Vehicle for protecting passengers and method of operating the same

Abstract

Various embodiments of the present disclosure relate to a vehicle for protecting a passenger and an operating method thereof. Disclosed is a vehicle for protecting a passenger, including: a plurality of passenger protection devices; a first sensor obtaining passenger information related to a passenger of the vehicle; a second sensor obtaining vehicle motion information in a case of collision with another object; and a processor operatively connected to the passenger protection devices, the first sensor, and the second sensor, and the processor estimating passenger behavior information in the case of collision based on the passenger information and the vehicle motion information; and controlling an operation of at least one of the plurality of passenger protection devices based on the estimated passenger behavior information; and the passenger information can include at least one of state information about a seat of the passenger and information about whether the passenger is wearing a seat belt.

Description

Technical Field

[0001] Various embodiments of this disclosure describe passenger protection devices in vehicles and their operation methods. Background Technology

[0002] Recently, advanced driver assistance systems (ADAS) have been developed to assist drivers. ADAS has several sub-technology categories and provides convenience for drivers. This type of ADAS is also known as autonomous driving or ADS (automatic driving system).

[0003] When a vehicle is autonomously driven via ADS (Advanced Driver Assistance Systems), passengers can engage in activities other than driving. Therefore, seats in autonomous vehicles can be rotated to allow passengers to comfortably perform other activities. For example, the driver's seat in an autonomous vehicle can be rotated to face the rear or side of the vehicle instead of the front.

[0004] On the other hand, vehicles are equipped with passenger protection devices (or safety devices) that activate in the event of a collision. For example, conventional vehicles use values ​​obtained from acceleration and / or angular velocity sensors to assess the severity of a collision, and activate passenger protection devices, such as airbags, when the severity of the collision exceeds a threshold. Summary of the Invention

[0005] Because vehicle seats can rotate freely, the actual injury a passenger sustains in a collision can depend not only on the intensity and direction of the impact but also on their behavior during the collision. Passenger behavior following a collision can vary depending on the seat's position and whether the passenger is wearing a seatbelt. However, because traditional passenger protection devices do not consider seat conditions or seatbelt use, situations may arise where passengers are not properly protected.

[0006] Therefore, various embodiments of this disclosure disclose a method and apparatus for controlling a passenger protection device by estimating the behavior of indoor occupants in a vehicle during a collision.

[0007] Various embodiments of this disclosure provide methods and apparatus for estimating passenger collision patterns based on passenger behavior in a collision scenario, using information about the state of the passenger seat and information about the seat belts in the vehicle.

[0008] The technical problems intended to be solved in this disclosure are not limited to those mentioned above, and those skilled in the art can precisely understand other technical problems not mentioned above from the description provided below.

[0009] According to various embodiments of this disclosure, one embodiment is a vehicle for protecting passengers, comprising: a plurality of passenger protection devices; a first sensor for acquiring passenger information related to the passengers of the vehicle; a second sensor for acquiring vehicle motion information in the event of a collision with another object; and a processor operatively connected to the passenger protection devices, the first sensor, and the second sensor, wherein the processor estimates passenger behavior information in the event of a collision based on the passenger information and the vehicle motion information; and controls the operation of at least one of the plurality of passenger protection devices based on the estimated passenger behavior information; and the passenger information may include at least one of information regarding the status of the passenger's seat and information regarding whether the passenger is wearing a seatbelt.

[0010] According to embodiments of this disclosure, the status information of a passenger's seat may include at least one of the following: seat tilt angle, seat rotation angle, seat sliding position, longitudinal distance between the seat and the dashboard, lateral distance between the seat and another seat, and lateral distance between the seat and the door.

[0011] According to embodiments of this disclosure, vehicle motion information may include at least one of yaw rate, pitch rate, roll rate, and acceleration applied to the vehicle in the event of a collision.

[0012] According to embodiments of this disclosure, the processor updates passenger parameters of a passenger behavior model based on passenger information; passenger behavior information under collision conditions is estimated by inputting passenger information and vehicle motion information into the passenger behavior model whose passenger parameters have been updated; and the passenger parameters may include at least one of a pitch rotation coefficient of a specified mass, a roll rotation coefficient of a specified mass, and a linear motion coefficient of a specified mass.

[0013] According to embodiments of this disclosure, the designated mass may include at least one of the head, chest, and lower body.

[0014] According to embodiments of this disclosure, passenger behavior information includes at least one of state information of a specified mass regarding rotational motion and state information of a specified mass regarding linear motion, wherein the state information regarding rotational motion includes at least one of pitch angle, pitch rate, roll angle, and roll rate, and the state information regarding linear motion may include the amount of movement.

[0015] According to embodiments of this disclosure, the processor determines a passenger collision mode based on passenger behavior information; determines at least one passenger protection device to be operated among a plurality of passenger protection devices based on the determined passenger collision mode; and controls the determined passenger protection device to operate; and the passenger collision mode may include at least one of a roll behavior amplification mode, a pitch behavior amplification mode, and a submarine mode.

[0016] According to embodiments of this disclosure, when at least one of the roll angle and roll rate of a specified mass point is greater than a specified roll threshold, the processor can determine the roll behavior amplification mode as a passenger collision mode; when at least one of the pitch angle and pitch rate of a specified mass point is greater than a specified pitch threshold, the processor can determine the pitch behavior amplification mode as a passenger collision mode; and when the movement amount of a specified mass point is greater than a specified movement amount threshold, the processor can determine the slump mode as a passenger collision mode.

[0017] According to embodiments of this disclosure, passenger parameters can be updated based on a lookup table representing standard male and female rotational motion coefficients for each seat condition.

[0018] According to embodiments of this disclosure, the processor can check at least one rotational motion coefficient corresponding to the seat status information of a passenger in a lookup table, and can obtain updated passenger parameters by applying a correction coefficient based on whether the passenger is wearing a seatbelt to the at least one rotational motion coefficient.

[0019] According to various embodiments of this disclosure, another embodiment is a method for operating a vehicle to protect passengers, comprising: acquiring passenger information related to passengers in the vehicle; acquiring vehicle motion information in the event of a collision with another object; estimating passenger behavior information in the event of a collision based on the passenger information and the vehicle motion information; and controlling the operation of at least one of a plurality of passenger protection devices disposed in the vehicle based on the estimated passenger behavior information, wherein the passenger information may include at least one of information regarding the status of the passenger's seat and information regarding whether the passenger is wearing a seatbelt.

[0020] According to embodiments of this disclosure, the passenger seat status information included in the passenger information may include at least one of the following: seat tilt angle, seat rotation angle, seat sliding position, longitudinal distance between the seat and the dashboard, lateral distance between the seat and another seat, and lateral distance between the seat and the door.

[0021] According to embodiments of this disclosure, vehicle motion information in a collision situation may include at least one of yaw rate, pitch rate, roll rate, and acceleration applied to the vehicle in a collision situation.

[0022] According to embodiments of this disclosure, estimating passenger behavior information may include updating passenger parameters of a passenger behavior model based on passenger information; passenger behavior information under collision conditions is estimated by inputting passenger information and vehicle motion information into a passenger behavior model whose passenger parameters have been updated, and the passenger parameters may include at least one of a pitch rotation coefficient of a specified mass, a roll rotation coefficient of a specified mass, and a linear motion coefficient of a specified mass.

[0023] According to embodiments of this disclosure, the estimated passenger behavior information may include at least one of state information of a specified mass regarding rotational motion and state information of a specified mass regarding linear motion, and the state information regarding rotational motion may include at least one of pitch angle, pitch rate, roll angle, and roll rate, and the state information regarding linear motion may include the amount of movement.

[0024] According to embodiments of this disclosure, controlling the operation of at least one of a plurality of passenger protection devices may include: determining a passenger collision mode based on passenger behavior information; determining at least one passenger protection device to be operated among the plurality of passenger protection devices based on the determined passenger collision mode; and controlling the determined passenger protection device to operate, wherein the passenger collision mode may include at least one of a roll behavior amplification mode, a pitch behavior amplification mode, and a slump mode.

[0025] According to embodiments of this disclosure, determining a passenger collision mode may include: determining a roll behavior amplification mode as a passenger collision mode when at least one of the roll angle or roll rate of a specified mass point is greater than a specified roll threshold; determining a pitch behavior amplification mode as a passenger collision mode when at least one of the pitch angle or pitch rate of a specified mass point is greater than a specified pitch threshold; and determining a slump mode as a passenger collision mode when the amount of movement of a specified mass point is greater than a specified amount of movement threshold.

[0026] According to embodiments of this disclosure, passenger parameters can be updated based on a lookup table representing standard male and female rotational motion coefficients for each seat condition.

[0027] According to embodiments of this disclosure, updating passenger parameters of a passenger behavior model may include: checking at least one rotational motion coefficient corresponding to the passenger's seat status information in a lookup table, and obtaining updated passenger parameters by applying a correction coefficient based on whether the passenger is wearing a seatbelt to the at least one rotational motion coefficient.

[0028] Beneficial effects

[0029] According to various embodiments of this disclosure, a vehicle can estimate a passenger collision pattern based on passenger behavior in a collision and control a passenger protection device based on the passenger collision pattern to enhance the performance of passenger protection. Attached Figure Description

[0030] Figure 1 This is a block diagram of a vehicle according to various embodiments of the present disclosure.

[0031] Figure 2 This is a graph illustrating examples of estimated passenger behavior in a vehicle according to various embodiments of the present disclosure.

[0032] Figure 3A and Figure 3B This is a diagram illustrating examples of parameters for updating a passenger behavior model in a vehicle according to various embodiments of the present disclosure.

[0033] Figure 4 This is a block diagram illustrating a multi-mass model of passenger behavior in a vehicle according to various embodiments of the present disclosure.

[0034] Figure 5A This is a diagram illustrating examples of longitudinal model calculations for estimating passenger behavior information according to various embodiments of this implementation.

[0035] Figure 5B This is a diagram illustrating examples of lateral model calculations for estimating passenger behavior information according to various embodiments of this implementation.

[0036] Figure 5C This is a diagram illustrating examples of determining passenger collision patterns according to various embodiments of this embodiment.

[0037] Figure 6 This is a flowchart illustrating various embodiments of this implementation of a passenger protection device in a vehicle by estimating passenger behavior information.

[0038] Figure 7 This is a flowchart illustrating various embodiments of this implementation for determining passenger collision patterns in a vehicle. Detailed Implementation

[0039] In the following, various embodiments of the present disclosure will be described in detail with reference to the accompanying drawings, without regard to the reference numerals in the drawings. Parts that are the same as or similar to the embodiments described above are indicated by the same reference numerals, and redundant descriptions thereof are omitted.

[0040] For ease of writing, the suffixes “module” and “part” used herein are generally used for components and may be used together or interchangeably, and these terms themselves do not have any distinguishing meaning or function. Furthermore, the terms “module” or “part” may refer to a software component or hardware component such as a Field Programmable Gate Array (FPGA) or Application-Specific Integrated Circuit (ASIC). A “part” or “module” implements certain functions. However, a “part” or “module” is not intended to be limited to software or hardware. A “part” or “module” may be configured to be placed in addressable storage media or configured to be retrieved by one or more processors. Thus, for example, a “part” or “module” may include components such as software components, object-oriented software components, class components, and task components, and may include processes, functions, attributes, procedures, subroutines, program code segments, drivers, firmware, microcode, circuits, data, databases, data structures, tables, arrays, and variables. Components and functions provided in a “part” or “module” may be combined with a smaller number of components and “parts” or “modules,” or may be further divided into additional components and “parts” or “modules.”

[0041] The method or algorithm steps described in relation to some embodiments of the present invention can be directly implemented by hardware and software modules executed by a processor, or by a combination thereof. The software modules can reside on RAM, flash memory cards, ROM, EPROM, EEPROM, resistors, hard disks, removable disks, CD-ROMs, or any other type of recording medium known to those skilled in the art. An exemplary recording medium is coupled to the processor, and the processor can read information from the recording medium and record information in a storage medium. In another embodiment, the recording medium can be integrally formed with the processor. The processor and the recording medium can reside within an application-specific integrated circuit (ASIC). The ASIC can reside inside the user terminal.

[0042] Furthermore, in describing the embodiments, descriptions of relevant known technologies will be omitted if they are deemed unnecessarily obscuring the essence of this disclosure. It should also be understood that the accompanying drawings are for illustrative purposes only, and the technical concept of this disclosure is not limited by the drawings; the drawings include all modifications, equivalent substitutions, or replacements included within the spirit and scope of the invention.

[0043] While terms such as "first" and "second" can be used to describe various components, these components are not limited to the terms mentioned above. These terms are only used to distinguish one component from others.

[0044] The phrase "connected to" or "accessed to" another component includes both cases where a component is directly connected to or directly accessed to another component, and cases where another component intervenes between them. Conversely, the phrase "directly connected to" or "directly accessed to" another component indicates that no other component intervenes between them.

[0045] In the following description, the vehicle may be an automated driving system (ADS) equipped with and capable of autonomous driving. For example, the vehicle may be able to perform at least one of steering, acceleration, deceleration, lane changing, and stopping by the ADS without driver intervention. For example, the ADS may include at least one of, for example, a pedestrian detection and collision reduction system (PDCMS), a lane change decision assistance system (LCDAS), a land departure warning system (LDWS), adaptive cruise control (ACC), a lane keeping assist system (LKAS), a road boundary departure prevention system (RBDPS), a cornering speed warning system (CSWS), a forward vehicle collision warning system (FVCWS), and low-speed follow (LSF).

[0046] Figure 1 This is a block diagram of a vehicle according to various embodiments of the present disclosure.

[0047] Figure 1 The vehicle configuration shown is one embodiment, and each component may be configured as a chip, a component, or an electronic circuit, or a combination of a chip, a component, and / or an electronic circuit. According to the embodiment, Figure 1 Some components shown can be divided into multiple components and constructed as different chips, different components, or different electronic circuits, and some components can be combined to form a chip, a component, or an electronic circuit. According to the embodiment, certain details may be omitted. Figure 1 Some of the components shown in the image may be added. Figure 1 Other components not shown. Figure 1 Among the components, at least some of them will refer to Figures 2 to 5C To describe. Figure 2 This is a graph illustrating examples of estimated passenger behavior in a vehicle according to various embodiments of the present disclosure. Figure 3A and Figure 3B This is a diagram illustrating examples of parameters for updating a passenger behavior model in a vehicle according to various embodiments of the present disclosure. Figure 4 This is a block diagram illustrating a multi-mass model of passenger behavior in a vehicle according to various embodiments of the present disclosure. Figure 5A This is a diagram illustrating examples of longitudinal model calculations for estimating passenger behavior information according to various embodiments of this implementation. Figure 5BThis is a diagram illustrating examples of lateral model calculations for estimating passenger behavior information according to various embodiments of this implementation. Figure 5C This is a diagram illustrating examples of determining passenger collision patterns according to various embodiments of this embodiment.

[0048] refer to Figure 1 The vehicle 100 may include a sensor unit 110, a processor 120, a passenger protection device 130, and a storage unit 140.

[0049] According to various embodiments, sensor unit 110 may use multiple sensors to generate data related to vehicle status, vehicle interior and / or vehicle external environment. According to this embodiment, sensor unit 110 may include vehicle information acquisition sensor 112 and passenger information acquisition sensor 114.

[0050] Vehicle information acquisition sensor 112 can detect collisions between a vehicle and an object (e.g., another vehicle, pedestrian, obstacle, etc.) and generate a collision detection signal. Vehicle information acquisition sensor 112 includes at least one sensor and can acquire information about the direction of the collision, the intensity of the collision, and the vehicle's motion in the initial stage of the collision (or during the collision). For example, vehicle information acquisition sensor 112 may include at least one of a low-gravity sensor that measures the gravitational acceleration applied to the vehicle, a front collision sensor (FIS) disposed on the front surface of the vehicle and detecting acceleration due to the collision, a side collision sensor (SIS) disposed on the side of the vehicle and detecting acceleration due to the collision, and an angular velocity sensor that detects yaw, roll, and pitch. Vehicle motion information may include gravitational acceleration obtained from the low-gravity sensor, acceleration obtained from the front collision sensor, acceleration obtained from the side collision sensor, and at least one of yaw rate, roll rate, and pitch rate obtained from the angular velocity sensor. Since vehicle motion information is information acquired from the sensors in the initial stage of the collision, it may be referred to as collision sensor information.

[0051] Passenger information acquisition sensor 114 can acquire passenger information including the state information of a passenger's seat in the vehicle and the passenger's restraint information. The state information of the passenger's seat can indicate the passenger's posture and / or position, and the restraint information can indicate whether the passenger is restrained by the seat. For example, the seat state information includes at least one of the following: seat tilt angle (or seat inclination), seat rotation angle (or seat rotation state), seat sliding position (seat position), lateral distance between the seat and the dashboard, lateral distance between the seat and another seat, and lateral distance between the seat and the door. For example, the seat tilt angle can indicate the angle of the seat back. The seat rotation angle can indicate how much the seat has rotated to the left or right based on the time the seat faces the front of the vehicle. The seat sliding position can indicate how much the seat has moved forward or backward from a specified reference position. The specified reference position can be set and / or changed by the designer. For example, the specified reference position can be the position of the steering wheel, the position of the dashboard, or the basic position of the corresponding seat, but is not limited to these. The passenger restraint information can include information about whether the passenger is wearing a seatbelt.

[0052] Sensor unit 110 may also include at least one sensor other than those described above. For example, sensor unit 110 may also include at least one of a camera (not shown) for capturing the environment outside the vehicle, a radio detection and ranging (RADAR), a light detection and ranging (LIDAR) sensor for detecting objects around the vehicle, or a position measurement sensor for measuring the vehicle's position. The listed sensors are merely examples for illustrative purposes, and the sensors described in this document are not limited thereto.

[0053] The processor 120 can control the overall operation of the vehicle 100. According to this embodiment, the processor 120 may include an electronic control unit (ECU) capable of overall control of components within the vehicle 100. For example, the processor 120 may include a central processing unit (CPU) or a microprocessor unit (MCU) capable of performing arithmetic operations.

[0054] According to various embodiments, the processor 120 can estimate passenger behavior information in a collision scenario based on passenger information and vehicle motion information obtained from the sensor unit 110, and can control the operation of the passenger protection device 130 based on the estimated passenger behavior information. Figure 2 As shown, according to this embodiment, the passenger behavior estimation unit 122 of the processor 120 can use the multi-mass model 234 of passenger behavior to estimate passenger behavior information in the event of a collision, and can control the passenger protection device 240 based on the estimated passenger behavior information.

[0055] The passenger behavior estimation unit 122 can obtain vehicle motion information 210 from the vehicle information acquisition sensor 112 and passenger information 220 from the passenger information acquisition sensor 114.

[0056] The passenger behavior estimation unit 122 can estimate passenger behavior 230 based on vehicle motion information and passenger information. The passenger behavior estimation unit 122 can update the model parameters 231 used for passenger behavior estimation based on passenger information. For example, the passenger behavior estimation unit 122 can update the passenger parameters of the multi-mass model 234 of passenger behavior. The passenger parameters of the multi-mass model 234 of passenger behavior can include rotational motion coefficients and / or linear motion coefficients of a specified mass among multiple mass points in the passenger's body parts. In the examples of this disclosure, the specified mass points can include the head, chest, and lower body (or hips). For example, the passenger parameters can include at least one of the rotational motion coefficients of the head and / or chest pitch and / or lateral tilt, and the linear motion coefficients of the lower body. According to this embodiment, the passenger parameters can include one of the following: head pitch damping coefficient, head pitch stiffness coefficient, head lateral tilt damping coefficient, head lateral tilt stiffness coefficient, chest pitch damping coefficient, chest pitch stiffness coefficient, chest lateral tilt damping coefficient, chest lateral tilt stiffness coefficient, hip linear damping coefficient, and hip linear stiffness coefficient.

[0057] According to this implementation, passenger parameters can be updated using a model parameter lookup table obtained and evaluated in advance through SLED testing and a model parameter calculation formula based on the lookup table. For example, the model parameter lookup table can represent the standard male and female rotational motion coefficients for each seating condition. The model parameter lookup table can be formed as a graph representing the standard male and female rotational motion coefficients for each seating condition. Here, seating conditions can include at least one of the seat tilt angle, seat rotation angle, and seat sliding position.

[0058] like Figure 3A and Figure 3BAs shown, the passenger behavior estimation unit 122 can update the head pitch and rotation motion coefficients. For example, the passenger behavior estimation unit 122 can check the seat tilt angle included in the current passenger information and the head pitch damping coefficient R1 corresponding to the passenger gender in a graph or lookup table representing the standard male and female head pitch damping coefficient R1 301 for each seat tilt angle 303. The passenger behavior estimation unit 122 can check the seat rotation angle included in the current passenger information and the head pitch damping coefficient R2 corresponding to the passenger gender in a graph or lookup table representing the standard male and female head pitch damping coefficient R2 311 for each seat rotation angle 305. The passenger behavior estimation unit 122 can obtain the updated head pitch damping coefficient 311 by adding the confirmed R1 and R2 and applying a correction coefficient 321 (e.g., approximately 0.3) based on whether the passenger is wearing a seatbelt. Here, the correction coefficient 0.3 can be a value applied when the passenger is not wearing a seatbelt. For example, the correction coefficient can be 1 when the passenger is wearing a seatbelt. Such correction coefficients are merely examples, and the various embodiments of this disclosure are not limited thereto.

[0059] As another example, passenger behavior estimation unit 122 can examine the seat tilt angle included in the current passenger information and the head pitch stiffness coefficient S1 corresponding to the passenger gender in a graph or lookup table representing the standard male and female head pitch stiffness coefficient S1 351 for each seat tilt angle 353. Passenger behavior estimation unit 122 can also examine the seat rotation angle included in the current passenger information and the head pitch stiffness coefficient S2 corresponding to the passenger gender in a graph or lookup table representing the standard male and female head pitch stiffness coefficient S2 361 for each seat rotation angle 355. Passenger behavior estimation unit 122 can obtain an updated head pitch stiffness coefficient 381 by adding the confirmed R1 and R2 and applying a correction factor 371 (e.g., approximately 0.4) based on whether the passenger is wearing a seatbelt.

[0060] Passenger behavior estimation unit 122 can be used in Figure 3A and Figure 3B The method shown updates other model parameters using a calculation formula for a single model parameter obtained in advance through a sled test.

[0061] For example, passenger behavior estimation unit 122 can examine the seat tilt angle included in the current passenger information and the head tilt damping coefficient R'1 corresponding to the passenger gender in a graph or lookup table representing the standard male and female head tilt damping coefficient R'1 for each seat tilt angle. Passenger behavior estimation unit 122 can also examine the seat rotation angle included in the current passenger information and the head tilt damping coefficient R'2 corresponding to the passenger gender in a graph or lookup table representing the standard male and female head tilt damping coefficient R'2 for each seat rotation angle. Passenger behavior estimation unit 122 can obtain an updated head tilt damping coefficient by adding the confirmed R'1 and R'2 and applying a correction factor based on whether the passenger is wearing a seatbelt.

[0062] As another example, the passenger behavior estimation unit 122 can examine the seat tilt angle included in the current passenger information and the chest tilt stiffness coefficient S1 corresponding to the passenger gender in a graph or lookup table representing the standard male and female chest tilt stiffness coefficient S'1 for each seat tilt angle. The passenger behavior estimation unit 122 can also examine the seat rotation angle included in the current passenger information and the chest tilt stiffness coefficient S'2 corresponding to the passenger gender in a graph or lookup table representing the standard male and female chest tilt stiffness coefficient S'2 for each seat rotation angle. The passenger behavior estimation unit 122 can obtain an updated chest tilt stiffness coefficient by adding the confirmed S'1 and S'2 and applying a correction factor based on whether the passenger is wearing a seatbelt.

[0063] As described above, the passenger behavior estimation unit 122 can be compared with... Figure 3A and Figure 3B The same method shown is used to update the head roll damping coefficient, head roll stiffness coefficient, chest pitch damping coefficient, chest pitch stiffness coefficient, chest roll damping coefficient, chest roll stiffness coefficient, hip linear damping coefficient, and hip linear stiffness coefficient.

[0064] The passenger behavior estimation unit 122 can estimate passenger behavior information 233 by inputting passenger information and vehicle motion information into a multi-mass model 234 of passenger behavior and using the multi-mass model 234 of passenger behavior. Passenger behavior information may include state information regarding the rotational and / or linear motion of a specified mass (e.g., head, chest, lower body) among multiple mass points in a passenger's body parts. For example, passenger behavior information may include at least one of the following: head pitch angle, head roll angle, head pitch rate, head roll rate, chest pitch angle, chest roll angle, chest pitch rate, chest roll rate, and the amount of movement of the lower body in the longitudinal direction.

[0065] like Figure 4As shown, according to this embodiment, the multi-mass model 234 of passenger behavior may include a longitudinal model 410 of passenger behavior and a lateral model 420 of passenger behavior. The longitudinal model 410 of passenger behavior can output at least one of the following through longitudinal model calculation: head pitch angle, head pitch rate, chest pitch angle, chest pitch rate, and lower body movement. For example, the longitudinal model calculation 412 can be configured to... Figure 5B The same applies. For example, a longitudinal model of passenger behavior 410 can be constructed using the chest pitch damping coefficient (b). x,c ), chest pitch stiffness coefficient (c x,c Head pitch damping coefficient (b) x,h Head pitch stiffness coefficient (c) x,h ), hip linear damping coefficient (b) x,hip ) and hip linear stiffness coefficient (c x,hip The longitudinal model is calculated using at least one of the following as input variables to output at least one of the following: head pitch angle, head pitch rate, chest pitch angle, chest pitch rate, and lower body movement.

[0066] The lateral model 420 of passenger behavior can output at least one of the following: head tilt angle, head tilt rate, chest tilt angle, and chest tilt rate, through the lateral model calculation 422. For example, the lateral model calculation 422 can be configured to... Figure 5B The same. For example, the lateral model 420 of passenger behavior can be obtained by using the head roll damping coefficient (b). y,h ), head roll stiffness coefficient (c y,h ), chest tilt damping coefficient (b) y,c ) and chest tilt stiffness coefficient (c y,c The lateral model is calculated using at least one of the following as input variables to output at least one of the following: head tilt angle, head tilt rate, chest tilt angle, and chest tilt rate.

[0067] The passenger behavior estimation unit 122 can determine the passenger collision mode 235 based on passenger behavior information. The passenger collision mode may include at least one of a pitch behavior amplification mode, a roll behavior amplification mode, and a slump mode. When the pitch angle and / or pitch rate of the head and / or chest is greater than the pitch threshold in the passenger behavior information, the passenger behavior estimation unit 122 can determine the pitch behavior amplification mode as the passenger collision mode. When the roll angle and / or roll rate of the head and / or chest is greater than the roll threshold in the passenger behavior information, the passenger behavior estimation unit 122 can determine the roll behavior amplification mode as the passenger collision mode. For example, as... Figure 5CAs shown, when the pitch rate and / or roll rate of the head and / or chest pitch angle and / or roll angle correspond to the first region 550, the passenger behavior estimation unit 122 can determine the pitch and / or roll amplification mode.

[0068] When the amount of movement of the lower body in the longitudinal direction is greater than the movement threshold in the passenger behavior information, the passenger behavior estimation unit 122 can determine the sinking mode as the passenger collision mode.

[0069] The passenger behavior estimation unit 122 can control the passenger protection device 240 based on at least one of passenger behavior information and passenger collision mode. According to this embodiment, the passenger behavior estimation unit 122 can determine at least one passenger protection device to be operated based on the passenger collision mode, and operate at least one determined passenger protection device. The passenger protection device may include, for example, at least one of an airbag and a PSB (pre-seat belt). The above are merely examples, and the passenger protection device according to various embodiments of this disclosure is not limited to airbags and PSBs. For example, passenger behavior information and / or passenger collision mode can be used to operate and / or control other devices installed in the vehicle.

[0070] The passenger protection device 130 may include means for protecting passengers. For example, the passenger protection device 130 may include multiple airbags and / or multiple PSBs.

[0071] Storage unit 140 can store various programs and data for the operation of the vehicle and / or the operation of processor 120. According to this embodiment, storage unit 140 can store various programs and data necessary for determining and operating passenger protection devices based on passenger behavior information and / or passenger collision patterns. For example, storage unit 140 can store information about combinations of passenger protection devices corresponding to passenger behavior information and / or passenger collision patterns.

[0072] The above description illustrates controlling passenger protection devices based on passenger behavior information and / or passenger collision patterns; however, various embodiments of this disclosure are not limited thereto. For example, the processor 120 may control the operation of other components disposed in the vehicle based on passenger behavior information and / or passenger collision patterns.

[0073] Figure 6This is a flowchart illustrating various embodiments of this implementation that control a passenger protection device in a vehicle by estimating passenger behavior information. In the following embodiments, each operation may be performed sequentially, but not necessarily sequentially. For example, the order of each operation may be changed, and at least two operations may be performed in parallel. Furthermore, the following operations may be performed by a processor 120 and / or at least one other component (e.g., sensor unit 110) disposed in the vehicle 100, or may be embodied in instructions that can be implemented by the processor 120 and / or at least one other component (e.g., sensor unit 110).

[0074] refer to Figure 6 The vehicle 100 can obtain passenger information from step 610. According to this embodiment, the vehicle 100 can obtain passenger information through a first sensor installed in the vehicle (e.g., [missing information]). Figure 1 The passenger information acquisition sensor 114 acquires passenger information. Passenger information may include information about the passenger's seat status within the vehicle and information about the passenger's restraints. For example, information about the passenger's seat status may include at least one of the following: seat tilt angle (or seat inclination), seat rotation angle (or seat rotation state), seat sliding position (seat position), lateral distance between the seat and the dashboard, lateral distance between the seat and another seat, and lateral distance between the seat and the door. For example, information about the passenger's restraints may include whether the passenger is wearing a seatbelt. According to this embodiment, passenger information may be acquired periodically or continuously, or it may be acquired upon detection of a selected event. The selected event may include at least one of the following: an event predicting a collision between the vehicle 100 and another object, an event indicating the predicted collision time has arrived, and an event indicating a collision has been detected.

[0075] In step 620, vehicle 100 can update the parameters of the passenger behavior model. According to this embodiment, vehicle 100 can update the parameters of the model used to estimate passenger behavior based on passenger information. For example, such as... Figure 2 As shown, vehicle 100 can update passenger parameters of the multi-mass model 234 of passenger behavior based on passenger information. Passenger parameters may include at least one of the rotational motion coefficients of the head and / or chest pitch and / or lateral tilt and the linear motion coefficients of the lower body.

[0076] In step 630, vehicle 100 can obtain passenger behavior information based on a passenger behavior model using vehicle motion information and passenger information. According to this embodiment, vehicle 100 can obtain passenger behavior information from a second sensor installed in the vehicle (e.g., ...). Figure 1The vehicle information acquisition sensor 112) obtains vehicle motion information in the initial stage of a collision. For example, when a collision is detected, the vehicle 100 can obtain information about the acceleration and angular velocity applied to the vehicle in the initial stage. According to this embodiment, the vehicle 100 can obtain passenger behavior information by using a passenger information model with passenger information and vehicle motion information as input variables. At this time, the passenger information model can be a model that applies the passenger parameters updated in step 620. Passenger behavior information may include state information about the rotational motion and / or linear motion of a specified mass (e.g., head, chest, lower body) among a plurality of mass points in the passenger's body parts. For example, passenger behavior information may include state information about the rotational motion of the head, the rotational motion of the chest, and the linear motion of the lower body (e.g., hip). The state information about the rotational motion of the head may include at least one of the head pitch angle, head pitch rate, head roll angle, and head roll rate. The state information about the rotational motion of the chest may include at least one of the chest pitch angle, chest roll rate, chest roll angle, and chest roll rate. State information regarding the linear motion of the lower body can include the amount of movement of the lower body in the longitudinal direction.

[0077] In step 640, vehicle 100 may determine the passenger collision mode based on passenger behavior information. The passenger collision mode may include at least one of a pitch behavior amplification mode, a roll behavior amplification mode, and a slump mode. (Refer to the following...) Figure 7 Provides detailed instructions for determining the passenger collision pattern.

[0078] In step 650, vehicle 100 can control passenger protection devices based on passenger collision patterns. According to this embodiment, vehicle 100 can determine at least one passenger protection device to be operated based on passenger collision patterns, and can operate at least one determined passenger protection device.

[0079] Figure 7 This is a flowchart illustrating various embodiments of this implementation for determining passenger collision patterns in a vehicle. Figure 7 The operation can be Figure 6 The detailed operation of step 640 is described below. In the following embodiments, each operation may be performed sequentially, but not necessarily sequentially. For example, the order of each operation may be changed, and at least two operations may be performed in parallel. Furthermore, the following operations may be performed by the processor 120 and / or at least one other component (e.g., sensor unit 110) disposed in the vehicle 100, or may be embodied in instructions that can be implemented by the processor 120 and / or at least one other component (e.g., sensor unit 110).

[0080] refer to Figure 7In step 701, vehicle 100 can check whether a roll behavior metric is greater than a specified roll threshold by comparing it with a specified roll threshold. For example, vehicle 100 can check metrics related to roll behavior in passenger behavior information and compare at least one metric selected from the roll behavior-related metrics with the specified roll threshold. For example, the roll behavior-related metrics may be head roll angle, head roll rate, chest roll angle, and chest roll rate. According to this embodiment, the roll threshold may include a threshold for each roll behavior-related metric. For example, the roll threshold may include at least one threshold selected from a threshold for head roll angle, a threshold for head roll rate, a threshold for chest roll angle, and a threshold for chest roll rate.

[0081] In step 703, when the roll behavior metric is greater than the roll threshold, the vehicle 100 can determine the roll behavior amplification mode as the passenger collision mode.

[0082] In step 705, when the roll behavior metric is equal to or less than the roll threshold, the vehicle 100 can check whether the pitch behavior metric is greater than the specified pitch threshold by comparing the pitch behavior metric with the specified pitch threshold. For example, the vehicle 100 can check metrics related to pitch behavior in passenger behavior information and can compare at least one metric selected from the pitch behavior-related metrics with the specified pitch threshold. For example, the pitch behavior-related metrics may be the head pitch angle, head pitch rate, chest pitch angle, and chest pitch rate. According to this embodiment, the pitch threshold may include a threshold for each metric related to pitch behavior. For example, the pitch threshold may include at least one selected from a threshold for the head pitch angle, a threshold for the head pitch rate, a threshold for the chest pitch angle, and a threshold for the chest pitch rate.

[0083] In step 707, when the pitch behavior metric is greater than the pitch threshold, the vehicle 100 can determine the pitch behavior amplification mode as the passenger collision mode.

[0084] In step 709, when the pitch behavior metric is equal to or less than the pitch threshold, the vehicle 100 can check whether the lower body movement is greater than a specified movement threshold by comparing the lower body movement amount with the movement amount threshold. For example, the vehicle 100 can check the lower body movement amount as a metric related to the linear behavior of the lower body in passenger behavior information and compare the lower body movement amount with the specified movement amount threshold.

[0085] In step 711, when the amount of lower body movement is greater than a specified movement threshold, the vehicle 100 can determine the sinking mode as a passenger collision mode.

[0086] When the amount of lower body movement is equal to or less than a specified movement threshold, the vehicle 100 can determine that the passenger collision mode has not fallen into the roll behavior amplification mode, pitch behavior amplification mode, or slump mode, and can terminate the operation used to determine the passenger collision mode.

[0087] In the above description, a vehicle equipped with an ADS (Autonomous Driving System) is used as an example for illustration. However, vehicles according to various embodiments of this disclosure do not necessarily have an ADS. For example, various embodiments of this disclosure do not support autonomous driving; however, they can be applied to vehicles equipped with passenger protection devices.

[0088] Figure Labels

[0089] 100: Vehicles

[0090] 110: Sensor Unit

[0091] 112: Vehicle Information Acquisition Sensor

[0092] 114: Passenger Information Acquisition Sensor

[0093] 120: Processor

[0094] 122: Passenger Behavior Estimation Unit

[0095] 130: Passenger protection device

[0096] 140: Storage unit

Claims

1. A vehicle for protecting passengers, comprising: Multiple passenger protection devices; A first sensor is configured to acquire passenger information related to the passengers of the vehicle; The second sensor is configured to acquire vehicle motion information in the event of a collision with another object; as well as The processor is operatively connected to the passenger protection device, the first sensor, and the second sensor. The processor is configured as follows: The passenger parameters of the passenger behavior model are updated based on the passenger information, and the passenger parameters include the tilt and rotation motion coefficients of a specified mass. Passenger behavior information under collision conditions is estimated by inputting the passenger information and the vehicle motion information into the passenger behavior model whose passenger parameters are updated. The operation of at least one of the plurality of passenger protection devices is controlled based on the estimated passenger behavior information, and The passenger information includes information about the passenger's seat status, and The status information regarding the passenger's seat includes at least one of the following: the seat's rotation angle, the lateral distance between the seat and another seat, and the lateral distance between the seat and the vehicle door.

2. The vehicle according to claim 1, wherein, The information regarding the passenger's seat status also includes at least one of the seat's tilt angle, the seat's sliding position, and the longitudinal distance between the seat and the dashboard.

3. The vehicle according to claim 1, wherein, The vehicle motion information includes at least one of yaw rate, pitch rate, roll rate, and acceleration applied to the vehicle in the event of a collision.

4. The vehicle according to claim 1, in, The passenger parameters also include at least one of the pitch and rotation motion coefficients of the specified mass and the linear motion coefficients of the specified mass.

5. The vehicle according to claim 1, wherein, The designated mass includes at least one of the head, chest, and lower body.

6. The vehicle according to claim 5, wherein, The passenger behavior information includes at least one of the state information of the specified mass regarding rotational motion and the state information of the specified mass regarding linear motion. The state information regarding rotational motion includes at least one of pitch angle, pitch rate, roll angle, and roll rate. The state information regarding linear motion includes the amount of movement.

7. The vehicle according to claim 6, wherein, The processor is also configured to: The passenger collision pattern is determined based on the passenger behavior information. Based on the determined passenger collision mode, at least one passenger protection device to be operated among the plurality of passenger protection devices is determined. as well as Control the operation of the determined passenger protection devices; and The passenger collision mode includes at least one of the following: a roll behavior amplification mode, a pitch behavior amplification mode, and a slump mode.

8. The vehicle according to claim 7, wherein, The processor is also configured to: When at least one of the roll angle and roll rate of the specified mass point is greater than a specified roll threshold, the roll behavior amplification mode is determined as the passenger collision mode, and When at least one of the pitch angle and pitch rate of the specified mass point is greater than the specified pitch threshold, the pitch behavior amplification mode is determined as the passenger collision mode, and When the movement of the specified mass point is greater than the specified movement threshold, the sinking mode is determined as the passenger collision mode.

9. The vehicle according to claim 1, wherein, The passenger parameters are updated based on a lookup table of standard male and female rotational motion coefficients representing each seat condition.

10. The vehicle according to claim 9, wherein, The processor is also configured to: Check at least one rotational motion coefficient corresponding to the passenger's seat status information in the lookup table, and Updated passenger parameters are obtained by applying a correction factor based on whether the passenger is wearing a seatbelt to the at least one rotational motion coefficient.

11. A method for operating a vehicle to protect passengers, comprising: Obtain passenger information related to the passengers in the vehicle; Obtain vehicle motion information in the event of a collision with another object; The passenger parameters of the passenger behavior model are updated based on the passenger information, and the passenger parameters include the tilt and rotation motion coefficients of a specified mass. Passenger behavior information in a collision scenario is estimated by inputting the passenger information and the vehicle motion information into the passenger behavior model whose passenger parameters are updated. as well as The operation of at least one of a plurality of passenger protection devices installed in the vehicle is controlled based on the estimated passenger behavior information. The passenger information includes information about the passenger's seat status, and The status information regarding the passenger's seat includes at least one of the following: the seat's rotation angle, the lateral distance between the seat and another seat, and the lateral distance between the seat and the vehicle door.

12. The method according to claim 11, wherein, The information regarding the passenger's seat status also includes at least one of the seat's tilt angle, the seat's sliding position, and the longitudinal distance between the seat and the dashboard.

13. The method according to claim 11, wherein, The vehicle motion information includes at least one of yaw rate, pitch rate, roll rate, and acceleration applied to the vehicle in the event of a collision.

14. The method according to claim 11, in, The passenger parameters also include at least one of the pitch and rotation motion coefficients of the specified mass and the linear motion coefficients of the specified mass.

15. The method according to claim 11, wherein, The designated mass includes at least one of the head, chest, and lower body.

16. The method according to claim 15, wherein, The passenger behavior information includes at least one of the state information of the specified mass regarding rotational motion and the state information of the specified mass regarding linear motion. The state information regarding rotational motion includes at least one of pitch angle, pitch rate, roll angle, and roll rate. The state information regarding linear motion includes the amount of movement.

17. The method according to claim 16, wherein, Controlling the operation of at least one of a plurality of passenger protection devices includes: The passenger collision pattern is determined based on the passenger behavior information. Based on the determined passenger collision mode, at least one passenger protection device is determined among the plurality of passenger protection devices to be operated; and Operate the designated passenger protection devices. The passenger collision mode includes at least one of the following: a roll behavior amplification mode, a pitch behavior amplification mode, and a slump mode.

18. The method according to claim 17, wherein, Determining passenger collision patterns includes: When at least one of the roll angle or roll rate of the specified mass point is greater than the specified roll threshold, the roll behavior amplification mode is determined as the passenger collision mode. When at least one of the pitch angle or pitch rate of the specified mass point is greater than a specified pitch threshold, the pitch behavior amplification mode is determined as the passenger collision mode; and When the movement of the specified mass point is greater than the specified movement threshold, the sinking mode is determined as the passenger collision mode.

19. The method according to claim 11, wherein, The passenger parameters are updated based on a lookup table of standard male and female rotational motion coefficients representing each seat condition.

20. The method according to claim 19, wherein, The passenger parameters for updating the passenger behavior model include: Check at least one rotational motion coefficient corresponding to the passenger's seat status information in the lookup table, and Updated passenger parameters are obtained by applying a correction factor based on whether the passenger is wearing a seatbelt to the at least one rotational motion coefficient.

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