Vehicle airbag control method and device, electronic equipment and storage medium

Abstract

The application discloses a kind of control method, device, electronic equipment and storage medium of vehicle safety air bag, wherein the method comprises: obtaining the image information of vehicle occupant, vehicle driving information, collision information and environmental information;Using preset safety air bag detection model, image information, vehicle driving information, collision information and environmental information are analyzed, and the control parameter of vehicle safety air bag is obtained, wherein the control parameter includes: the deployment duration information of vehicle safety air bag, inflation speed information, deployment angle information;Vehicle safety air bag is controlled using control parameter.The present application solves the technical problems of poor protection and low intelligent level of vehicle safety air bag in related art when controlling vehicle safety air bag.

Description

Technical Field

[0001] The present invention relates to the field of vehicle technology, and in particular to a control method, device, electronic device and storage medium for a vehicle airbag. Background Art

[0002] Amidst the rapid development of the automotive industry, vehicle safety performance has received unprecedented attention. Airbags, in particular, play a crucial role in passive safety systems. They rapidly inflate in the event of a collision, providing cushioning protection and mitigating or preventing serious injuries. However, existing airbag deployment timing relies primarily on simple mechanical signals, such as the seatbelt buckle and seat position, which presents significant limitations.

[0003] This limitation manifests itself in two key aspects: First, the decision-making basis is too simplistic, relying primarily on the seatbelt buckle and seat rail position. It fails to consider key factors such as the occupant's relative position to the vehicle's airbag and whether the seatbelt is fastened. This results in inaccurate airbag deployment timing and reduced protective effectiveness. Second, the inability to identify detailed occupant information, such as height and age, limits precise control of the passenger airbag and prevents personalized protection for different occupants.

[0004] To address the above-mentioned problems, no effective solutions have been proposed so far. Summary of the Invention

[0005] Embodiments of the present invention provide a vehicle airbag control method, device, electronic device, and storage medium to at least solve the technical problems in related arts of poor protection and low intelligence level of vehicle airbags when controlling vehicle airbags.

[0006] According to one embodiment of the present invention, a method for controlling a vehicle airbag is provided, comprising: acquiring image information of a vehicle occupant, vehicle driving information, collision information, and environmental information; analyzing the image information, vehicle driving information, collision information, and environmental information using a preset airbag detection model to obtain control parameters of the vehicle airbag, wherein the control parameters include: deployment duration information, inflation speed information, and deployment angle information of the vehicle airbag; and controlling the vehicle airbag using the control parameters.

[0007] Optionally, the preset airbag detection model comprises an image detection module and a preset neural network module; the control parameter of the vehicle airbag is obtained by analyzing the image information, the vehicle driving information, the collision information and the environment information by using the preset airbag detection model; the position information, the body feature information and the seat belt state information of the vehicle occupant are obtained by analyzing the image information by using the image detection module; the deployment duration information is determined by analyzing the position information, the body feature information, the seat belt state information, the vehicle driving information, the collision information and the environment information by using the preset neural network module; and the inflation speed information and the deployment angle information are determined based on the position information and the body feature information.

[0008] Optionally, the preset neural network module comprises an input layer, a hidden layer and an output layer; the deployment duration information is determined by analyzing the position information, the body feature information, the seat belt state information, the vehicle driving information, the collision information and the environment information by using the preset neural network module; the input vector is obtained by inputting the position information, the body feature information, the seat belt state information, the vehicle driving information, the collision information and the environment information into the input layer; the feature information is obtained by performing feature extraction on the input vector by using the hidden layer; and the deployment duration information is obtained by performing linear regression analysis on the feature information by using the output layer.

[0009] Optionally, the image detection module comprises an edge detection algorithm, a contour tracking algorithm and a face recognition algorithm; the position information, the body feature information and the seat belt state information of the vehicle occupant are obtained by analyzing the image information by using the image detection module; the feature points of the vehicle occupant are obtained by detecting the image information by using the edge detection algorithm; the position information is obtained by detecting the feature points by using the contour tracking algorithm; the body feature information is obtained by recognizing the image information by using the face recognition algorithm; and the seat belt state information is determined based on the shape and position of the seat belt in the image information.

[0010] Optionally, the inflation speed information and the deployment angle information are determined based on the position information and the body feature information; the distance information and the direction information between the vehicle occupant and the installation position of the vehicle airbag are determined based on the position information; the inflation speed information is determined based on the distance information and the weight information and the age information in the body feature information; and the deployment angle information is determined based on the direction information and the height information in the body feature information.

[0011] Optionally, the control method of the vehicle airbag further comprises: obtaining an execution result of the vehicle airbag in response to the vehicle airbag being in a deployed state; and adjusting the network parameters in the preset airbag detection model by using the execution result.

[0012] According to another aspect of an embodiment of the present invention, a vehicle airbag control device is also provided, including: an acquisition module, which acquires image information of vehicle occupants, vehicle driving information, collision information and environmental information; an analysis module, which uses a preset airbag detection model to analyze the image information, vehicle driving information, collision information and environmental information to obtain control parameters of the vehicle airbag, wherein the control parameters include: deployment time information, inflation speed information, and deployment angle information of the vehicle airbag; and a control module, which uses the control parameters to control the vehicle airbag.

[0013] Optionally, the analysis module is also used to: use the image detection module to analyze the image information to obtain the vehicle occupant's position information, body feature information and seat belt status information; use a preset neural network module to analyze the position information, body feature information, seat belt status information, vehicle driving information, collision information and environmental information to determine the deployment time information; the vehicle airbag control device also includes a determination module for determining the inflation speed information and deployment angle information based on the position information and body feature information.

[0014] Optionally, the analysis module is also used to: input position information, body feature information, seat belt status information, vehicle driving information, collision information and environmental information into the input layer to obtain an input vector; use the hidden layer to extract features of the input vector to obtain feature information; use the output layer to perform linear regression analysis on the feature information to obtain expansion time information.

[0015] Optionally, the vehicle airbag control device also includes a detection module, which is used to: detect image information using an edge detection algorithm to obtain feature points of the vehicle occupants; detect feature points using a contour tracking algorithm to obtain position information; identify image information using a face recognition algorithm to obtain body feature information; the determination module is also used to determine seat belt status information based on the shape and position of the seat belt in the image information.

[0016] Optionally, the determination module is also used to: determine the distance information and direction information between the vehicle occupant and the vehicle airbag installation position based on the position information; determine the inflation speed information based on the distance information and the weight information and age information in the body feature information; determine the deployment angle information based on the direction information and the height information in the body feature information.

[0017] Optionally, the acquisition module is also used to obtain the execution result of the vehicle airbag in response to the vehicle airbag being in a deployed state; the vehicle airbag control device also includes a processing module for adjusting the network parameters in the preset airbag detection model using the execution result.

[0018] According to another aspect of an embodiment of the present invention, an electronic device is provided, including: a memory storing an executable program; and a processor for running the program, wherein the program executes the above-mentioned vehicle airbag control method when running.

[0019] According to another aspect of an embodiment of the present invention, a computer-readable storage medium is provided. The computer-readable storage medium includes a stored executable program, wherein when the executable program runs, the device where the storage medium is located is controlled to execute the above-mentioned vehicle airbag control method.

[0020] According to another aspect of an embodiment of the present invention, a computer program product is provided, including a computer program. When the computer program is executed by a processor, the computer program implements the above-mentioned vehicle airbag control method.

[0021] In an embodiment of the present invention, a method of obtaining image information of vehicle occupants, vehicle driving information, collision information and environmental information is adopted, and a preset airbag detection model is used to analyze the image information, vehicle driving information, collision information and environmental information to obtain control parameters of the vehicle airbag. Finally, the vehicle airbag is controlled using the control parameters, thereby achieving the purpose of providing more timely and effective protection for the occupants when a collision occurs, and being able to automatically adjust the control parameters of the vehicle airbag according to the specific circumstances of the occupants, thereby achieving the technical effect of reducing the risk of occupant injury and improving the intelligence and personalization level of vehicle airbag control, and thus solving the technical problems in the related art of poor protection and low intelligence level of vehicle airbags when controlling vehicle airbags. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of this application. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0023] Figure 1 is a flow chart of a method for controlling a vehicle airbag according to one embodiment of the present invention;

[0024] Figure 2 is a flow chart of another vehicle airbag control method according to one embodiment of the present invention;

[0025] Figure 3 The figure is a structural block diagram of a vehicle airbag control device according to one embodiment of the present invention. DETAILED DESCRIPTION

[0026] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of the present invention.

[0027] It should be noted that the terms "first", "second", etc. in the description and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that the numbers used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0028] According to an embodiment of the present invention, a method embodiment of a vehicle airbag control method is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0029] The method embodiment can be executed in an electronic device or similar computing device that includes a memory and a processor. Taking running on a vehicle terminal as an example, the vehicle terminal may include one or more processors (processors may include but are not limited to central processing units (CPUs), graphics processing units (GPUs), digital signal processing (DSP) chips, microcontroller units (MCUs), field programmable gate arrays (FPGAs), neural network processors (NPUs), tensor processing units (TPUs), artificial intelligence (AI) type processors, etc.) and a memory for storing data. Optionally, the vehicle terminal may also include transmission equipment, input and output equipment, and display equipment for communication functions. Those skilled in the art will understand that the above structural description is only illustrative and does not limit the structure of the vehicle terminal. For example, the vehicle terminal may also include more or fewer components than described above, or have a configuration different from that described above.

[0030] The memory can be used to store computer programs, such as software programs and modules of application software, such as the computer program corresponding to the vehicle airbag control method in the embodiments of the present invention. The processor executes the computer program stored in the memory to execute various functional applications and data processing, thereby implementing the vehicle airbag control method described above. The memory can include high-speed random access memory (RAM) and non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory can further include memory located remotely from the processor, which can be connected to the mobile terminal via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0031] The transmission device is used to receive or send data via a network. Specific examples of the aforementioned network may include a wireless network provided by the mobile terminal's communications provider. In one embodiment, the transmission device includes a network interface controller (NIC), which can be connected to other network devices via a base station to enable communication with the Internet. In one embodiment, the transmission device may be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0032] The display device can be, for example, a touch-screen liquid crystal display (LCD) and a touch display (also referred to as a "touch screen" or "touch display screen"). The liquid crystal display can enable the user to interact with the user interface of the mobile terminal. In some embodiments, the mobile terminal has a graphical user interface (GUI), and the user can interact with the GUI by finger contacts and / or gestures on the touch-sensitive surface. The human-computer interaction functions here optionally include the following interactions: creating web pages, drawing, word processing, making electronic documents, games, video conferencing, instant messaging, sending and receiving emails, call interfaces, playing digital videos, playing digital music and / or web browsing, etc. The executable instructions for performing the above-mentioned human-computer interaction functions are configured / stored in a computer program product or readable storage medium executable by one or more processors.

[0033] Figure 1 FIG. 1 is a flow chart of a method for controlling a vehicle airbag according to one embodiment of the present invention. Figure 1 As shown, the method includes the following steps:

[0034] Step S10, acquiring vehicle occupant image information, vehicle driving information, collision information, and environmental information;

[0035] In step S10, the image information of the vehicle occupant is captured by an in-vehicle camera and used to analyze the distance between the occupant and the steering wheel, and the position of the head and chest, so as to predict the movement of the occupant during a collision.

[0036] The above-mentioned vehicle driving information includes but is not limited to: vehicle speed, acceleration, driving direction, braking force, etc. Vehicle driving information helps to judge the vehicle's driving status, such as sudden braking, sharp turns, etc., and thus predict possible collisions.

[0037] The above-mentioned collision information includes but is not limited to the type of collision (frontal, side, rollover, etc.), intensity, collision direction, etc. Collision information is crucial for determining the deployment time and inflation speed of the vehicle's airbags.

[0038] The above-mentioned environmental information includes but is not limited to information such as road conditions and weather conditions. For example, rainy and snowy weather may affect the seating position and posture of the occupants. Environmental information, including weather conditions, light intensity, road conditions, etc., is obtained through sensors outside the vehicle (such as rain sensors, light sensors) and cameras. Environmental information helps to determine whether the working mode of the airbag needs to be adjusted to adapt to different external conditions. If the road surface is slippery or the weather is bad, the vehicle control system will adjust the triggering conditions of the airbag so that it can be deployed even in minor collisions, because in this case the vehicle is more likely to lose control. If the ambient light is dim, the vehicle control system will activate the airbag in advance to compensate for the driver's delayed reaction time. Under different road conditions, the vehicle control system will adjust the deployment angle and inflation speed of the airbag to adapt to the irregular movements of the occupants.

[0039] Step S12: Analyzing the image information, vehicle driving information, collision information, and environmental information using a preset airbag detection model to obtain control parameters for the vehicle airbag, wherein the control parameters include: deployment duration information, inflation speed information, and deployment angle information for the vehicle airbag;

[0040] In step S12, the deployment duration information is used to indicate the time required for the airbag to fully deploy after a collision. By precisely controlling the deployment duration information, the airbag is ensured to be fully deployed before the occupant collides with a vehicle interior structure (such as the steering wheel or dashboard).

[0041] The inflation speed information is used to indicate the speed at which the airbag inflates during deployment. This speed is controlled to ensure the airbag provides adequate cushioning force while preventing excessive injury to the occupants.

[0042] The deployment angle is used to indicate the inclination angle of the surface of the airbag relative to the vertical direction when the airbag is inflated and deployed.

[0043] Specifically, the system determines the severity of the collision, current environmental information, and vehicle driving information based on collision data. Combined with occupant image information, it calculates the optimal airbag deployment time to ensure full deployment before the occupant makes contact with the airbag. By analyzing the occupant's image information, it sets the appropriate inflation speed to provide adequate protection without causing excessive injury. Based on the occupant's image information, it also determines the airbag deployment angle to ensure optimal protection.

[0044] Step S14: Control the vehicle airbag using the control parameters.

[0045] Specifically, the airbag is precisely controlled based on the generated control parameters: The electronic control unit (ECU) precisely controls the airbag's triggering time to ensure it deploys at the optimal moment. The airbag's inflation speed is adjusted by varying the ignition timing or fuel quantity of the gas generator inside the airbag. The airbag's deployment angle can be adjusted to suit the occupant's specific position and posture by changing the airbag's design or using deformable materials. For example, the deployment angle can be controlled by changing the material and structure of the airbag's internal separator layer. An adjustable valve is installed in the airbag's exhaust hole. Depending on the degree of valve opening, the pressure release rate inside the airbag will also vary, affecting the airbag's deployment angle.

[0046] Based on the above steps S10 to S14, the image information of the vehicle occupants, the vehicle driving information, the collision information and the environmental information are obtained, and the image information, the vehicle driving information, the collision information and the environmental information are analyzed using a preset airbag detection model to obtain the control parameters of the vehicle airbag. Finally, the vehicle airbag is controlled using the control parameters, thereby achieving the purpose of providing more timely and effective protection for the occupants when a collision occurs, and being able to automatically adjust the control parameters of the vehicle airbag according to the specific circumstances of the occupants, thereby achieving the technical effect of reducing the risk of occupant injury and improving the intelligence and personalization level of vehicle airbag control, and thus solving the technical problems in the related art of poor protection and low intelligence level of the vehicle airbag when controlling the vehicle airbag.

[0047] Optionally, in step S12, the preset airbag detection model includes: an image detection module and a preset neural network module. The preset airbag detection model is used to analyze image information, vehicle driving information, collision information, and environmental information to obtain vehicle airbag control parameters including:

[0048] Step S121, analyzing the image information using an image detection module to obtain the vehicle occupant's position information, physical feature information, and seat belt status information;

[0049] In step S121, the position information is used to represent the position of the vehicle occupant and the position information of key parts of the vehicle occupant, such as the head, chest, and pelvis. Position information can affect the way and time of contact between the airbag and the occupant.

[0050] The above-mentioned physical characteristic information includes age information, height information and weight information of the vehicle occupants.

[0051] The seat belt status information is used to indicate whether the vehicle occupant has fastened the seat belt and the tightness of the seat belt.

[0052] Step S122, using a preset neural network module to analyze the position information, body feature information, seat belt status information, vehicle driving information, collision information, and environmental information to determine deployment duration information;

[0053] Specifically, the pre-set neural network module is a deep learning model that processes and analyzes diverse information from the image detection module and vehicle sensors. By learning from a large amount of collision data and occupant injury conditions, the deep learning model can predict the optimal airbag deployment time under specific conditions. The pre-set neural network module combines occupant position information, physical characteristics, and seatbelt status with vehicle travel, collision, and environmental information to predict the optimal airbag deployment time.

[0054] Step S123 : determining inflation speed information and deployment angle information based on the position information and the body feature information.

[0055] Specifically, the system uses the vehicle occupant's positional information (such as the locations of key areas like the head, chest, and pelvis) and the known position of the airbag to calculate the straight-line distance between the occupant and the airbag installation location. Combined with the distance information and the occupant's height, the system determines the airbag's inflation speed and deployment angle, ensuring the airbag deploys at the appropriate time and direction to protect the occupant.

[0056] Based on the above steps S121 to S123, the image information is analyzed by an image detection module to obtain the position information, body feature information and seat belt status information of the vehicle occupant; and the position information, body feature information, seat belt status information, vehicle driving information, collision information and environmental information are analyzed by a preset neural network module to determine the deployment duration information; finally, the inflation speed information and deployment angle information are determined based on the position information and body feature information, thereby achieving the purpose of providing more timely and effective protection for the occupant when a collision occurs, and being able to automatically adjust the control parameters of the vehicle airbag according to the specific situation of the occupant, thereby achieving the technical effect of reducing the risk of occupant injury and improving the intelligence and personalization level of vehicle airbag control, and thus solving the technical problems of poor protection and low intelligence level of vehicle airbags when controlling vehicle airbags in related technologies.

[0057] Optionally, in step S122, the preset neural network module includes an input layer, a hidden layer, and an output layer. The preset neural network module is used to analyze the position information, body feature information, seat belt status information, vehicle driving information, collision information, and environmental information to determine the expansion duration information, including:

[0058] Step S1221: inputting position information, body feature information, seat belt status information, vehicle driving information, collision information, and environmental information into the input layer to obtain an input vector;

[0059] Step S1222, using the hidden layer to extract features from the input vector to obtain feature information;

[0060] Step S1223: Use the output layer to perform linear regression analysis on the feature information to obtain expansion duration information.

[0061] Specifically, the system collects information about the occupants' location, physical characteristics, seatbelt status, vehicle travel, collision, and environmental conditions. This information is converted into numerical data and fed into the input layer of a pre-set neural network. Each input variable corresponds to a node in the input layer, forming a high-dimensional input vector.

[0062] Specifically, the input vector is forward propagated through the hidden layer, which consists of multiple neurons, each representing a feature extraction unit. Each neuron in the hidden layer is connected to a node in the input layer, and each connection has a weight. These weights are adjusted during the training of the pre-set neural network to minimize prediction error. The output of the neuron is typically transformed using a nonlinear activation function to introduce nonlinear characteristics and improve the expressive power of the pre-set neural network model. The main task of the hidden layer is to extract feature information from the input vector that helps predict the output. Feature information is a high-dimensional, abstract representation of the input vector that can capture key patterns and trends in the input vector. The feature information extracted from the hidden layer is passed to the output layer.

[0063] Specifically, the output layer consists of a set of weights and a bias. The feature information is linearly combined using the weights and, combined with the bias, generates a predicted airbag deployment duration. This value reflects the time it takes for the airbag to fully deploy after a collision, providing optimal protection, given the current vehicle state and conditions.

[0064] Based on the above steps S1221 to S1223, the position information, body feature information, seat belt status information, vehicle driving information, collision information and environmental information are input into the input layer to obtain an input vector; the hidden layer is used to extract features of the input vector to obtain feature information; the output layer is used to perform linear regression analysis on the feature information to obtain deployment time information. The preset neural network module is capable of processing high-dimensional data containing multiple variables, which enables the system to simultaneously consider information of multiple dimensions such as position information, body feature information, seat belt status, vehicle driving status, collision characteristics and environmental factors. By more accurately controlling the airbag deployment time, unnecessary or untimely airbag deployment can be reduced, thereby reducing maintenance costs and improving occupant safety.

[0065] Optionally, in step S121, the image detection module comprises an edge detection algorithm, a contour tracking algorithm and a face recognition algorithm, and the image information is analyzed by using the image detection module to obtain the position information, body feature information and seat belt state information of the vehicle occupant, including:

[0066] In step S1211, the edge detection algorithm is used to detect the image information to obtain the feature points of the vehicle occupant.

[0067] In step S1211, the edge detection algorithm includes but is not limited to Canny edge detector, Sobel operator, Prewitt operator, etc.

[0068] Specifically, the edge detection algorithm is used to identify the obvious boundaries in the image, which usually correspond to the boundaries of different objects or regions. The edge detection algorithm is applied to the image captured by the in-vehicle camera to identify the boundaries between the occupant's body and the seat, door, etc. The output of the edge detection algorithm is a set of feature points, which can outline the contour of the occupant's body and the key body parts such as the head, chest and pelvis.

[0069] In step S1212, the contour tracking algorithm is used to detect the feature points to obtain the position information.

[0070] In step S1212, the contour tracking algorithm includes but is not limited to Level Set Method, Kalman filter, etc.

[0071] Specifically, the contour tracking algorithm is used to track and analyze the feature points in the image to determine the position information of the occupant. By using the feature points of the vehicle occupant, the contour tracking algorithm can track the body contour of the occupant and the positions of the key body parts such as the head, chest and pelvis. The contour tracking algorithm is used to track the positions of these feature points and analyze the changes of the feature points over time. Even if the driver's head moves slightly or tilts, the contour tracking algorithm can continue to accurately track these points.

[0072] In step S1213, the face recognition algorithm is used to recognize the image information to obtain the body feature information.

[0073] In step S1213, the face recognition algorithm includes but is not limited to DeepFace, DeepID series, etc.

[0074] Specifically, facial recognition algorithms are used to extract facial features from images to estimate physical characteristics such as age, height, and weight. Facial recognition algorithms analyze facial features such as the position and shape of the eyes, nose, and mouth, as well as facial contours and texture, to output the occupant's age, height, and weight.

[0075] Step S1214: Determine the seat belt status information based on the shape and position of the seat belt in the image information.

[0076] Specifically, the system determines seatbelt status information, including whether the occupant is buckled and the tension of the seatbelt, based on the shape and position of the seatbelt. By analyzing the image of the seatbelt, it can quickly identify whether the seatbelt is buckled and whether the strap is tight.

[0077] Based on the above steps S1211 to S1214, the image information is detected using an edge detection algorithm to obtain feature points of the vehicle occupants, the feature points are detected using a contour tracking algorithm to obtain position information, the image information is recognized using a face recognition algorithm to obtain body feature information, and the seat belt status information is determined based on the shape and position of the seat belt in the image information. This can ensure that in the event of a collision, the airbag can adapt to occupants of different body sizes and provide more personalized safety protection.

[0078] Optionally, in step S123, determining the inflation speed information and the deployment angle information based on the position information and the body feature information includes:

[0079] Step S1231, determining distance information and direction information between the vehicle occupant and the vehicle airbag installation location based on the position information;

[0080] Specifically, the straight-line distance between the occupant's key body part and the airbag installation location is calculated using a coordinate calculation method in three-dimensional space (such as the Euclidean distance formula). This is achieved by calculating the vector between the key body part and the airbag installation location and comparing it with a reference direction (such as the longitudinal axis of the vehicle).

[0081] Step S1232, determining inflation speed information based on the distance information and the weight information and age information in the physical characteristic information;

[0082] Specifically, the airbag inflation speed is determined by the distance between the occupant's key body parts and the nearest airbag surface, combined with the occupant's age and weight, which are included in the vehicle's occupant's physical characteristics. If the distance is shorter, the occupant is heavier, or their age is within a preset range, a faster inflation speed is required to ensure the airbag can provide adequate protection in a timely manner.

[0083] Step S1232: Determine the expansion angle information based on the direction information and the height information in the body feature information.

[0084] Specifically, the optimal airbag deployment angle is determined based on the directional information relative to the vehicle's airbag installation location and the height information from the body characteristics. This ensures that the airbag properly covers key body parts of the occupant, such as the chest and head, during inflation. For taller occupants, the airbag deployment angle needs to be larger to ensure coverage of the occupant's upper torso. For example, a driver is 180 cm tall, weighs 80 kg, and is 35 years old. They are positioned 25 cm in front of the steering wheel. The distance between the driver's head and the airbag in the steering wheel is calculated to be 25 cm, perpendicular to the driver's chest. Taking into account the driver's weight, age, and head-to-head distance, a faster inflation speed (full inflation within 100 milliseconds) and a more perpendicular deployment angle (30 degrees) are required to ensure rapid coverage of the driver's chest and head. For example, a front passenger is 160 cm tall, weighs 50 kg, and is 60 years old. They are positioned 35 cm in front of the passenger-side airbag. The calculated distance between the front passenger's head and the passenger-side airbag is 35 cm, with a slightly tilted orientation. Based on the passenger's weight, age, and distance between their head and the passenger-side airbag, a slightly slower inflation rate (full inflation within 150 milliseconds) and a slightly tilted deployment angle (45 degrees to the passenger's chest) are used to accommodate the passenger's body size and position.

[0085] Based on the above steps 1231 to S1232, the distance information and direction information between the vehicle occupant and the vehicle airbag installation position are determined based on the position information, and the inflation speed information is determined based on the distance information and the weight information and age information in the physical characteristic information; the deployment angle information is determined based on the direction information and the height information in the physical characteristic information. By precisely controlling the inflation speed and deployment angle of the airbag, it is possible to ensure that the airbag provides timely and effective protection for the occupant in the event of a collision, reduce the risk of injury, and dynamically adjust the airbag deployment strategy according to the height, weight and age of different occupants, so that the deployment of the airbag is more adapted to the physical characteristics of the occupant, thereby providing more personalized protection.

[0086] Optionally, the vehicle airbag control method further includes:

[0087] Step S151 , in response to the vehicle airbag being in a deployed state, obtaining an execution result of the vehicle airbag; and adjusting network parameters in a preset airbag detection model using the execution result.

[0088] In step S151, the aforementioned network parameters are used to characterize the model parameters of the preset neural network module and the algorithm parameters of the image detection module in the preset airbag detection model. The model parameters of the preset neural network module include the weights and biases of the neural network, which directly affect the network's predictive capabilities. Algorithm parameters refer to parameters in image detection algorithms (such as edge detection and contour tracking), such as thresholds and kernel sizes.

[0089] Specifically, after the airbag deploys, relevant execution result data is collected, including the deployment time, inflation speed, deployment angle, and occupant injuries. By analyzing the airbag deployment results, the protective effectiveness of the airbag is evaluated. For example, if the occupant is seriously injured, it may indicate that the airbag deployment parameters need further optimization.

[0090] Specifically, the neural network model is retrained using the collected execution result data. By adjusting the model parameters in the network, the network can more accurately predict the optimal time for airbag deployment. For example, if the actual airbag deployment time is later than the predicted time, the prediction of the deployment time can be optimized by adjusting the network parameters. Based on the execution result data, the algorithm parameters in the image detection module are adjusted. For example, if it is found that the threshold of the edge detection algorithm is set too high, resulting in the loss of feature points, the detection effect of the feature points can be improved by lowering the threshold. The adjusted algorithm parameters can enable the image detection module to more accurately identify and track key body parts of vehicle occupants.

[0091] Specifically, after adjusting network parameters, the model needs to be validated to ensure that the adjusted model provides better protection. This validation can be performed through methods such as cross-validation and A / B testing. Once validated, the adjusted model is deployed in the vehicle, replacing the original preset airbag detection model.

[0092] Based on the above step S151, in response to the vehicle airbag being in the deployed state, the execution result of the vehicle airbag is obtained; the network parameters in the preset airbag detection model are adjusted using the execution result, and learning and self-improvement can be performed according to the actual airbag deployment result, ensuring that the airbag control strategy is always in the best state, which can not only provide better occupant protection, but also enhance the user's trust in vehicle safety and improve user satisfaction.

[0093] Figure 2 FIG. 1 is a flow chart of another method for controlling a vehicle airbag according to one embodiment of the present invention. Figure 2 As shown, the method includes the following steps:

[0094] Step S201, acquiring vehicle occupant image information, vehicle driving information, collision information, and environmental information;

[0095] Step S202: Detect the image information using an edge detection algorithm to obtain feature points of the vehicle occupants;

[0096] Step S203, detecting feature points using a contour tracking algorithm to obtain position information;

[0097] Step S204, using a face recognition algorithm to identify the image information to obtain body feature information;

[0098] Step S205, determining seat belt status information based on the shape and position of the seat belt in the image information;

[0099] Step S206 , inputting the position information, body feature information, seat belt status information, vehicle driving information, collision information, and environmental information into the input layer to obtain an input vector;

[0100] Step S207, using the hidden layer to extract features from the input vector to obtain feature information;

[0101] Step S208: using the output layer to perform linear regression analysis on the feature information to obtain expansion duration information;

[0102] Step S209, determining distance information and direction information between the vehicle occupant and the vehicle airbag installation location based on the position information;

[0103] Step S210, determining inflation speed information based on the distance information and the weight information and age information in the physical characteristic information;

[0104] Step S211, determining the expansion angle information based on the direction information and the height information in the body feature information;

[0105] Step S212: Control the vehicle airbag using the control parameters.

[0106] Based on the above steps S201 to S212, the image information of the vehicle occupants, the vehicle driving information, the collision information and the environmental information are obtained, and the preset airbag detection model is used to analyze the image information, the vehicle driving information, the collision information and the environmental information to obtain the control parameters of the vehicle airbag. Finally, the control parameters are used to control the vehicle airbag, thereby achieving the purpose of providing more timely and effective protection for the occupants when a collision occurs, and being able to automatically adjust the control parameters of the vehicle airbag according to the specific circumstances of the occupants, thereby achieving the technical effect of reducing the risk of occupant injury and improving the intelligence and personalization level of vehicle airbag control, and thus solving the technical problems in the related technology of poor protection and low intelligence level of vehicle airbags when controlling vehicle airbags.

[0107] Through the description of the above embodiments, those skilled in the art can clearly understand that the method according to the above embodiment can be implemented by means of software plus the necessary general hardware platform, and of course it can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes a number of instructions for enabling a terminal device (which can be a mobile phone, computer, server, or network device, etc.) to execute the methods described in each embodiment of the present invention.

[0108] The present invention also provides a vehicle airbag control device for implementing the aforementioned embodiments and preferred embodiments. Details already described will not be repeated. As used below, the term "module" may refer to a combination of software and / or hardware that implements a predetermined function. While the devices described in the following embodiments are preferably implemented using software, implementation using hardware, or a combination of software and hardware, is also possible and contemplated.

[0109] Figure 3 FIG. 1 is a structural block diagram of a vehicle airbag control device according to one embodiment of the present invention. Figure 3 As shown, the device includes:

[0110] An acquisition module 301 acquires image information of vehicle occupants, vehicle driving information, collision information, and environmental information;

[0111] An analysis module 302 is configured to analyze image information, vehicle driving information, collision information, and environmental information using a preset airbag detection model to obtain control parameters for the vehicle airbag, wherein the control parameters include: deployment duration information, inflation speed information, and deployment angle information for the vehicle airbag;

[0112] The control module 303 is used to control the vehicle airbag using control parameters.

[0113] Optionally, the analysis module 302 is also used to: use the image detection module to analyze the image information to obtain the vehicle occupant's position information, body feature information and seat belt status information; use a preset neural network module to analyze the position information, body feature information, seat belt status information, vehicle driving information, collision information and environmental information to determine the deployment time information; the vehicle airbag control device also includes a determination module 304, which is used to determine the inflation speed information and deployment angle information based on the position information and body feature information.

[0114] Optionally, the analysis module 302 is also used to: input position information, body feature information, seat belt status information, vehicle driving information, collision information and environmental information into the input layer to obtain an input vector; use the hidden layer to extract features of the input vector to obtain feature information; use the output layer to perform linear regression analysis on the feature information to obtain expansion time information.

[0115] Optionally, the vehicle airbag control device also includes a detection module 305, which is used to: detect image information using an edge detection algorithm to obtain feature points of the vehicle occupants; detect feature points using a contour tracking algorithm to obtain position information; identify image information using a face recognition algorithm to obtain body feature information; the determination module 304 is also used to determine seat belt status information based on the shape and position of the seat belt in the image information.

[0116] Optionally, the determination module 304 is also used to: determine the distance information and direction information between the vehicle occupant and the vehicle airbag installation position based on the position information; determine the inflation speed information based on the distance information and the weight information and age information in the physical characteristic information; determine the inflation speed information based on the direction information and the height information in the physical characteristic information.

[0117] Optionally, the acquisition module 301 is further used to obtain the execution result of the vehicle airbag in response to the vehicle airbag being in a deployed state; the vehicle airbag control device also includes a processing module 306, which is used to adjust the network parameters in the preset airbag detection model using the execution result.

[0118] It should be noted that the above modules can be implemented through software or hardware. For the latter, it can be implemented in the following ways, but not limited to: the above modules are all located in the same processor; or the above modules are located in different processors in any combination.

[0119] According to another aspect of an embodiment of the present invention, an electronic device is provided, including: a memory storing an executable program; and a processor for running the program, wherein the program executes the above-mentioned vehicle airbag control method when running.

[0120] Optionally, in this embodiment, the processor may be configured to execute the following steps through a computer program:

[0121] Step S1, acquiring vehicle occupant image information, vehicle driving information, collision information, and environmental information;

[0122] Step S2: Analyzing the image information, vehicle driving information, collision information, and environmental information using a preset airbag detection model to obtain control parameters for the vehicle airbag, wherein the control parameters include: deployment duration information, inflation speed information, and deployment angle information for the vehicle airbag;

[0123] Step S3: Control the vehicle airbag using the control parameters.

[0124] According to another aspect of an embodiment of the present invention, a computer-readable storage medium is provided. The computer-readable storage medium includes a stored executable program, wherein when the executable program runs, the device where the storage medium is located is controlled to execute the above-mentioned vehicle airbag control method.

[0125] Optionally, in this embodiment, the storage medium may be configured to store a computer program for performing the following steps:

[0126] Step S1, acquiring vehicle occupant image information, vehicle driving information, collision information, and environmental information;

[0127] Step S2: Analyzing the image information, vehicle driving information, collision information, and environmental information using a preset airbag detection model to obtain control parameters for the vehicle airbag, wherein the control parameters include: deployment duration information, inflation speed information, and deployment angle information for the vehicle airbag;

[0128] Step S3: Control the vehicle airbag using the control parameters.

[0129] Optionally, in this embodiment, the above-mentioned storage medium may include but is not limited to: a USB flash drive, a read-only memory (ROM), a random access memory (RAM), a mobile hard disk, a magnetic disk or an optical disk, and other media that can store computer programs.

[0130] According to another aspect of an embodiment of the present invention, a computer program product is provided, including a computer program. When the computer program is executed by a processor, the computer program implements the above-mentioned vehicle airbag control method.

[0131] Optionally, in this embodiment, the computer program product may be configured as a computer program for executing the following steps:

[0132] Step S1, acquiring vehicle occupant image information, vehicle driving information, collision information, and environmental information;

[0133] Step S2: Analyzing the image information, vehicle driving information, collision information, and environmental information using a preset airbag detection model to obtain control parameters for the vehicle airbag, wherein the control parameters include: deployment duration information, inflation speed information, and deployment angle information for the vehicle airbag;

[0134] Step S3: Control the vehicle airbag using the control parameters.

[0135] The serial numbers of the above embodiments of the present invention are for description only and do not represent the advantages or disadvantages of the embodiments.

[0136] In the above embodiments of the present invention, the description of each embodiment has its own focus. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0137] In the above embodiments of the present invention, the description of each embodiment has its own focus. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0138] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only exemplary. For example, the division of the units can be a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.

[0139] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple units. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0140] In addition, the functional units in the various embodiments of the present invention may be integrated into a single processing unit, each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0141] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, magnetic disk or optical disk, etc. Various media that can store program codes.

[0142] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as within the scope of protection of the present invention.

Claims

1. A method for controlling a vehicle airbag, characterized in that: include: Acquire image information of vehicle occupants, vehicle driving information, collision information, and environmental information; Analyzing the image information, the vehicle driving information, the collision information, and the environmental information using a preset airbag detection model to obtain control parameters of the vehicle airbag, wherein the control parameters include: deployment duration information, inflation speed information, and deployment angle information of the vehicle airbag, and the preset airbag detection model includes: an image detection module and a preset neural network module; controlling the vehicle airbag using the control parameters; The image information, the vehicle driving information, the collision information, and the environmental information are analyzed using the preset airbag detection model to obtain the control parameters of the vehicle airbag, including: Analyzing the image information using the image detection module to obtain the vehicle occupant's position information, physical feature information, and seat belt status information; Analyzing the position information, the body feature information, the seat belt status information, the vehicle driving information, the collision information, and the environmental information using the preset neural network module to determine the deployment duration information; Based on the position information, the distance information and direction information between the vehicle occupant and the vehicle airbag installation position are determined; based on the distance information and the weight information and age information in the physical characteristic information, the inflation speed information is determined; based on the direction information and the height information in the physical characteristic information, the deployment angle information is determined.

2. The vehicle airbag control method according to claim 1, characterized in that: The preset neural network module includes an input layer, a hidden layer, and an output layer. The preset neural network module is used to analyze the position information, the body feature information, the seat belt status information, the vehicle driving information, the collision information, and the environmental information to determine the deployment duration information. Inputting the position information, the body feature information, the seat belt status information, the vehicle driving information, the collision information, and the environment information into the input layer to obtain an input vector; Using the hidden layer to extract features from the input vector to obtain feature information; The output layer is used to perform linear regression analysis on the feature information to obtain the expansion duration information.

3. The vehicle airbag control method according to claim 1, characterized in that: The image detection module includes: an edge detection algorithm, a contour tracking algorithm and a face recognition algorithm. The image detection module is used to analyze the image information to obtain the position information, the body feature information and the seat belt status information of the vehicle occupant, including: Detecting the image information using the edge detection algorithm to obtain feature points of the vehicle occupant; Detecting the feature points using the contour tracking algorithm to obtain the position information; Using the face recognition algorithm to identify the image information to obtain the body feature information; The seat belt status information is determined based on the shape and position of the seat belt in the image information.

4. The vehicle airbag control method according to claim 1, characterized in that: The method further comprises: In response to the vehicle airbag being in a deployed state, obtaining an execution result of the vehicle airbag; The execution result is used to adjust network parameters in the preset airbag detection model.

5. A vehicle airbag control device, characterized in that: include: An acquisition module, which acquires image information of vehicle occupants, vehicle driving information, collision information, and environmental information; an analysis module, configured to analyze the image information, the vehicle driving information, the collision information, and the environmental information using a preset airbag detection model to obtain control parameters for the vehicle airbag, wherein the control parameters include: deployment duration information, inflation speed information, and deployment angle information of the vehicle airbag; and the preset airbag detection model includes: an image detection module and a preset neural network module; a control module, configured to control the vehicle airbag using the control parameters; Among them, the analysis module is also used to: use the image detection module to analyze the image information to obtain the position information, physical feature information and seat belt status information of the vehicle occupant; use the preset neural network module to analyze the position information, the physical feature information, the seat belt status information, the vehicle driving information, the collision information and the environmental information to determine the deployment time information; determine the distance information and direction information between the vehicle occupant and the vehicle airbag installation position based on the position information; determine the inflation speed information based on the distance information and the weight information and age information in the physical feature information; determine the deployment angle information based on the direction information and the height information in the physical feature information.

6. An electronic device, characterized in that: include: a memory storing an executable program; A processor is configured to run the program, wherein the program, when running, executes the vehicle airbag control method according to any one of claims 1 to 4.

7. A computer-readable storage medium, characterized in that The computer-readable storage medium includes a stored executable program, wherein when the executable program is run, the device where the storage medium is located is controlled to execute the vehicle airbag control method according to any one of claims 1 to 4.

8. A computer program product, characterized in that The invention comprises a computer program, which implements the vehicle airbag control method according to any one of claims 1 to 4 when being executed by a processor.

Images