Method and system for tuning a vibrating seat

Abstract

Methods and systems for tuning a vibrating seat are described, including generating vehicle customer relationship management (VCRM) data based on biometric information of an occupant and music preference information of the occupant, generating a vibrating seat tuning parameter based on the generated VCRM data, adjusting and tuning an envelope function based on the vibrating seat tuning parameter and real-time received biometric information of the occupant and music preference information of the occupant, and operating the vibrating seat based on the tuned envelope function.

Description

Technical Field

[0001] This embodiment relates to a vibrating seat, and more specifically, to a vehicle to which a vibrating seat tuning method is applied. Background Technology

[0002] As living standards improve, people are spending more time on personal leisure activities and enjoying greater convenience, leading to a rapid shift in car culture. In recent years, the time spent in vehicles has increased, transforming them from simple means of transportation into a cultural medium serving as personal living spaces.

[0003] As the time spent in vehicles increases, the physiological burden on vehicle occupants also increases. In particular, vibrations transmitted from the road surface are transmitted to the human body through the vehicle body. Vibrations generated when driving in areas with poor road conditions can cause damage to the body or nervous system, and may therefore lead to symptoms such as fatigue, headaches, back pain, and decreased vision.

[0004] Seat suspension is designed to address these issues by supporting occupants while maintaining maximum comfort in the seat. This type of seat suspension is designed to minimize and absorb shocks or swaying generated when driving on uneven roads, thereby improving occupant comfort.

[0005] Recently, there has been a growing demand for a more optimized music playback environment while in a vehicle. One example of this demand is a vibrating seat. A vibrating seat is a device that improves the satisfaction of vehicle occupants during audio playback by installing a vibration generating device in the vehicle seat and generating vibrations corresponding to the currently playing audio.

[0006] However, typical vibrating seats do not reflect the occupant's physiological condition or musical preferences, and therefore users may experience a sense of disharmony. Summary of the Invention

[0007] To address the aforementioned problems, embodiments of this disclosure aim to provide a method and system for tuning a resonant vibrating seat that provides customized vibration seat operation for the occupant by reflecting the occupant's biometric information and music preferences.

[0008] The technical objectives achieved by this disclosure are not limited to those described above, and other technical objectives not mentioned will be clearly understood by those skilled in the art from the following description.

[0009] To achieve the above-mentioned technical objectives, according to one embodiment of the present disclosure, a method for tuning a vibrating seat is provided, the method comprising: generating vehicle customer relationship management (VCRM) data based on occupant biometric information and occupant music preference information; generating vibrating seat tuning parameters based on the generated VCRM data; adjusting and tuning an envelope function based on the vibrating seat tuning parameters and occupant biometric information and occupant music preference information received in real time; and operating the vibrating seat based on the tuned envelope function.

[0010] According to the implementation method, generating VCRM data may include: determining physiological status based on occupant biometric information; generating physiological status parameters based on the determined physiological status; determining psychological preferences based on occupant music preference information; generating psychological preference parameters based on the determined psychological preferences; and generating vibration seat tuning parameters by matching the physiological status parameters with the psychological preference parameters.

[0011] According to an implementation, determining a physiological condition based on occupant biometric information may include: determining a tension level based on facial expression by comparing the occupant's facial image information with determination logic based on facial expression; determining a tension level based on pulse and / or heart rate by comparing the occupant's pulse and heart rate information with determination logic based on pulse and / or heart rate; and determining the physiological condition by performing a weighted summation of the tension level based on facial expression and the tension level based on pulse and / or heart rate.

[0012] According to the implementation method, generating physiological condition parameters based on the determined physiological condition may include: generating first bandpass filter parameters; generating first compressor parameters; and generating first output gain parameters.

[0013] According to the implementation method, determining psychological preferences based on occupant music preference information may include: collecting new playback information accumulated by occupants in the vehicle; collecting previously stored playback information by occupants; and determining psychological preferences by comparing the new playback information and the previously played playback information with music preference determination logic.

[0014] According to the implementation method, generating psychological preference parameters based on the determined psychological preferences may include: generating second bandpass filter parameters; generating second compressor parameters; and generating second output gain parameters.

[0015] According to an implementation, generating vibration seat tuning parameters by matching physiological condition parameters with psychological preference parameters may include: generating a third bandpass filter parameter by performing a weighted summation on a first bandpass filter parameter and a second bandpass filter parameter; generating a third compressor parameter by performing a weighted summation on a first compressor parameter and a second compressor parameter; and generating a third output gain parameter by performing a weighted summation on a first output gain parameter and a second output gain parameter.

[0016] According to the implementation, adjusting and tuning the envelope function may include: receiving audio output in real time inside the vehicle; applying a bandpass filter configured with third bandpass filter parameters to the received audio; applying a compressor configured with third compressor parameters to the audio to which the bandpass filter was applied; extracting the envelope by applying a low-pass filter to the audio to which the compressor was applied; generating a sine signal; and generating a vibrating seat signal by performing convolution on the extracted envelope and the generated sine signal.

[0017] According to the implementation method, adjusting and tuning the envelope function may further include: receiving vehicle driving data including driving mode information and speed information; adjusting the vibration seat tuning parameters based on the driving mode information; and adjusting the output gain weight based on the speed information.

[0018] According to one embodiment of this disclosure, safety and ride comfort can be improved by operating a vibration seat customized for the occupants during driving.

[0019] The effects that can be obtained from this disclosure are not limited to those described above, and other effects not mentioned will be clearly understood by those skilled in the art from the following description. Attached Figure Description

[0020] Figure 1 It is an overall block diagram of an autonomous driving control system applicable to any embodiment of the autonomous driving device according to this disclosure.

[0021] Figure 2 This is a diagram illustrating an example of an autonomous driving device applied to a vehicle according to any embodiment of the present disclosure.

[0022] Figure 3 , Figure 4 , Figure 5 , Figure 6 , Figure 7 and Figure 8 This is a diagram illustrating a vibration seat tuning system according to an embodiment of the present disclosure.

[0023] Figure 9 This is a diagram illustrating a method for generating a vibrating seat signal according to an embodiment of the present disclosure.

[0024] Figure 10 and Figure 11 This is a flowchart describing a method for tuning a resonant seat according to an embodiment of the present disclosure. Detailed Implementation

[0025] In the following, embodiments of the present disclosure will be described in detail with reference to the accompanying drawings, enabling those skilled in the art to readily practice the disclosure. However, the present disclosure may be implemented in various different forms and is not limited to the embodiments described herein. Furthermore, in the drawings, components unrelated to the description will be omitted for clarity of explanation, and identical or similar components will be denoted by the same reference numerals throughout the description.

[0026] Throughout the description, when a component is referred to as "including" an element, it does not mean that the component excludes other elements, but may mean that the component includes other elements, unless otherwise stated.

[0027] Figure 1 It is an overall block diagram of an autonomous driving control system applicable to any embodiment of the autonomous driving device according to this disclosure. Figure 2 This is a diagram illustrating an example of an autonomous driving device applied to a vehicle according to any embodiment of the present disclosure.

[0028] First, refer to Figure 1 and Figure 2 The structure and function of an autonomous driving control system (e.g., an autonomous vehicle) applicable to an autonomous driving device according to this embodiment are described.

[0029] like Figure 1 As shown, an autonomous vehicle 1000 can be implemented based on an autonomous driving integrated controller 600. The autonomous driving integrated controller 600 sends and receives the data necessary for the autonomous driving control of the vehicle through a driving information input interface 101, a driving information input interface 201, an occupant output interface 301, and a vehicle control output interface 401. However, the autonomous driving integrated controller 600 may also be referred to herein as a controller, a processor, or simply a controller.

[0030] The autonomous driving integrated controller 600 can obtain driving information through the driving information input interface 101, in either the vehicle's autonomous driving mode or manual driving mode, based on the occupant's operation of the user input unit 100. For example... Figure 1 As shown, the user input unit 100 may include a driving mode switch 110 and a control panel 120 (e.g., a navigation terminal installed in the vehicle or a smartphone or tablet held by the occupant). Therefore, driving information may include the vehicle's driving mode information and navigation information.

[0031] For example, the vehicle's driving mode (i.e., automatic driving mode / manual driving mode or sport mode / economy mode / safety mode / normal mode) determined by the occupant's operation of the driving mode switch 110 can be sent as driving information to the automatic driving integrated controller 600 through the driving information input interface 101.

[0032] In addition, navigation information (such as the destination entered by the occupant through the control panel 120 and the route to the destination (e.g., the shortest or preferred route selected by the occupant among the candidate routes to the destination)) can be sent as driving information to the autonomous driving integrated controller 600 through the driving information input interface 101.

[0033] The control panel 120 can be implemented as a touchscreen panel providing a user interface (UI) through which occupants input or modify information for the vehicle's autonomous driving control. In this case, the driving mode switch 110 can be implemented as a touch button on the control panel 120.

[0034] Furthermore, the autonomous driving integrated controller 600 can obtain driving information indicating the vehicle's driving status through the driving information input interface 201. Driving information may include the steering angle formed when the occupant operates the steering wheel, the accelerator pedal travel or brake pedal travel formed when the occupant depresses the accelerator or brake pedal, and various types of information indicating the vehicle's driving status and behavior, such as vehicle speed, acceleration, yaw, pitch, and roll. Figure 1 As shown, driving information can be detected by the driving information detection unit 200, which includes a steering angle sensor 210, an accelerator pedal position sensor (APS) / pedal driving sensor (PTS) 220, a vehicle speed sensor 230, an acceleration sensor 240, and a yaw / pitch / roll sensor 250.

[0035] In addition, vehicle driving information may include vehicle location information. The vehicle's location information can be obtained through a Global Positioning System (GPS) receiver 260 applied to the vehicle. This driving information can be sent to the automated driving integrated controller 600 via the driving information input interface 201, and can be used to control the vehicle's driving in either automated driving mode or manual driving mode.

[0036] The autonomous driving integrated controller 600 can send driving status information provided to the occupants to the output unit 300 via the occupant output interface 301, whether the vehicle is in autonomous driving mode or manual driving mode. That is, the autonomous driving integrated controller 600 sends the vehicle's driving status information to the output unit 300, allowing the occupants to check the vehicle's autonomous driving or manual driving status based on the driving status information output by the output unit 300. The driving status information may include various types of information indicating the vehicle's driving status, such as the vehicle's current driving mode, transmission range, and speed.

[0037] If it is determined that it is necessary to warn the driver in either autonomous or manual driving mode, based on the above driving status information, the autonomous driving integrated controller 600 sends the warning information to the output unit 300 via the occupant output interface 301, enabling the output unit 300 to output a warning to the driver. To provide an audible and visual output of this driving status information and warning information, the output unit 300 may include, for example: Figure 1 The speaker 310 and the display 320 are shown. In this case, the display 320 can be implemented as the same device as the control panel 120, or it can be implemented as a separate device from the control panel 120.

[0038] Furthermore, the autonomous driving integrated controller 600 can send control information for vehicle driving control to the lower control system 400 applied to the vehicle via the vehicle control output interface 401, whether the vehicle is in autonomous driving mode or manual driving mode. For example... Figure 1 As shown, the lower control system 400 for vehicle driving control may include an engine control system 410, a braking control system 420, and a steering control system 430. The automated driving integrated controller 600 can send engine control information, braking control information, and steering control information as control information to the corresponding lower control systems 410, 420, and 430 via the vehicle control output interface 401. Therefore, the engine control system 410 can control the vehicle's speed and acceleration by increasing or decreasing the fuel supplied to the engine. The braking control system 420 can control the vehicle's braking by controlling the vehicle's braking force. The steering control system 430 can control the vehicle's steering through a steering mechanism applied to the vehicle (e.g., an electronic power steering (MDPS) system).

[0039] As described above, the autonomous driving integrated controller 600 according to this embodiment can obtain driving information based on the driver's operation and driving information indicating the vehicle's driving state through the driving information input interface 101 and the driving information input interface 201, respectively, and sends driving state information and warning information generated based on the autonomous driving algorithm to the output unit 300 through the occupant output interface 301. Furthermore, the autonomous driving integrated controller 600 can send control information generated based on the autonomous driving algorithm to the lower control system 400 through the vehicle control output interface 401, thereby executing vehicle driving control.

[0040] To ensure stable autonomous driving, it is necessary to continuously monitor the vehicle's driving state by accurately measuring the driving environment and to control driving based on the measured environment. Therefore, such as... Figure 1 As shown, the autonomous driving device according to this embodiment may include a sensor unit 500, which is used to detect objects near the vehicle, such as nearby vehicles, pedestrians, roads or fixed facilities (e.g., traffic lights, road signs, traffic signs or building fences).

[0041] like Figure 1 As shown, the sensor unit 500 may include one or more of a LiDAR sensor 510, a radar sensor 520, and a camera sensor 530 to detect nearby objects outside the vehicle.

[0042] LiDAR sensor 510 can send laser signals to the periphery of the vehicle and detect nearby objects outside the vehicle by receiving signals reflected and returned from a corresponding object. LiDAR sensor 510 can detect nearby objects within a preset distance, a preset vertical field of view, and a preset horizontal field of view, which are predefined according to their specifications. LiDAR sensor 510 may include a front LiDAR sensor 511, a top LiDAR sensor 512, and a rear LiDAR sensor 513 respectively installed at the front, top, and rear of the vehicle; however, the installation location of each LiDAR sensor and the number of LiDAR sensors installed are not limited to specific embodiments. A threshold for determining the validity of the laser signal reflected and returned from the corresponding object can be pre-stored in the memory (not shown) of the autonomous driving integrated controller 600. The autonomous driving integrated controller 600 can determine the position (including distance to the corresponding object), speed, and direction of movement of the corresponding object by measuring the time taken for the laser signal sent by LiDAR sensor 510 to reflect and return from the corresponding object.

[0043] Radar sensor 520 can radiate electromagnetic waves around the vehicle and detect nearby objects outside the vehicle by receiving signals reflected and returned from a corresponding object. Radar sensor 520 can detect nearby objects within a preset distance, a preset vertical field of view, and a preset horizontal field of view, which are predefined according to their specifications. Radar sensor 520 may include a front radar sensor 521, a left radar sensor 522, a right radar sensor 523, and a rear radar sensor 524 respectively installed at the front, left, right, and rear of the vehicle; however, the installation location of each radar sensor and the number of radar sensors installed are not limited to specific embodiments. The autonomous driving integrated controller 600 can determine the position (including distance to the corresponding object), speed, and direction of movement of a corresponding object by analyzing the power of the electromagnetic waves transmitted and received by the radar sensor 520.

[0044] The camera sensor 530 can detect nearby objects outside the vehicle by photographing the periphery of the vehicle, and detect nearby objects within a preset distance, preset vertical field of view and preset horizontal field of view, which are predefined according to their specifications.

[0045] The camera sensor 530 may include a front camera sensor 531, a left camera sensor 532, a right camera sensor 533, and a rear camera sensor 534 respectively installed at the front, left, right, and rear of the vehicle. However, the installation location of each camera sensor and the number of camera sensors installed are not limited to specific embodiments. The autonomous driving integrated controller 600 can determine the position (including distance to the corresponding object), speed, and direction of movement of a corresponding object by applying predefined image processing to the images captured by the camera sensor 530.

[0046] Furthermore, an internal camera sensor 535 for capturing images of the vehicle's interior can be installed at a predetermined location within the vehicle (e.g., at a rearview mirror). The autonomous driving integrated controller 600 can monitor the occupant's behavior and status based on images captured by the internal camera sensor 535, and output guidance or warnings to the occupant via the output unit 300.

[0047] like Figure 1 As shown, the sensor unit 500 may include an ultrasonic sensor 540 in addition to the LiDAR sensor 510, radar sensor 520 and camera sensor 530, and also employs various types of sensors together with these sensors to detect objects near the vehicle.

[0048] Figure 2An example is shown in which, to aid understanding of this embodiment, a front LiDAR sensor 511 or a front radar sensor 521 is mounted at the front of the vehicle, a rear LiDAR sensor 513 or a rear radar sensor 524 is mounted at the rear of the vehicle, and a front camera sensor 531, a left-side camera sensor 532, a right-side camera sensor 533, and a rear camera sensor 534 are mounted at the front, left, right, and rear of the vehicle, respectively. However, as described above, the mounting location of each sensor and the number of sensors mounted are not limited to this specific embodiment.

[0049] In addition, to determine the status of occupants within the vehicle, the sensor unit 500 may also include biosensors for detecting the occupants' biological signals (e.g., heart rate, electrocardiogram, respiration, blood pressure, body temperature, electroencephalogram, photoplethysmography (or pulse wave), and blood glucose). Biosensors may include heart rate sensors, electrocardiogram sensors, respiration sensors, blood pressure sensors, body temperature sensors, electroencephalogram sensors, photoplethysmography sensors, and blood glucose sensors.

[0050] Finally, the sensor unit 500 further includes a microphone 550, which has an internal microphone 551 and an external microphone 552 for different purposes.

[0051] The internal microphone 551 can be used, for example, to analyze the voice of occupants inside the autonomous vehicle 1000 based on AI or to immediately respond to direct voice commands from occupants.

[0052] Conversely, the external microphone 552 can be used, for example, to analyze various sounds generated from the exterior of the autonomous vehicle 1000 using various analysis tools such as deep learning to respond appropriately for safe driving.

[0053] For reference, Figure 2 The symbols shown can perform operations similar to those in [the diagram]. Figure 1 The symbols shown in the figure have the same or similar functions. Figure 2 A more detailed description of the relationship with Figure 1 The relative positional relationships of each component (based on the interior of the autonomous vehicle 1000).

[0054] Figure 3 , Figure 4 , Figure 5 , Figure 6 , Figure 7 and Figure 8 This is a diagram illustrating a vibration seat tuning system according to an embodiment of the present disclosure.

[0055] refer to Figure 3 , Figure 4 and Figure 5The vibration seat tuning system can consist of a server 2000 and a vibration seat 3000.

[0056] The server 2000 according to embodiments of the present disclosure may include an occupant biometric information storage device 2100, an occupant music preference storage device 2200, a database 2300, a physiological condition parameter generator 2400, a psychological preference parameter generator 2500, and a parameter matcher 2600.

[0057] Server 2000 can be a vehicle customer relationship management (VCRM) server. Server 2000 can continuously receive vehicle input data.

[0058] For this purpose, vehicle input data can include image information received from an occupant information collector. The occupant information collector can collect occupant information from in-cabin sensors or cameras located within the vehicle.

[0059] Vehicle input data may include steering wheel / seatbelt information received from an occupant information collector. The occupant information collector may use sensors mounted on the steering wheel to collect pulse and / or heart rate, and sensors mounted on the seatbelts to collect heart rate, and combine the collected information to calculate pulse and / or heart rate.

[0060] Vehicle input data can include information obtained from the vehicle's audio playback recorder, which continuously accumulates each occupant's music choices and genre preferences after the vehicle is sold.

[0061] In addition, the server 2000 can receive external input data. This external input data may include streaming audio playback information, recordings, etc., obtained from the previous playback information database 2300.

[0062] According to an embodiment of the present disclosure, the occupant biometric information storage device 2100 can combine image information with pulse and / or heart rate based on vehicle input data, and store the combined information in a database 2300.

[0063] According to an embodiment of the present disclosure, the occupant music preference storage device 2200 can combine accumulated music selections with previous music selection information based on vehicle input data and external input data, and store the combined information in a database 2300.

[0064] The database 2300 according to embodiments of this disclosure may be a VCRM database. Specifically, the database 2300 may include a database storage device 2310, a physiological condition determiner 2320, and a psychological preference determiner 2330.

[0065] The database storage device 2310 according to embodiments of the present disclosure can store facial image information and pulse and / or heart rate measurement information received from the occupant biometric information storage device 2100. Furthermore, the database storage device 2310 can store pre-trained stress level determination logic.

[0066] Specifically, the tension level determination logic may include logic based on facial expressions and logic based on pulse and / or heart rate. Both the facial expression-based and pulse and / or heart rate-based logic can run within the VCRM server. Data can be continuously accumulated in the VCRM server for data learning and real-time updates. Therefore, after standardizing data from multiple general subjects to generate comparison groups, the standardized data can be compared with occupant information.

[0067] For example, the determination logic based on facial expressions could be a tension level determination logic pre-trained by deep learning based on changes in the facial expressions / facial temperature of occupants in an image.

[0068] For example, the determination logic based on pulse and / or heart rate could be a tension level determination logic pre-trained based on the occupant's pulse and heart rate (BPM).

[0069] Furthermore, the database storage device 2310 according to embodiments of this disclosure can store newly accumulated playback information collected from the vehicle audio playback recorder. The database storage device 2310 can also store previously accumulated playback information received from the previous playback information DB. Additionally, the database storage device 2310 can store pre-trained preference determination logic.

[0070] Specifically, the logic for determining music preferences can be based on categorizing songs selected by the user into genres according to accumulated information. For example, genres could include folk, hip-hop, jazz, etc.

[0071] The physiological condition determiner 2320 according to embodiments of this disclosure can determine the physiological condition of an occupant using occupant biometric information and occupant music preference information stored in the database storage device 2310, as well as pre-trained physiological condition determination logic. For this purpose, the physiological condition determiner 2320 can use determination logic pre-trained through AI-based machine learning / deep learning to determine the occupant's physiological condition.

[0072] Specifically, the physiological condition determiner 2320 can load facial image information captured by a camera and stored in the database storage device 2310. The physiological condition determiner 2320 can determine the occupant's tension level based on facial expression-based determination logic pre-stored in the database storage device 2310. To this end, the physiological condition determiner 2320 can quantify the tension level by comparing the facial expression-based determination logic with the facial image information.

[0073] For example, tension levels can be categorized into levels 1 through 10 based on calmness and tension. Strong calmness corresponds to level 1, moderate calmness to level 2, slight calmness to level 3, very mild tension to level 4, slightly mild tension to level 5, mild tension to level 6, moderate tension to level 7, strong tension to level 8, fairly strong tension to level 9, and extreme tension to level 10.

[0074] Furthermore, facial expression-based determination logic can analyze facial expression data in real time to determine occupant stress, fatigue, and mood changes. This logic can continuously improve its analysis model by updating existing training data in real time to reflect new facial expression information. It can also automatically optimize analysis criteria by comparing general user data with global standard data using expanded data. For example, it can provide low-frequency vibrations for comfort in the case of smiling facial expressions and high-frequency blocking vibrations to alleviate tension in the case of tense facial expressions.

[0075] Furthermore, the physiological condition determiner 2320 can load pulse and / or heart rate biometric information stored in the database storage device 2310. The physiological condition determiner 2320 can determine the occupant's stress level based on pulse and / or heart rate determination logic stored in the database storage device 2310. To this end, the physiological condition determiner 2320 can quantify the stress level by comparing the pulse and / or heart rate determination logic with the pulse and / or heart rate measurement information.

[0076] The pulse and / or heart rate-based determination logic can monitor pulse and heart rate change patterns in real time to assess physiological status. This logic can operate in conjunction with the VCRM server 2000 through real-time data updates and more accurately identify a user's current condition by comparing it with average data from other users.

[0077] The logic based on pulse and / or heart rate can analyze expanded data to learn from data from multiple users, thereby detecting abnormal conditions or providing feedback to maintain optimal conditions.

[0078] For example, when the heart rate increases rapidly, determination logic based on pulse and / or heart rate can provide a sense of stability by reducing vibration intensity or changing vibration patterns.

[0079] In addition, the physiological condition determiner 2320 can perform a weighted summation of the determined tension level based on facial expression and the determined tension level based on pulse and / or heart rate.

[0080] Subsequently, the physiological condition determiner 2320 can determine the value based on the weighted summation result to finally determine the physiological condition.

[0081] According to embodiments of the present disclosure, the psychological preference determiner 2330 can determine the occupant's psychological preferences using pre-trained psychological condition-based determination logic stored in the database storage device 2310. To this end, the psychological preference determiner 2330 can collect playlist information continuously accumulated in the vehicle and use the determination logic pre-trained through AI-based machine learning / deep learning, along with previously played information stored in the database storage device 2310, to determine the occupant's preferences.

[0082] Specifically, the psychological preference determiner 2330 can load newly accumulated playback information and previously accumulated playback information. The psychological preference determiner 2330 can perform a weighted summation on the newly accumulated playback information and the previously accumulated playback information. Furthermore, the psychological preference determiner 2330 can load music preference determination logic.

[0083] The psychological preference determiner 2330 can ultimately determine psychological preferences based on the weighted summation result and music preference determination logic.

[0084] The physiological condition parameter generator 2400 according to embodiments of this disclosure can generate bandpass filter parameters. Specifically, the physiological condition parameter generator 2400 can reset the low-pass point of the filter to pre-stored DB information based on the quantified stress level.

[0085] For example, when the physiological condition corresponds to a stress level of 3.5, the physiological condition parameter generator 2400 can set the bandpass filter parameters to 1500 Hz.

[0086] Furthermore, the physiological condition parameter generator 2400 can generate compressor parameters. Specifically, the physiological condition parameter generator 2400 can reset the filter's input / output compressor application points to pre-stored DB information based on the quantified stress level.

[0087] Ultimately, the physiological condition parameter generator 2400 can adjust the final output gain value based on the pre-tuned DB.

[0088] The psychological preference parameter generator 2500 according to embodiments of the present disclosure can generate bandpass filter parameters.

[0089] In addition, the psychological preference parameter generator 2500 can generate compressor parameters.

[0090] Ultimately, the psychological preference parameter generator 2500 can adjust the final output gain value based on the pre-tuned DB.

[0091] The parameter matcher 2600 according to an embodiment of the present disclosure can receive the result of performing a weighted summation on the bandpass filter parameters generated by the physiological condition parameter generator 2400 and the bandpass filter parameters generated by the psychological preference parameter generator 2500.

[0092] In addition, parameter matcher 2600 can receive the result of performing a weighted summation on the compressor parameters generated by physiological condition parameter generator 2400 and the compressor parameters generated by psychological preference parameter generator 2500.

[0093] In addition, parameter matcher 2600 can receive the result of a weighted summation of the output gain value adjusted by physiological condition parameter generator 2400 and the output gain value adjusted by psychological preference parameter generator 2500.

[0094] Finally, the parameter matcher 2600 can apply weights to match these parameters.

[0095] refer to Figure 6 The vibration seat 3000 according to embodiments of the present disclosure may include an envelope function adjuster 3100, an envelope function tuner 3200, and a vibration seat operator 3300.

[0096] The envelope function adjuster 3100 according to this disclosure can receive image information and steering wheel / seat belt information in real time from the occupant information collector.

[0097] In addition, the envelope function adjuster 3100 can receive information from the vehicle audio playback recorder about the music choices and genre preferences of each occupant that have been continuously accumulated since the vehicle was sold.

[0098] In addition, the envelope function adjuster 3100 can receive driving mode, speed, torque, accelerator value, etc. from the vehicle driving data input device. For example, driving modes may include Eco mode, Normal / Comfort mode, Sport mode, Off-road mode, Snow / Sand mode, etc.

[0099] The envelope function adjuster 3100 can readjust the envelope function based on vehicle driving data input information preset by the parameter matcher 2600, which includes bandpass / compressor / gain information.

[0100] like Figure 7 As shown, the envelope function adjuster 3100 can readjust the values ​​of these parameters according to each driving mode based on driving mode information received from the vehicle driving data input device. Specifically, the envelope function adjuster 3100 can adjust the bandpass filter parameters, compressor parameters, and gain parameters.

[0101] In addition, the envelope function adjuster 3100 can further adjust the output gain weight for each bandwidth based on vehicle speed information.

[0102] The envelope function tuner 3100 can send the result of a weighted summation of the readjusted parameter values ​​and gain weights to the envelope function tuner 3200.

[0103] refer to Figure 8 The envelope function adjuster 3100 includes an audio input unit 3110, a bandpass filter applicator 3120, and a compressor applicator 3130, and the envelope function tuner 3200 includes an envelope extractor 3210, a sine signal synthesizer 3220, and an output gain tuner 3230.

[0104] Figure 9 This is a diagram illustrating a method for generating a vibrating seat signal according to an embodiment of the present disclosure.

[0105] like Figure 9 As shown in (a), the audio input 3110 of the envelope function adjuster 3100 can use a microphone to collect input sound signals inside the vehicle.

[0106] like Figure 9 As shown in (b), the bandpass filter applicator 3120 can convert the time domain of the input audio signal to the frequency domain, and then use a bandpass filter to attenuate the signal components in a specific bandwidth. For example, the bandpass filter can be a low-pass filter.

[0107] like Figure 9 As shown in (c), the compressor applicator 3130 can convert the frequency domain of a signal that has been applied with a bandpass filter to the time domain, and then use the compressor to reduce the signal level.

[0108] like Figure 9 As shown in (d), the envelope extractor 3210 of the envelope function tuner 3200 can use a low-pass filter to extract the envelope of the signal.

[0109] like Figure 9As shown in (e), the sine signal synthesizer 3220 can generate a sine wave with a predetermined period.

[0110] like Figure 9 As shown in (f), the output gain tuner 3230 can perform convolution on the envelope signal extracted by the envelope extractor 3210 and the sine wave to generate the vibration seat signal.

[0111] Therefore, the envelope function tuner 3100 according to the embodiments of this disclosure can apply tuning parameters adjusted by the server, and the envelope function tuner 3200 can perform tuning using the envelope function.

[0112] In this regard, the envelope function tuner 3200 according to the embodiments of this disclosure can apply information processed by the VCRM server in the form of parameters by involving bandpass filtering / compression / low-pass filtering / sine signal generation.

[0113] Furthermore, the vibration seat operator 3300 according to the embodiments of this disclosure can operate the vibration seat based on the vibration seat signal generated by the envelope function tuner 3200.

[0114] Figure 10 and Figure 11 This is a flowchart describing a method for tuning a resonant seat according to an embodiment of the present disclosure.

[0115] refer to Figure 10 and Figure 11 The server 2000 of the vibration seat tuning system can receive accumulated vehicle input data (S110).

[0116] Server 2000 can load previous music playback information (S120).

[0117] Server 2000 can store VCRM server data, such as accumulated vehicle input data and loaded previous music playback information (S130). For example, server 2000 can generate VCRM data based on occupant biometric information and occupant music preference information.

[0118] Server 2000 can determine physiological status based on VCRM server data (S140). To this end, server 2000 can determine physiological status based on occupant biometric information. Specifically, server 2000 can determine the level of tension based on facial expression by comparing the occupant's facial image information with determination logic based on facial expression, determine the level of tension based on pulse and / or heart rate by comparing the occupant's pulse and heart rate information with determination logic based on pulse and / or heart rate, and determine physiological status by performing a weighted summation of the tension level based on facial expression and the tension level based on pulse and / or heart rate.

[0119] Server 2000 can generate physiological condition parameters based on the determined physiological condition information (S145).

[0120] Furthermore, the server 2000 can generate physiological condition parameters based on the determined physiological condition. Specifically, the server 2000 can generate first bandpass filter parameters, first compressor parameters, and first output gain parameters.

[0121] In addition, Server 2000 can determine psychological preferences based on VCRM server data (S150).

[0122] Furthermore, server 2000 can determine psychological preferences based on occupant music preference information. Specifically, server 2000 can collect new playback information accumulated by occupants within the vehicle, collect previously stored playback information by occupants, and compare the new playback information and previous playback information with music preference determination logic to determine psychological preferences.

[0123] Server 2000 can generate psychological preference parameters based on the determined psychological preference information (S155).

[0124] Server 2000 can generate psychological preference parameters based on the determined psychological preferences. Specifically, server 2000 can generate second bandpass filter parameters, second compressor parameters, and second output gain parameters.

[0125] Server 2000 can apply weights to the generated physiological condition parameters and psychological preference parameters to perform parameter matching (S160).

[0126] Specifically, server 2000 can generate third bandpass filter parameters by performing a weighted summation on the first bandpass filter parameters and the second bandpass filter parameters, generate third compressor parameters by performing a weighted summation on the first compressor parameters and the second compressor parameters, and generate third output gain parameters by performing a weighted summation on the first output gain parameters and the second output gain parameters.

[0127] Server 2000 can generate vibration seat tuning parameters by matching physiological condition parameters with psychological preference parameters.

[0128] Server 2000 can generate a database based on parameter matching results (S170).

[0129] The vibration seat 3000 of the vibration seat tuning system can receive accumulated vehicle input data in real time (S210).

[0130] The vibrating seat 3000 can load previous music playback information (S220).

[0131] The vibration seat 3000 can adjust the envelope function (S230) based on the accumulated vehicle input data received in real time, the previously played music information, and the parameter matching results received from the server 2000. Specifically, the vibration seat 3000 can receive audio output in real time inside the vehicle, apply a bandpass filter configured with third bandpass filter parameters to the received audio, and apply a compressor configured with third compressor parameters to the audio on which the bandpass filter has been applied.

[0132] The vibration seat 3000 can perform envelope function tuning based on the envelope function adjustment result (S240). Specifically, the vibration seat 3000 can extract the envelope by applying a low-pass filter to the audio with a compressor applied, generate a sine wave, and generate a vibration seat signal by performing convolution between the extracted envelope and the generated sine wave.

[0133] The vibration seat 3000 can operate based on the envelope function tuning result (S250). Specifically, the vibration seat 3000 can operate based on the generated vibration seat signal.

[0134] That is, the technical concept of this disclosure can be applied to the entire autonomous vehicle, or it can be applied only to some components within the autonomous vehicle. The scope of this disclosure should be determined according to the matters described in the claims.

[0135] In another aspect of this disclosure, the above-described proposals or operations can be provided as code that can be implemented, executed, or performed by a "computer" (including a comprehensive concept such as a system-on-a-chip (SoC), microprocessor, etc.) or application storing or containing code, a computer-readable storage medium, a computer program product, etc. Furthermore, such a configuration also falls within the scope of this disclosure.

[0136] A detailed description of preferred embodiments of the present disclosure as disclosed above is provided to allow those skilled in the art to implement and practice the present disclosure. While the present disclosure has been described and illustrated herein with reference to preferred embodiments, those skilled in the art will understand that various modifications and changes can be made to the present disclosure without departing from its scope. For example, those skilled in the art can use the various components described in the above embodiments in combination with each other.

[0137] Therefore, this disclosure is not intended to be limited to the embodiments described herein, but rather to provide the broadest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for adjusting a resonant vibration seat, the method comprising: Based on the occupants' biometric information and their music preferences, vehicle customer relationship management (VCRM) data is generated. Based on the generated VCRM data, generate vibration seat tuning parameters; Based on the vibration seat tuning parameters and the real-time received occupant biometric information and occupant music preference information, the envelope function is adjusted and tuned. as well as The vibrating seat operates based on a tuned envelope function.

2. The method according to claim 1, wherein, Generating the VCRM data includes: Physiological status is determined based on the occupant's biometric information; Physiological condition parameters are generated based on the determined physiological conditions; Determine psychological preferences based on the occupants' music preference information; Psychological preference parameters are generated based on the determined psychological preferences; and The vibration seat tuning parameters are generated by matching the physiological condition parameters with the psychological preference parameters.

3. The method according to claim 2, wherein, Determining the physiological condition based on the occupant's biometric information includes: The level of tension based on facial expressions is determined by comparing the occupant's facial image information with facial expression-based determination logic. The level of stress based on pulse and / or heart rate is determined by comparing the occupant's pulse and heart rate information with pulse and / or heart rate-based determination logic; and The physiological condition is determined by performing a weighted summation of the tension level based on facial expression and the tension level based on pulse and / or heart rate.

4. The method according to claim 3, wherein, The generation of the physiological condition parameters based on the determined physiological condition includes: Generate the parameters for the first bandpass filter; Generate the first compressor parameters; and Generate the first output gain parameter.

5. The method according to claim 4, wherein, Determining the psychological preference based on the occupant's music preference information includes: Collect new playback information accumulated by the occupants within the vehicle; Collect the occupants' pre-stored previous playback information; and The psychological preference is determined by comparing the new playback information and the previous playback information with music preference determination logic.

6. The method according to claim 5, wherein, The psychological preference parameters are generated based on the determined psychological preferences, including: Generate the parameters for the second bandpass filter; Generate the second compressor parameters; and Generate a second output gain parameter.

7. The method according to claim 6, wherein, Generating the vibration seat tuning parameters by matching the physiological condition parameters with the psychological preference parameters includes: The third bandpass filter parameters are generated by performing a weighted summation on the first bandpass filter parameters and the second bandpass filter parameters. The third compressor parameters are generated by performing a weighted summation on the first and second compressor parameters; and The third output gain parameter is generated by performing a weighted summation on the first output gain parameter and the second output gain parameter.

8. The method according to claim 7, wherein, Adjusting and tuning the envelope function includes: Real-time audio output is received inside the vehicle; The bandpass filter configured with the third bandpass filter parameters is applied to the received audio; The compressor configured with the third compressor parameters is applied to the audio that has applied the bandpass filter; The envelope is extracted by applying a low-pass filter to the audio audio with the compressor applied. Generate a sinusoidal signal; and The vibration seat signal is generated by performing convolution on the extracted envelope and the generated sinusoidal signal.

9. The method according to claim 8, wherein, Adjusting and tuning the envelope function further includes: Receive vehicle driving data, including driving mode information and speed information; Adjust the vibration seat tuning parameters based on the driving mode information; and The output gain weight is adjusted based on the speed information.

10. A system for adjusting a resonant vibrating seat, the system comprising: The server is configured as follows: Based on the occupants' biometric information and their music preferences, vehicle customer relationship management (VCRM) data is generated; and Based on the generated VCRM data, vibration seat tuning parameters are generated; and The vibration seat is configured as follows: Based on the vibration seat tuning parameters and the real-time received occupant biometric information and occupant music preference information, the envelope function is adjusted and tuned. and The operation is based on the tuned envelope function.

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