Tailgate control method, device, electronic device and medium

Abstract

The present application relates to the technical field of vehicle body domain control, and provides a tailgate control method, device, electronic device, and medium. The method comprises: acquiring sensor data from a plurality of sensors on a target vehicle; extracting feature data from each sensor data, and analyzing the similarity between each feature data; determining the correlation relationship between all feature data based on the similarity; inputting all feature data containing the correlation relationship into a target support vector machine, so that the target support vector machine performs fusion calculation on the feature data and outputs the fusion result; and controlling the movement of the tailgate based on the fusion result and user instructions. The present application compensates for the deficiency of a single sensor by extracting feature data from a plurality of sensors of the tailgate, analyzing the similarity, determining the correlation relationship, and performing fusion calculation, thereby ensuring stable and accurate obstacle detection under complex environmental conditions. Through timely and accurate obstacle detection, the safety hazard of continuous tailgate movement due to untimely detection is eliminated.

Description

Technical Field

[0001] The present application relates to the field of vehicle body domain control technology, and in particular to a tailgate control method, device, electronic device, and medium. Background Art

[0002] With the increasing electrification of vehicles, electric tailgates are becoming increasingly popular due to their convenience. However, electric tailgates can fail to accurately detect obstacles during closing, potentially causing injury or damage. To address this issue, electric tailgates have been equipped with anti-pinch systems. These systems typically use anti-pinch logic based on electrical parameter changes in the tailgate's drive motor to detect obstacles, or they utilize some type of sensor, such as an infrared or ultrasonic sensor.

[0003] These systems can work effectively under specific conditions, but each has its limitations, resulting in suboptimal obstacle detection performance of the anti-pinch system. The accuracy of the sensors is greatly affected by the external environment. For example, infrared sensors are easily interfered with by strong heat sources, and ultrasonic sensors have poor detection effects in windy or temperature-changing environments. The anti-pinch logic of the drive motor has a high delay and cannot prevent the tailgate from closing in time. Once the user has not left the tailgate's range of motion, he or she is likely to be pinched.

[0004] Therefore, how to provide a solution to the above technical problems is a problem that those skilled in the art need to solve. Summary of the Invention

[0005] In view of this, embodiments of the present application provide a tailgate control method, device, electronic device, and medium to solve the problem of unsatisfactory obstacle detection effect of the tailgate in the prior art.

[0006] According to a first aspect of an embodiment of the present application, a tailgate control method is provided, comprising:

[0007] Acquiring sensor data from a plurality of sensors on the target vehicle; the sensors are used to monitor obstacles in the movement path of the tailgate of the target vehicle, and the sensors include a plurality of laser distance sensors, infrared sensors, image acquisition sensors, and touch sensors;

[0008] Extract the feature data of each sensor data and analyze the similarity between the feature data;

[0009] According to the similarity, determine the association relationship between all feature data;

[0010] Input all feature data containing the association relationship into the target support vector machine, so that the target support vector machine performs fusion calculation on the feature data and outputs the fusion result; the fusion result includes whether there is an obstacle on the motion path and the location of the obstacle;

[0011] Based on the fusion results and user instructions, the tailgate movement is controlled.

[0012] According to a second aspect of the embodiments of the present application, a tailgate control device is provided, comprising:

[0013] an acquisition module for acquiring sensor data from a plurality of sensors on the target vehicle; the sensors are used to monitor obstacles in the movement path of the tailgate of the target vehicle, and the sensors include a plurality of laser distance sensors, infrared sensors, image acquisition sensors, and touch sensors;

[0014] The data association module is used to extract the feature data of each sensor data, analyze the similarity between each feature data, and determine the association relationship between all feature data based on the similarity;

[0015] The data fusion module is used to input all feature data containing association relationships into the target support vector machine, so that the target support vector machine performs fusion calculation on the feature data and outputs the fusion result; the fusion result includes whether there are obstacles on the motion path and the location of the obstacles;

[0016] The control module is used to control the movement of the tailgate based on the fusion results and user instructions.

[0017] According to a third aspect of an embodiment of the present application, an electronic device is provided, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above method when executing the computer program.

[0018] According to a fourth aspect of the embodiments of the present application, a computer-readable storage medium is provided, which stores a computer program. When the computer program is executed by a processor, the steps of the above method are implemented.

[0019] The beneficial effects of the embodiments of the present application compared with the prior art include at least the following: the embodiments of the present application make up for the shortcomings of a single sensor by extracting feature data, analyzing similarity, determining correlation and performing fusion calculations from multiple sensors of the tailgate, ensuring stable and accurate obstacle detection effects under complex environmental conditions, and eliminating the safety hazards caused by the continuous movement of the tailgate due to untimely detection through timely and accurate obstacle detection. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following briefly introduces the drawings required for use in the embodiments or descriptions of the prior art. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0021] Figure 1 This is a schematic diagram of an application scenario of an embodiment of the present application;

[0022] Figure 2 1 is a flow chart of a tailgate control method provided in an embodiment of the present application;

[0023] Figure 3 is a schematic diagram of the relative position relationship of a target vehicle provided in an embodiment of the present application;

[0024] Figure 4 Schematic diagram of the structure of a tailgate control device provided in an embodiment of the present application;

[0025] Figure 5 This is a structural diagram of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0026] In the following description, specific details such as specific system structures and techniques are provided for purposes of illustration rather than limitation to facilitate a thorough understanding of the embodiments of the present application. However, it will be apparent to those skilled in the art that the present application may be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to avoid obscuring the description of the present application with unnecessary detail.

[0027] A tailgate control method, device, electronic device, and medium according to embodiments of the present application will be described in detail below with reference to the accompanying drawings.

[0028] Figure 1 1 is a schematic diagram of an application scenario of an embodiment of the present application. The application scenario may include a first terminal device 101, a second terminal device 102, a third terminal device 103, a server 104, and a network 105.

[0029] The first terminal device 101, the second terminal device 102, and the third terminal device 103 can be hardware or software. When the first terminal device 101, the second terminal device 102, and the third terminal device 103 are hardware, they can be various electronic devices with a display screen and support communication with the server 104, including but not limited to vehicle systems, smart phones, tablet computers, laptop portable computers, and desktop computers; when the first terminal device 101, the second terminal device 102, and the third terminal device 103 are software, they can be installed in the above electronic devices. The first terminal device 101, the second terminal device 102, and the third terminal device 103 can be implemented as multiple software or software modules, or as a single software or software module, and this embodiment of the application is not limited to this. Furthermore, various applications can be installed on the first terminal device 101, the second terminal device 102, and the third terminal device 103, such as data processing applications, instant messaging tools, social platform software, search applications, shopping applications, etc.

[0030] Server 104 can be a server that provides various services, such as a backend server that receives requests sent by terminal devices that establish communication connections with it. The backend server can receive and analyze the requests sent by the terminal devices, and generate processing results. Server 104 can be a single server, a server cluster consisting of multiple servers, or a cloud computing service center, and the embodiments of the present application are not limited thereto.

[0031] It should be noted that the server 104 can be either hardware or software. When the server 104 is hardware, it can be various electronic devices that provide various services to the first terminal device 101, the second terminal device 102, and the third terminal device 103. When the server 104 is software, it can be multiple software or software modules that provide various services to the first terminal device 101, the second terminal device 102, and the third terminal device 103, or it can be a single software or software module that provides various services to the first terminal device 101, the second terminal device 102, and the third terminal device 103, and this embodiment of the application is not limited to this.

[0032] The network 105 can be a wired network connected by coaxial cable, twisted pair and optical fiber, or it can be a wireless network that can interconnect various communication devices without wiring, such as Bluetooth, Near Field Communication (NFC), infrared, etc., which is not limited in the embodiments of the present application.

[0033] It should be noted that the specific types, quantities and combinations of the first terminal device 101, the second terminal device 102, the third terminal device 103, the server 104 and the network 105 can be adjusted according to the actual needs of the application scenario, and the embodiments of the present application do not limit this.

[0034] Figure 2 It is a flow chart of a tailgate control method provided in an embodiment of the present application. Figure 2 The tailgate control method is executed by a controller located on the target vehicle, which can be Figure 1 The first terminal device, the second terminal device or the third terminal device is implemented. Figure 2 As shown, the tailgate control method includes:

[0035] S201: Acquire sensor data from multiple sensors on the target vehicle; the sensors are used to monitor obstacles in the movement path of the tailgate of the target vehicle, and the sensors include multiple sensors selected from the group consisting of laser distance sensors, infrared sensors, image acquisition sensors, and touch sensors;

[0036] S202: extracting feature data of each sensor data and analyzing the similarity between each feature data;

[0037] S203: Determine the association relationship between all feature data based on the similarity;

[0038] S204: Inputting all feature data containing the association relationship into the target support vector machine, so that the target support vector machine performs fusion calculation on the feature data and outputs a fusion result; the fusion result includes whether there is an obstacle on the motion path and the location of the obstacle;

[0039] S205: Control the tailgate movement based on the fusion result and the user instruction.

[0040] It can be understood that in this embodiment, the target vehicle is equipped with multiple sensors. After obtaining the sensor data of each sensor, all sensor data are analyzed and processed to achieve data fusion of the sensor data, thereby improving the accuracy and reliability of obstacle detection, solving the limitations of a single sensor, and ensuring stable obstacle detection effects under complex environmental conditions. The method of this embodiment is continuously performed throughout the entire process of the tailgate movement, ultimately achieving a reliable and safe control effect of the tailgate movement.

[0041] It will be understood that all sensors in this embodiment are located on the target vehicle. These sensors are used to monitor the movement path of the target vehicle's tailgate for obstacles based on their own characteristics. The specific placement of the sensors is also determined based on the desired use of the sensors in the method of this embodiment. The tailgate's movement path includes both the opening path corresponding to the tailgate opening process and the closing path corresponding to the tailgate closing process. The sensor configuration of this embodiment's method enables obstacle detection for both the opening and closing paths of the tailgate. In other words, the target vehicle's movement trajectory within and outside the target vehicle relative to the tailgate can be detected as an obstacle by the sensors of this embodiment.

[0042] Specifically, a laser distance sensor measures the distance between the tailgate and obstacles by emitting a laser beam and receiving a reflected laser beam. Due to the laser beam's small divergence angle, it has very high linearity. This enables the laser distance sensor to accurately locate obstacles in the tailgate's path, even if the obstacles are far away or small in size. During the tailgate's opening and closing process, the laser distance sensor continuously monitors obstacles in the tailgate's path in real time, measuring distances and providing feedback at extremely high speeds, enabling rapid obstacle identification and response. Furthermore, laser distance sensors are generally highly adaptable to environmental conditions and can operate in a variety of lighting conditions, including direct sunlight and low-light environments.

[0043] Infrared sensors identify obstacles in the tailgate's path by detecting infrared radiation and can detect the heat emitted by living organisms. Because the body temperature of humans and animals is typically higher than the surrounding environment, infrared sensors can identify this temperature difference and thus detect living organisms in the tailgate's closing path. Infrared sensors use a non-contact detection method, meaning they can identify the presence of an obstacle before the tailgate makes contact. Furthermore, infrared sensors do not rely on visible light and can operate even at night or in low-light conditions, making them an all-weather safety component in comprehensive sensing systems.

[0044] Image acquisition sensors, also known as visual cameras, capture a real-time video stream of the tailgate's surroundings, providing continuous visual data as sensor data. Image processing technology analyzes the images in this visual data to identify the shape, size, and type of obstacles. Furthermore, motion tracking technology monitors the movement of obstacles, and this information can be used to predict their future locations, enabling proactive safety measures.

[0045] The laser distance sensor measures the precise distance between the tailgate and the obstacle, the infrared sensor detects heat changes in the tailgate's path, and the image acquisition sensor captures real-time video of the tailgate's surroundings. These sensors work together to collect environmental data about the tailgate's surroundings from various angles and dimensions. The distance data provided by the laser distance sensor can be combined with the obstacle image identified by the image acquisition sensor and the heat changes detected by the infrared sensor to obtain more comprehensive obstacle information.

[0046] Furthermore, when the tailgate is fully closed and engaged with the tailgate lock, there are blind spots for the laser distance sensor, infrared sensor, and image acquisition sensor. If someone accidentally places their hand at the bottom of the tailgate, they could be trapped. Therefore, the method of this embodiment can further eliminate the safety hazard of trapping accidents by acquiring sensor data from a touch sensor. Specifically, the touch sensor is embedded in the tailgate's sealing strip in a long strip shape to detect obstacles when the tailgate is fully closed and engaged with the tailgate lock. The tailgate's sealing strip is located at the edge of the tailgate and is used to ensure a tight seal between the tailgate and the vehicle's body. To ensure that the touch sensor can detect no biological objects trapped at any point of contact between the tailgate and the vehicle's body when the tailgate is closed, the error between the touch sensor's sensing range and the length of the sealing strip, when embedded in the strip shape, is no greater than a predetermined error. The predetermined error is a positive number close to zero. Therefore, the touch sensor's sensing range corresponds to the contact range between the sealing strip and the vehicle's body, ensuring that the touch sensor can detect any biological object trapped at any point between the tailgate and the vehicle's body.

[0047] Touch sensors generally use a capacitive method, with a continuous capacitive field on their surface. When a finger touches the touch sensor surface, the conductivity of the human body causes a change in the local capacitance of the surface. The touch sensor detects this change and converts it into an electrical signal, which serves as sensor data for the touch sensor. The method of this embodiment, after determining the presence of an obstacle based on the touch sensor's sensor data, can control the tailgate to stop its current closing motion and reverse, causing the tailgate to move outward a certain distance, thereby releasing the trapped biological part. By installing a touch sensor at the bottom of the tailgate, even when the tailgate is about to close completely, contact with a biological part can be effectively detected, allowing timely action to prevent pinching accidents.

[0048] See also Figure 3 As shown, Figure 3 A schematic diagram of the relative position relationship between the target vehicle's body, tailgate, and various sensors, wherein 301 is the target vehicle's body structure, 302 is the target vehicle's tailgate, 303 is the installation location of the laser distance sensor, infrared sensor, and image acquisition sensor, which is in the middle of the tailgate, and 304 is the installation location of the touch sensor, which is at the bottom of the tailgate. It is understandable that, except for Figure 3 In addition to this position example, the number and position of various sensors can be further set and adjusted according to actual conditions, reliability requirements, and detection range requirements, which are not limited here.

[0049] It can be understood that after the sensor data of multiple sensors are acquired in step S201, feature data extraction, similarity analysis, correlation relationship determination, and fusion calculation are performed on the sensor data in steps S202-S204 to obtain a judgment about the obstacle, and finally the tailgate movement is controlled in step S205.

[0050] The purpose of step S203 is data association. This involves identifying feature data belonging to different sensors but corresponding to the same obstacle based on the similarity of the feature data. This means identifying which feature data describe the same obstacle. The similarity between the feature data determined in step S202 is a prerequisite for step S203.

[0051] It is understandable that before extracting feature data, the sensor data may be first processed briefly to improve data quality in preparation for subsequent analysis. Specifically, before extracting feature data of each sensor data and analyzing the similarity between the feature data, the following steps may also be performed:

[0052] Preprocessing the sensor data includes at least one of data cleaning, noise filtering, data standardization, and data normalization;

[0053] Time synchronize all sensor data.

[0054] Specifically, data cleaning refers to identifying and correcting erroneous data points. Types of errors include the loss of abnormal numerical data. For example, the sensor data of a laser distance sensor suddenly jumps to an unreasonable value. This may indicate noise or a fault and needs to be corrected or deleted. Noise filtering, because sensor data may contain noise caused by environmental interference or equipment errors, noise filtering refers to applying filtering algorithms to remove this noise and improve the signal-to-noise ratio of the data. Data standardization, because different sensors may provide data in different units or ranges, the data needs to be converted to a unified standard or range. Data normalization refers to scaling the data to a specific range, usually between 0 and 1, which helps to eliminate the impact of differences in the magnitude of data from different sensors, making the data easier to compare and fuse. Sensor data preprocessing can ensure that the received data is accurate, consistent, and comparable, laying a solid foundation for efficient and accurate data fusion.

[0055] Specifically, time synchronization, since sensors may collect data at different frequencies, time synchronization of the data is performed to ensure that sensor data from different sensors correspond to the same time point.

[0056] Specifically, step S202 extracts feature data from each sensor data. The feature data includes the distance measurement data of the obstacle corresponding to the laser distance sensor, the heat change data corresponding to the infrared sensor, and the visual image data corresponding to the image acquisition sensor, which can be recorded as data sets X, Y and Z respectively. The similarity analysis between the feature data can be achieved through the principal component analysis (PCA) method. Principal component analysis is a multivariate statistical analysis method for unsupervised learning. Its main principle is to project high-dimensional data into a low-dimensional space, extract the main factors of the data, and reveal its essential characteristics. This analysis method can efficiently find the main parts of the data, reduce the dimensionality of the originally complex data, and retain the main features of the data. Therefore, this embodiment can be based on the idea of ​​principal component analysis and evaluate the similarity of different sensor data by observing their distribution in a low-dimensional space.

[0057] Specifically, the process of extracting feature data of each sensor data and analyzing the similarity between the feature data includes:

[0058] Extract and standardize the feature data of each sensor data;

[0059] All feature data are combined into a comprehensive feature matrix, and the comprehensive feature matrix is ​​subjected to data dimensionality reduction to obtain a projection matrix in a low-dimensional space after dimensionality reduction;

[0060] Based on the projection matrix, the similarity between each feature data is analyzed.

[0061] In an exemplary embodiment, the feature data of the laser distance sensor, the infrared sensor, and the image acquisition sensor are merged into a comprehensive feature matrix, each row of data is the feature data of a sensor, and the feature data has been standardized. After the standardization, each feature data is expressed in the form of a mean of 0 and a variance of 1.

[0062] Specifically, the process of data dimensionality reduction includes:

[0063] Calculate the covariance matrix of the comprehensive feature matrix. The covariance matrix describes the linear relationship between each feature data;

[0064] Perform eigenvalue decomposition on the covariance matrix to obtain eigenvalues ​​and eigenvectors. The eigenvalues ​​represent the variance of each principal component, and the eigenvectors represent the direction of the principal component.

[0065] Sort the eigenvalues ​​and eigenvectors by eigenvalue from large to small. The larger the eigenvalue corresponding to the sorted eigenvector, the greater the variance in the direction of the eigenvector.

[0066] According to the target dimension of the projection matrix after dimensionality reduction, the eigenvectors corresponding to the eigenvalues ​​of the corresponding number of dimensions are selected from front to back in the sorted order. For example, if the target dimension is 2, the eigenvectors corresponding to the first two eigenvalues ​​are selected, and finally the projection matrix is ​​constructed, thereby achieving the effect of projecting the original feature data into the new low-dimensional space.

[0067] It can be understood that if the feature data from different sensors are clustered together in low-dimensional space, they have high similarity and correspond to the same obstacle. If the feature data from different sensors are far apart in low-dimensional space, they have low similarity and are not the same obstacle. The degree of clustering of each feature data can be described by similarity: higher similarity indicates higher clustering, and lower similarity indicates lower clustering. Similarity is calculated using the eigenvectors in the projection matrix.

[0068] It can be understood that after knowing the similarity between each feature data, step S203 is executed to determine the correlation between the feature data based on the similarity. This correlation is used to indicate the probability that the feature data corresponds to the same obstacle, thereby eliminating duplicate information about the same obstacle as much as possible and concentrating the feature data so that the data input into the target support vector machine in step S204 reflects accurate, comprehensive and non-redundant information.

[0069] It is understood that step S204 performs fusion calculation on the feature data through the target support vector machine and outputs the fusion result. The target support vector machine is obtained by training a support vector machine (SVM) based on the data fusion calculation requirements of the method of this embodiment. The specific training process of the support vector machine includes:

[0070] Sample feature fusion: Extract sample feature vectors of the laser distance sensor, infrared sensor, and image acquisition sensor. The sample feature vector is the sample feature data containing the association relationship obtained after the sample sensor data is processed in steps S202-S203. All sample feature vectors are fused to form a comprehensive feature vector.

[0071] Data Annotation: To train the support vector machine, the comprehensive feature vectors must be annotated. This annotation is based on the actual application scenario, including the presence and location of obstacles. It can also include the classification of obstacles, such as people, vehicles, or other objects. If the annotation result indicates the absence of obstacles, the location and classification of the obstacles are invalid and can be omitted.

[0072] Dataset partitioning: The dataset is divided into training and testing sets for training and evaluating support vector machines.

[0073] Training support vector machine: Use the comprehensive feature vectors and corresponding annotations in the training set to train the support vector machine. Since the sensor data of the laser distance sensor, infrared sensor and image acquisition sensor have complex nonlinear relationships, the kernel function of the support vector machine is selected as the radial basis function (RBF) kernel. The RBF kernel is one of the most commonly used kernel functions, which can handle complex nonlinear relationships and is suitable for nonlinearly separable data. In addition, the support vector machine also needs to be parameter optimized. Parameter optimization is the process of selecting the best hyperparameters (regularization parameter C and kernel parameter gamma). The regularization parameter C controls the complexity of the model, and the kernel parameter gamma controls the influence range of a single training sample. In this embodiment, grid search is used for parameter optimization. The optimized regularization parameter C = 10 and the kernel parameter gamma = 0.01 are used to train the support vector machine.

[0074] Model evaluation: Use the trained support vector machine to predict the test set and calculate the model evaluation indicators, including accuracy, precision, recall, and F1 score.

[0075] When the evaluation index of the model evaluation meets the target index, the trained support vector machine can be used as the target vector machine in the method of this embodiment. If the evaluation index does not meet the target index, the support vector machine needs to be trained again until the evaluation index meets the target index.

[0076] It can be understood that since different types of sensors have different data sensitivities and reliabilities, the feature data can be weighted based on the weights of different sensors before being input into the target support vector machine. Here, the weight of the sensor is the preset weight in the preset configuration area. The preset configuration area is read and the value of the preset weight in the preset configuration area is assigned to the current weight of the sensor.

[0077] Furthermore, taking into account the limitations of each sensor in a special environment, before performing step S204, the reliability of the sensor data can be analyzed according to the current real-time environment, and the weight of the characteristic data of the corresponding sensor can be adjusted based on the reliability. The adjustment here mainly refers to lowering the weight of a certain sensor if it is negatively interfered with by the current real-time environment and its sensor data may be inaccurate.

[0078] Therefore, before inputting all the feature data containing the association relationship into the target support vector machine, it also includes:

[0079] Get the preset weight of each sensor as the current weight;

[0080] Get the real-time environmental status corresponding to each sensor;

[0081] Determine whether the preset weights correspond to the real-time environment status;

[0082] If so, each feature data is weighted based on the current weight;

[0083] If not, the current weight of each sensor is adjusted according to the real-time environmental status, and then each feature data is weighted based on the current weight.

[0084] Specifically, the real-time environmental state corresponding to the laser distance sensor is the content of solid and liquid particles in the air, the real-time environmental state corresponding to the infrared sensor is the distance between the infrared sensor and the strong heat source, and the real-time environmental state corresponding to the image acquisition sensor is the maximum brightness of the environment received by the image acquisition sensor;

[0085] The process of adjusting the weight of each sensor according to the real-time environmental status includes:

[0086] Based on the negative correlation between the solid and liquid particulate matter content in the air and the current weight of the laser distance sensor, the current weight of the laser distance sensor is adjusted;

[0087] Based on the positive correlation between the heat source distance and the current weight of the infrared sensor, the current weight of the infrared sensor is adjusted;

[0088] Based on the negative correlation between the maximum brightness of the environment and the current weight of the image acquisition sensor, the weight of the image acquisition sensor is adjusted.

[0089] It is understandable that when adjusting weights, the main consideration is to lower the weight of a sensor when it receives negative interference from the current real-time environment, causing its sensor data to be inaccurate. The correlation between the above weights and the real-time environmental state is not necessarily linear, but may be stepped. The stepped relationship means that the weight remains unchanged within certain ranges of the real-time environmental state, and the weight is only adjusted after it exceeds the range. Therefore, specific weight adjustments can also include:

[0090] When the content of solid and liquid particles in the air is greater than the preset content, the weight of the laser distance sensor is reduced;

[0091] When the heat source distance is less than the preset distance, the weight of the infrared sensor is lowered;

[0092] When the maximum brightness of the environment is greater than the preset brightness, the weight of the image acquisition sensor is reduced.

[0093] It's understandable that the initial weight assigned to each feature data point is based on the reliability and accuracy of the corresponding sensor. Laser distance sensors typically have high precision and resolution, so they should be given a higher weight under ideal conditions. However, in adverse weather conditions (such as heavy fog or rain), their performance may degrade, and their weight should be reduced. The impact of adverse weather on laser distance sensors is actually the interference of solid and liquid particles in the air on laser propagation, so this can be assessed by the concentration of solid and liquid particles. Infrared sensors detect heat changes to identify obstacles and are suitable for low-light or nighttime environments. However, their accuracy decreases under interference from strong heat sources, so their weight should be reduced. Image acquisition sensors may perform poorly in strong light or backlight conditions, so their weight should be reduced. Whether they are subject to strong light or backlight interference can be assessed by measuring the maximum ambient brightness. In practical applications, weight assignment should comprehensively consider these factors and be dynamically adjusted based on real-time environmental conditions. Regularly analyze historical sensor data to evaluate their performance under different environmental conditions and adjust initial weights and dynamic adjustment rules accordingly. For example, the initial weights can be set as follows: the initial weight of the laser distance sensor is 0.4, the initial weight of the infrared sensor is 0.3, and the initial weight of the image acquisition sensor is 0.3. When the solid and liquid particulate matter content in the air is greater than the preset content, the weight of the laser distance sensor is lowered to 0.2, and the weights of the infrared sensor and image acquisition sensor can be increased to 0.4. In the presence of strong heat source interference, if the heat source distance is less than the preset distance, the weight of the infrared sensor is lowered to 0.2, while the weight of the laser distance sensor remains at 0.4, and the weight of the image acquisition sensor can be increased to 0.4. In strong light or backlight conditions, if the maximum ambient brightness is greater than the preset brightness, the weight of the image acquisition sensor is lowered to 0.2, the weight of the laser distance sensor remains at 0.4, and the weight of the infrared sensor is increased to 0.4.

[0094] It is understandable that when the weight of a certain sensor is lowered due to a specific real-time environmental state, the weights of other sensors can remain unchanged or be increased accordingly according to the actual situation, for example:

[0095] When the content of solid and liquid particles in the air is greater than the preset content, the weight of the laser distance sensor is lowered, and the weight of the infrared sensor and image acquisition sensor is increased;

[0096] When the heat source distance is less than the preset distance, the weight of the infrared sensor is lowered, the weight of the laser distance sensor remains unchanged, and the weight of the image acquisition sensor is increased;

[0097] When the maximum brightness of the environment is greater than the preset brightness, the weight of the image acquisition sensor is lowered, the weight of the laser distance sensor remains unchanged, and the weight of the infrared sensor is increased.

[0098] Therefore, in this embodiment, the negative impact of various environmental conditions on the sensor is mainly considered to lower the weight. The amount of weight change when lowering the weight can be set according to actual conditions or user needs. The changes in the weights of other sensors can also be set according to actual conditions or user needs, and there is no restriction here.

[0099] It can be understood that step S204 ultimately obtains a fusion result, including whether there are obstacles on the motion path and the position of the obstacle when there are obstacles. In addition, the fusion result can also include the classification of obstacles, which is specifically set according to user needs and actual conditions, and is achieved by training the target support vector machine.

[0100] It is understandable that after obtaining the fusion result, the fusion result is used as the obstacle detection result to participate in the control process of the tailgate movement. The control of the tailgate movement is usually triggered by a user instruction, and a driving information for the motion motor is generated according to the user instruction. After receiving the driving information, the motion motor runs to control the tailgate to move in the direction corresponding to the user instruction. The real-time position of the tailgate and the obstacle detection result of the tailgate are used as control feedback factors to correct the control process of the tailgate movement. The real-time position of the tailgate can be described by the real-time opening of the tailgate. Therefore, the process of controlling the movement of the tailgate based on the fusion result and the user instruction includes:

[0101] Acquiring the real-time position of the tailgate by controlling the motion motor of the tailgate or the position sensor of the tailgate;

[0102] Based on the fusion results, user instructions and the real-time position of the tailgate, the motion motor is driven to control the movement of the tailgate.

[0103] It is understood that if the fusion results indicate that there are no obstacles on the current motion path, the user's command can be completed directly at a faster speed. If the fusion results indicate that there is an obstacle on the current motion path but the obstacle is far away from the tailgate, the user's command can be temporarily maintained at a faster speed. If there is an obstacle on the current motion path and it is close to the tailgate, the tailgate's movement speed can be reduced, and a warning prompt, such as a voice prompt or a beep, can be issued to inform the user of the obstacle on the current motion path. If there is an obstacle on the current motion path and it is very close to the tailgate, the tailgate's current movement is directly stopped and the motor is driven to reverse, causing the tailgate to move in the opposite direction to prevent the obstacle from being trapped between the vehicle body and the tailgate. In addition, the sensor data of the touch sensor can reflect whether a biological part is contacting the touch sensor. Therefore, if the touch sensor indicates that contact is currently detected, that is, a biological part is contacting the touch sensor, to avoid pinching, the tailgate should be immediately controlled to open by moving in the opposite direction.

[0104] Specifically, based on the fusion results, user instructions, and the real-time position of the tailgate, the motion motor is driven to control the tailgate movement process, including:

[0105] When there are no obstacles on the motion path, the motion motor is driven based on the user command to control the tailgate to move at a first preset speed and in a direction corresponding to the user command;

[0106] When there is an obstacle on the motion path, the real-time obstacle distance between the tailgate and the obstacle is determined based on the position of the obstacle and the real-time position of the tailgate;

[0107] When the real-time obstacle distance is greater than the first safety distance, the motion motor is driven based on the user instruction to control the tailgate to move at a first preset speed and in a direction corresponding to the user instruction;

[0108] When the real-time obstacle distance is greater than the second safety distance and not greater than the first safety distance, the motion motor is driven based on the user command to control the tailgate to move at a second preset speed and in a direction corresponding to the user command, and an alarm is issued at the same time;

[0109] When the real-time obstacle distance is not greater than the second safety distance or the sensor data of the touch sensor indicates that contact is detected, the driving direction of the motion motor is changed to control the tailgate to move in the opposite direction to the direction corresponding to the user command;

[0110] The first safety distance is greater than the second safety distance, and the first preset speed is greater than the second preset speed.

[0111] It can be understood that here the first safety distance and the second safety distance are both positive numbers, the first preset speed and the second preset speed are both positive numbers, and the normal movement direction of the tailgate corresponds to the user instruction.

[0112] Furthermore, in this embodiment, a security log record and a user interaction interface can also be provided.

[0113] The safety log records all tailgate-related events and operations, including obstacle detection data, tailgate movement status, issued warnings, and implemented safety measures. This log is crucial for post-event analysis and system maintenance. Through these steps, the intelligent control module ensures safe tailgate operation, effectively prevents trapping accidents, and provides essential user feedback and system maintenance information.

[0114] The user interface provides a platform for drivers and passengers to control and manage the tailgate status. The following functions can be achieved by the user interface:

[0115] Real-time information display: The user interface can display the current status of the tailgate, including the tailgate opening, the speed and direction of the tailgate movement, as well as the location and distance of obstacles.

[0116] Warnings and prompts: The user interface can issue warning prompts to remind users of potential pinching risks.

[0117] Manual control: The user interaction interface may provide a command option for controlling the tailgate, so that the user can issue a user command through the command option to open or close the tailgate, or stop the movement of the tailgate when necessary.

[0118] System Settings: Users can access system settings through the interactive interface and adjust the tailgate's first safety distance, second safety distance, first preset speed, second preset speed, and other related parameters to suit personal preferences and different usage scenarios.

[0119] Event Log Viewing: Users can view the tailgate's historical operation history through the user interface, including obstacle detection events, tailgate movement records, and safety measures activation. Through these features, the user interface provides an intuitive and easy-to-use platform for users to better monitor and control tailgate safety, while also enhancing the user experience of the entire anti-pinch control system.

[0120] The method of the embodiment of the present application compensates for the shortcomings of a single sensor by extracting feature data, analyzing similarities, determining correlations, and performing fusion calculations from multiple sensors of the tailgate, thereby ensuring stable and accurate obstacle detection under complex environmental conditions. Through timely and accurate obstacle detection, the safety hazard caused by the continuous movement of the tailgate due to untimely detection is eliminated.

[0121] All of the above optional technical solutions can be combined in any way to form optional embodiments of the present application, and will not be described in detail here. It should be understood that the order of the sequence numbers of the steps in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0122] The following are device embodiments of the present application, which can be used to implement the method embodiments of the present application. For details not disclosed in the device embodiments of the present application, please refer to the method embodiments of the present application.

[0123] Figure 4 Schematic diagram of a tailgate control device provided in an embodiment of the present application. Figure 4 As shown, the tailgate control device includes:

[0124] An acquisition module 401 is configured to acquire sensor data from a plurality of sensors on the target vehicle; the sensors are configured to monitor obstacles in the movement path of the tailgate of the target vehicle, and the sensors include a plurality of laser distance sensors, infrared sensors, image acquisition sensors, and touch sensors;

[0125] The data association module 402 is used to extract feature data of each sensor data, analyze the similarity between each feature data, and determine the association relationship between all feature data based on the similarity;

[0126] The data fusion module 403 is used to input all feature data containing the association relationship into the target support vector machine, so that the target support vector machine performs fusion calculation on the feature data and outputs a fusion result; the fusion result includes whether there is an obstacle on the motion path and the location of the obstacle;

[0127] The control module 404 is used to control the movement of the tailgate based on the fusion result and the user instruction.

[0128] The device of the embodiment of the present application compensates for the shortcomings of a single sensor by extracting feature data, analyzing similarities, determining correlations, and performing fusion calculations from multiple sensors of the tailgate, thereby ensuring stable and accurate obstacle detection effects under complex environmental conditions. Through timely and accurate obstacle detection, the safety hazard caused by the continuous movement of the tailgate due to untimely detection is eliminated.

[0129] In an exemplary embodiment, before inputting all feature data containing association relationships into the target support vector machine, the data fusion module is further configured to:

[0130] Get the preset weight of each sensor as the current weight;

[0131] Get the real-time environmental status corresponding to each sensor;

[0132] Determine whether the preset weights correspond to the real-time environment status;

[0133] If so, each feature data is weighted based on the current weight;

[0134] If not, the current weight of each sensor is adjusted according to the real-time environmental status, and then each feature data is weighted based on the current weight.

[0135] In an exemplary embodiment, the real-time environmental state corresponding to the laser distance sensor is the content of solid and liquid particulate matter in the air, the real-time environmental state corresponding to the infrared sensor is the distance between the infrared sensor and a strong heat source, and the real-time environmental state corresponding to the image acquisition sensor is the maximum ambient brightness received by the image acquisition sensor;

[0136] The data fusion module is specifically used for:

[0137] Based on the negative correlation between the solid and liquid particulate matter content in the air and the current weight of the laser distance sensor, the current weight of the laser distance sensor is adjusted;

[0138] Based on the positive correlation between the heat source distance and the current weight of the infrared sensor, the current weight of the infrared sensor is adjusted;

[0139] Based on the negative correlation between the maximum brightness of the environment and the current weight of the image acquisition sensor, the weight of the image acquisition sensor is adjusted. In an exemplary embodiment, the data association module is specifically used to:

[0140] Extract and standardize the feature data of each sensor data;

[0141] All feature data are combined into a comprehensive feature matrix, and the comprehensive feature matrix is ​​subjected to data dimensionality reduction to obtain a projection matrix in a low-dimensional space after dimensionality reduction;

[0142] Based on the projection matrix, the similarity between each feature data is analyzed.

[0143] In an exemplary embodiment, before extracting feature data of each sensor data and analyzing the similarity between the feature data, the data association module is further configured to:

[0144] Preprocessing the sensor data includes at least one of data cleaning, noise filtering, data standardization, and data normalization;

[0145] Time synchronize all sensor data.

[0146] In an exemplary embodiment, the control module is specifically configured to:

[0147] Acquiring the real-time position of the tailgate by controlling the motion motor of the tailgate or the position sensor of the tailgate;

[0148] Based on the fusion results, user instructions and the real-time position of the tailgate, the motion motor is driven to control the movement of the tailgate.

[0149] In an exemplary embodiment, the control module is specifically configured to:

[0150] When there are no obstacles on the motion path, the motion motor is driven based on the user command to control the tailgate to move at a first preset speed and in a direction corresponding to the user command;

[0151] When there is an obstacle on the motion path, the real-time obstacle distance between the tailgate and the obstacle is determined based on the position of the obstacle and the real-time position of the tailgate;

[0152] When the real-time obstacle distance is greater than the first safety distance, the motion motor is driven based on the user instruction to control the tailgate to move at a first preset speed and in a direction corresponding to the user instruction;

[0153] When the real-time obstacle distance is greater than the second safety distance and not greater than the first safety distance, the motion motor is driven based on the user command to control the tailgate to move at a second preset speed and in a direction corresponding to the user command, and an alarm is issued at the same time;

[0154] When the real-time obstacle distance is not greater than the second safety distance or the sensor data of the touch sensor indicates that contact is detected, the driving direction of the motion motor is changed to control the tailgate to move in the opposite direction to the direction corresponding to the user command;

[0155] The first safety distance is greater than the second safety distance, and the first preset speed is greater than the second preset speed.

[0156] Figure 5 Schematic diagram of the electronic device 5 provided in the embodiment of the present application. Figure 5 As shown, the electronic device 5 of this embodiment includes: a processor 501, a memory 502, and a computer program 503 stored in the memory 502 and executable by the processor 501. When the processor 501 executes the computer program 503, the steps of the above-mentioned method embodiments are implemented. Alternatively, when the processor 501 executes the computer program 503, the functions of the modules / units in the above-mentioned device embodiments are implemented.

[0157] The electronic device 5 may be a desktop computer, a notebook, a PDA, a cloud server, or other electronic device. The electronic device 5 may include but is not limited to a processor 501 and a memory 502. Those skilled in the art will appreciate that Figure 5 This is merely an example of the electronic device 5 and does not limit the electronic device 5 . The electronic device 5 may include more or fewer components than shown in the figure, or different components.

[0158] The processor 501 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc.

[0159] The memory 502 can be an internal storage unit of the electronic device 5, such as a hard disk or memory of the electronic device 5. The memory 502 can also be an external storage device of the electronic device 5, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) card, a flash memory card, etc. The memory 502 can also include both an internal storage unit of the electronic device 5 and an external storage device. The memory 502 is used to store computer programs and other programs and data required by the electronic device.

[0160] Those skilled in the art will clearly understand that for the sake of convenience and brevity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In actual applications, the above-mentioned functions can be distributed and completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The above-mentioned integrated units can be implemented in the form of hardware or in the form of software functional units.

[0161] If the integrated module / 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 present application implements all or part of the process in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. The computer program may include computer program code, which may be in source code form, object code form, executable file or some intermediate form. The computer-readable storage medium may include: any entity or device capable of carrying computer program code, recording medium, U disk, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal and software distribution medium, etc.

[0162] The above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present application, and should all be included in the scope of protection of the present application.

Claims

1. A tailgate control method, characterized in that: include: Acquiring sensor data from a plurality of sensors on a target vehicle; the sensors are used to monitor obstacles in a movement path of a tailgate of the target vehicle, and the sensors include a plurality of laser distance sensors, infrared sensors, image acquisition sensors, and touch sensors; Extracting characteristic data of each sensor data, and analyzing similarity between each characteristic data; Determining the association relationship between all the feature data based on the similarity; Obtain the preset weights and corresponding real-time environmental states of each of the sensors, and determine whether the preset weights correspond to the real-time environmental states, the real-time environmental states corresponding to the laser distance sensor, the infrared sensor, and the image acquisition sensor are respectively the content of solid and liquid particulate matter in the air, the heat source distance between the infrared sensor and a strong heat source, and the maximum brightness of the environment; if so, perform weighted processing on each of the feature data based on the preset weights; if not, adjust the preset weights according to the real-time environmental state to obtain the current weights, and then perform weighted processing on each of the feature data based on the current weights; wherein, based on the negative correlation between the content of solid and liquid particulate matter in the air and the weight of the laser distance sensor, adjust the weight of the laser distance sensor; based on the positive correlation between the heat source distance and the weight of the infrared sensor, adjust the weight of the infrared sensor; based on the negative correlation between the maximum brightness of the environment and the weight of the image acquisition sensor, adjust the weight of the image acquisition sensor; inputting all the feature data containing the association relationship into a target support vector machine, so that the target support vector machine performs a fusion calculation on the feature data and outputs a fusion result; the fusion result includes whether there is an obstacle on the motion path and the position of the obstacle; Based on the fusion result and the user instruction, the tailgate movement is controlled.

2. The method according to claim 1, characterized in that The process of extracting feature data of each sensor data and analyzing the similarity between the feature data includes: Extracting and standardizing feature data of each of the sensor data; Combining all the feature data into a comprehensive feature matrix, and performing data dimensionality reduction on the comprehensive feature matrix to obtain a projection matrix in a low-dimensional space after dimensionality reduction; Based on the projection matrix, similarities between the feature data are analyzed.

3. The method according to claim 1, characterized in that Before extracting the feature data of each sensor data and analyzing the similarity between the feature data, the method further includes: Preprocessing the sensor data, wherein the preprocessing includes at least one of data cleaning, noise filtering, data standardization, and data normalization; All the sensor data are time synchronized.

4. The method according to any one of claims 1 to 3, characterized in that The process of controlling the tailgate movement based on the fusion result and the user instruction includes: Acquiring a real-time position of the tailgate by controlling a motion motor for the tailgate or a position sensor for the tailgate; Based on the fusion result, the user instruction and the real-time position of the tailgate, the motion motor is driven to control the movement of the tailgate.

5. The method according to claim 4, characterized in that The process of driving the motion motor to control the movement of the tailgate based on the fusion result, the user instruction and the real-time position of the tailgate includes: When there is no obstacle on the movement path, driving the movement motor based on the user instruction to control the tailgate to move at a first preset speed and in a direction corresponding to the user instruction; When the obstacle exists on the motion path, determining a real-time obstacle distance between the tailgate and the obstacle based on the position of the obstacle and the real-time position of the tailgate; When the real-time obstacle distance is greater than a first safety distance, driving the motion motor based on the user instruction to control the tailgate to move at the first preset speed and in the direction corresponding to the user instruction; When the real-time obstacle distance is greater than the second safety distance and not greater than the first safety distance, driving the motion motor based on the user command to control the tailgate to move at a second preset speed and in a direction corresponding to the user command, and issuing a warning prompt at the same time; When the real-time obstacle distance is not greater than the second safety distance or the sensor data of the touch sensor indicates that contact is detected, changing the driving direction of the motion motor to control the tailgate to move in a direction opposite to the direction corresponding to the user command; The first safety distance is greater than the second safety distance, and the first preset speed is greater than the second preset speed.

6. A tailgate control device, characterized in that: include: an acquisition module for acquiring sensor data from a plurality of sensors on the target vehicle; the sensors are used to monitor obstacles in the movement path of the tailgate of the target vehicle, and the sensors include a plurality of laser distance sensors, infrared sensors, image acquisition sensors, and touch sensors; a data association module, configured to extract feature data of each of the sensor data, analyze similarities between the feature data, and determine association relationships between all of the feature data based on the similarities; a data fusion module, configured to obtain preset weights and corresponding real-time environmental states of each of the sensors, and determine whether the preset weights correspond to the real-time environmental states, wherein the real-time environmental states corresponding to the laser distance sensor, the infrared sensor, and the image acquisition sensor are respectively the solid and liquid particulate matter content in the air, the heat source distance between the infrared sensor and a strong heat source, and the maximum brightness of the environment; if so, weighting processing is performed on each of the feature data based on the preset weights; if not, adjusting the preset weights according to the real-time environmental state to obtain current weights, and then weighting processing is performed on each of the feature data based on the current weights; wherein the weight of the laser distance sensor is adjusted based on the negative correlation between the solid and liquid particulate matter content in the air and the weight of the laser distance sensor; the weight of the infrared sensor is adjusted based on the positive correlation between the heat source distance and the weight of the infrared sensor; and the weight of the image acquisition sensor is adjusted based on the negative correlation between the maximum brightness of the environment and the weight of the image acquisition sensor; all the feature data containing the association relationships are input into a target support vector machine, so that the target support vector machine performs fusion calculation on the feature data and outputs a fusion result; the fusion result includes whether there is an obstacle on the motion path and the location of the obstacle; A control module is used to control the movement of the tailgate based on the fusion result and user instructions.

7. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 5 are implemented.

8. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 5 are implemented.

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