Method and apparatus for generating control logic for vehicle safety devices

Abstract

A control logic derivation method and apparatus are provided. The method can include obtaining vehicle behavior data associated with a vehicle crash through a driving simulation of the vehicle, obtaining a load level applied to the vehicle and an occupant of the vehicle by applying the obtained vehicle behavior data to an accident scene reproduction model, obtaining a severity value of the vehicle crash based on the load level, estimating occupant behavior and injury based on a safety analysis model, configuring a full factorial design of experiment (DOE) based on the occupant behavior and injury, estimating one or more injury trend values associated with the vehicle crash based on the full factorial DOE, and determining a control logic and a duration in which the control logic is to be activated based on the injury trend values.

Description

Cross-references to related applications

[0001] This application claims priority and benefit to Korean Patent Application No. 10-2025-0058013, filed with the Korean Intellectual Property Office on May 2, 2025, the entire contents of which are incorporated herein by reference. Technical Field

[0002] This disclosure relates to vehicle safety equipment, and more specifically, to a method and apparatus for deriving control logic for vehicle safety equipment. Background Technology

[0003] In some implementations, under standard crash test conditions with seat positions and angles fixed, vehicle safety equipment controls are manually executed based on the vehicle's crash characteristics. This control method can be designed for reproducibility under limited test conditions and precise test environments. Therefore, this method may have limitations when testing diverse real-world passenger seating positions and usage conditions. However, in real-world road environments, passenger usage environments can vary, including different passenger physical conditions, seat positions, seat angles, and seating habits. In other words, complex driving environments and on-site accident scenarios may exist that are difficult to predict and test. Therefore, some implementations utilizing safety equipment control logic based solely on typical test conditions may struggle to adequately ensure passenger protection performance.

[0004] The descriptions in this background section are intended only to enhance the understanding of the background of this disclosure and should not be construed as an admission that such content corresponds to prior art known to those skilled in the art. Summary of the Invention

[0005] This disclosure attempts to provide a method and apparatus for deriving control logic for vehicle safety equipment, which can provide active and optimized safety equipment control logic that reflects the passenger's seating position and on-site usage conditions.

[0006] According to one or more exemplary embodiments of this disclosure, a method performed by a vehicle's device may include: obtaining vehicle behavior data through a driving simulation of the vehicle, the vehicle behavior data being associated with a vehicle collision; obtaining load levels applied to the vehicle and its passengers by applying the obtained vehicle behavior data to an accident scenario reproduction model; obtaining a severity value of the vehicle collision based on the load levels applied to the vehicle and passengers, the severity value indicating a severe accident scenario; estimating passenger behavior and injuries based on a safety analysis model that considers the seating positions of passengers in a severe accident scenario; configuring a full factorial design of experiment (DOE) based on the passenger behavior and injuries; estimating one or more injury trend values ​​based on the full factorial DOE, the injury trend values ​​being associated with a vehicle collision; determining control logic and a duration based on the injury trend values, during which the control logic will be activated; and controlling the operation of the vehicle based on the control logic. The design factors of the full factorial DOE may include the seating positions of the passengers.

[0007] Estimating one or more injury trend values ​​may include identifying the body parts most affected by vehicle braking or collision.

[0008] Estimating one or more injury trend values ​​may include plotting the location of a human body part on the x-axis at the time of a vehicle collision and plotting the injury value associated with the human body part on the y-axis.

[0009] The method may further include determining the location of a human body part based on: a first indicator associated with the driver's seat of the vehicle, corresponding to the shortest distance between the tip of the passenger's nose and the steering wheel of the vehicle; and a second indicator associated with the passenger seat of the vehicle, corresponding to the shortest distance between the tip of the nose of the crash test dummy and the crash pad.

[0010] The method may further include: determining the injury value associated with the body part based on the Head Injury Criterion (HIC) value. The HIC value can be determined based on the integral of the acceleration of the head of a crash test dummy in a vehicle over time.

[0011] Determining the duration can include classifying the location of human body parts at the time of a vehicle collision based on HIC values.

[0012] Determining the duration can further include classifying the position of the body parts at the time of the vehicle collision based on the instantaneous acceleration value of the body parts exceeding a threshold.

[0013] Determining the control logic may include controlling the vehicle's safety equipment to adjust the position of human body parts at the time of a vehicle collision to belong to different time intervals in a graph that includes one or more injury trend values.

[0014] Determining the control logic may include controlling the vehicle's safety equipment to adjust the injury values ​​of human body parts at the time of a vehicle collision to belong to different time intervals in a graph, which includes one or more injury trend values.

[0015] Estimating one or more injury trend values ​​may include: performing a first analysis based on the results of a full factorial DOE analysis using at least one of a correlation plot, an analysis of mean plot (ANOM), a response surface, and a serpentine plot; and performing a second analysis based on the amount of movement of a crash test dummy in a vehicle.

[0016] According to one or more exemplary embodiments of this disclosure, a device for a vehicle may include: a processor; and a memory that can store at least one instruction configured to, when executed by the processor, cause the device to: perform a driving simulation of the vehicle; obtain vehicle behavior data associated with a vehicle collision through the driving simulation; obtain a load level applied to the vehicle and its passengers by applying the obtained vehicle behavior data to an accident scene reproduction model; obtain a severity value of the vehicle collision based on the load levels applied to the vehicle and passengers, the severity value indicating a severe accident scenario; estimate passenger behavior and injuries based on a safety analysis model that considers the seating positions of passengers in a severe accident scenario; configure a full factorial design (DOE) based on passenger behavior and injuries; estimate one or more injury trend values ​​associated with a vehicle collision based on the full factorial design (DOE); determine control logic and a duration based on the injury trend values, during which the control logic will be activated; and control the operation of the vehicle based on the control logic. The design factors of the full factorial design (DOE) may include the seating positions of the passengers.

[0017] The at least one instruction can be configured, when run by the processor, to cause the device to estimate one or more injury trend values ​​by determining the body parts most affected by vehicle braking or collision.

[0018] The at least one instruction can be configured, when run by the processor, to cause the device to estimate one or more injury trend values ​​by plotting the location of the human body parts at the time of the vehicle collision on the x-axis and plotting the injury values ​​associated with the human body parts on the y-axis.

[0019] The at least one instruction can be configured, when executed by the processor, to further cause the device to: determine the position of a human body part based on: a first indicator, associated with the driver's seat of the vehicle, corresponding to the shortest distance between the tip of the passenger's nose and the steering wheel of the vehicle; and a second indicator, associated with the passenger seat of the vehicle, corresponding to the shortest distance between the tip of the nose of the crash test dummy and the crash pad.

[0020] At least one instruction can be configured, when executed by the processor, to further enable the device to: determine an injury value associated with a body part based on a Head Injury Criterion (HIC) value. The HIC value can be determined based on the integral of the acceleration of the head of a crash test dummy in a vehicle over time.

[0021] The at least one instruction can be configured, when run by the processor, to cause the device to determine the duration by classifying the location of human body parts at the time of a vehicle collision based on HIC values.

[0022] The at least one instruction can be configured, when executed by the processor, to cause the device to further classify the position of the human body parts at the time of the vehicle collision based on the duration of the instantaneous acceleration value of the body parts exceeding a threshold.

[0023] The at least one instruction can be configured, when executed by the processor, to cause the device to determine control logic by controlling the vehicle's safety equipment to adjust the position of human body parts at the time of a vehicle collision to belong to different time intervals in a graph that includes one or more injury trend values.

[0024] The at least one instruction can be configured, when executed by the processor, to cause the device to determine control logic by controlling the vehicle's safety equipment to adjust the injury values ​​of human body parts at the time of a vehicle collision to belong to different time intervals in a graph, which includes one or more injury trend values.

[0025] According to one or more exemplary embodiments of the present disclosure, a non-transitory computer-readable medium may store instructions that, when executed by a computing device, cause the computing device to: obtain vehicle behavior data associated with a vehicle collision through a driving simulation of a vehicle collision; identify, based on the vehicle behavior data, a relationship between one or more locations of a passenger's body parts and corresponding injury severity values; determine control logic for vehicle safety equipment based on the relationship between one or more locations of the body parts and corresponding injury severity values; and control the vehicle safety equipment based on the control logic. Attached Figure Description

[0026] Figure 1 A block diagram is shown for a control logic derivation device used to describe an exemplary vehicle safety device.

[0027] Figure 2 A flowchart is shown to describe a control logic derivation method for an exemplary vehicle safety device.

[0028] Figure 3 , Figure 4 , Figure 5 and Figure 6A view is shown illustrating an example implementation of a method and device for describing the control logic derivation of vehicle safety equipment.

[0029] Figure 7 A block diagram illustrating an example computing device is shown. Detailed Implementation

[0030] In the following description, the present disclosure will be described more fully with reference to the accompanying drawings, in which one or more exemplary embodiments of the present disclosure are illustrated. As will be appreciated by those skilled in the art, the exemplary embodiments may be modified in various different ways without departing from the spirit or scope of the present disclosure. Therefore, the drawings and description are to be considered illustrative in nature and not restrictive. Throughout the specification, the same reference numerals denote the same elements.

[0031] Furthermore, throughout the specification and claims, unless expressly stated otherwise, the word "comprising" and variations such as "including" or "containing" shall be understood to mean including the stated elements but not excluding any other elements. Terms including ordinal numbers such as first, second, etc., shall be used only to describe the individual components and shall not be construed as limiting those components. Terms are used only to distinguish one component from others.

[0032] For the purposes of this application and claims, the exemplary phrases “at least one of A; B; or C” or “at least one of A, B, or C” are used, which means “at least one A, or at least one B, or at least one C, or any combination of at least one A, at least one B, and at least one C.” Furthermore, exemplary phrases such as “at least one of A, B, or C,” “at least one of A, B, and C,” and “at least one of A, B, or C” as used herein may refer to each of the listed items or all possible combinations of the listed items. For example, “at least one of A or B” may refer to (1) at least one A; (2) at least one B; or (3) at least one A and at least one B.

[0033] Unless otherwise defined, the terms used herein, including technical or scientific terms, may have the meaning commonly understood by one of ordinary skill in the art to which this disclosure pertains.

[0034] As used herein, expressions such as “including,” “may include,” “contain,” “may contain,” “have,” and “may have” are intended to indicate the presence of features (e.g., functionality, operation, component, etc.) and do not exclude the presence of other additional features. That is, these expressions should be understood as open-ended terms that include the possibility of including other examples.

[0035] Unless otherwise stated in the context, the singular expression used herein may include the meaning of the plural, and this also applies to the singular expressions described in the claims.

[0036] Unless otherwise specified in the context, expressions such as “first” or “second” as used herein are used to distinguish one object from another when referring to multiple similar objects, and do not limit the order or importance of the objects. For example, multiple chips according to this disclosure can be distinguished from each other by referring to them respectively as “first chip” and “second chip”.

[0037] As used herein, the term "cell" can refer to a software or hardware component, such as a field-programmable gate array (FPGA), an application-specific integrated circuit (ASIC), etc. However, "cell" is not limited to hardware or software. A "cell" can be configured to be stored in addressable memory or to run one or more processors. A "cell" can include components such as software components, object-oriented software components, class components, and task components, as well as processors, functions, properties, programs, subroutines, program code segments, drivers, firmware, microcode, circuits, data, databases, data structures, tables, arrays, and variables.

[0038] As used herein, the expression “based on” is intended to describe one or more factors that influence the behavior or operation described in the phrase or sentence that includes the expression, and the expression does not exclude any additional factors that influence the behavior or operation that influences the determination or decision.

[0039] As used herein, when describing a component (e.g., a first component) as “connected” or “linked” to another component (e.g., a second component), this may mean that the component is not only directly connected or linked to the other component, but also connected or linked through yet another component (e.g., a third component).

[0040] Depending on the context, as used herein, the expression "configured as" can have meanings such as "set up as," "possess the ability to," "modify as," "enable," or "enable." This expression is not limited to the meaning of "specifically designed in hardware." For example, a processor configured to perform a specific operation can refer to a general-purpose processor capable of performing that operation by running software, or to a special-purpose computer constructed through programming to perform that operation.

[0041] According to the Society of Automotive Engineers (SAE), the automation levels of autonomous vehicles can be classified as follows: At Level 0, the SAE classification corresponds to "No Automation," where the automated system intervenes temporarily in emergency situations (e.g., automatic emergency braking) and / or only provides warnings (e.g., blind spot warning, lane departure warning, etc.), requiring the driver to operate the vehicle. At Level 1, the SAE classification corresponds to "Driver Assistance," where the system performs some driving functions (e.g., steering, acceleration, braking, lane centering, adaptive cruise control, etc.), while in normal operating conditions, the driver operates the vehicle and is required to determine the system's operating status and / or timing, perform other driving functions, and respond to (e.g., handle) emergency situations. At Level 2, the SAE classification corresponds to "Partial Automation," where the system performs steering, acceleration, and / or braking under driver supervision, requiring the driver to determine the system's operating status and / or timing, perform other driving functions, and respond to (e.g., handle) emergency situations. At Level 3 of automated driving, the SAE classification standard can correspond to "conditional automation," where the system drives the vehicle under limited conditions (e.g., performing driving functions such as steering, acceleration, and / or braking), but transfers driving control to the driver when the required conditions are not met, requiring the driver to determine the system's operating state and / or timing, and to take over control in emergency situations, but otherwise does not operate the vehicle (e.g., steering, acceleration, and / or braking). At Level 4 of automated driving, the SAE classification standard can correspond to "high automation," where the system performs all driving functions and requires the driver to take over control of the vehicle only in emergency situations. At Level 5 of automated driving, the SAE classification standard can correspond to "full automation," where the system performs all driving functions without any assistance from the driver, including in emergency situations, and does not require the driver to perform any driving functions other than determining the system's operating state. Although this disclosure can apply the SAE classification standard to automated driving classification, other classification methods and / or algorithms can be used in one or more configurations described herein. One or more features associated with automated driving control can be activated based on configured automated driving control settings (e.g., based on automated driving classification, selection of the vehicle's automated driving level, etc.).

[0042] Vehicle operation can be controlled based on one or more features described herein (e.g., applying vehicle behavior data to an accident scene reconstruction model). Vehicle control can include various operational controls associated with the vehicle (e.g., autonomous driving control, sensor control, braking control, braking time control, acceleration control, rate of change of acceleration control, warning timing control, forward collision warning timing control, etc.).

[0043] One or more auxiliary devices (e.g., engine brakes, exhaust brakes, hydraulic reducers, electric reducers, regenerative brakes, etc.) can also be controlled, for example, based on one or more features described herein (e.g., applying vehicle behavior data to an accident scene reconstruction model). Similarly, one or more communication devices (e.g., modems, network adapters, radio transceivers, antennas, etc., capable of communicating via one or more wired or wireless communication protocols such as Ethernet, Wi-Fi, Near Field Communication (NFC), Bluetooth, Long Term Evolution (LTE), 5G New Radio (NR), Vehicle-to-Everything (V2X) etc.) can also be controlled, for example, based on one or more features described herein (e.g., applying vehicle behavior data to an accident scene reconstruction model).

[0044] Minimum Risk Maneuvering (MRM) operations can also be controlled, for example, based on one or more features described herein (e.g., applying vehicle behavior data to an accident scene reconstruction model). Minimum Risk Maneuvering operations (e.g., minimum risk maneuver, minimum risk control) can be maneuvers of the vehicle to minimize (e.g., reduce) the risk of collision with surrounding vehicles, thereby achieving a reduced (e.g., minimum) risk state. Minimum Risk Maneuvering can be an operation that can be activated during autonomous driving when the driver is unable to respond to an intervention request. During Minimum Risk Maneuvering, one or more processors of the vehicle can control the vehicle's driving operations for a set time period.

[0045] Bias driving can also be controlled, for example, based on one or more features described herein (e.g., applying vehicle behavior data to an accident scene reconstruction model). The driving control device can perform bias driving control. To perform bias driving, the driving control device can control the vehicle to stay within the lane by maintaining the lateral distance between the vehicle's center position and the lane center. For example, the driving control device can control the vehicle to stay within the lane, rather than at the lane center.

[0046] The driving control equipment can identify a target lateral distance for offset driving control. For example, the target lateral distance may include an intentionally adjusted lateral distance that the vehicle can aim to maintain during maneuvers such as lane changes, relative to a reference point such as the center of the lane or another vehicle. This adjustment can be made to improve the vehicle's stability, safety, and / or performance under different driving conditions. For example, during a lane change, the driving control system may offset the lateral distance to maintain a safer distance from adjacent vehicles, taking into account factors such as vehicle speed, road conditions, and / or the presence of obstacles.

[0047] The level of autonomous driving and / or activation / deactivation of autonomous driving can also be controlled, for example, based on one or more features described herein (e.g., applying vehicle behavior data to an accident scene reconstruction model). The driving control device can perform autonomous driving level control (e.g., changing the level of autonomous driving, changing the required user attention, etc.) or deactivate autonomous driving. For example, by changing the required user attention, the driver can be instructed to place their hands on the steering wheel more frequently (e.g., at least once within threshold time periods such as 5 seconds, 30 seconds, 1 minute). By changing the required user attention, the driver can be instructed to look forward more frequently (e.g., at least once within threshold time periods such as 5 seconds, 30 seconds, 1 minute). By changing the level of autonomous driving, one or more video contents may not be displayed on the vehicle's monitors.

[0048] One or more sensors (e.g., IMU sensors, cameras, LIDAR, RADAR, blind spot monitoring sensors, lane departure warning sensors, parking sensors, light sensors, rain sensors, traction control sensors, anti-lock braking system sensors, tire pressure monitoring sensors, seat belt sensors, airbag sensors, fuel sensors, emission sensors, throttle position sensors, inverters, converters, motor controllers, power distribution units, high-voltage wiring and connectors, auxiliary power modules, charging interfaces, etc.) can also be controlled based on one or more features described herein (e.g., applying vehicle behavior data to an accident scene reconstruction model).

[0049] Operational control for autonomous driving of a vehicle may include various driving controls of the vehicle performed by vehicle control units (e.g., acceleration, deceleration, steering control, gear shifting control, braking system control, traction control, stability control, cruise control, lane keeping assist control, collision avoidance system control, emergency braking assist control, traffic sign recognition control, adaptive headlight control, driver warning control, autonomous driving operation design domain (ODD), enabling and / or disabling autonomous driving modes, etc.). One or more auxiliary devices (e.g., engine brakes, exhaust brakes, hydraulic reducers, electric reducers, regenerative brakes, etc.) may also be controlled, for example, based on one or more features described herein (e.g., applying vehicle behavior data to an accident scene reconstruction model). One or more communication devices (e.g., modems, network adapters, radio transceivers, antennas, etc., capable of communicating via one or more wired or wireless communication protocols such as Ethernet, Wi-Fi, Near Field Communication (NFC), Bluetooth, Long Term Evolution (LTE), 5G New Radio (NR), Vehicle-to-Everything (V2X), etc.) may also be controlled, for example, based on one or more features described herein (e.g., assessing the driver's engagement status).

[0050] Vehicles actively controlled by an autonomous driving system can be referred to as autonomous vehicles, host vehicles, or autonomous vehicles. Autonomous vehicles can also be referred to as autonomous driving cars, autonomous vehicles (AC), driverless cars, driverless taxis, robotic cars, or robotic vehicles. An autonomous vehicle can be a vehicle equipped with an autonomous driving system. Optionally, the autonomous driving system can control the autonomous vehicle, for example, from an external and / or remote device such as a server. The autonomous vehicle can be remotely controlled (e.g., navigating, driving, etc.) partially or entirely by a remote human driver. A vehicle in front of an autonomous vehicle (e.g., in the same lane as the autonomous vehicle) can be referred to as a vehicle in front (e.g., directly in front), a leading vehicle, a vehicle in the lead, or a vehicle ahead. A vehicle following an autonomous vehicle (e.g., in the same lane as the autonomous vehicle) can be referred to as a vehicle behind, a vehicle following, a vehicle following, or a vehicle behind. An adjacent vehicle can refer to any vehicle located in any direction (e.g., in front, behind, left, right, diagonally opposite, etc.) of the autonomous vehicle, provided (e.g., regardless of distance from the autonomous vehicle) there are no other vehicles (e.g., intervening vehicles) between it and the autonomous vehicle. Optionally, in some cases, only those vehicles located within a threshold distance of the autonomous vehicle (e.g., the line-of-sight and / or detection limits of one or more sensors of the autonomous vehicle) can be referred to as neighboring vehicles. A target vehicle can be any vehicle close to the autonomous vehicle (e.g., within the threshold distance). A target vehicle can be any vehicle actively or passively monitored, identified, marked, tracked, and / or analyzed by the autonomous driving system, once or multiple times, sporadically or continuously. The threshold distance can be, for example, the line-of-sight and / or detection limits of one or more sensors of the autonomous vehicle, but the threshold distance can be a value smaller than the line-of-sight and / or detection limits of one or more sensors of the autonomous vehicle (e.g., an adjustable value). A target vehicle can be, for example, a vehicle in front, a vehicle behind, a vehicle in a lane different from the autonomous vehicle's lane of travel (e.g., a vehicle on the left, a vehicle on the right, a vehicle diagonally opposite, etc.), and / or an adjacent vehicle (e.g., regardless of distance from the autonomous vehicle and / or regardless of whether there are intervening vehicles between the target vehicle and the autonomous vehicle). Target vehicles can also be referred to as surrounding vehicles, nearby vehicles, external vehicles, another vehicle (other vehicles), etc.

[0051] Terms such as "component," "part," and "module" described in this specification can refer to a unit capable of performing at least one function or operation described in this specification, and this can be implemented by hardware (e.g., circuitry, control circuitry, processor, etc.), software, or a combination of hardware or circuitry and software. Furthermore, at least some of the components or functions of the control logic derivation method and device for vehicle safety equipment according to the exemplary embodiments described below can be implemented as a program or software, and this program or software can be stored in a computer-readable recording medium or storage medium.

[0052] Figure 1 A block diagram is shown for a control logic derivation device used to describe an exemplary vehicle safety device.

[0053] Reference Figure 1 The control logic derivation device 10 for vehicle safety equipment can be implemented as a computing device, which includes a processor and a memory. For example, the control logic derivation device 10 can be implemented as shown below. Figure 7 The computing device 50 described herein. In this case, the processor may correspond to the processor 510 of the computing device 50, and the memory may correspond to the memory 530 of the computing device 50. Optionally, the control logic derivation device 10 may include one or more non-transitory computer-readable media and one or more processors, the non-transitory computer-readable media containing instructions for the processor to execute the instructions to perform operations. In this document, operations may include the configurations, functions, steps, etc., described in this specification with respect to the control logic derivation method and device according to exemplary embodiments. In this specification, the term "module" is used to logically distinguish these operations performed by the control logic derivation method and device according to this disclosure.

[0054] The control logic derivation device 10 may include a vehicle behavior data extraction module 11, a severe scenario determination module 12, an injury trend analysis module 13, and a control logic derivation module 14.

[0055] The vehicle behavior data extraction module 11 can extract (e.g., obtain) vehicle behavior data related to the occurrence of a collision (e.g., vehicle impact) by performing a driving simulation of the vehicle. The vehicle behavior data extraction module 11 can perform driving simulations in specific accident scenarios or in non-specific random traffic environments, based on pre-prepared simulation tools or a joint simulation environment with pre-prepared vehicles, such as intersection collisions, rear-end collisions, overlapping collisions occurring during lane changes, etc.

[0056] The vehicle behavior data extraction module 11 can continuously collect dynamic behavior information of the test vehicle and surrounding vehicles during the simulation, such as real-time position, speed, acceleration, steering angle, and braking operation. It can also extract (e.g., obtain) vehicle behavior data corresponding to a time period from, for example, 5 seconds before the collision to 1 second after the collision, based on the time of the collision (e.g., vehicle impact). In some exemplary embodiments, the vehicle behavior data can be stored in a standardized file format (e.g., comma-separated values ​​(.csv)).

[0057] The severe scenario determination module 12 can input vehicle behavior data extracted by the vehicle behavior data extraction module 11 into the accident scenario reproduction model. It can determine (e.g., calculate) the load level applied to the vehicle and passengers, and determine the severity of the accident to identify a severe accident scenario. Specifically, the severe scenario determination module 12 can configure the accident reproduction model using physical characteristic information of the test vehicle and the relative vehicle, as well as pre-collision vehicle behavior data and accident scenario information provided by the vehicle behavior data extraction module 11, as accident scenario input values. Furthermore, the severe scenario determination module 12 can determine (e.g., calculate) the load level applied to the vehicle and passengers by evaluating the configured accident scenario reproduction model, and can also obtain vehicle behavior data at the time after the collision (during the collision). Based on such load determination (e.g., calculation) results and vehicle dynamics analysis results, the severity of each accident scenario (e.g., severity value) can be quantitatively evaluated, and if the severity (e.g., severity value) exceeds a specific reference, the severe scenario determination module 12 can determine the accident scenario as a severe accident scenario. The severe accident scenario derived at this time can correspond to a scenario with high impact intensity and a high probability of causing serious injury to passengers.

[0058] The injury trend analysis module 13 can use a safety analysis model to assess passenger behavior and injuries, taking into account the passenger's seating position in a serious accident scenario. In this case, seating position can be considered as a variable by considering factors such as seat position, seat back angle, and seat depth, which reflect the passenger's actual usage conditions. Furthermore, the injury trend analysis module 13 can configure a full factorial experimental design (DOE) based on data about passenger behavior and injuries, according to design factors including seating position. Additionally, the injury trend analysis module 13 can analyze injury trends (e.g., one or more injury trend values ​​indicating the likelihood of a passenger suffering injury) based on the full factorial DOE.

[0059] In this paper, full factorial design of experiment (DOE) can represent an experimental design technique that performs analysis by configuring all possible combinations of the levels of each factor for all design factors included in the experiment. Full factorial design of experiment (DOE) has the advantage of being able to quantitatively assess not only the main effect of each factor but also the interaction effects between factors, and can be used to precisely analyze the impact of design factors on passenger behavior and injury levels.

[0060] For example, if the collision pattern has six levels (e.g., frontal collision, right corner collision, small overlap collision, offset collision, left corner collision, and low-speed collision) and the seating position has 18 levels, then full factorial DOE can quantitatively assess passenger behavior and injury response for all combinations of variables by performing analysis on each of the 108 conditions. This process identifies the probability of injury for each combination and can detect trends showing a sharp increase in injury under specific conditions, allowing for the identification of risk conditions requiring control logic.

[0061] The injury trend analysis module 13 can perform a first correlation analysis based on the analysis results obtained through a full factorial design (DOE) using techniques such as correlation plots, analysis of means plots (ANOM), response surfaces, or serpentine plots. It can also perform a second analysis based on the movement of a dummy (e.g., a crash test dummy). Therefore, the first correlation analysis can intuitively derive the relationship between design factors and variables that have a major impact on injury, and the second analysis can further analyze injury sensitivity based on location-based data such as the dummy's head movement distance and changes in seating position.

[0062] The injury trend analysis module 13 can deduce (e.g., determine) the body parts (e.g., body parts) that are most affected (e.g., most vulnerable) by vehicle braking or a collision. For example, if an impact analysis based on the interpretation results shows that the head experiences higher acceleration than other body parts during braking or a collision, head injuries can be identified as a part that should be given priority.

[0063] The injury trend analysis module 13 can analyze injury trends (e.g., one or more injury trend values ​​indicating the likelihood of injury to a passenger) by defining the position of a human body part at the time of a collision as the x-axis (e.g., plotting the position of a human body part at the time of a vehicle collision on the x-axis) and defining the injury value of the human body part as the y-axis (e.g., plotting the injury value associated with the human body part on the y-axis). In this paper, the position value of the human body part can be determined using a first index and a second index. The first index is defined as the shortest distance between the tip of the nose of a passenger in the driver's seat and the steering wheel, and the second index is defined as the shortest distance between the tip of the nose of a dummy in the passenger seat (e.g., the nose of a crash test dummy sitting in the passenger seat) and the impact pad. Using these first and second indices, the passenger's seating position can be quantitatively represented. The injury value of the human body part can be determined using a Head Injury Criterion (HIC) value, which can be determined (e.g., calculated) by integrating the acceleration of the dummy's head over time (e.g., determining the integral of the dummy's head acceleration over time). The HIC can be an index that uses numbers to represent the degree of injury. For example, a larger HIC value may indicate a greater degree of injury.

[0064] The control logic derivation module 14 can derive (e.g., determine) the control logic application range and the control logic non-application range from the damage trend analyzed by the damage trend analysis module 13.

[0065] The control logic derivation module 14 can classify the location of human body parts (e.g., the head) at the time of collision based on HIC values ​​to derive (e.g., determine) control logic application and non-application zones. In this case, for example, a reference HIC value of HIC 560 can be applied, which is 80% of HIC 700 corresponding to AIS Level 3, and if this reference value is exceeded, it can be deduced as a risk zone requiring the application of control logic. Therefore, the control logic derivation module 14 can identify the head position in the relevant zone and derive (e.g., determine, generate, etc.) control logic that minimizes the risk of injury.

[0066] The control logic derivation module 14 can classify the position of human body parts during a collision based on the HIC value, and can further derive the control logic application range and the control logic non-application range by considering a bottoming-out index, which is defined as the situation where the instantaneous acceleration value of the head (e.g., a body part) exceeds a threshold (e.g., 80G). A bottoming-out phenomenon can refer to the following situation: during a vehicle collision, the head of a dummy (e.g., a crash test dummy) directly contacts the internal structure of the vehicle, resulting in rapid injury, and the control logic derivation module 14 can derive the range where this phenomenon may occur as the risk range requiring control logic application.

[0067] The control logic derivation module 14 can derive the control logic activated within the derived control logic application range. The derived control logic can be set based on the specifications of the safety equipment installed in the vehicle, as well as response time, operating pressure, and deployment methods. Furthermore, it can design control methods such as advance operation, delayed operation, and load adjustment in response to each application range.

[0068] The control logic derivation module 14 can control the safety equipment before the collision time to change the position of human body parts at the time of collision to different intervals (e.g., different durations or intervals) in a graph analyzing injury trends. In other words, the vehicle's safety equipment can be controlled to adjust (e.g., move) the position of human body parts at the time of collision to different time intervals in a graph analyzing one or more injury trend values. For example, by controlling the direction of a passenger's head to a non-dangerous zone, it may be possible to move the passenger out of the danger zone at the time of collision. This pre-control can be control logic that reduces the risk of injury, especially in the time before a collision (e.g., a vehicle impact).

[0069] The control logic derivation module 14 can change the injury value of the human body parts at the time of collision to belong to different intervals in the injury trend analysis graph by controlling the safety equipment after the collision time. In this case, by controlling to reduce the load applied to the head, the injury value can be moved from the existing risk interval to a non-risk interval. This can be a response control in post-collision situations, or it can be the control logic that immediately follows the collision to protect passengers.

[0070] By reflecting passenger seating positions and on-site usage conditions, it is possible to derive and apply optimized safety equipment control logic based on collision scenarios. Therefore, it is possible to implement active safety control systems capable of flexibly responding to actual accident situations, rather than passive control methods based on existing fixed test conditions. Furthermore, by analyzing passenger positions and injury trends at the time of a collision and applying control strategies suitable for pre-collision and in-collision conditions, it is possible to help reduce key injury indicators, such as head injuries, below reference values ​​and improve vehicle safety performance.

[0071] Figure 2 A flowchart is shown to describe a control logic derivation method for an exemplary vehicle safety device.

[0072] For convenience, Figure 2 This is described through an example, in which something such as... Figure 1 One or more processors in the modules shown are used to perform the steps. Figure 2 One, some, or all of the steps or parts thereof of the exemplary method may be performed by one or more other circuits (e.g., processors, circuits, control circuits, etc.). Figure 2 One or more steps of the exemplary method may be omitted, performed in a different order and / or modified in another way, and / or one or more additional steps may be added.

[0073] Reference Figure 2 According to the method used to generate the virtual scene, the processor can extract vehicle behavior data related to (e.g., associated with) the occurrence of a collision by performing a driving simulation of the vehicle (S201). The processor can input the extracted vehicle behavior data into an accident scene reproduction model and determine (e.g., calculate) the load level applied to the vehicle and passengers to determine the severity of the accident (e.g., severity value) and determine a severe accident scenario (S202). The processor can determine the load level, for example, by applying the obtained vehicle behavior data to the accident scene reproduction model. The processor can determine the severity value of the accident based on the load level, which indicates a severe accident scenario. The processor can use a safety analysis model to assess passenger behavior and injuries, which takes into account the seating position of passengers in a severe accident scenario (S203). The processor can configure a full factorial design element (DOE) based on data about passenger behavior and injuries, according to design factors including seating position (S204). The processor can analyze injury trends (e.g., injury values ​​or grades) based on the full factorial design element (DOE) (S205). The processor can deduce (e.g., generate) the control logic application range and the control logic non-application range from the damage trend (S206). The processor can deduce (e.g., generate) the control logic activated in the control logic application range (S207).

[0074] For more detailed information relating to the methods described above, please refer to the description of one or more other exemplary embodiments included in this specification; therefore, redundant descriptions will be omitted.

[0075] Figure 3 , Figure 4 , Figure 5 and Figure 6 A view is shown illustrating an example implementation of a method and device for describing the control logic derivation of vehicle safety equipment.

[0076] Reference Figure 3 and Figure 4 This illustrates the application range of control logic derived from head injury values. A reference HIC value can be applied as HIC 560, which is 80% of HIC 700 corresponding to AIS Level 3. If this reference value is exceeded, it can be derived as a risk range where control logic needs to be applied.

[0077] Reference Figure 5 The derived safety control logic can be divided into operational control before the collision time (e.g., pre-collision) and operational control after the collision time (e.g., during the collision), and each control operation can have the following directionality. First, in the pre-collision operation, the position of the passenger's head before the collision can be controlled so that the position is in zone B. Based on the injury trend analysis map, zone B has a relatively low risk, thereby reducing the risk of injury in advance. In the during-collision operation, in response to the collision that has already occurred, safety equipment control can be executed to reduce the head injury value of the passenger located in zone A or zone C to below the reference injury value.

[0078] Reference Figure 6 The integrated safety control logic of safety equipment can be formed by three operations: inference, evaluation, and control.

[0079] In the inference operation, the passenger's actual seating position can be determined (e.g., calculated) by utilizing seat-related information such as seat position, height, and seat back angle. In the evaluation operation, passenger behavior estimation logic can be used to predict the passenger's dynamic behavior based on vehicle behavior factors such as vehicle control CAN signals, braking deceleration force, and steering information. In the control operation, safety equipment control logic derived according to this disclosure can be driven. By comprehensively considering the passenger's initial position and predicted behavior, this logic can determine the optimal safety equipment control strategy for an accident situation. If it is determined that using only existing standard safety equipment in a collision situation would result in excessive passenger behavior and injury, the control logic can minimize passenger injury by responding appropriately to the situation and adjusting the operating time, method, load, etc., of the safety equipment.

[0080] Depending on the passenger's seating position at the time of a collision (e.g., impact), the safety equipment can be controlled as follows: When the passenger's seating position is further forward than the standard seating position, the safety equipment can be set to activate earlier and operate with a load equal to or less than that of the standard safety equipment. If the passenger's seating position is further rearward than the standard seating position, the safety equipment can be delayed in operation and adjusted to activate with a load greater than that of the standard safety equipment. Furthermore, safety equipment utilizing new technologies such as dual-depth airbags and pre-safe seats can be activated in a separate control method different from the control method described herein.

[0081] Figure 7 A block diagram illustrating an exemplary computing device is shown.

[0082] Reference Figure 7 The computing device 50 can be used to implement control logic derivation methods and devices for vehicle safety equipment. The computing device 50 can be implemented as various forms of electronic devices, servers, or similar devices, and its functions can be implemented through a combination of software and hardware.

[0083] The computing device 50 may include at least one of a processor 510, a memory 530, a user interface input device 540, a user interface output device 550, and a storage device 560, which communicate with each other via a bus 520. The computing device 50 may also include a network interface 570 electrically connected to the network 40. The network interface 570 can send signals to or receive signals from other components via the network 40.

[0084] Processor 510 can be implemented as various types of computing devices, such as microcontroller units (MCUs), application processors (APs), central processing units (CPUs), graphics processing units (GPUs), neural processing units (NPUs), quantum processing units (QPUs), etc. Processor 510 can be a semiconductor device that executes instructions stored in memory 530 or storage device 560, and can play a key role in the system. The program code and data stored in memory 530 or storage device 560 can instruct processor 510 to perform specific tasks, thereby enabling the general operation of the system. Processor 510 can be configured to implement the above-mentioned... Figures 1 to 6 The various functions and methods described.

[0085] Memory 530 and storage device 560 may include various forms of volatile or non-volatile storage media for storing and accessing system data. For example, memory 530 may include read-only memory (ROM) 531 and random access memory (RAM) 532. Memory 530 may be embedded within processor 510, in which case data transfer between memory 530 and processor 510 can be very fast. Memory 530 may be located externally to processor 510, in which case memory 530 may be connected to processor 510 via various data buses or interfaces. Such connection may be made through various means, such as a high-speed peripheral component interconnect (PCIe) interface for high-speed data transfer, or through a memory controller.

[0086] At least some of the components or functions of the control logic derivation method and device for vehicle safety equipment can be implemented as programs or software running on computing device 50, and the programs or software can be stored on a computer-readable recording medium or storage medium. Specifically, the computer-readable recording medium or storage medium can be a program recorded on a computer including processor 510, which executes programs or commands stored in memory 530 or storage device 560 to perform operations included in the implementation scheme of the method and device for deriving the control logic of vehicle safety equipment based on model profiling.

[0087] The control logic derivation method of the vehicle safety equipment and at least some of the components or functions of the equipment can be implemented using the hardware or circuitry of the computing device 50, or can be implemented as separate hardware or circuitry that can be electrically connected to the computing device 50.

[0088] The computing device 50 may be provided with one or more non-transitory computer-readable media containing executable instructions that, when executed by one or more processors of the computing device 50, cause the computing device 50 to perform operations. In this document, operations may include the configurations, functions, steps, etc., described herein with respect to the control logic derivation methods and devices for vehicle safety equipment.

[0089] By reflecting passenger seating positions and on-site usage conditions, it is possible to derive and apply optimized safety equipment control logic based on collision scenarios. Therefore, it is possible to implement active safety control systems capable of flexibly responding to actual accident situations, rather than passive control methods based on existing fixed test conditions. Furthermore, by analyzing passenger positions and injury trends during a collision (e.g., impact) and applying control strategies suitable for pre-collision and in-collision conditions, it is possible to help reduce key injury indicators, such as head injuries, below reference values ​​and improve vehicle safety performance.

[0090] A method for deriving control logic for vehicle safety equipment, executed by a computing device including a processor and memory, includes: the processor extracting vehicle behavior data related to a collision by performing a driving simulation of the vehicle; the processor inputting the extracted vehicle behavior data into an accident scenario reproduction model to calculate the load levels applied to the vehicle and passengers to determine the severity of the accident and identify a severe accident scenario; the processor evaluating passenger behavior and injuries using a safety analysis model that considers the seating position of passengers in a severe accident scenario; the processor configuring a full factorial experimental design (DOE) based on data regarding passenger behavior and injuries, according to design factors including seating position; the processor analyzing injury trends based on the full factorial DOE; the processor deriving the control logic application range and the control logic non-application range from the injury trends; and the processor deriving the control logic activated within the control logic application range.

[0091] Analyzing injury trends can include, by the processor, deducing the body parts most affected by vehicle braking or collision.

[0092] Analyzing injury trends can include: using a processor to analyze injury trends by defining the position of the human body part at the time of collision as the x-axis and the injury value of the human body part as the y-axis.

[0093] The position values ​​of human body parts can be determined using a first index and a second index. The first index is defined as the shortest distance between the tip of the nose of the passenger in the driver's seat and the steering wheel, and the second index is defined as the shortest distance between the tip of the nose of the dummy in the passenger seat and the anti-collision pad.

[0094] The injury value of a human body part can be determined using the Head Injury Criterion (HIC) value, which is calculated by integrating the acceleration of a dummy's head over time.

[0095] The derivation of the control logic application range and the control logic non-application range may include: the processor classifying the positions of human body parts at the time of collision based on the HIC value to derive the control logic application range and the control logic non-application range.

[0096] The derivation of the control logic application range and the control logic non-application range may include: the processor deriving the control logic application range and the control logic non-application range by classifying the position of the human body parts at the time of collision according to the HIC value and by taking into account the bottoming index, which is defined as the case where the instantaneous value of head acceleration exceeds 80G.

[0097] The derivation of the control logic activated in the control logic application range may include: by the processor, through the control of safety equipment before the collision time point, changing the position of the human body parts at the collision time point to belong to different intervals in the graph analyzing the injury trend.

[0098] The derivation of the control logic activated within the control logic application range can include: the processor, through the control of safety equipment after the collision time point, changing the damage value of the human body part at the collision time point to belong to different intervals in the graph analyzing the damage trend.

[0099] Analyzing injury trends may further include: a first correlation analysis performed by the processor, based on the analysis results performed via full factorial DOE, using correlation plots, analysis of mean plots (ANOM), response surface methodology, or serpentine plot techniques; and a second analysis performed based on the movement of the dummy.

[0100] A control logic derivation device may include one or more non-transitory computer-readable media and one or more processors. The non-transitory computer-readable media contains instructions, and the processors are configured to execute the instructions to perform operations, wherein the operations may include: extracting vehicle behavior data related to a collision by performing a driving simulation of the vehicle; inputting the extracted vehicle behavior data into an accident scenario reproduction model to calculate the load levels applied to the vehicle and passengers to determine the severity of the accident and to identify a severe accident scenario; evaluating passenger behavior and injuries using a safety analysis model that considers the seating positions of passengers in a severe accident scenario; configuring a full factorial experimental design (DOE) based on data regarding passenger behavior and injuries, according to design factors including seating positions; analyzing injury trends based on the full factorial DOE; deriving control logic application intervals and control logic non-application intervals from the injury trends; and deriving control logic activated within the control logic application intervals.

[0101] Analyzing injury trends can include identifying the body parts most affected by vehicle braking or collisions.

[0102] Analyzing injury trends can include defining the position of the human body part at the time of collision as the x-axis and the injury value of the human body part as the y-axis.

[0103] The position values ​​of human body parts can be determined using a first index and a second index. The first index is defined as the shortest distance between the tip of the nose of the passenger in the driver's seat and the steering wheel, and the second index is defined as the shortest distance between the tip of the nose of the dummy in the passenger seat and the anti-collision pad.

[0104] The injury value of a human body part can be determined using the Head Injury Criterion (HIC) value, which is calculated by integrating the acceleration of a dummy's head over time.

[0105] The derivation of the control logic application range and the control logic non-application range may include: classifying the positions of human body parts during a collision based on the HIC value in order to derive the control logic application range and the control logic non-application range.

[0106] The derivation of the control logic application range and the control logic non-application range can include: classifying the position of human body parts at the time of collision based on the HIC value and additionally considering the bottoming-out index, which is defined as the case where the instantaneous value of head acceleration exceeds 80G.

[0107] The derivation of the control logic activated in the control logic application range may include: changing the position of the human body parts at the time of collision to different intervals in the graph analyzing the injury trend by controlling the safety equipment before the collision time point.

[0108] The derivation of the control logic activated within the control logic application range can include: changing the damage value of the human body part at the time of collision to a different range in the graph analyzing the damage trend by controlling the safety equipment after the collision time point.

[0109] A computer-readable medium may include one or more non-transitory computer-readable media containing instructions executable by a computing device, wherein the instructions, when executed by one or more processors of the computing device, cause the computing device to perform operations including: extracting vehicle behavior data related to a collision by performing a driving simulation of the vehicle; inputting the extracted vehicle behavior data into an accident scenario reproduction model to calculate load levels applied to the vehicle and passengers to determine the severity of the accident and to identify a severe accident scenario; evaluating passenger behavior and injuries using a safety analysis model that considers the seating positions of passengers in a severe accident scenario; configuring a full factorial experimental design (DOE) based on data regarding passenger behavior and injuries, according to design factors including seating positions; analyzing injury trends based on the full factorial DOE; deriving control logic application intervals and control logic non-application intervals from the injury trends; and deriving control logic activated within the control logic application intervals.

[0110] Although this disclosure has been described in conjunction with exemplary embodiments, it will be understood that this disclosure is not limited to the disclosed exemplary embodiments, but rather, is intended to cover various modifications and equivalent arrangements included within the spirit and scope of the appended claims.

Claims

1. A method performed by a device in a vehicle, the method comprising: Vehicle behavior data is obtained through driving simulation of the vehicle, and the vehicle behavior data is associated with vehicle collisions. By applying the obtained vehicle behavior data to an accident scene reconstruction model, the load level applied to the vehicle and the passengers in the vehicle is obtained; Based on the load levels applied to the vehicle and the passengers, a severity value for the vehicle collision is obtained, the severity value indicating a severe accident scenario; Based on a safety analysis model, passenger behavior and injuries are estimated, wherein the safety analysis model takes into account the passenger's seating position in the serious accident scenario; Based on the passenger behavior and the injury, a full factorial experimental design (DOE) is configured, wherein the design factors of the DOE include the passenger's seating position. Based on the full factorial DOE, one or more damage trend values ​​are estimated, which are associated with the vehicle collision. Based on the damage trend value, control logic and duration are determined, during which the control logic will be activated; and The operation of the vehicle is controlled based on the control logic.

2. The method according to claim 1, wherein, Estimating one or more of the aforementioned damage trend values ​​includes: Identify the body parts most affected by the braking or collision of the vehicle.

3. The method according to claim 2, wherein, Estimating one or more of the aforementioned damage trend values ​​includes: Plot the position of the human body part on the x-axis at the time of the vehicle collision, and plot the injury value associated with the human body part on the y-axis.

4. The method of claim 3, further comprising: The location of the human body part is determined based on the following: The first metric, associated with the driver's seat of the vehicle, corresponds to the shortest distance between the tip of the passenger's nose and the steering wheel of the vehicle, and The second metric, associated with the passenger seat of the vehicle, corresponds to the shortest distance between the tip of the nose of the crash test dummy and the crash pad.

5. The method of claim 3, further comprising: The injury value associated with the human body part is determined based on the Head Injury Criterion (HIC) value, wherein the HIC value is determined based on the integral of the acceleration of the head of the crash test dummy in the vehicle over time.

6. The method according to claim 5, wherein, Determining the duration includes: Based on the HIC value, the positions of the human body parts during the vehicle collision are classified.

7. The method according to claim 6, wherein, Determining the duration further includes: Furthermore, based on the instantaneous acceleration value of the body part exceeding a threshold, the position of the human body part at the time of the vehicle collision is classified.

8. The method according to claim 5, wherein, Determining the control logic includes: The vehicle safety equipment controls the vehicle to adjust the position of the human body parts at the time of the vehicle collision to belong to different time intervals in the graph, which includes one or more of the injury trend values.

9. The method according to claim 5, wherein, Determining the control logic includes: The vehicle safety equipment of the vehicle is controlled to adjust the injury values ​​of the human body parts at the time of the vehicle collision to belong to different time intervals in the graph, which includes one or more injury trend values.

10. The method according to claim 1, wherein, Estimating one or more of the aforementioned damage trend values ​​includes: Based on the analysis results of the full factorial DOE, at least one of the following was used to perform the first analysis: correlation plot, mean analysis plot (ANOM), response surface methodology, and serpentine plot; and The second analysis is performed based on the amount of movement of the crash test dummy in the vehicle.

11. A vehicle device comprising: processor; as well as The memory stores at least one instruction, which, when executed by the processor, causes the device to: Perform a driving simulation of the vehicle; The driving simulation is used to obtain vehicle behavior data, which is associated with vehicle collisions. By applying the obtained vehicle behavior data to an accident scene reconstruction model, the load level applied to the vehicle and the passengers in the vehicle is obtained; Based on the load levels applied to the vehicle and the passengers, a severity value for the vehicle collision is obtained, the severity value indicating a severe accident scenario; Based on a safety analysis model, passenger behavior and injuries are estimated, wherein the safety analysis model takes into account the passenger's seating position in the serious accident scenario; Based on the passenger behavior and the injury, a full factorial experimental design (DOE) is configured, wherein the design factors of the DOE include the passenger's seating position. Based on the full factorial DOE, one or more damage trend values ​​are estimated, which are associated with the vehicle collision. Based on the damage trend value, control logic and duration are determined, during which the control logic will be activated; and The operation of the vehicle is controlled based on the control logic.

12. The device according to claim 11, wherein, When the at least one instruction is executed by the processor, the device estimates one or more of the damage trend values ​​by: Identify the body parts most affected by the braking or collision of the vehicle.

13. The device according to claim 12, wherein, When the at least one instruction is executed by the processor, the device estimates one or more of the damage trend values ​​by: Plot the position of the human body part on the x-axis at the time of the vehicle collision, and plot the injury value associated with the human body part on the y-axis.

14. The device according to claim 13, wherein, When the at least one instruction is executed by the processor, it further causes the device to: The location of the human body part is determined based on the following: The first metric, associated with the driver's seat of the vehicle, corresponds to the shortest distance between the tip of the passenger's nose and the steering wheel of the vehicle, and The second metric, associated with the passenger seat of the vehicle, corresponds to the shortest distance between the tip of the nose of the crash test dummy and the crash pad.

15. The device according to claim 13, wherein, When the at least one instruction is executed by the processor, it further causes the device to: The injury value associated with the human body part is determined based on the Head Injury Criterion (HIC) value, which is determined by integrating the acceleration of the head of the crash test dummy in the vehicle over time.

16. The device according to claim 15, wherein, When the at least one instruction is executed by the processor, the device determines the duration by: Based on the HIC value, the positions of the human body parts during the vehicle collision are classified.

17. The device according to claim 16, wherein, When the at least one instruction is executed by the processor, the device further determines the duration by: Furthermore, based on the instantaneous acceleration value of the body part exceeding a threshold, the position of the human body part at the time of the vehicle collision is classified.

18. The device according to claim 15, wherein, When the at least one instruction is executed by the processor, the device determines the control logic by: The vehicle safety equipment controls the vehicle to adjust the position of the human body parts at the time of the vehicle collision to belong to different time intervals in the graph, which includes one or more of the injury trend values.

19. The device according to claim 15, wherein, When the at least one instruction is executed by the processor, the device determines the control logic by: The vehicle safety equipment of the vehicle is controlled to adjust the injury values ​​of the human body parts at the time of the vehicle collision to belong to different time intervals in the graph, which includes one or more injury trend values.

20. A non-transitory computer-readable medium storing instructions that, when executed by a computing device, cause the computing device to: Vehicle behavior data is obtained through driving simulation of vehicle collisions, and the vehicle behavior data is associated with the vehicle collisions. Based on the vehicle behavior data, identify the relationship between one or more body parts of the passenger in the vehicle and the corresponding injury severity value; Based on the relationship between one or more locations of the body parts and the corresponding injury severity values, the control logic of the vehicle safety equipment is determined; and Based on the control logic, the vehicle safety equipment is controlled.

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