Vehicle front cabin cover unlocking method, device, equipment and medium

Abstract

The invention provides a vehicle front hatch cover unlocking method, device and equipment and a medium, and the method comprises the steps: obtaining vehicle maintenance data, vehicle abnormal data and image information outside a vehicle, predicting the opening demand of a front hatch cover of a target person according to the vehicle maintenance data and the vehicle abnormal data, and determining the opening demand of the front hatch cover of the target person; if the prediction result shows that the target person has the potential demand for opening the front hatch cover, the image information is recognized, the behavior intention of the target person is determined based on the recognition result, and when the behavior intention of the target person is to open the front hatch cover of the vehicle, the front hatch cover of the vehicle is controlled to be unlocked; by judging the potential demand and the actual behavior intention of the target person, the actual demand that the target person needs to open the front hatch cover of the vehicle is accurately recognized, the front hatch cover is controlled to be automatically unlocked, the trouble that the front hatch cover cannot be normally opened due to the fact that manual unlocking is forgotten is avoided, and convenience is provided for a user.

Description

Technical Field

[0001] The present application relates to the field of intelligent control technology, and in particular to a method, device, equipment and medium for unlocking a vehicle hood. Background Art

[0002] With the rapid development of modern automobile technology, the design of the vehicle hood has far exceeded its basic functionality. Convenience and safety have gradually become two aspects that users attach great importance to. As an important part of the vehicle, the opening method of the hood (commonly known as the engine hood or hood) directly affects the user's daily experience. For a long time, the unlocking of the vehicle hood mainly depends on the manual intervention of the driver, such as the unlocking button set in the car, or remote control operation using the car key.

[0003] However, this traditional unlocking method sometimes seems inadequate in actual application and lacks sufficient intelligence and humanization. Especially when the user forgets to manually unlock the front hatch due to carelessness, busy work or other reasons, he often encounters the embarrassing situation of not being able to open the front hatch in time, which not only brings unnecessary trouble to the user, but also may affect the user's travel plan and mood to a certain extent. Summary of the invention

[0004] In view of the above-mentioned shortcomings of the prior art, the present application provides a vehicle front hood unlocking method, device, equipment and medium to solve the above-mentioned technical problems.

[0005] The present application provides a method for unlocking a vehicle hood, the method comprising: acquiring vehicle maintenance data, vehicle abnormality data, and image information outside the vehicle, the vehicle maintenance data comprising at least one of maintenance data of preset maintenance items and vehicle fluid maintenance data, the image information comprising portrait information of at least one target person; predicting the target person's need to open the hood based on the vehicle maintenance data and the vehicle abnormality data; if the prediction result shows that the target person has a potential need to open the hood, identifying the image information to determine the target person's behavioral intention based on the identification result; and when the target person's behavioral intention is to open the vehicle hood, controlling the vehicle hood to unlock.

[0006] In one embodiment of the present application, the target person's need to open the hood is predicted based on the vehicle maintenance data and the vehicle abnormality data, including: obtaining the current time, the current remaining amount of vehicle fluid, and a door signal; comparing the current time with the maintenance data of the preset maintenance item to determine whether the current time is at a maintenance time point; based on the vehicle fluid maintenance data, predicting the fluid replenishment demand state under the current vehicle fluid remaining amount state; judging whether the vehicle has an accident or failure within a preset time based on the abnormal data, and evaluating the target person's willingness to repair in combination with the door signal; if the current time is at a maintenance time point, or there is a need for fluid replenishment, or the target person has an intention to repair, then it is determined that the target person has a potential need to open the hood.

[0007] In one embodiment of the present application, the portrait information is identified to determine the target person's behavioral intention based on the identification result, including: extracting facial feature points, body feature points, and hand feature points in the portrait information of the target person; identifying the relative position relationship between the facial feature points to determine the orientation of the target person; when the target person is facing the front hood of the vehicle, detecting whether the target person is bending over based on the change in the angle formed by the body feature points; if a bending over is detected, identifying the target person's gesture based on the hand feature points, and determining the target person's behavioral intention based on the orientation, bending state, and gesture of the target person.

[0008] In one embodiment of the present application, before identifying the relative position relationship between each facial feature point to determine the orientation of the target person, it also includes: obtaining the initial coordinates of each facial feature point; taking the initial coordinates as the center, cropping a first cropped image area containing all the initial coordinates, and preliminarily positioning each facial feature point in the first cropped image area to obtain the first updated coordinates; taking the first updated coordinates as the new center point, cropping a cropped image area containing all the updated coordinates again, and re-positioning each facial feature point in the cropped image area again to obtain the again updated coordinates; repeating the above cropping and positioning process until the acquired facial feature point coordinates meet the preset accuracy conditions.

[0009] In one embodiment of the present application, based on the change of the angle formed by the body feature points, detecting whether the target person has a bending action includes: obtaining the coordinates of each body feature point, the body feature points including at least one waist feature point, one chest feature point, and one leg feature point; taking the waist feature point as the vertex, calculating the angle formed between the chest feature point and the leg feature point; if the angle is less than a preset angle threshold, determining that the target person has a bending action.

[0010] In one embodiment of the present application, the gesture of the target person is identified based on the hand feature points, and the behavioral intention of the target person is determined in combination with the orientation, bending state and gesture of the target person, including: when the target person is facing the hood of the vehicle, and the target person is in a bending state, and the target person's gesture matches at least one preset gesture, then it is determined that the target person has the intention to open the hood of the vehicle, and the preset gesture includes at least one of the following: knocking on the hood, a preset specific hood opening gesture, and hand touching the hood sensor at the front of the vehicle.

[0011] In one embodiment of the present application, before controlling the unlocking of the front hood of the vehicle, it also includes: pre-logging into the owner's account and entering the authorization information, wherein the authorization information includes at least one of facial information and image information; based on the authorization information, authenticating the target person who has the behavioral intention to open the front hood of the vehicle; if the verification is passed, sending a request to unlock the front hood instruction to the body domain controller to trigger the body domain controller to identify the operating status of the front hood of the vehicle; when the operating status of the front hood is an openable state, executing the unlocking operation of the front hood.

[0012] The present application provides a vehicle hood unlocking device, the device comprising: a data acquisition module, used to obtain vehicle maintenance data, vehicle abnormality data, and image information outside the vehicle, the vehicle maintenance data comprising at least one of maintenance data of preset maintenance items and vehicle fluid maintenance data, and the image information comprising at least one portrait; a demand prediction module, used to predict the hood opening demand of a target person based on the vehicle maintenance data and the vehicle abnormality data; a behavior intention recognition module, used to identify the image information when the prediction result shows that the target person has a potential demand to open the hood, so as to determine the behavior intention of the target person based on the recognition result; an unlocking control module, used to control the vehicle hood to unlock when the target person's behavior intention is to open the vehicle hood.

[0013] The present application provides an electronic device, including a processor, a memory and a communication bus; the communication bus is used to connect the processor and the memory; the processor is used to execute a computer program stored in the memory to implement the vehicle front hood unlocking method as described above.

[0014] The present application provides a computer-readable storage medium, characterized in that a computer program is stored thereon, and the computer program is used to enable a computer to execute the vehicle front hood unlocking method as described above.

[0015] Beneficial effects of the present application: The vehicle hood unlocking method proposed in the present application, by comprehensively analyzing the vehicle maintenance data, vehicle abnormality data and real-time external image information of the vehicle hood, the system can intelligently predict the target person's need to open the hood, and further confirm the target person's actual behavioral intention through image recognition technology, and then open the hood based on the target person's actual behavioral intention. Among them, demand-based recognition greatly reduces the inconvenience encountered by users due to forgetting to unlock manually, making the opening of the vehicle hood smoother and more natural, significantly improving the user's daily car experience; in addition, its double verification mechanism effectively avoids the risk of misoperation or illegal unlocking, and provides additional security for the vehicle and user property. In summary, this method not only solves the pain point of users forgetting to unlock the hood due to negligence, but also improves the convenience, safety and efficiency of vehicle use through intelligent and precise technical means.

[0016] It should be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The drawings herein are incorporated into the specification and constitute a part of the specification, showing embodiments consistent with the present application, and together with the specification, are used to explain the principles of the present application. Obviously, the drawings described below are only some embodiments of the present application, and for those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative work. In the drawings:

[0018] Figure 1 is a schematic diagram of an implementation environment of a vehicle front hood unlocking method shown in an exemplary embodiment of the present application;

[0019] Figure 2 is a schematic diagram of the implementation steps of a method for unlocking a vehicle front hood according to an exemplary embodiment of the present application;

[0020] Figure 3 is a schematic diagram of a target person's facial orientation recognition step of a vehicle front hood unlocking method shown in an exemplary embodiment of the present application;

[0021] Figure 4 is a block diagram of a vehicle front hood unlocking device shown in an exemplary embodiment of the present application;

[0022] Figure 5 A schematic diagram of the structure of a computer system suitable for implementing an electronic device of an embodiment of the present application is shown. DETAILED DESCRIPTION

[0023] The following will describe the implementation methods of the present application with reference to the accompanying drawings and preferred embodiments. Those skilled in the art can easily understand other advantages and effects of the present application from the contents disclosed in this specification. The present application can also be implemented or applied through other different specific implementation methods, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present application. It should be understood that the preferred embodiments are only for illustrating the present application, not for limiting the scope of protection of the present application.

[0024] It should be noted that the illustrations provided in the following embodiments are only schematic illustrations of the basic concept of the present application, and thus the drawings only show components related to the present application rather than being drawn according to the number, shape and size of components in actual implementation. In actual implementation, the type, quantity and proportion of each component may be changed at will, and the component layout may also be more complicated.

[0025] In the following description, a large number of details are discussed to provide a more thorough explanation of the embodiments of the present application. However, it is obvious to those skilled in the art that the embodiments of the present application can be implemented without these specific details. In other embodiments, well-known structures and devices are shown in the form of block diagrams rather than in detail to avoid making the embodiments of the present application difficult to understand.

[0026] First of all, it should be explained that CNN is a type of feedforward neural network (Feedforward Neural Networks) that includes convolution calculations and has a deep structure. It is one of the representative algorithms of deep learning.

[0027] DCNN is an extension of CNN. It improves the depth of the model by stacking multiple convolutional layers, pooling layers, and fully connected layers, thereby improving the accuracy of image classification. DCNN simulates the working principle of the human brain's visual system and achieves efficient feature extraction and classification by performing multi-level convolution and pooling operations on images.

[0028] OpenPose,OpenPose is an open source library for real-time detection of body, feet, hands, and facial key points.

[0029] Figure 1 It is a schematic diagram of an implementation environment of a vehicle hood unlocking method shown in an exemplary embodiment of the present application.

[0030] like Figure 1As shown, the implementation environment of the vehicle hood unlocking method mainly includes a data acquisition module 101 and a computer module 102. Among them, the data acquisition module 101 is responsible for collecting multi-dimensional data related to the unlocking of the vehicle hood. These data cover the vehicle maintenance data of the vehicle hood, vehicle abnormality data, and real-time image information outside the vehicle. Specifically, the vehicle maintenance data of the vehicle hood may come from the vehicle's historical usage records, user preference settings, etc.; the vehicle abnormality data is provided in real time by the status monitoring sensors of various systems of the vehicle, such as the engine status, the door status, etc.; and the image information outside the vehicle is captured by high-definition cameras installed around the vehicle. The data acquisition module 101 can be an integration of various sensors and cameras built into the vehicle, or it can be a remote device connected through a vehicle-mounted communication network. This application does not impose any restrictions on the specific configuration and connection method of the data acquisition module 201.

[0031] The computer module 102 is responsible for efficiently processing various types of data collected by the data acquisition module 101. It first uses advanced algorithm models to predict whether the target person has a potential need to open the front hood based on the vehicle maintenance data and vehicle abnormality data of the vehicle front hood. If the prediction results show that there is such a need, the computer module 102 will immediately perform in-depth recognition of the image information outside the vehicle to accurately determine the actual behavioral intention of the target person. In this process, the computer module will use image recognition technology to analyze the target person's movements, postures, etc., so as to determine whether he really intends to open the front hood of the vehicle. Once it is confirmed that the target person's behavioral intention is consistent with the prediction, the computer module 102 will quickly generate an unlocking instruction to control the vehicle's front hood to automatically unlock. This design not only avoids the inconvenience caused by the user forgetting to unlock manually, but also greatly improves the user experience through intelligent and automated methods.

[0032] It is worth noting that the computer module 102 can be a high-performance computing unit built into the vehicle, such as an onboard computer, an embedded system, etc., or a remote server connected to the vehicle through cloud computing technology. This application also does not impose any restrictions on the specific type and deployment method of the computer module 102 to ensure the flexibility and wide applicability of the solution.

[0033] Figure 2 It is a schematic diagram of the implementation steps of a method for unlocking a vehicle hood according to an exemplary embodiment of the present application.

[0034] like Figure 2 As shown, in an exemplary embodiment, the vehicle front hood unlocking method includes at least steps S210 to S240, which are described in detail as follows:

[0035] Step S210, acquiring vehicle maintenance data, vehicle abnormality data, and image information outside the vehicle, wherein the vehicle maintenance data includes at least one of maintenance data of preset maintenance items and vehicle fluid maintenance data, and the image information includes portrait information of at least one target person.

[0036] In one embodiment of the present application, vehicle maintenance data, vehicle abnormality data, and image information outside the vehicle are collected and processed respectively, as follows:

[0037] About obtaining vehicle maintenance data: The maintenance data of the preset maintenance items include the maintenance cycle of the vehicle's regular maintenance and the specific time of each vehicle maintenance. The preset maintenance items are some items that require opening the front hood for maintenance operations. According to the factory settings of different models, the preset maintenance items are different. This application does not impose any specific restrictions on the specific project content of its preset maintenance items. The vehicle fluids include windshield water, coolant, and engine oil and other vehicle fluids. The vehicle fluid maintenance data includes the remaining amount of vehicle fluids in this category each time any vehicle fluid is added. Therefore, by connecting to the vehicle manufacturer's cloud service, the vehicle's regular maintenance records are automatically synchronized. These records include but are not limited to timestamps of key maintenance items such as oil changes, brake system inspections, and vehicle fluid changes. In addition, the owner or driver can also manually enter or confirm the maintenance time through the in-vehicle APP to ensure the accuracy of the data. The vehicle's built-in sensors continuously monitor the consumption of fluids such as fuel tanks, vehicle fluid tanks, and brake oil pots, and record the time of each addition or replacement of fluids, as well as information such as the remaining amount of fluids when each fluid is added.

[0038] About obtaining vehicle abnormality data: The vehicle is equipped with a comprehensive condition monitoring system, including engine fault monitoring, electrical system fault monitoring, tire pressure monitoring, etc. When the system detects any abnormality (such as engine fault code, low tire pressure, etc.), it will immediately record the abnormal data, including the type of abnormality, occurrence time, duration, etc., and may be accompanied by an alarm prompt.

[0039] Regarding obtaining image information: Multi-angle high-definition cameras are installed on the outside of the vehicle, especially on the front and sides of the vehicle, to ensure that the target person approaching the vehicle can be captured. The camera has night vision and wide-angle functions to ensure clear imaging under different lighting conditions and viewing angles. Then, using advanced face recognition technology, the images captured by the camera are analyzed in real time to identify the portrait information of at least one target person. The recognition results include the facial features, position, movements, etc. of the target person, providing key data for subsequent behavioral intention judgment. Then, all the collected data (vehicle maintenance data, abnormal data, image information) will be sent to the vehicle's built-in computer module or remote server for fusion processing. Through the comprehensive analysis of the data by the algorithm model, the system can more accurately predict the target person's need to open the front hood, and control the unlocking of the front hood based on their behavioral intentions.

[0040] It can be understood that through the above embodiments, the system can comprehensively and accurately obtain vehicle maintenance data, vehicle abnormality data, and vehicle external image information containing target person portrait information. These data provide the system with a rich basis for decision-making, enabling it to intelligently judge the potential needs and actual behavioral intentions of the target person, thereby realizing the automatic unlocking function of the vehicle's front hood, greatly improving the user's convenience and safety.

[0041] Step S220: predicting the hood opening demand of the target person based on the vehicle maintenance data and the vehicle abnormality data.

[0042] In one embodiment of the present application, the target person's need to open the hood is predicted based on vehicle maintenance data and vehicle abnormality data, including: obtaining the current time, the current remaining amount of vehicle fluid, and a door signal; comparing the current time with the maintenance data of a preset maintenance item to determine whether the current time is at a maintenance time point; based on the vehicle fluid maintenance data, predicting the fluid replenishment demand state under the current vehicle fluid remaining amount state; judging whether the vehicle has an accident or failure within a preset time based on the abnormal data, and evaluating the target person's intention to repair in combination with the door signal; if the current time is at a maintenance time point, or there is a need for fluid replenishment, or the target person has an intention to repair, then it is determined that the target person has a potential need to open the hood.

[0043] In a specific embodiment of the present application, first, the factory information of the battery pack and the motor is accurately obtained from the database of the vehicle manufacturer, including key parameters such as the production date, expected life and maintenance cycle, and the data calculated by the battery pack, motor sensor and intelligent diagnosis system according to the life prediction model is received in real time, and the maintenance reminder information issued by the maintenance reminder system is closely combined to accurately determine the regular maintenance time of the battery pack and the motor; and through the high-sensitivity sensor built into the vehicle, the user's windshield washer fluid and coolant usage and cycle are continuously recorded to obtain the current remaining amount of these two liquids in real time, providing a solid data foundation for subsequent demand forecasting. In addition, the working status of the motor and the battery pack is monitored in real time, and once any fault signal is detected, it will be immediately recorded and reported. At the same time, through the vehicle's safety system or collision sensor, the system can quickly obtain the signal of the vehicle accident to ensure a comprehensive grasp of the vehicle status.

[0044] Then, based on the collected data, the current time is first obtained and accurately compared with the regular maintenance time of the battery pack and motor to determine whether it is currently at the maintenance time point or approaching the maintenance cycle. Next, based on the user's past windshield washer fluid and coolant usage habits and the current remaining amount of windshield washer fluid or coolant, it is predicted whether windshield washer fluid or coolant needs to be added in the future usage cycle. In the event of a vehicle failure or accident, these abnormal data are immediately recorded and combined with the door signal (such as the door being opened after the accident) to evaluate whether the target person has the intention to repair the vehicle.

[0045] Finally, based on all the above information, the target person's need to open the front hood is determined: if the current time is at the maintenance time point, it is determined that there is a need for regular maintenance; if there is a need for fluid replenishment, it is determined that there is a need to add windshield washer fluid or coolant; if the vehicle breaks down or has an accident and the door signal indicates an intention to overhaul, it is determined that there is a need to open the front hood for overhaul.

[0046] It is understandable that through the above embodiments, the maintenance data of preset maintenance items, the user's vehicle fluid maintenance data, vehicle abnormality data, door signals and other multi-dimensional information can be comprehensively considered to accurately predict the target person's need to open the front hood. This significantly improves the user's convenience and can automatically adjust vehicle-related settings according to the prediction results, such as opening the front hood in advance, thereby improving the user experience.

[0047] Step S230: If the prediction result shows that the target person has a potential need to open the front hood, the image information is recognized to determine the behavior intention of the target person based on the recognition result.

[0048] In one embodiment of the present application, portrait information is recognized to determine the target person's behavioral intention based on the recognition result, including: extracting facial feature points, body feature points, and hand feature points in the target person's portrait information; identifying the relative position relationship between the facial feature points to determine the target person's orientation; when the target person is facing the front hood of the vehicle, detecting whether the target person is bending over based on the change in the angle formed by the body feature points; if a bending over is detected, identifying the target person's gesture based on the hand feature points, and determining the target person's behavioral intention based on the target person's orientation, bending state, and gesture.

[0049] In a specific embodiment of the present application, advanced facial recognition technology is first used to accurately extract the facial feature points of the target person from the image captured by the camera. These key points include eyes, nose, mouth, and facial contours, etc., which provide a solid foundation for subsequent analysis. Next, through the human posture recognition algorithm, the system further identifies and extracts the body feature points of the target person, such as shoulders, elbows, knees and other key parts, to construct a human skeleton model, which makes it possible to monitor the movements of the target person. It is particularly noteworthy that the system pays special attention to the hand area, and extracts hand feature points in detail, including fingertips, palm centers, etc., which lays a solid foundation for subsequent gesture recognition.

[0050] After acquiring these key portrait information, the system begins to analyze the relative position relationship between facial feature points, especially the arrangement of the eyes, nose and mouth, and the direction of the facial contour, so as to accurately determine the direction of the target person. When the system detects that the target person is facing the front hood of the vehicle, it continues to monitor the changes in the angles formed by the body feature points, especially the angle between the shoulders and waist, and the angle between the knees and the ground. When these angles undergo specific changes, that is, the angle between the shoulders and waist gradually decreases, and the angle between the knees and the ground gradually increases, the system can accurately determine that the target person is bending over. Once the bending action is detected, the target person's gestures, such as grabbing, pointing, waving, etc., are further identified by analyzing the relative position of the fingertips and the center of the palm, as well as the degree of bending of the fingers.

[0051] Finally, the target person's behavioral intention is comprehensively judged based on the target person's orientation, bending state, and recognized gestures. For example, if the target person faces the front hood of the vehicle, bends over, and makes a grabbing gesture with his hand, it is determined that the target person intends to open the front hood or perform some operation; if a pointing gesture is made, it may be indicating or checking a component inside the front hood.

[0052] Figure 3 It is a schematic diagram of the target person's facial orientation recognition steps of a vehicle hood unlocking method shown as an exemplary embodiment of the present application.

[0053] like Figure 3 As shown in the figure, the recognition of facial feature points requires multiple positioning to obtain the precise positioning of each feature point. Specifically: first, the image containing the portrait information of the target person is extracted to obtain multiple facial feature points of the target person, and the coarse positioning of each facial feature point is determined. Then, based on the coarse positioning, the facial feature points are further processed to obtain the corresponding initial positioning and precise positioning. Finally, the orientation of the target person is obtained by processing the precise positioning of each feature point.

[0054] In one embodiment of the present application, identifying the relative position relationship between each facial feature point to determine the orientation of the target person includes: when a signal of the driver opening the door is detected, starting a timer, and estimating the action time required for the driver to reach the preset position according to the driver's dynamic parameters, the driver's dynamic parameters at least including the driver's step length and gait; during the action time, collecting the driver's face image in the driver's portrait information to obtain multiple facial feature points. Obtain the initial coordinates of each facial feature point; with the initial coordinates as the center, cropping the initial cropped image area containing all the initial coordinates, and preliminarily positioning each facial feature point in the initial cropped image area to obtain the initial updated coordinates; with the initial updated coordinates as the new center point, cropping the cropped image area containing all the updated coordinates again, and re-positioning each facial feature point in the cropped image area again to obtain the re-updated coordinates; repeating the above cropping and positioning process until the obtained facial feature point coordinates meet the preset accuracy conditions. Finally, based on the precise positioning coordinates of each facial feature point, the relative position relationship between each facial feature point is calculated to determine the facial orientation of the target person.

[0055] In a specific embodiment of the present application, first, the built-in camera or other sensors of the vehicle are used to accurately capture the driver's gait information, including key dynamic parameters such as step length and gait pattern. These parameters are crucial to the system because they will be used to estimate the time required for the driver to move from the door to a preset position (such as in front of the driver's seat).

[0056] Next, based on the driver's dynamic parameters, a timer is started and the time window required for the driver to reach the preset location is accurately estimated. This time window is crucial for the subsequent portrait information collection, because it ensures that the system can obtain sufficient and high-quality facial data before the driver reaches the preset location. And within the estimated action time, the driver's facial images are continuously and quickly collected through the camera inside the vehicle. These images will be pre-processed, such as grayscale, binarization, etc., so that the subsequent facial recognition algorithm can more accurately identify facial feature points and record their initial coordinates.

[0057] Subsequently, a first cropped image region containing all the initial coordinates is cropped with the initial coordinates as the center, and the facial feature points are preliminarily located in this region to obtain the first updated coordinates. Next, a smaller cropped image region containing all the updated coordinates is cropped with the initial updated coordinates as the new center point, and a second positioning is performed in this region to obtain the updated coordinates again. The above process is repeated, and each iteration uses more accurate coordinates as the center point, crops a smaller image region, and performs more precise positioning until the obtained facial feature point coordinates meet the preset accuracy conditions.

[0058] Finally, based on the precise positioning coordinates of each facial feature point finally obtained, the relative position relationship between these feature points is calculated, such as the relative position and distance of key feature points such as eyes, nose, and mouth. Through these relative position relationships, the system will use a preset algorithm or model to accurately determine the driver's facial orientation. This process may involve comparing a preset facial orientation template, or using a machine learning algorithm to identify different orientation patterns, thereby ensuring that the system can accurately and quickly identify the driver's facial orientation. This application does not impose any restrictions on its specific calculation steps.

[0059] In a specific embodiment, in order to accurately determine the driver's facial orientation, a facial recognition algorithm is first used to identify feature points of the driver's facial images continuously captured by the vehicle's internal camera. These feature points cover key parts such as the eyes, nose, mouth, and eyebrows, and the precise coordinates of each identified feature point in the image are recorded as the basis for subsequent calculation of relative position relationships.

[0060] Next, the relative position relationship of the feature points is calculated. First, one or more reference points that are relatively stable when the facial orientation changes, such as the center of the eye or the vertex of the nose, are selected; then, for each other feature point, its coordinates relative to the reference point are calculated, which usually involves simple geometric operations, such as calculating the horizontal and vertical distances between two points. In addition, in order to eliminate the influence of different driver facial sizes and camera distances on the calculation results, these relative coordinates can also be normalized by dividing the coordinate values ​​by a certain reference value (such as facial width or height) to obtain a relative ratio.

[0061] After the relative position relationship of the feature points is obtained, the facial orientation is determined. To this end, one or more facial orientation models are pre-established, which describe the relative position relationship of facial feature points under different orientations. For example, when looking straight ahead, the relative positions of the eyes and nose will present a specific proportional relationship. The calculated relative position relationship of the feature points is compared with these pre-built orientation models, and the best matching orientation model is found by calculating the similarity or distance metric (such as Euclidean distance, cosine similarity, etc.). Finally, the best matching orientation model is selected as the driver's facial orientation. If the calculated relative position relationship of the feature points is closest to the model looking straight ahead, the system will determine that the driver's facial orientation is looking straight ahead.

[0062] Finally, in order to improve the accuracy of judgment, iterative optimization can be performed to perform feature point recognition, relative position relationship calculation and facial orientation judgment multiple times, and adjust parameters or optimize algorithms based on the previous results. In addition, information from other sources can be integrated, such as the driver's body posture, head rotation angle, etc., which can be obtained through other sensors or cameras inside the vehicle.

[0063] In a specific implementation of the present application, DCNN facial feature points are used to predict the facial orientation of a human face, and the facial position and subsequent posture are described based on the five feature points of the face (corners of the eyes, nose, and corners of the mouth): a multi-stage cascade method is usually used to gradually approximate the real feature point position from coarse to fine. The following are the three specific stages of DCNN predicting the five feature points of a human face, as well as its basic process and calculation formula:

[0064] The first stage: coarsely locate the facial feature points. Use a convolutional neural network (such as the F1 network) to extract features from the entire face image; and output a feature vector through the network, which specifically contains the predicted coordinates of the five feature points, as shown below:

[0065] Output = f(Input) Formula (1)

[0066] Among them, f() represents the forward propagation process of the network, Input is the input face image, and Output is the feature point coordinate vector output by the network.

[0067] Due to the whole process from the user getting off the car to walking to the front of the car to open the front hood, the facial features collected by the camera arranged at the windshield are from the initial side face (that is, only one side of the eye corner, mouth corner and nose, a total of 3 feature points) to the face completely facing the windshield (5 feature points); therefore, the timing starts after the driver's door opening signal is recognized, and the timing time is determined by the user's walking parameters such as step length and gait to determine how much time it takes for the user to walk to the front of the car; during this time, the number of facial feature points is continuously collected and input into the image, and the coordinate vector is calculated.

[0068] In the second stage, the facial feature points are initially located. Based on the output of the first stage, a small area image containing the feature points is cropped with the predicted feature points as the center; multiple CNNs (such as EN1 and NM1 networks) are used to initially locate the feature points in these small area images;

[0069] New_Coordinates=CNN(Cropped_Image) Formula (2)

[0070] Where CNN represents a specific convolutional neural network, Cropped_Image is the cropped image area, and New_Coordinates are the updated feature point coordinates.

[0071] In the third stage, the facial feature points are precisely located. Based on the output of the second stage, a smaller image area is cropped again; in the finely located CNN, a series of calculation steps are performed, including convolution operations, activation functions, and pooling operations, which are repeated in each convolution layer until the network outputs the final feature point coordinates.

[0072] The convolution operation is as follows:

[0073] (I*K)[x,y]=∑m∑nI[xm.yn]·k[m,n] Formula (3)

[0074] Where I is the input image, K is the convolution kernel, * indicates that convolution is done, [x, y] is the coordinate of the output feature map, m is the number of steps the convolution kernel slides in the horizontal direction, and n is the number of steps the convolution kernel slides in the vertical direction.

[0075] The activation function is specifically ReLU:

[0076] ReLU(z)=max(0,z) Formula (4)

[0077] Here, z is an input value in the neural network.

[0078] It can be understood that in the simplest case, z can be a single input value in a neural network, such as the sum of the weighted inputs of a neuron; or in other common cases, z can be a vector or matrix, in which case the ReLU function is applied element by element, for example, for each element zi in the vector or matrix, the ReLU function calculates max(0, zi) and combines all the results into a new vector or matrix.

[0079] Pooling operations such as maximum pooling:

[0080] P[x, y] = max m，n (I[xm,yn]) Formula (5)

[0081] Among them, P is the feature map after pooling, I is the feature map, x is the horizontal coordinate of the target position of the pooling operation, y is the vertical coordinate of the target position of the pooling operation, and m and n are the window sizes that define the pooling operation.

[0082] It can be understood that the input feature map (Input Feature Map), in the convolutional neural network, will obtain one or more feature maps after being processed by the convolution layer, and I refers to one of these feature maps. In addition, m and n are the window sizes that define the pooling operation. In the maximum pooling, m and n usually take the same value, indicating the size of the pooling window in the horizontal and vertical directions. For example, if a 2x2 pooling window is used, then m = 2, n = 2.

[0083] In one embodiment of the present application, based on the change of the angle formed by the body feature points, detecting whether the target person has a bending action includes: obtaining the coordinates of each body feature point, the body feature points including at least one waist feature point, one chest feature point, and one leg feature point; taking the waist feature point as the vertex, calculating the angle formed between the chest feature point and the leg feature point; if the angle is less than a preset angle threshold, determining that the target person has a bending action.

[0084] In a specific embodiment of the present application, after detecting that the user is facing the windshield, the user's distance from the front of the car is detected in real time; after the distance between the user and the front of the car reaches a threshold, OpenPose is used to detect whether the user has made a bending action: the human skeleton key point (and body feature point) data is extracted from the above-collected image information. The data includes the coordinates of various parts of the body, such as the head, shoulders, elbows, wrists, hips, knees, etc., and a function is defined to calculate the angle formed by three key points (for example, chest, waist, knees), and the function is specifically as follows:

[0085]

[0086] Among them, θ represents the final angle formed, A and C represent one of the key points (and one of the chest feature point and the knee feature point), and B represents the vertex of the angle (and the waist feature point).

[0087] In addition, a bending threshold is set. For example, when the waist angle (the angle between the chest and the knee) is less than 150 degrees, it can be considered that the human body is bending. By comparing the calculated angle with the threshold, it is determined whether the bending action has occurred. If the angle is less than the threshold, it is considered that the bending action is detected.

[0088] In one embodiment of the present application, the gesture of the target person is identified based on the hand feature points, and the behavioral intention of the target person is determined in combination with the target person's orientation, bending state and gesture, including: when the target person is facing the hood of the vehicle, and the target person is in a bending state, and the target person's gesture matches at least one preset gesture, then it is determined that the target person has the intention to open the hood of the vehicle, and the preset gesture includes at least one of the following: knocking on the hood, a preset specific hood opening gesture, and the hand touching the hood sensor at the front of the vehicle.

[0089] In one embodiment of the present application, a high-definition camera is used to accurately capture the hand image of the target person, and then a deep learning algorithm is used to carefully extract the various feature points of the hand, including key information such as the precise position of the fingertips, the outline of the palm, and the degree of bending of the finger joints. At the same time, the orientation and bending state of the target person are continuously monitored, and the body posture of the target person is accurately judged by analyzing the relative position relationship between facial feature points and body feature points, as well as monitoring the changes in the angles of body feature points such as shoulders and waist.

[0090] When entering the gesture recognition stage, the system has already stored a series of preset gesture models closely related to opening the front hood of the vehicle. These models cover the simulated action of tapping the front hood, specific gestures for opening the front hood (such as simulating the action of pulling the door or pressing the switch), and the behavior of touching the front hood sensor at the front of the vehicle. When the hand feature points of the target person match any of these preset gesture models, it will be preliminarily judged that the target person may be performing an action related to opening the front hood.

[0091] Finally, in the stage of determining the behavioral intention, the system will comprehensively consider the target person's orientation, bending state, and recognized gestures. If the target person is facing the front hood of the vehicle, and the body is in a bent state, and the hand feature points match at least one preset gesture model, then it is determined that the target person has a clear intention to open the front hood of the vehicle. Once this intention is determined, the corresponding response mechanism is immediately triggered, such as quickly unlocking the front hood, thereby achieving accurate recognition and timely response to the target person's behavioral intention.

[0092] In a specific embodiment, when the target person walks towards the vehicle, the system captures his facial and body features through the camera and determines that he is facing the front hood of the vehicle and is in a bent state. Subsequently, the system observes that the target person's gesture matches the preset "specific front hood opening gesture", that is, simulating the action of pulling the door or pressing the front hood switch. Based on this comprehensive information, the system quickly determines that the target person intends to open the front hood, and immediately unlocks the front hood, and sends a notification to the user to inform that the front hood is ready.

[0093] Step S240, when the target person's behavioral intention is to open the vehicle's hood, control the vehicle's hood to be unlocked.

[0094] In one embodiment of the present application, before controlling the unlocking of the vehicle's hood, it also includes: pre-logging into the owner's account and entering the authorization information, the authorization information including at least one of facial information and image information; based on the authorization information, authenticating the target person who has the behavioral intention to open the vehicle's hood; if the verification is successful, sending a request to unlock the hood to the body domain controller to trigger the body domain controller to identify the operating status of the vehicle's hood; if the current hood operating state is an openable state, the hood unlocking operation is executed.

[0095] In one embodiment of the present application, the owner must first log in to the owner's account through the vehicle's central control system or mobile phone APP to ensure the legitimacy of subsequent operations. After successfully logging in, the owner can pre-enter the information of the authorized person under the account, which at least includes facial information and / or image information. The system will save this information and use it for subsequent identity verification.

[0096] When the vehicle is in a normal power-on state, the SRR radar controller and camera module will monitor the vehicle's surroundings in real time to ensure safety. The camera module uses advanced facial recognition technology to match the faces of people approaching the vehicle and quickly determine whether they are authorized. At the same time, the module also uses human posture recognition technology to carefully analyze the behavior of the target person and accurately determine whether he has a clear intention to open the front hood, such as bending over, reaching out, and other iconic movements.

[0097] Once the target person's facial information perfectly matches the pre-stored authorized person information, and his behavior also conforms to the preset mode of opening the front hatch, the system will consider that the identity authentication has been successfully passed. At this time, the camera module will send a command to the vehicle body domain controller (VIU) without hesitation to request unlocking the front hatch. After receiving this command, the VIU will immediately carry out a series of security checks, including key information such as the vehicle's power mode, gear position, and anti-theft status to ensure that the vehicle is currently in an operable and safe state. Subsequently, the VIU will also carefully identify the operating status of the front hatch to confirm whether it is in a ready-to-open state. When the VIU confirms that everything is ready and the front hatch is indeed in an openable state, it will immediately perform the unlocking operation, and the authorized person can easily open the front hatch for subsequent operations or maintenance. The entire process is efficient, intelligent, safe and reliable, fully demonstrating the advancement and convenience of modern vehicle intelligent management systems.

[0098] In a specific embodiment, it is assumed that the car owner has entered his or her facial information and some specific behavior patterns in the system in advance. One day, the car owner approaches the vehicle and prepares to open the hood. The system captures the owner's face through the camera module and matches it with the pre-stored information. At the same time, the system also recognizes that the car owner is bending over and reaching out to the hood, which is consistent with the preset behavior pattern of opening the hood. Therefore, the system quickly passes the identity authentication, and the camera module sends an unlock request to the VIU. After confirming that the vehicle status is safe and the hood can be opened, the VIU immediately performs the unlocking operation, and the car owner is able to open the hood smoothly.

[0099] It can be understood that the method proposed in the above embodiment not only ensures that only authorized personnel can open the front hatch, but also improves the safety and convenience of operation through real-time environment detection and behavior recognition.

[0100] Figure 4 is a block diagram of a vehicle front hood unlocking device shown in an exemplary embodiment of the present application. The device can be applied to Figure 1 The device may also be applicable to other exemplary implementation environments and specifically configured in other devices, and this embodiment does not limit the implementation environment to which the device is applicable.

[0101] like Figure 4 As shown, the exemplary vehicle hood unlocking device includes: a data acquisition module 410 , a demand prediction module 420 , a behavior intention recognition module 430 , and an unlocking control module 440 .

[0102] Among them, the data acquisition module 410 is used to obtain vehicle maintenance data, vehicle abnormality data, and image information outside the vehicle, the vehicle maintenance data includes at least one of the maintenance data of preset maintenance items and vehicle fluid maintenance data, and the image information includes at least one portrait; the demand prediction module 420 is used to predict the target person's demand for opening the front hood based on the vehicle maintenance data and the vehicle abnormality data; the behavior intention recognition module 430 is used to identify the image information when the prediction result shows that the target person has a potential demand to open the front hood, so as to determine the target person's behavior intention based on the recognition result; the unlocking control module 440 is used to control the unlocking of the vehicle front hood when the target person's behavior intention is to open the vehicle front hood.

[0103] In a specific embodiment of the present application, the vehicle hood unlocking device can also be divided into a first module, a second module, and a third module. The first module is a command acquisition module, which is used to acquire the hood control command input by the user in the vehicle through the human-computer interaction module; the second module is a radar module, which is used to detect whether there is a dynamic command from the user outside the vehicle; the third module is a camera module, which is used to predict the static gestures and behaviors of the user outside the vehicle, and match them with the behaviors in the pre-stored library and identify the behaviors to control the hood unlocking command.

[0104] In a specific implementation, the radar module (radar controller SRR) works continuously, and the front radar module of the vehicle detects objects in the preset area extending outward from the front of the vehicle to detect whether there is a user's dynamic action in front of the vehicle within a specified time period. When the user's dynamic action is any dynamic action in the pre-stored dynamic action library, the corresponding type of front hood control instruction is output. When the radar module SSR detects that there is a user's behavior outside the vehicle, the camera module (vehicle camera module MRC) is called to collect and identify the operable user and behavior. After the face of the user outside the vehicle collected by the camera module passes the authorized face recognition, the camera module collects and compares in real time whether the static behavior collected outside the vehicle within the preset time period is the preset behavior; if it is the preset behavior. The camera module extracts features of the collected static behavior of the user outside the vehicle; compares the extracted behavior with each behavior in the pre-stored behavior database, and determines that the collected behavior matches the front hood unlocking behavior. In the pre-stored gesture database, the same gesture contains multiple data corresponding to people of different genders, ages, weights and heights.

[0105] It should be noted that the vehicle hood unlocking device provided in the above embodiment and the vehicle hood unlocking method provided in the above embodiment belong to the same concept, wherein the specific manner in which each module and unit performs the operation has been described in detail in the method embodiment, and will not be repeated here. In actual application, the vehicle hood unlocking device provided in the above embodiment can allocate the above functions to different functional modules as needed, that is, divide the internal structure of the device into different functional modules to complete all or part of the functions described above, and this is not limited here.

[0106] An embodiment of the present application also provides an electronic device, comprising: one or more processors; a storage device for storing one or more programs, when the one or more programs are executed by the one or more processors, the electronic device implements the vehicle hood unlocking method provided in the above-mentioned embodiments.

[0107] Figure 5 The structure diagram of the computer system suitable for implementing the electronic device of the embodiment of the present application is shown. It should be noted that: Figure 5 The computer system 500 of the electronic device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0108] like Figure 5 As shown, the computer system 500 includes a central processing unit (CPU) 501, which can perform various appropriate actions and processes according to the program stored in the read-only memory (ROM) 502 or the program loaded from the storage part 508 to the random access memory (RAM) 503, such as executing the method described in the above embodiment. In the RAM 503, various programs and data required for system operation are also stored. The CPU 501, the ROM 502 and the RAM 503 are connected to each other through the bus 504. The input / output (I / O) interface 505 is also connected to the bus 504.

[0109] The following components are connected to the I / O interface 505: an input section 506 including a keyboard, a mouse, etc.; an output section 507 including a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker; a storage section 508 including a hard disk, etc.; and a communication section 509 including a network interface card such as a LAN (Local Area Network) card, a modem, etc. The communication section 509 performs communication processing via a network such as the Internet. A drive 510 is also connected to the I / O interface 505 as needed. A removable medium 511, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 510 as needed so that a computer program read therefrom is installed into the storage section 508 as needed.

[0110] In particular, according to an embodiment of the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present application includes a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a computer program for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through a communication section 509, and / or installed from a removable medium 511. When the computer program is executed by a central processing unit (CPU) 501, various functions defined in the system of the present application are executed.

[0111] It should be noted that the computer-readable medium shown in the embodiment of the present application can be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium can be, for example, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media can include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM), a flash memory, an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present application, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, wherein a computer-readable computer program is carried. This propagated data signal can take a variety of forms, including but not limited to an electromagnetic signal, an optical signal, or any suitable combination of the above. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium, which may send, propagate or transmit a program for use by or in conjunction with an instruction execution system, apparatus or device. A computer program contained on a computer-readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wired, etc., or any suitable combination of the above.

[0112] The flowchart and block diagram in the accompanying drawings illustrate the possible architecture, functions and operations of the system, method and computer program product according to various embodiments of the present application. Wherein, each box in the flowchart or block diagram can represent a module, a program segment, or a part of the code, and the above-mentioned module, program segment, or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flowchart, and the combination of boxes in the block diagram or flowchart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0113] The units involved in the embodiments described in this application may be implemented by software or hardware, and the units described may also be set in a processor. The names of these units do not, in some cases, constitute limitations on the units themselves.

[0114] Another aspect of the present application provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor of a computer, the computer executes the vehicle front hood unlocking method as described above. The computer-readable storage medium may be included in the electronic device described in the above embodiment, or may exist independently without being assembled into the electronic device.

[0115] Another aspect of the present application also provides a computer program product or a computer program, which includes a computer instruction stored in a computer-readable storage medium. A processor of a computer device reads the computer instruction from the computer-readable storage medium, and the processor executes the computer instruction, so that the computer device executes the vehicle front hood unlocking method provided in each of the above embodiments.

[0116] The above embodiments are merely illustrative of the principles and effects of the present application, and are not intended to limit the present application. Anyone familiar with the technology may modify or change the above embodiments without violating the spirit and scope of the present application. Therefore, all equivalent modifications or changes made by a person of ordinary skill in the art without departing from the spirit and technical ideas disclosed in the present application shall still be covered by the claims of the present application.

Claims

1. A method for unlocking a vehicle hood, characterized in that: The method comprises: Acquiring vehicle maintenance data, vehicle abnormality data, and image information outside the vehicle, wherein the vehicle maintenance data includes at least one of maintenance data of preset maintenance items and vehicle fluid maintenance data, and the image information includes portrait information of at least one target person; Predicting a front hood opening demand of a target person based on the vehicle maintenance data and the vehicle abnormality data; If the prediction result shows that the target person has a potential need to open the front hatch, the image information is identified to determine the behavior intention of the target person based on the identification result; When the target person's behavioral intention is to open the vehicle's front hood, the vehicle's front hood is controlled to be unlocked.

2. The vehicle hood unlocking method according to claim 1, characterized in that: Predicting the front hood opening demand of the target person according to the vehicle maintenance data and the vehicle abnormality data includes: Get the current time, the current remaining amount of vehicle fluid, and the door signal; Compare the current time with the maintenance data of the preset maintenance item to determine whether the current time is at a maintenance time point; Based on the vehicle fluid maintenance data, predicting the fluid replenishment demand state under the current vehicle fluid remaining state; Determine whether the vehicle has an accident or failure within a preset time based on the abnormal data, and evaluate the target personnel's willingness to perform maintenance in combination with the door signal; If the current time is at a maintenance time point, or there is a need for fluid replenishment, or the target person has an intention to perform maintenance, it is determined that the target person has a potential need to open the front hatch.

3. The vehicle front hood unlocking method according to claim 2, characterized in that: The portrait information is identified to determine the behavior intention of the target person based on the identification result, including: Extracting facial feature points, body feature points, and hand feature points from the portrait information of the target person; Identify the relative position relationship between facial feature points to determine the orientation of the target person; When the target person is facing the front hood of the vehicle, detecting whether the target person is bending over according to the change of the angle formed by the body feature points; If a bending action is detected, the gesture of the target person is identified based on the hand feature points, and the behavioral intention of the target person is determined in combination with the direction, bending state and gesture of the target person.

4. The vehicle front hood unlocking method according to claim 3, characterized in that: Before identifying the relative position relationship between the facial feature points and determining the orientation of the target person, the following steps are also included: Get the initial coordinates of each facial feature point; Taking the initial coordinates as the center, cropping a first cropped image region including all the initial coordinates, and preliminarily locating each facial feature point in the first cropped image region to obtain the first updated coordinates; Using the first updated coordinates as the new center point, crop the cropped image area containing all the updated coordinates again, and perform secondary positioning of each facial feature point in the cropped image area again to obtain the updated coordinates again; Repeat the above cropping and positioning process until the obtained facial feature point coordinates meet the preset accuracy conditions.

5. The vehicle hood unlocking method according to claim 3, characterized in that: According to the change of the angle formed by the body feature points, detecting whether the target person has a bending action includes: Acquire coordinates of each body feature point, wherein the body feature points include at least a waist feature point, a chest feature point, and a leg feature point; Taking the waist feature point as the vertex, calculating the angle formed between the chest feature point and the leg feature point; If the angle is smaller than a preset angle threshold, it is determined that the target person is bending over.

6. The vehicle hood unlocking method according to claim 3, characterized in that: The gesture of the target person is identified according to the hand feature points, and the behavioral intention of the target person is determined in combination with the direction, bending state and gesture of the target person, including: When the target person is facing the hood of the vehicle and is in a bent-over state, and the target person's gesture matches at least one preset gesture, it is determined that the target person has the intention to open the hood of the vehicle, and the preset gesture includes at least one of the following: knocking on the hood, a preset specific hood opening gesture, and hand touching the hood sensor at the front of the vehicle.

7. The vehicle front hood unlocking method according to any one of claims 1 to 6, characterized in that: Before controlling the vehicle's front hood unlocking, it also includes: Log in to the vehicle owner account in advance and enter the authorization information, wherein the authorization information includes at least one of facial information and image information; Based on the authorized person information, identity verification is performed on the target person who has the behavioral intention to open the front hatch of the vehicle; If the verification is successful, a command requesting unlocking of the front hood is sent to the body domain controller to trigger the body domain controller to identify the operation status of the front hood of the vehicle; When the front hatch operation state is an openable state, an unlocking operation of the front hatch is performed.

8. A vehicle front hood unlocking device, characterized in that: The device comprises: a data acquisition module, used to acquire vehicle maintenance data, vehicle abnormality data, and image information outside the vehicle, wherein the vehicle maintenance data includes at least one of maintenance data of preset maintenance items and vehicle fluid maintenance data, and the image information includes at least one human portrait; A demand prediction module, used for predicting the front hood opening demand of a target person according to the vehicle maintenance data and the vehicle abnormality data; a behavior intention recognition module, configured to recognize the image information when the prediction result shows that the target person has a potential need to open the front hatch, so as to determine the behavior intention of the target person based on the recognition result; The unlocking control module is used to control the unlocking of the vehicle's front hood when the target person's behavioral intention is to open the vehicle's front hood.

9. An electronic device, characterized in that: It comprises a processor, a memory and a communication bus; the communication bus is used to connect the processor and the memory; the processor is used to execute a computer program stored in the memory to implement the vehicle hood unlocking method as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that: A computer program is stored thereon, and the computer program is used to enable a computer to execute the vehicle front hood unlocking method as described in any one of claims 1-7.

Images