received in any form, including audible, voice, or tactile input. Furthermore, the computer can interact with the user by sending documents to and receiving documents from the device used by the user; for example, by sending a web page to a web browser on the user's client device in response to a request received from a web browser.

[0037] refer to Figure 1-5A seat sensor system 10 is configured for use in a vehicle 100 and is integrally formed in one or more seats 102 of the vehicle 100. The seats 102 of the vehicle 100 include a seat base 104, a backrest 106, an armrest 107, and a headrest 108. The seat sensor system 10 includes a plurality of seat sensors 12 disposed in at least one of the seat base 104, backrest 106, armrest 107, and headrest 108. The seat sensors 12 may also be disposed in a floor space 109 adjacent to the seat 102. For example, the seat sensors 12 disposed in the floor space 109 can detect pressure exerted from the placement of an object 200, such as a bag or box, and / or pressure exerted by the occupant's feet. As described in more detail below, the plurality of seat sensors 12 at each location of the seat 102 are configured to detect an object 200 on, near, or adjacent to the seat 102. The seat sensor 12 assists the seat sensor system 10 in classifying the object 200 along the changing position of the seat 102 (i.e., in the headrest 108, backrest 106, and / or seat base 104), as described below.

[0038] The seat sensor system 10 includes a controller 14 that includes data processing hardware 16. The data processing hardware 16 is configured to execute a seat sensor application 18. The seat sensor application 18 receives sensor data 20 from each of a plurality of seat sensors 12. The seat sensor system 10 also includes memory hardware 22 in communication with the data processing hardware 16. The memory hardware 22 stores instructions that, when executed on the data processing hardware 16, cause the data processing hardware 16 to perform an operation. The memory hardware 22 also stores an activation threshold 24 associated with the seat sensor application 18. The seat sensor application 18 can typically remain inactive until the sensor data 20 exceeds the activation threshold 24 stored in the memory hardware 22.

[0039] For example, sensor data 20 is received at data processing hardware 16, and data processing hardware 16 can determine whether sensor data 20 exceeds an activation threshold 24 for seat sensor application 18. If sensor data 20 does not exceed the activation threshold 24, data processing hardware 16 continues to monitor sensor data 20 and keeps seat sensor application 18 inactive. When sensor data 20 exceeds the activation threshold 24, seat sensor application 18 is activated by data processing hardware 16. Sensor data 20 is captured by each individual seat sensor 12 and communicates with data processing hardware 16 of controller 14.

[0040] Further reference Figure 1-5Due to the linear configuration of the seat sensors 12 in seat 102, the seat sensors 12 are configured to capture individual measurements (i.e., individual sensor data 20). The seat sensors 12 may be linear piezoelectric sensors 12, each of which independently captures sensor data 20. Each seat sensor 12 is individually sampled for sensor data 20 and associated with a physical location on seat 102. For example, an individual seat sensor 12 may be associated with one of the seat base 104, backrest 106, and headrest 108, and also with a specific location at one of the seat base 104, backrest 106, and headrest 108. Each of the seat sensors 12 provides sensor data 20 associated with a discrete location along seat 102, allowing the seat sensor application 18 to utilize the sensor data 20 to classify detected objects 200.

[0041] Sensor data 20 may include, but is not limited to, pressure data 20a, spatial data 20b, and / or time data 20c. Each aspect of sensor data 20 can be used to evaluate and classify objects based on each aspect of sensor data 20 (i.e., pressure data 20a, spatial data 20b, and time data 20c). For example, a first seat sensor 12a may be disposed on a first side 110 of seat 102 in seat base 104, and a second seat sensor 12b may be disposed on a second side 112 of seat 102 in backrest 106. Sensor data 20 captured by the first seat sensor 12a and sensor data 20 captured by the second seat sensor 12b communicate with data processing hardware 16. If sensor data 20 exceeds an activation threshold 24, seat sensor application 18 is activated. In response, seat sensor application 18 may aggregate sensor data 20 from the first seat sensor 12a with sensor data 20 from the second seat sensor 12b.

[0042] For example, the seat sensor application 18 can perform an aggregation function 30, during which the seat sensor application 18 receives and records sensor data 20 over a predetermined time period 32. Once aggregated, the seat sensor application 18 performs a classification function 34 on the aggregated sensor data 20. The classification function 34 is configured to classify detected objects 200 into object classes 36. Object classes 36 include living objects 36a and inanimate objects 36b. Living objects 36a include live objects such as adults, children, and / or pets. For example, living objects 36a may include adult 36a1, child 36a2, and pet 36a3. Inanimate objects 36b include non-living objects such as packages, bags, boxes, and / or any other non-living objects. Object classes 36 may be stored in memory hardware 22 and communicate with the seat sensor application 18 during the execution of the classification function 34.

[0043] Seat sensor application 18 performs classification function 34 based on sensor data 20 to classify object 200 at seat 102 into one of object classes 36. For example, seat sensor application 18 can identify object 200 as a living object and initially classify object 200 in living class 36a. Living class 36 has multiple subclasses, allowing seat sensor application 18 to continue aggregating and classifying sensor data 20 until one of the subclasses (i.e., adult 36a1, child 36a2, or pet 36a3) is identified. For example, based on the pressure applied to seat 102, pressure data 20a of sensor data 20 can indicate that object 200 is an adult, and classification function 34 can classify object 200 into adult 36a1 of living class 36a. In other instances, pressure data 20a can indicate that object 200 is not an adult, and classification function 34 can use spatial data 20b and temporal data 20c to distinguish between child class 36a2 and pet class 36a3.

[0044] Spatial data 20b can indicate the general distribution of objects 200 along seat 102, and temporal data 20c indicates how the distribution of objects 200 changes over time. Classification function 34 can be programmed to identify pets (i.e., animals such as cats or dogs) that may move or change position on seat 102 more frequently than children. However, classification function 34 can utilize a larger aggregation of data points from sensor data 20 to distinguish between children class 36a2 and pet class 36a3.

[0045] For example, seat sensor application 18 can analyze sensor data 20 at a first time point 38a and a second time point 38b, and compare the sensor data 20 at the first time point 38a with the sensor data 20 at the second time point 38b. The first time point 38a and the second time point 38b can span a predetermined time frame, allowing a first set of sensor data 20 to be captured during the first time point 38a and a second set of sensor data 20 to be captured during the second time point 38b. The extended duration provides seat sensor application 18 with a range of data points corresponding to the positioning of object 200 on seat 102. While classification function 34 can utilize the comparison of sensor data 20 at the corresponding time points 38a, 38b to classify object 200 into one of child class 36a2 and pet class 36a3, classification function 34 can also use the comparison for other classification operations (i.e., classifying object 200 into other living class 36a and / or non-living class 36b).

[0046] Depending on the object 200, the object 200 may be displaced or otherwise moved relative to the seat 102. The seat sensors 12 can detect changes in the position of the object 200 because each seat sensor 12 captures sensor data 20 individually and independently. The classification function 34 can result in identifying a change 40 in the position of the object 200 relative to the seat 102 based on a comparison of sensor data 20 at a first time point 38a with sensor data 20 at a second time point 38b. For example, the first seat sensor 12a can detect sensor data 20 at the first time point 38a, and the second seat sensor 12b can detect sensor data 20 at the second time point 38b.

[0047] The seat sensor application 18 is configured to continuously perform a cyclic aggregation function 30 and a classification function 34 for a preset cycle duration 42. The preset cycle duration 42 is configured for the seat sensor application 18 to verify the determined object class 36 using sensor data 20 continuously collected from each seat sensor 12. By performing the aggregation function 30 and the classification function 34 for the preset cycle duration 42, the seat sensor application 18 can minimize the possibility of misclassification of the object 200 by aggregating and analyzing the sensor data 20 collected at multiple time points.

[0048] Further reference Figure 1-5 The seat sensor application 18 is used in combination with the seat sensor system 10 and the monitoring application 50. The monitoring application 50 can be used to monitor various aspects of objects 200 within the vehicle 100. For example, the monitoring application 50 may include a seat belt monitor 52 configured to detect whether the seat belt 120 of the seat 102 is engaged when object 200 is present. The seat belt monitor 52 can be triggered based on object class 36 to monitor the seat belt status 122 of the seat belt 120. For example, the seat sensor application 18 can generate a signal 44 corresponding to object 200 based on object class 36. The signal 44 communicates with the monitoring application 50, and the monitoring application 50 can issue a notification 54 at the user interface 124 of the vehicle 100.

[0049] If object 200 is classified into one of the living classes 36a, the monitoring application 50 can trigger the seatbelt monitor 52 to determine the status of the seatbelt 120. In some instances, the seatbelt monitor 52 can be configured to exclude the pet class 36a3. However, the user of the seat sensor system 10 can customize the monitoring application 50 to reflect user preferences for seatbelts 120 activated in the pet class 36a3. If object 200 is classified into the living class 36a and the seatbelt 120 is detected as disengaged, the monitoring application 50 can issue a notification 54 to warn the occupant to engage the seatbelt 120.

[0050] refer to Figure 6 The diagram illustrates an example flowchart for a seat sensor system 10, where a controller 14 receives sensor data 20 from multiple seat sensors 12 at 600. At 602, data processing hardware 16 determines whether the sensor data 20 exceeds an activation threshold 24. If not, at 600, the data processing hardware 16 continues to monitor the sensor data 20 received at the controller 14. If the sensor data 20 exceeds the activation threshold 24, at 604, the data processing hardware 16 activates a seat sensor application 18. In response, the seat sensor application 18 aggregates the sensor data 20 at 606 and classifies objects 200 based on the aggregated sensor data 20 at 608. The seat sensor application 18 performs a preset loop duration 42 of aggregation function 30 and classification function 34 at 610 until the seat sensor application 18 determines an object class 36 at 612.

[0051] refer to Figure 7 The illustration shows an exemplary method 700 for operating a seat sensor system 10. At 702, a controller 14 receives sensor data 20 associated with the seat 102 of the vehicle 100 from a plurality of seat sensors 12. The plurality of seat sensors 12 are disposed in at least one of the seat base 104, backrest 106, and headrest 108 of the seat 102, and each seat sensor 12 is configured to independently capture sensor data 20. At 704, data processing hardware 16 determines that the sensor data 20 exceeds an activation threshold 24 of a seat sensor application 18. At 706, the data processing hardware 16 activates the seat sensor application 18 based on the sensor data 20 exceeding the activation threshold 24. At 708, the seat sensor application 18 aggregates the sensor data 20, and at 710, analyzes the sensor data 20 at a first time point 38a and a second time point 38b.

[0052] At 712, the seat sensor application 18 compares the sensor data 20 at a first time point 38a with the sensor data 20 at a second time point 38b, and at 714, based on the aggregated sensor data 20, classifies the object 200 at the seat 102 into object class 36. Object class 36 includes one of inanimate class 36b and animate class 36a. At 716, the controller 14 generates a signal 44 corresponding to the object 200 via the seat sensor application 18 based on the object class 36.

[0053] Refer again Figure 1-7 The seat sensor system 10 advantageously assists in identifying and classifying objects 200 located on the seat 102 of the vehicle 100. Classification function 34 can distinguish between inanimate and animate objects 200, allowing object class 36 to be used to determine whether a notification 54 should be issued. Notification 54 may relate to the seatbelt status 122 and can be projected via the user interface 124 of the vehicle 100. Notification 54 may also relate to other operational states associated with the seat 102, including but not limited to the position of object 200 on the seat 102. Therefore, the seat sensor system 10 can also be used to monitor the safe positioning of object 200 on the seat 102.

[0054] The independent detection of sensor data 20 by each individual seat sensor 12 advantageously provides the seat sensor application 18 with precise information about the object 200 on the seat 102. The seat sensor application 18 can utilize the specific sensor data 20 from each seat sensor 12 to compare the detection of the object 200 at a precise location on the seat 102. For example, small deviations in the sensor data 20 from adjacent seat sensors 12 can inform the classification of the object 200 and provide improved accuracy for the classification function 34. Furthermore, the repeated looping assistance of performing the aggregation function 30 and the classification function 34 improves the classification of the object 200 based on repeated analysis of the sensor data 20 received at the seat sensor application 18.

[0055] Several implementations have been described. However, it will be understood that various modifications can be made without departing from the spirit and scope of this disclosure. Therefore, other implementations are also within the scope of the following claims.

[0056] The foregoing description has been provided for illustrative and descriptive purposes. It is not intended to be exhaustive or limiting of this disclosure. Individual elements or features of a particular configuration are generally not limited to that particular configuration, but where applicable, they are interchangeable and can be used in the chosen configuration even if not specifically shown or described. This can also be varied in many ways. Such variations should not be considered a departure from this disclosure, and all such modifications are intended to be included within the scope of this disclosure.

Claims

1. A computer-implemented method, when executed by data processing hardware, causes the data processing hardware to perform operations, said operations including: Receive sensor data associated with the vehicle's seats from multiple seat sensors; Determine if sensor data exceeds the activation threshold for seat sensor applications; The seat sensor application is activated based on sensor data exceeding the activation threshold. Aggregate sensor data via seat sensor application; Based on aggregated sensor data, objects at the seats are classified into object classes; and Signals corresponding to objects are generated by the seat sensor application based on the object class.

2. The method of claim 1, wherein, Sensor data includes pressure data, spatial data, and temporal data.

3. The method of claim 2, wherein, The object classification includes analyzing sensor data at a first time point and a second time point, and comparing the sensor data at the first time point with the sensor data at the second time point.

4. The method of claim 3, wherein, Compare sensor data at the first time point with sensor data at the second time point, including identifying changes in the location of the object.

5. The method of claim 4, wherein, The change in the marked position includes detecting sensor data at a first time point via a first seat sensor and detecting sensor data at a second time point via a second seat sensor.

6. The method according to claim 1, wherein, The multiple seat sensors are linear piezoelectric sensors, and each seat sensor is configured to independently capture sensor data.

7. The method according to claim 1, wherein, The seat sensors among the multiple seat sensors are located in one or more of the seat base, backrest, headrest, armrests, and floor space near the seat.

8. The method according to claim 1, wherein, The object classification includes identifying object classes based on sensor data, with object classes including one of the following: children, adults, pets, and inanimate objects.

9. The method according to claim 1, wherein, The generated signals include issuing notifications at the vehicle's user interface.

10. A seat sensor system, comprising: Data processing hardware; and Memory hardware that communicates with data processing hardware, the memory hardware storing instructions that, when executed on the data processing hardware, cause the data processing hardware to perform operations, the operations including: Receive sensor data associated with the vehicle’s seats from multiple seat sensors, which are located in at least one of the seat base, backrest and headrest of the seat; Determine if sensor data exceeds the activation threshold for seat sensor applications; The seat sensor application is activated based on sensor data exceeding the activation threshold. Aggregate sensor data via seat sensor application; Based on aggregated sensor data, objects at the seats are classified into object classes, which include one of two categories: inanimate or animate. Signals corresponding to objects are generated by the seat sensor application based on the object class.

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