Vehicle demisting method and device

Abstract

The invention relates to the technical field of vehicle control, and discloses a vehicle demisting method and device. The method comprises the following steps: acquiring associated data of a target vehicle; wherein the associated data of the target vehicle comprises at least one of in-vehicle environment data and out-vehicle environment data; according to the associated data of the target vehicle, determining whether the target vehicle needs to be defogged; and if the target vehicle needs to be defogged, the target vehicle is controlled to be defogged.

Description

Technical Field

[0001] The present invention relates to the technical field of vehicle control, and in particular to a vehicle defogging method and device. Background Art

[0002] With the rapid development of the automobile industry, the safety and comfort of vehicles have become the focus of increasing attention of consumers. In cold or humid weather conditions, key parts of the vehicle such as the front windshield, side windows and rearview mirrors are prone to fogging, which seriously affects the driver's field of vision and increases the hidden danger of driving safety. Therefore, effectively removing fog from the vehicle has become an important part of ensuring driving safety.

[0003] At present, the fog on the vehicle is usually handled by manual defogger. In this process, the driver is required to manually remove the fog by using a defogging device, cloth or hot water after discovering the fog on the vehicle. This process is highly dependent on the driver's vision and judgment, that is, the driver needs to observe the fogging of the vehicle with the naked eye before deciding what defogger measures to take.

[0004] However, the lag of manual defogger is mainly reflected in the time difference between the driver noticing the fog and reacting to it. Since the driver needs to perceive the fog through vision before deciding to take action (such as turning on the wipers), this process often leads to a delay in the defogger operation. In extreme weather conditions, this lag may seriously threaten driving safety.

[0005] Therefore, how to improve the efficiency of vehicle defogger becomes a problem that needs to be solved. Summary of the invention

[0006] In view of this, the present invention provides a vehicle defog method and device.

[0007] In a first aspect, the present invention provides a vehicle defog method, the method comprising: obtaining associated data of a target vehicle; wherein the associated data of the target vehicle comprises at least one of in-vehicle environment data and out-vehicle environment data; determining whether the target vehicle needs to be defogged based on the associated data of the target vehicle; if the target vehicle needs to be defogged, controlling the target vehicle to perform defog.

[0008] The vehicle defog method provided in this embodiment determines whether the target vehicle has a condition that causes the window to fog up through at least one of the vehicle interior environment data and the vehicle exterior environment data. Once the condition that causes the window to fog up is detected, the target vehicle can be immediately controlled to perform defog, without the driver having to manually operate the defog component of the target vehicle, thereby reducing the delay of the defog operation. In addition, the defog operation is intelligently adjusted according to the vehicle interior and exterior environment data, thereby effectively improving the vehicle defog effect.

[0009] In one possible implementation, the in-vehicle environmental data of the target vehicle includes: in-vehicle temperature data and in-vehicle humidity data; the out-vehicle environmental data includes: out-vehicle temperature data and out-vehicle humidity data; and based on the associated data of the target vehicle, determining whether the target vehicle needs to be defogged, including: determining the temperature difference between the out-vehicle temperature data and the in-vehicle temperature data based on the out-vehicle temperature data and the in-vehicle temperature data; determining the out-vehicle relative humidity based on the out-vehicle humidity data and the maximum humidity corresponding to the out-vehicle temperature data; determining the in-vehicle relative humidity based on the in-vehicle humidity data and the maximum humidity corresponding to the in-vehicle temperature data; if at least one of the out-vehicle relative humidity and the in-vehicle relative humidity is greater than the relative humidity threshold, and the temperature difference is greater than the temperature difference threshold, it is determined that the target vehicle needs to be defogged.

[0010] The vehicle defogging method provided in this embodiment can sense the subtle changes in the vehicle's internal and external environment in real time through the temperature difference between the indoor temperature and the outdoor temperature, the relative humidity inside the vehicle, and the relative humidity outside the vehicle. That is, whether it is a slight fluctuation in temperature or an increase or decrease in humidity, the subtle changes in the vehicle's internal and external environment can be quickly captured, thereby accurately determining whether the vehicle needs to be defogged based on the subtle changes in the vehicle's internal and external environment.

[0011] In one possible implementation, the external environment data includes rainfall data; and determining whether the target vehicle needs to be defogged based on the associated data of the target vehicle includes: when the rainfall data is not less than a rainfall threshold, determining that the target vehicle needs to be defogged.

[0012] The vehicle defogger method provided in this embodiment can distinguish the rainy day conditions in which the vehicle is located, such as heavy rain, moderate rain, light rain, etc., by detecting rainfall data in the external environment data of the vehicle, and can determine the distribution of raindrops on the glass parts of the vehicle, so that when only considering the rainfall data, it is also possible to accurately determine whether the vehicle needs to be defogged.

[0013] In a possible implementation, the associated data of the target vehicle also includes: first image data to be tested of the glass components of the target vehicle; determining whether the target vehicle needs to be defogged based on the associated data of the target vehicle, including: determining a first defogger judgment result based on the first image data to be tested of the glass components of the target vehicle, annotated image data corresponding to the first image data to be tested, and a target fogging threshold; wherein the annotated image data corresponding to the first image data to be tested is image data of the glass components of the target vehicle collected when the target vehicle does not need to be defogged; determining whether the target vehicle needs to be defogged based on the first defogger judgment result.

[0014] The vehicle defogger method provided in the present embodiment introduces first image data of the glass parts of the target vehicle to be tested, adds conditions for determining whether the vehicle needs to be defogged, and combines the annotated image data corresponding to the first image data to be tested and the defogger judgment result determined by the target fogging threshold as a basis for determining whether the vehicle needs to be defogged, thereby improving the accuracy of the vehicle defogger judgment.

[0015] In a possible implementation, a first defogger judgment result is determined based on first image data of a glass part of a target vehicle to be tested, annotated image data corresponding to the first image data to be tested, and a target fogging threshold, including: determining a first grayscale gradient component in a horizontal direction and a second grayscale gradient component in a vertical direction for each pixel in the first image data to be tested; determining a gradient amplitude corresponding to each pixel according to the first grayscale gradient component and the second grayscale gradient component; determining a grayscale gradient mean corresponding to the first image data to be tested according to the gradient amplitude corresponding to each pixel; determining a first defogger judgment result based on a ratio of the grayscale gradient mean to a target grayscale gradient and a target fogging threshold; wherein the target grayscale gradient is calculated from the annotated image data corresponding to the first image data to be tested; determining whether the target vehicle needs to be defogged based on the first defogger judgment result, including: determining that the target vehicle needs to be defogged when the ratio of the grayscale gradient mean to the target grayscale gradient is less than the target fogging threshold.

[0016] The vehicle defogging method provided in this embodiment can determine the precise information of the grayscale change of each pixel in the image by determining the first grayscale gradient component in the horizontal direction and the second grayscale gradient component in the vertical direction of each pixel in the image data to be tested. Then, according to the grayscale gradient components in the horizontal and vertical directions of each pixel and the mean of the gradient amplitudes of all pixels in the image data to be tested, the grayscale gradient mean is obtained, and the ratio of the grayscale gradient mean to the target grayscale gradient is compared with the fogging threshold, so as to quantify the difference in grayscale gradient between the image to be tested and the target image, thereby accurately determining whether the vehicle needs to be defogged.

[0017] In one possible implementation, determining whether the target vehicle needs to be defogged based on the first defog judgment result includes: using a pre-trained defog judgment model to obtain a second defog judgment result based on associated data; and determining whether the target vehicle needs to be defogged based on the first defog judgment result and the second defog judgment result.

[0018] The vehicle defogger method provided in the present embodiment introduces first image data to be tested of the glass parts of the target vehicle, annotated image data corresponding to the first image data to be tested, and a first defogger judgment result determined by a target fogging threshold as a judgment condition for determining whether the vehicle needs to be defogged. Whether the vehicle needs to be defogged is determined not only by the first defogger judgment result, but also by a combination of the first defogger judgment result and a second defogger judgment result of a pre-trained defogger judgment model. This avoids misjudgment of vehicle defoggering caused by inaccurate judgment of the defogger judgment model, thereby improving the accuracy of vehicle defogger judgment.

[0019] In a possible implementation, the method also includes: inputting second image data to be tested of the glass parts of the target vehicle into a pre-trained defogger judgment model to obtain a third defogger judgment result; repeatedly performing a search operation for a target fogging threshold until the target fogging threshold is determined, the search operation for the target fogging threshold including: determining a fourth defogger judgment result targeted by the search operation based on the second image data to be tested of the glass parts of the target vehicle, the annotated image data corresponding to the second image data to be tested, and the candidate fogging threshold targeted by the search operation; when the third defogger judgment result and the fourth defogger judgment result are different, determining the candidate fogging threshold targeted by the next search operation based on a preset value and the candidate fogging threshold targeted by the search operation; when the third defogger judgment result and the fourth defogger judgment result are the same, determining the candidate fogging threshold targeted by the search operation as the target fogging threshold.

[0020] The vehicle defog method provided in this embodiment, when the third defog judgment result and the fourth defog judgment result are different, indicates that the fourth defog judgment result obtained by image processing is inaccurate, and it is necessary to adjust the candidate fogging threshold to determine the target fogging threshold, that is, continuously adjust the candidate fogging threshold for the target fogging threshold search operation according to the preset value, so that when the third defog judgment result and the fourth defog judgment result are the same, the target fogging threshold is accurately determined.

[0021] In one possible implementation, determining whether the target vehicle needs to be defogged is performed based on the associated data of the target vehicle, including: determining fogging judgment parameters corresponding to the associated data of the target vehicle based on the associated data of the target vehicle; wherein the fogging judgment parameters include temperature parameters and humidity parameters, and the associated data of the target vehicle are data that changes in real time; determining whether the target vehicle needs to be defogged based on a comparison result between the fogging judgment parameters and the associated data of the target vehicle.

[0022] In the vehicle defog method provided by the present embodiment, the temperature parameters and the humidity parameters can be changed through associated data. The associated data is data that changes in real time, that is, different temperature parameters and humidity parameters can be determined according to different associated data, and then the associated data and the fogging judgment parameters corresponding to the associated data can be used to accurately determine whether the target vehicle needs to be defogged.

[0023] In a possible implementation, the method further includes: inputting the associated data into a pre-trained defogging judgment model to obtain a predicted defogging probability corresponding to the associated data, the predicted defogging probability indicating the probability that defogging is required under the circumstances indicated by the associated data; determining a fifth defogging judgment result corresponding to the associated data based on a comparison result between the predicted defogging probability corresponding to the associated data and a defogging probability threshold; and updating the parameters of the pre-trained defogging judgment model when the fifth defogging judgment result corresponding to the associated data is different from the labeled result of the associated data.

[0024] The vehicle defogging method provided in this embodiment continuously updates the parameters of the model through associated data, so that the model can better adapt to various actual situations and improve the accuracy of determining whether the target vehicle needs to be defogged. In addition, the performance of the pre-trained defogging judgment model when facing new and unseen data often depends on its generalization ability. By continuously updating the parameters of the pre-trained defogging judgment model with the associated data of the new target vehicle and the corresponding annotation results, the model's adaptability to new data can be enhanced and its generalization performance can be improved.

[0025] In a possible implementation, the associated data also includes: at least one of the own vehicle data and other vehicle data; the own vehicle data includes at least one of the seasonal data of the target vehicle, the regional location of the target vehicle, and the vehicle speed data; the other vehicle data includes at least one of the seasonal data of other vehicles, the regional location of other vehicles, and the vehicle speed data.

[0026] The vehicle defog method provided in this embodiment has a more comprehensive understanding of the current environmental conditions by introducing data of more dimensions (season, regional location and vehicle speed), since the possibility of fogging of vehicle windows may vary significantly in different seasons, different regions and different vehicle speeds, thereby improving the accuracy of determining whether the target vehicle needs to be defogged.

[0027] In a possible implementation, image data collected when the target vehicle meets preset conditions is used as annotated image data corresponding to the first image data to be tested; wherein the preset conditions include: the current weather is foggy and precipitation-free, the visibility of the glass parts of the target vehicle is greater than a visibility threshold, the relative humidity is lower than a relative humidity threshold, and the defogger function of the target vehicle is not turned on.

[0028] The vehicle defogger method provided in the present embodiment can truly reflect the clarity state of the vehicle's glass parts in the current weather without fogging, precipitation, visibility of the vehicle's glass parts greater than a visibility threshold and other interference factors through image data collected under a preset state, and can accurately determine the images of the vehicle's glass parts that do not need to be defogged, so as to more accurately predict whether the vehicle needs to be defogged through the images of the glass parts that do not need to be defogged.

[0029] In a second aspect, the present invention provides a vehicle defogger device, which includes: an acquisition module, used to acquire associated data of a target vehicle; wherein the associated data of the target vehicle includes at least one of in-vehicle environment data and out-of-vehicle environment data; a determination module, used to determine whether the target vehicle needs to be defogged based on the associated data of the target vehicle; and a control module, used to control the target vehicle to perform defogger if the target vehicle needs to be defogged.

[0030] In a third aspect, the present invention provides a computer device, comprising: a memory and a processor, the memory and the processor are communicatively connected to each other, computer instructions are stored in the memory, and the processor executes the vehicle defog method of the first aspect or any corresponding embodiment thereof by executing the computer instructions.

[0031] In a fourth aspect, the present invention provides a computer-readable storage medium having computer instructions stored thereon, the computer instructions being used to enable a computer to execute the vehicle defog method of the first aspect or any corresponding embodiment thereof.

[0032] In a fifth aspect, the present invention provides a computer program product, comprising computer instructions for causing a computer to execute the vehicle defog method of the first aspect or any corresponding embodiment thereof.

[0033] In a sixth aspect, the present invention provides a vehicle comprising a memory and a processor, the memory and the processor being communicatively connected to each other, the memory storing computer instructions, and the processor executing the vehicle defog method of the first aspect or any corresponding embodiment thereof by executing the computer instructions. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0035] Figure 1 is a schematic flow chart of a vehicle defogging method according to an embodiment of the present invention; Figure 2 is a flow chart of another vehicle defogging method according to an embodiment of the present invention; Figure 3 is a schematic diagram of a vehicle defogging method according to an embodiment of the present invention; Figure 4 It is a schematic diagram of the hardware structure of a computer device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0036] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0037] According to an embodiment of the present invention, an embodiment of a vehicle defog method is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0038] In this embodiment, a vehicle defogging method is provided, which can be specifically executed by a computer device on the vehicle, for example, it can be executed by a vehicle controller, etc. Figure 1 is a flow chart of a vehicle defogging method according to an embodiment of the present invention, such as Figure 1 As shown, the process includes the following steps: Step S101, obtaining the associated data of the target vehicle; wherein the associated data of the target vehicle includes at least one of the in-vehicle environment data and the out-vehicle environment data.

[0039] The target vehicle may be any vehicle to which the method provided by the embodiments of the present disclosure may be applied.

[0040] The vehicle's associated data can represent various information about the vehicle's internal and external environments. The vehicle's associated data includes at least one of the vehicle's internal environment data and the vehicle's external environment data, and specifically includes the following multiple situations: Situation 1: vehicle's internal environment data; Situation 2: vehicle's external environment data; Situation 3: vehicle's internal environment data and vehicle's external environment data.

[0041] As an example, for the above situation 1: the in-car environment data may include in-car humidity and in-car temperature. For example: the in-car humidity is 90% and the in-car temperature is 22°C. The judgment logic may be that if the in-car humidity is higher than a preset threshold (such as 85%) and the in-car temperature is moderate (avoiding extreme high or low temperatures affecting the judgment), the glass fogs up due to excessive humidity in the car, and the vehicle needs to be defogged.

[0042] As an example, for the above situation 1: the in-vehicle environment data may include the in-vehicle air quality. The judgment logic may be that poor air quality leads to poor ventilation in the vehicle, which indirectly increases the risk of fogging, and then it is judged that the vehicle needs to be defogged.

[0043] As an example, for the above situation 2: the vehicle external environment data may include the vehicle external temperature and the vehicle external humidity. Its judgment logic may be that if the vehicle external temperature is low and the humidity is high, it is determined that the vehicle needs to be defogged.

[0044] As an example, for the above situation 2: the vehicle external environment data may include rainfall data. Its judgment logic may be that when the current rainfall is judged to be heavy rain, it is determined that the vehicle needs to be defogged.

[0045] As an example, for the above situation three: the in-vehicle environment data includes: in-vehicle temperature data, in-vehicle humidity data; the out-vehicle environment data includes: out-vehicle temperature data, out-vehicle humidity data. Its judgment logic can be to determine whether the vehicle needs to be defogged based on the temperature difference between the inside and outside of the vehicle and the relative humidity.

[0046] In a possible implementation, the associated data of the vehicle may also include vehicle speed data and position data of the target vehicle. Among them, whether the target vehicle needs to be defogged can be determined by a defog judgment model. Among them, the above-mentioned cases 1 to 3, the vehicle speed data and the position data of the target vehicle can all be used as inputs of the defog judgment model when determining whether the target vehicle needs to be defogged.

[0047] As an example, the defogging judgment model may represent a pre-trained model used to determine whether the target vehicle is to be defogged. The defogging judgment model may be a support vector machine (SVM), a neural network model (such as a convolutional neural network), a random forest model, etc., which are not specifically limited here.

[0048] Step S102, determining whether the target vehicle needs to be defogged based on the associated data of the target vehicle.

[0049] It can be seen from the above content that each of the above situations 1 to 3 has a corresponding method for judging whether the target vehicle needs to be defogged. For example: the in-car environment data may include the in-car humidity, and the out-car environment data may include the out-car humidity. The judgment method may be when the relative humidity of the in-car humidity and / or the out-car humidity is greater than the relative humidity threshold, it is judged that the vehicle needs to be defogged, etc. For another example: the in-car environment data may include the in-car temperature, and the out-car environment data may include the out-car temperature. The judgment method may be when the temperature difference between the in-car temperature and the out-car temperature is greater than the temperature threshold, it is judged that the vehicle needs to be defogged, etc.

[0050] Step S103: if the target vehicle needs to be defogged, control the target vehicle to be defogged.

[0051] After determining that the vehicle needs to be defogged, the defog function of the vehicle can be executed by controlling the controller of the target vehicle.

[0052] As an example, you can turn on the front windshield defogger, side window defogger, etc. You can also adjust the temperature and wind speed of the air conditioning system, and you can turn on the vehicle's wipers, etc., which are not specifically limited here.

[0053] The vehicle defog method provided in this embodiment determines whether the target vehicle has a condition that causes the window to fog up through at least one of the vehicle interior environment data and the vehicle exterior environment data. Once the condition that causes the window to fog up is detected, the target vehicle can be immediately controlled to perform defog, without the driver having to manually operate the defog component of the target vehicle, thereby reducing the delay of the defog operation. In addition, the defog operation is intelligently adjusted according to the vehicle interior and exterior environment data, thereby effectively improving the vehicle defog effect.

[0054] In a possible implementation, taking the above situation three as an example, when the vehicle defogging method is implemented by a defogging judgment model, it may specifically include: When the user manually turns on the defog function, the first associated data of the target vehicle is obtained, and the defog judgment model is trained according to the first associated data of the target vehicle to obtain an updated defog judgment model, wherein the associated data includes in-vehicle environment data, out-of-vehicle environment data, vehicle speed data and position data of the target vehicle; when the second associated data of the target vehicle is obtained and the user does not manually turn on the defog function, the second associated data of the target vehicle is input into the defog judgment model to determine the defog judgment result; based on the defog judgment result, it is determined whether the target vehicle needs to be defogged, and when it is determined that the target vehicle needs to be defogged, the target vehicle is controlled to be defogged.

[0055] It should be noted that the defog judgment model is a pre-trained defog judgment model. The first associated data and the second associated data may be different associated data. For example, the first associated data may include temperature data and humidity data; wherein the temperature data in the first associated data may be M1, and the humidity data in the first associated data may be N1; the second associated data may also include temperature data and humidity data; wherein the temperature data in the second associated data may be M2, and the humidity data in the second associated data may be N2.

[0056] The user may manually turn on the defog function by clicking a defog function button, etc. After the user manually turns on the defog function, the vehicle controller may respond to the user's manual triggering operation of turning on the defog function and turn on the electric heater configured for the vehicle, thereby achieving the purpose of defog.

[0057] In specific implementation, during the driving process of the vehicle, the defogger judgment model needs to determine in real time whether the vehicle needs to be defogged according to the first associated data of the target vehicle. Among them, when the user manually turns on the defogger function, the user's defogger demand is that the vehicle needs to be defogged, and the defogger judgment result of the defogger judgment model is that the vehicle does not need to be defogged, that is, the defogger judgment result of the defogger judgment model is inaccurate, and the defogger judgment result violates the user's defogger demand. Therefore, it is necessary to train the defogger judgment model to improve the accuracy of the defogger judgment model and obtain an updated defogger judgment model.

[0058] As an example, when the defogging judgment model may be a support vector machine, the training process of the defogging judgment model may be: First, the geographic locations need to be partitioned and seasonally differentiated on the cloud platform and encoded using specific hexadecimal values, which are then used in the neural network model code to differentiate data between regions and seasons.

[0059] The in-vehicle environment data, the outside environment data, the vehicle speed data and the position data of the target vehicle are input into the neural network model as features, and whether to start the defog function is used as the target value (0 or 1). The data set is divided into training set, test set and validation set through the train_test_split function, and the training set is input as the model training data. The model is trained through the Support Vector Machine (SVM) class function in the sklearn library function.

[0060] The validation set separated from the dataset is used to verify the defogging judgment model. The hyperparameters of the machine learning model are adjusted through grid search to reduce misclassification and clarify the decision boundary. The model learning results are accepted through accuracy and recall rate indicators.

[0061] The test set can be used to verify the output results of the updated defog judgment model to obtain a more complete classification of scenes in which the user enables or does not enable the defog function, so that it can be applied to actual new data.

[0062] The usage process of the updated defog judgment model can be: the current season is spring, and user A is driving on the coastal highway in Guangdong. The temperature difference between the inside and outside of the car seriously affects the line of sight, so user A manually turns on the defog (corresponding to the southern coast, spring, heavy rain, and highway respectively, and the matching user's behavior is to manually turn on the defog function-assignment 1). The usage process of the updated defog judgment model can be that when the current season is spring, user A is driving on the coastal highway in Guangdong, and the temperature difference between the inside and outside of the car seriously affects the line of sight, it is determined that the target vehicle needs to be defogged; user A drives back to his hometown in Hubei in winter, stops to rest in a rest area on the way, and heavy rain suddenly occurs, but user A has no need for defog, so the matching user behavior result does not turn on defog (assignment 0), and the usage process of the updated defog judgment model can be that when the current season is winter, user A drives back to his hometown in Hubei, stops to rest in a rest area on the way, and heavy rain suddenly occurs, and it is determined that the target vehicle does not need to be defogged.

[0063] The vehicle defogger method provided in the present embodiment trains a defogger judgment model by acquiring in-vehicle environment data, out-vehicle environment data, vehicle speed data and target vehicle position data, and determines whether the vehicle needs to be defogged based on the defogger judgment model. The method can intelligently determine whether the vehicle needs to be defogged, and can immediately control the vehicle to be defogged when the vehicle meets the defogger conditions, thereby improving the efficiency of vehicle defogger.

[0064] In addition, it is considered that the user manually turning on the defog function reflects that the judgment result of the defog judgment model is inconsistent with the user's defog demand. When the user manually turns on the defog function, the defog judgment model needs to be trained according to the associated data of the target vehicle to improve the accuracy of the defog judgment model, so that the defog judgment model can better meet the user's defog needs, thereby improving the accuracy of vehicle defog.

[0065] In this embodiment, a vehicle defogging method is provided, which can be specifically executed by a computer device on the vehicle, for example, by a vehicle controller. Figure 2 FIG. 1 is a flow chart of another vehicle defogging method according to an embodiment of the present invention. Figure 2 As shown, the process includes the following steps: Step S201, obtain the associated data of the target vehicle. Figure 1 Step S101 of the illustrated embodiment will not be described in detail here.

[0066] Step S202, determining whether the target vehicle needs to be defogged based on the associated data of the target vehicle.

[0067] Specifically, the in-vehicle environment data of the target vehicle includes: in-vehicle temperature data and in-vehicle humidity data; the out-vehicle environment data includes: out-vehicle temperature data and out-vehicle humidity data; wherein the above step S202 includes: Step S2021, determining the temperature difference between the outside temperature data and the inside temperature data according to the outside temperature data and the inside temperature data.

[0068] The outside temperature data may represent the current temperature information of the vehicle's external environment. The inside temperature data may represent the current temperature information of the vehicle's internal environment. In specific implementation, when determining the outside temperature data and the inside temperature data, the inside temperature data may be subtracted from the outside temperature data to obtain a temperature difference.

[0069] As an example, when the outside temperature is 20°C and the inside temperature is 15°C, the temperature difference may be 5°C.

[0070] Step S2022, determining the relative humidity outside the vehicle according to the maximum humidity corresponding to the humidity data outside the vehicle and the temperature data outside the vehicle.

[0071] The vehicle's external humidity data can represent the current humidity information of the vehicle's external environment. The vehicle's external humidity data can represent the current humidity information of the vehicle's external environment. The maximum humidity can represent the maximum amount of water vapor that the air can contain under the current temperature information of the vehicle's external environment, which can be expressed by the humidity when the relative humidity reaches 100%. In specific implementation, the outdoor relative humidity can be calculated using the vehicle's external humidity data and the maximum humidity at the corresponding vehicle's external temperature.

[0072] As an example, the outside relative humidity can be calculated by = (outside humidity / maximum humidity at outside temperature) * 100%.

[0073] As an example, it can also be determined by the following formula: RH1=e1 / E1×100%; where RH1 is the relative humidity outside the vehicle, e1 is the actual water vapor pressure, and E1 is the saturated water vapor pressure. The actual water vapor pressure can be determined by the humidity outside the vehicle, and the saturated water vapor pressure can be determined by the maximum humidity under the temperature outside the vehicle.

[0074] Step S2023, determining the relative humidity inside the vehicle according to the maximum humidity corresponding to the humidity data inside the vehicle and the temperature data inside the vehicle.

[0075] The in-vehicle humidity data can represent the current humidity information of the vehicle's internal environment. The in-vehicle humidity data can represent the current humidity information of the vehicle's internal environment. The maximum humidity can represent the maximum amount of water vapor that the air can contain under the current temperature information of the vehicle's internal environment, which can be expressed by the humidity when the relative humidity reaches 100%. In specific implementation, the indoor relative humidity can be calculated using the in-vehicle humidity data and the maximum humidity at the corresponding in-vehicle temperature.

[0076] Step S2024: If at least one of the relative humidity outside the vehicle and the relative humidity inside the vehicle is greater than the relative humidity threshold, and the temperature difference is greater than the temperature difference threshold, it is determined that the target vehicle needs to be defogged.

[0077] The relative humidity threshold may be a threshold for determining whether the relative humidity is too high. The relative humidity threshold may be 90%, etc., which is not specifically limited here. The temperature difference threshold may be a threshold for determining whether the temperature difference is too high. The temperature difference threshold may be 5°C, etc., which is not specifically limited here. Specifically, whether the target vehicle needs to be defogged is determined by determining at least one of the relative humidity outside the vehicle, the relative humidity inside the vehicle, and multiple factors of the temperature difference.

[0078] As an example, if a vehicle is parked overnight in cold and foggy weather, the outside temperature is -2°C and the outside humidity is 95%; the inside temperature and humidity are maintained at a certain level due to the passenger's use the previous night, and the inside temperature is 10°C and the inside humidity is 50%. Then it is determined that the vehicle needs to be defogged.

[0079] As an example, the relative humidity threshold is 90% and the temperature difference threshold is 5° C. If the relative humidity outside the vehicle is 95%, the relative humidity inside the vehicle is 65%, and the temperature difference is 8° C, then because the relative humidity outside the vehicle is greater than the threshold and the temperature difference is also greater than the threshold, it is determined that the vehicle needs to be defogged.

[0080] Step S203: If the target vehicle needs to be defogged, control the target vehicle to be defogged. Figure 1 Step S103 of the illustrated embodiment will not be described in detail here.

[0081] The vehicle defogging method provided in this embodiment can sense the subtle changes in the vehicle's internal and external environment in real time through the temperature difference between the indoor temperature and the outdoor temperature, the relative humidity inside the vehicle, and the relative humidity outside the vehicle. That is, whether it is a slight fluctuation in temperature or an increase or decrease in humidity, the subtle changes in the vehicle's internal and external environment can be quickly captured, thereby accurately determining whether the vehicle needs to be defogged based on the subtle changes in the vehicle's internal and external environment.

[0082] In a possible implementation, the vehicle exterior environment data includes rainfall data; wherein the above step S202 includes: when the rainfall data is not less than a rainfall threshold, determining that the target vehicle needs to be defogged.

[0083] The rainfall data can represent the real-time rainfall information of the environment in which the vehicle is located. The rainfall threshold can be used to indicate whether the current rainfall reaches the condition for triggering the defog operation.

[0084] In specific implementation, the rainfall information of the target vehicle's environment can be obtained in real time through a rain sensor or other meteorological data source installed on the vehicle. A reasonable rainfall threshold can be preset based on historical meteorological data, user preferences and other factors. The real-time rainfall data is compared with the rainfall threshold. If the rainfall data is not less than the rainfall threshold (i.e., the rainfall is large and may cause the windshield to fog), it is determined that the target vehicle needs to be defogged; conversely, if the rainfall data is less than the rainfall threshold, it is determined that the vehicle does not need to be defogged.

[0085] In a possible implementation, the rainfall threshold may be set according to different regions, or may be determined according to historical data, which is not specifically limited here and may be implemented by those skilled in the art.

[0086] As an example, in wet and rainy areas, the rainfall threshold can be set relatively high due to frequent and heavy rainfall. In dry and rainy areas, the rainfall threshold can be set relatively low due to sparse and small rainfall, to ensure that the defog operation can be triggered in time even under limited rainfall conditions.

[0087] As an example, the setting of the rainfall threshold can be further optimized by analyzing the historical meteorological data of the area where the target vehicle is located, especially the rainfall amount and rainfall frequency. For example, if it is found that the rainfall amount in a certain period of time in the historical data is often greater than a certain value and accompanied by the windshield fogging phenomenon, this value can be used as the rainfall threshold.

[0088] The vehicle defogger method provided in this embodiment can distinguish the rainy day conditions in which the vehicle is located, such as heavy rain, moderate rain, light rain, etc., by detecting rainfall data in the external environment data of the vehicle, and can determine the distribution of raindrops on the glass parts of the vehicle, so that when only considering the rainfall data, it is also possible to accurately determine whether the vehicle needs to be defogged.

[0089] In order to adjust the defogger power of the target vehicle, in a possible implementation, the above-mentioned vehicle defogger method may also include: when the rainfall data is not less than the rainfall threshold, inputting the rainfall data and the vehicle speed data into the defogger control model, determining the defogger power, and controlling the target vehicle to perform defogger based on the defogger power.

[0090] When the vehicle needs to be defogged, it is first necessary to detect whether the rainfall data is not less than the rainfall threshold. Among them, the rainfall threshold can indicate whether it is raining. When the rainfall data is not less than the rainfall threshold, the rainfall data and vehicle speed data are input into the defog control model to determine the defog power, and the vehicle is defogged according to the defog power. For example: when the speed is low and the rainfall is small, the defog heating power is reduced, otherwise, the highest power heating is used. The defog power can be used to adjust the temperature emitted by the vehicle's defog heater, thereby controlling the target vehicle to perform defog.

[0091] The vehicle defog method provided in this embodiment can dynamically adjust the defog power based on the real-time rainfall data and vehicle speed data through the defog control model. The defog power calculated by the model is more accurate, avoiding the problem of excessive defog or incomplete defog that may be caused by traditional fixed-power defog.

[0092] In a possible implementation, the associated data of the target vehicle further includes: first image data to be tested of a glass component of the target vehicle, wherein the above step S202 includes: Step S301, determining a first defogger judgment result according to first image data of a glass component of a target vehicle to be tested, annotated image data corresponding to the first image data to be tested, and a target fogging threshold; wherein the annotated image data is image data of a glass component of a target vehicle collected when the target vehicle does not need defogger.

[0093] The first image data to be tested may be image data of the front windshield, rear windshield, front camera, rearview mirror, etc. of the vehicle. The first image data to be tested may be real-time image data. Specifically, it may be detected by an image sensor of the vehicle, etc., and is not specifically limited here.

[0094] The target fogging threshold value can indicate that the grayscale gradient of the first image to be tested will not change due to fogging. The annotated image data corresponding to the first image data to be tested is the image data of the glass of the target vehicle collected when the target vehicle does not need to be defogged, that is, the annotated image data corresponding to the first image data to be tested can be pre-collected data.

[0095] In specific implementation, a camera or other image acquisition device may be used to acquire the first image data to be tested of the glass component of the target vehicle. The annotated image data of the glass component of the target vehicle acquired when defogging is not required may also be retrieved from the storage system.

[0096] When determining the first defogger judgment result, an image processing algorithm can be used to determine the difference between the grayscale gradient mean of the first image data to be tested and the grayscale gradient of the annotated image data corresponding to the first image data to be tested. According to the comparison result of the grayscale gradient ratio between the grayscale gradient mean of the first image data to be tested and the annotated image data corresponding to the first image data to be tested and the target fogging threshold, it is determined whether the clarity of the glass component in the first image data to be tested is lower than the baseline level of the clarity of the glass component in the annotated image data, and it is determined that the target vehicle needs to be defogged. For example: if the first image to be tested shows that there is significant fog or water droplets on the glass component, which affects the clarity of the field of view, the baseline level of the clarity of the glass component in the annotated image data corresponding to the first image data to be tested is no fog or water droplets, and the clarity of the glass component in the first image data to be tested is lower than the baseline level in the annotated image data corresponding to the first image data to be tested, then it is determined that the vehicle needs to be defogged.

[0097] Specifically, the above step S301 includes: Step S3011, determining a first grayscale gradient component in a horizontal direction and a second grayscale gradient component in a vertical direction of each pixel in the first image data to be tested.

[0098] A pixel point can represent the smallest unit in an image, and each pixel point contains information such as color and brightness. The first image data to be tested can be grayscale image data. The first grayscale gradient component can represent the brightness change rate in the horizontal direction. The second grayscale gradient component can represent the brightness change rate in the vertical direction.

[0099] In specific implementation, the grayscale difference between each pixel in the image data to be tested and its horizontally adjacent pixel can be calculated to obtain the horizontal grayscale gradient. The grayscale difference between each pixel in the image data to be tested and its vertically adjacent pixel can be calculated to obtain the vertical grayscale gradient.

[0100] Step S3012: Determine the gradient amplitude corresponding to each pixel point according to the first grayscale gradient component and the second grayscale gradient component.

[0101] The gradient amplitude of each pixel can be calculated by combining the grayscale gradient components in the horizontal and vertical directions according to the Pythagorean theorem or an equivalent method, wherein the gradient amplitude can indicate the comprehensive intensity of the brightness change of the pixel.

[0102] Step S3013: determining the grayscale gradient mean corresponding to the first image data to be tested according to the gradient amplitude corresponding to each pixel point.

[0103] Calculate the average value of the gradient amplitude of all pixels in the image to get the grayscale gradient mean. The grayscale gradient mean can indicate the degree of brightness change of the entire image and can be used to evaluate the clarity of the image.

[0104] Step S3014, based on the ratio of the grayscale gradient mean and the target grayscale gradient and the target fogging threshold, determine the third defogger judgment result, and when the ratio of the grayscale gradient mean and the target grayscale gradient is less than the target fogging threshold, determine that the target vehicle needs to be defogged, wherein the target grayscale gradient is calculated from the annotated image data corresponding to the first image data to be tested.

[0105] The target grayscale gradient is the grayscale gradient mean of the annotated image data corresponding to the first image data to be tested. In specific implementation, the ratio of the grayscale gradient mean of the second image data to be tested to the target grayscale gradient is calculated. The ratio of the grayscale gradient mean of the second image data to be tested to the target grayscale gradient is compared with the target fogging threshold. If the ratio of the grayscale gradient mean of the second image data to be tested to the target grayscale gradient is less than the target fogging threshold, the target vehicle needs to be defogged.

[0106] As an example, in the initial judgment stage, the target grayscale gradient G = grayscale gradient mean G0, and the fogging parameter G0 / G = 1 is calculated; when G0 is much smaller than G, it is judged that the probability of fogging is high at this time, and the vehicle needs to be defogged, and the output is 1 (the vehicle needs to be defogged), otherwise the output is 0 (the vehicle does not need to be defogged).

[0107] Step S302: Determine whether the target vehicle needs to be defogged based on the first defog judgment result.

[0108] After determining the first defogger judgment result, it can be directly determined whether the target vehicle needs to be defogged according to the first defogger judgment result. For example, if the first defogger judgment result is defogger, then it is determined that the target vehicle needs to be defogged.

[0109] Specifically, the above step S302 includes: Step S3021, using the pre-trained defogger judgment model, and obtaining a second defogger judgment result according to the associated data.

[0110] The associated data may be used as an input of a pre-trained defog judgment model, and the second defog judgment result may be used as an output of the pre-trained defog judgment model.

[0111] Step S3022, determining whether the target vehicle needs to be defogged based on the first defogger judgment result and the second defogger judgment result.

[0112] Whether the vehicle needs to be defogged can be determined by combining the first defog judgment result and the second defog judgment result. When any one or more of the first defog judgment result and the second defog judgment result indicate that the vehicle needs to be defogged, it is determined that the target vehicle needs to be defogged.

[0113] The vehicle defogger method provided in this embodiment introduces the first image data to be tested of the glass parts of the target vehicle, adds conditions for determining whether the vehicle needs to be defogged, and combines the defogger judgment result determined by the annotated image data and the target fogging threshold as a basis for determining whether the vehicle needs to be defogged, thereby improving the accuracy of the vehicle defogger judgment.

[0114] In addition, by determining the first grayscale gradient component in the horizontal direction and the second grayscale gradient component in the vertical direction of each pixel in the image data to be tested, the accurate information of the grayscale change of each pixel in the image can be determined. Then, according to the grayscale gradient components in the horizontal and vertical directions of each pixel and the mean of the gradient amplitudes of all pixels in the image data to be tested, the mean grayscale gradient is obtained, and the ratio of the grayscale gradient mean to the target grayscale gradient is compared with the fogging threshold, so as to quantify the difference in grayscale gradient between the image to be tested and the target image, thereby accurately determining whether the vehicle needs to be defogged.

[0115] In addition, the first test image data of the glass parts of the target vehicle, the annotated image data and the first defogger judgment result determined by the target fogging threshold are introduced as a judgment condition for determining whether the vehicle needs to be defogged. Whether the vehicle needs to be defogged is not determined only by the first defogger judgment result, but rather by the combination of the first defogger judgment result and the prediction result. This avoids the misjudgment of vehicle defoggering caused by inaccurate judgment of the defogger judgment model, thereby improving the accuracy of vehicle defogger judgment.

[0116] In a possible implementation, the method further includes: Step S401 : inputting the second to-be-tested image data of the glass member of the target vehicle into a pre-trained defogger judgment model to obtain a third defogger judgment result.

[0117] The third defogging judgment result can indicate whether the vehicle needs to be defogged by the defogging judgment model. Specifically, the second image data to be tested of the glass piece can be used as the input of the defogging judgment model, and the third defogging judgment result can be used as the output of the defogging judgment model.

[0118] Step S402, repeatedly performing a search operation of a target fogging threshold until the target fogging threshold is determined, the search operation of the target fogging threshold comprising: determining a fourth defogging judgment result targeted by the search operation according to the second image data to be tested of the glass member of the target vehicle, the annotated image data corresponding to the second image data to be tested, and the candidate fogging threshold targeted by the search operation; Step S403, when the third defogging judgment result and the fourth defogging judgment result are different, determining the candidate fogging threshold for the next search operation according to the preset value and the candidate fogging threshold for the search operation; The preset value can be set manually, and the preset value can be 2%, or 3%, etc., which is not specifically limited here.

[0119] In specific implementation, if the third defog judgment result is different from the fourth defog judgment result, it indicates that the fourth defog judgment result obtained by image processing is inaccurate, and the candidate fogging threshold needs to be adjusted according to a preset value to determine the candidate fogging threshold for the next search operation.

[0120] Step S404: when the third defogging judgment result and the fourth defogging judgment result are the same, the candidate fogging threshold targeted by the search operation is determined as the target fogging threshold.

[0121] When the third defog judgment result and the fourth defog judgment result are the same, that is, the candidate fogging threshold targeted by the search operation corresponding to the fourth defog judgment result can satisfy that the grayscale gradient of the second image to be tested will not change due to fogging, the candidate fogging threshold targeted by the search operation can be determined as the target fogging threshold.

[0122] The vehicle defog method provided in this embodiment, when the third defog judgment result and the fourth defog judgment result are different, indicates that the fourth defog judgment result obtained by image processing is inaccurate, and it is necessary to adjust the candidate fogging threshold to determine the target fogging threshold, that is, continuously adjust the candidate fogging threshold for the target fogging threshold search operation according to the preset value, so that when the third defog judgment result and the fourth defog judgment result are the same, the target fogging threshold is accurately determined.

[0123] In a possible implementation, the above step S202 includes: Step S501, based on the associated data of the target vehicle, determining the fogging judgment parameters corresponding to the associated data of the target vehicle; wherein the fogging judgment parameters include temperature parameters and humidity parameters, and the associated data of the target vehicle is data that changes in real time.

[0124] The fogging judgment parameter may represent the judgment condition for determining whether the vehicle needs to be defogged through the associated data. The fogging judgment parameter may include a temperature parameter and a humidity parameter.

[0125] In specific implementation, the fogging judgment parameter can be determined based on the associated data of the target vehicle, wherein the associated data changes in real time, that is, the latest associated information can be continuously obtained, so that the fogging judgment parameter can also be updated in real time.

[0126] As an example, the fogging judgment parameters corresponding to different associated data can be determined through a mapping table between fogging judgment parameters and associated data. Real-time analysis of fogging judgment parameters enables the target vehicle to determine whether the vehicle needs to be defogged in different regions, different temperatures, different humidity, etc.

[0127] Step S502, determining whether the target vehicle needs to be defogged based on the comparison result of the fogging judgment parameter and the associated data of the target vehicle.

[0128] After the fogging judgment parameter is determined, it can be determined whether defogger is required based on the comparison result of the fogging judgment parameter and the associated data of the target vehicle. For example, the temperature parameter in the fogging judgment parameter is T1, and the associated data includes the outside temperature and the inside temperature, wherein the difference between the outside temperature and the inside temperature is T2. When T2 is greater than T1, it is determined that the target vehicle needs to be defogged.

[0129] In the vehicle defog method provided by the present embodiment, the temperature parameters and the humidity parameters can be changed through associated data. The associated data is data that changes in real time, that is, different temperature parameters and humidity parameters can be determined according to different associated data, and then the associated data and the fogging judgment parameters corresponding to the associated data can be used to accurately determine whether the target vehicle needs to be defogged.

[0130] In a possible implementation, the method further includes: Step S601, inputting the associated data into a pre-trained defogging judgment model to obtain a predicted defogging probability corresponding to the associated data, where the predicted defogging probability indicates the probability of needing to perform defogging under the conditions indicated by the associated data.

[0131] Step S602: determining a fifth defogging judgment result corresponding to the associated data according to a comparison result between the predicted defogging probability corresponding to the associated data and a defogging probability threshold.

[0132] The associated data is input into the pre-trained defogger judgment model. The pre-trained defogger judgment model calculates the predicted defogger probability based on the input associated data, and compares the predicted defogger probability with the defogger probability threshold. If the predicted probability is greater than or equal to the defogger probability threshold, it is determined that the vehicle needs to be defogged; otherwise, it is determined that the vehicle does not need to be defogged. The comparison result of the predicted defogger probability and the defogger probability threshold is compared with the labeled result in the training data to determine whether the parameters of the pre-trained defogger judgment model need to be updated.

[0133] Step S603: when the fifth defog judgment result corresponding to the associated data is different from the labeled result of the associated data, the parameters of the pre-trained defog judgment model are updated.

[0134] If the fifth defogging judgment result corresponding to the associated data is different from the labeled result of the associated data, it means that the prediction performance of the pre-trained defogging judgment model needs to be improved. At this time, the parameters of the pre-trained defogging judgment model should be updated to improve the prediction accuracy of the model.

[0135] The vehicle defogging method provided in this embodiment continuously updates the parameters of the model through associated data, so that the model can better adapt to various actual situations and improve the accuracy of determining whether the target vehicle needs to be defogged. In addition, the performance of the pre-trained defogging judgment model when facing new and unseen data often depends on its generalization ability. By continuously updating the parameters of the pre-trained defogging judgment model with the associated data of the new target vehicle and the corresponding annotation results, the model's adaptability to new data can be enhanced and its generalization performance can be improved.

[0136] In a possible implementation, the associated data also includes: at least one of the own vehicle data and other vehicle data; the own vehicle data includes at least one of the seasonal data of the target vehicle, the regional location of the target vehicle, and the vehicle speed data; the other vehicle data includes at least one of the seasonal data of other vehicles, the regional location of other vehicles, and the vehicle speed data.

[0137] The associated data of the target vehicle may include not only the data of the target vehicle but also the data of other vehicles, wherein the data of other vehicles may be received by the cloud server and sent to the target vehicle.

[0138] The vehicle defog method provided in this embodiment has a more comprehensive understanding of the current environmental conditions by introducing data of more dimensions (season, regional location and vehicle speed), since the possibility of fogging of vehicle windows may vary significantly in different seasons, different regions and different vehicle speeds, thereby improving the accuracy of determining whether the target vehicle needs to be defogged.

[0139] In order to accurately determine the annotated image data, in a possible implementation, the method further includes: using the image data collected when the target vehicle meets preset conditions as the annotated image data; wherein the preset conditions include: the current weather is without fog and precipitation, the visibility of the glass parts of the target vehicle is greater than the visibility threshold, the relative humidity is lower than the relative humidity threshold, and the defog function of the target vehicle is not turned on.

[0140] No fog and no precipitation can represent the absence of fog and precipitation in the weather conditions. The visibility threshold can be n1 or n2, etc., which is not specifically limited here. The relative humidity can be represented by the ratio of the water vapor content in the air to the maximum amount of water vapor that the air may contain at the current temperature.

[0141] In specific implementation, the current weather conditions are obtained in real time through the weather sensors installed on the vehicle or the external weather data source. The visibility of glass parts such as the windshield is evaluated by the camera or sensor inside the vehicle. The relative humidity data of the current environment is obtained in real time through the humidity sensor on the vehicle. The control system inside the vehicle monitors whether the user has manually turned on the defogger function. The above information is combined to determine whether the current state meets the preset conditions (i.e., no fogging and no precipitation, the visibility of the glass parts is greater than the threshold, the relative humidity is lower than the relative humidity threshold, and the defogger function is not turned on). If the preset conditions are met, the camera on the vehicle is used to collect image data and save it as annotated image data.

[0142] The vehicle defogger method provided in this embodiment can truly reflect the clarity state of the vehicle's glass parts under interference factors such as no fogging, no precipitation, and the visibility of the vehicle's glass parts being greater than the visibility threshold in the current weather conditions through image data collected under preset conditions. It can accurately determine the image of the vehicle's glass parts that are not defogged, thereby more accurately predicting whether the vehicle needs defogger.

[0143] Please refer to Figure 3 , Figure 3 Schematic diagram of a vehicle defogging method according to an embodiment of the present invention.

[0144] Obtain the associated data of the vehicle. The associated data of the vehicle may include: temperature, humidity, rainfall and image data. By arranging temperature sensors inside and outside the vehicle to measure the real-time temperature and upload it to the cloud server, the Electronic Control Unit (ECU) can obtain the in-vehicle humidity value (H_in), the outside humidity value (H_out), the in-vehicle temperature value (T_in) and the outside temperature value (T_out) from the humidity sensor and the temperature sensor at intervals (such as 5s). The real-time humidity is compared with the maximum humidity at the current temperature through the humidity sensors inside and outside the vehicle, and the relative humidity inside and outside the vehicle is calculated respectively. Then, when any one of the relative humidity inside and outside the vehicle is greater than the relative humidity threshold (such as 90%), and the temperature difference is greater than the temperature difference threshold (such as 5°C), it is judged that there is a risk of fogging at present, and it is determined that the vehicle needs to be defogged.

[0145] The current rainfall information is tested by the rain sensor. According to the national standard definition of rainfall level, when the rainfall is ≥ moderate rain, it can be recorded as the vehicle needs to be defogged. When the vehicle activates the defog function, the wiper controller can be started in conjunction with the rain sensor to enable the wipers to clear the vehicle's field of view to maximize the clearing of the front and rear fields of view.

[0146] It is possible to determine whether the vehicle needs to be defogged based on the fogging judgment algorithm after the camera image is processed. When detecting that there is no fog, the image is first converted from a color image to a grayscale image, and the grayscale gradient value of each pixel is obtained through the edge detection operator, and the grayscale gradient amplitude is calculated, and the target grayscale gradient G of the grayscale gradient amplitude of all pixels is obtained. Different grayscale gradient mean values ​​G0 are calculated in the image obtained in the implementation. If G0 is significantly lower than G (that is, whether the ratio of the grayscale gradient mean value to the target grayscale gradient is less than the fogging threshold), it is judged that the probability of fogging is high at this time, and it is judged that the vehicle needs to be defogged.

[0147] For example: in winter, the air conditioner is turned on in the car, and the relative humidity is judged to be less than 90% when coming out of the underground parking lot, and it is judged that it is not raining outside. However, since the outside temperature is lower and there is a larger temperature difference than inside the car, the short-term temperature difference of the glass causes condensation of water vapor. At this time, the camera image recognition calculation parameter G0 / G is less than the threshold, and it is judged that the vehicle needs to be defogged.

[0148] The vehicle defog method provided in this embodiment determines whether there are conditions that cause fogging of the vehicle windows in the vehicle through any one of the vehicle interior environment data, vehicle exterior environment data, and the image data to be tested of the vehicle's glass parts. Once the fogging condition is detected, the defog program can be started immediately without the driver having to manually operate the vehicle's defog component, thereby reducing the delay of the defog operation. In addition, the defog operation is intelligently adjusted according to the vehicle interior and exterior environment data and the image data to be tested of the glass parts, thereby effectively improving the vehicle defog effect.

[0149] In this embodiment, a vehicle defogger is also provided, which is used to implement the above-mentioned embodiments and preferred embodiments, and the descriptions that have been made will not be repeated. As used below, the term "module" can implement a combination of software and / or hardware of a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, the implementation of hardware, or a combination of software and hardware, is also possible and conceivable.

[0150] The present embodiment provides a vehicle defogger device, which includes: an acquisition module, used to acquire associated data of a target vehicle; wherein the associated data of the target vehicle includes at least one of in-vehicle environment data and out-vehicle environment data; a determination module, used to determine whether the target vehicle needs to be defogged based on the associated data of the target vehicle; and a control module, used to control the target vehicle to perform defogger if the target vehicle needs to be defogged.

[0151] In some possible implementations, the in-vehicle environmental data of the target vehicle includes: in-vehicle temperature data and in-vehicle humidity data; the out-vehicle environmental data includes: out-vehicle temperature data and out-vehicle humidity data; wherein the determination module includes: a first determination unit, used to determine the temperature difference between the out-vehicle temperature data and the in-vehicle temperature data based on the out-vehicle temperature data and the in-vehicle temperature data; a second determination unit, used to determine the out-vehicle relative humidity based on the out-vehicle humidity data and the maximum humidity corresponding to the out-vehicle temperature data; a third determination unit, used to determine the in-vehicle relative humidity based on the in-vehicle humidity data and the maximum humidity corresponding to the in-vehicle temperature data; a fourth determination unit, used to determine that the target vehicle needs to be defogged if at least one of the out-vehicle relative humidity and the in-vehicle relative humidity is greater than the relative humidity threshold, and the temperature difference is greater than the temperature difference threshold.

[0152] In some possible implementations, the vehicle exterior environment data includes rainfall data; the determination module includes: a fifth determination unit, configured to determine that the target vehicle needs to be defogged when the rainfall data is not less than a rainfall threshold.

[0153] In some possible implementations, the associated data of the target vehicle also includes: first image data to be tested of the glass parts of the target vehicle, and the determination module includes: a sixth determination unit, used to determine a first defogger judgment result based on the first image data to be tested of the glass parts of the target vehicle, the annotated image data corresponding to the first image data to be tested, and the target fogging threshold; wherein the annotated image data corresponding to the first image data to be tested is the image data of the glass parts of the target vehicle collected when the target vehicle does not need defogger; a seventh determination unit, used to determine whether the target vehicle needs to be defogged based on the first defogger judgment result.

[0154] In one possible implementation, the sixth determination unit includes: a first determination subunit, used to determine the first grayscale gradient component in the horizontal direction and the second grayscale gradient component in the vertical direction of each pixel in the image data to be tested; a second determination subunit, used to determine the gradient amplitude corresponding to each pixel according to the first grayscale gradient component and the second grayscale gradient component; a third determination subunit, used to determine the grayscale gradient mean corresponding to the first image data to be tested according to the gradient amplitude corresponding to each pixel; a detection determination subunit, used to determine that the target vehicle needs to be defogged if the ratio of the grayscale gradient mean to the target grayscale gradient is less than a fogging threshold, wherein the target grayscale gradient is calculated from the annotated image data corresponding to the first image data to be tested.

[0155] In one possible implementation, the seventh determination unit includes: a fourth determination subunit, used to use a pre-trained defogger judgment model to obtain a second defogger judgment result according to associated data; a fifth determination subunit, used to determine whether the target vehicle needs to be defogged based on the first defogger judgment result and the second defogger judgment result.

[0156] In some possible implementations, the above-mentioned device also includes: a training module, which is used to input the second image data to be tested of the glass parts of the target vehicle into a pre-trained defogger judgment model to obtain a third defogger judgment result; a repeated execution module, which is used to repeatedly execute the search operation of the target fogging threshold until the target fogging threshold is determined, and the search operation of the target fogging threshold includes: determining the fourth defogger judgment result targeted by the search operation based on the second image data to be tested of the glass parts of the target vehicle, the annotated image data corresponding to the second image data to be tested, and the candidate fogging threshold targeted by the search operation; when the third defogger judgment result and the fourth defogger judgment result are different, determining the candidate fogging threshold targeted by the next search operation based on the preset value and the candidate fogging threshold targeted by the search operation; when the third defogger judgment result and the fourth defogger judgment result are the same, determining the candidate fogging threshold targeted by the search operation as the target fogging threshold.

[0157] In some possible implementations, the determination module includes: an eighth determination unit, used to determine the fogging judgment parameters corresponding to the associated data of the target vehicle based on the associated data of the target vehicle; wherein the fogging judgment parameters include temperature parameters and humidity parameters, and the associated data of the target vehicle are data that changes in real time; a ninth determination unit, used to determine whether the target vehicle needs to be defogged based on a comparison result between the fogging judgment parameters and the associated data of the target vehicle.

[0158] In some possible implementations, the above-mentioned device also includes: a training subunit, which is used to input the associated data into a pre-trained defogging judgment model to obtain a predicted defogging probability corresponding to the associated data, and the predicted defogging probability indicates the probability that defogging is required under the conditions indicated by the associated data; a comparison subunit, which is used to determine the fifth defogging judgment result corresponding to the associated data based on a comparison result between the predicted defogging probability corresponding to the associated data and a defogging probability threshold; and an updating subunit, which is used to update the parameters of the pre-trained defogging judgment model when the fifth defogging judgment result corresponding to the associated data is different from the labeled result of the associated data.

[0159] In some possible implementations, the associated data also includes: at least one of the own vehicle data and other vehicle data; the own vehicle data includes at least one of the seasonal data of the target vehicle, the regional location of the target vehicle, and the vehicle speed data; the other vehicle data includes at least one of the seasonal data of other vehicles, the regional location of other vehicles, and the vehicle speed data.

[0160] In some possible implementations, the above-mentioned device also includes: an annotated image data determination module, which is used to use the image data collected when the target vehicle meets preset conditions as the annotated image data corresponding to the first image data to be tested; wherein the preset conditions include: the current weather is without fog and precipitation, the visibility of the glass parts of the target vehicle is greater than the visibility threshold, the relative humidity is lower than the relative humidity threshold, and the defog function of the target vehicle is not turned on.

[0161] The further functional description of each of the above modules and units is the same as that of the above corresponding embodiments and will not be repeated here.

[0162] The vehicle defogger device in this embodiment is presented in the form of a functional unit, where the functional unit refers to an ASIC (Application Specific Integrated Circuit) circuit, a processor and memory that executes one or more software or fixed programs, and / or other devices that can provide the above functions.

[0163] An embodiment of the present invention further provides a computer device having the above-mentioned vehicle defogger device.

[0164] See also Figure 4 , Figure 4 is a schematic diagram of the structure of a computer device provided by an optional embodiment of the present invention, such as Figure 4 As shown, the computer device includes: one or more processors 10, a memory 20, and interfaces for connecting various components, including high-speed interfaces and low-speed interfaces. Various components are connected to each other using different buses for communication, and can be installed on a common mainboard or installed in other ways as needed. The processor can process the instructions executed in the computer device, including instructions stored in or on the memory to display the graphical information of the GUI on an external input / output device (such as, a display device coupled to the interface). In some optional embodiments, if necessary, multiple processors and / or multiple buses can be used together with multiple memories and multiple memories. Similarly, multiple computer devices can be connected, and each device provides some necessary operations (for example, as a server array, a group of blade servers, or a multi-processor system). Figure 4 A processor 10 is taken as an example.

[0165] The processor 10 may be a central processing unit, a network processor or a combination thereof. The processor 10 may further include a hardware chip. The hardware chip may be a dedicated integrated circuit, a programmable logic device or a combination thereof. The programmable logic device may be a complex programmable logic device, a field programmable gate array, a general purpose array logic or any combination thereof.

[0166] The memory 20 stores instructions executable by at least one processor 10, so that at least one processor 10 executes the method shown in the above embodiment.

[0167] The memory 20 may include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function; the data storage area may store data created according to the use of the computer device, etc. In addition, the memory 20 may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some optional embodiments, the memory 20 may optionally include a memory remotely arranged relative to the processor 10, and these remote memories may be connected to the computer device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0168] The memory 20 may include a volatile memory, such as a random access memory; the memory may also include a non-volatile memory, such as a flash memory, a hard disk or a solid state drive; the memory 20 may also include a combination of the above types of memory.

[0169] The computer device also includes an input device 30 and an output device 40. The processor 10, the memory 20, the input device 30 and the output device 40 may be connected via a bus or other means. Figure 4 The example of connecting through bus is taken in the following.

[0170] The input device 30 can receive input digital or character information, and generate key signal input related to the user settings and function control of the computer device, such as a touch screen, a keypad, a mouse, a track pad, a touch pad, an indicator bar, one or more mouse buttons, a trackball, a joystick, etc. The output device 40 may include a display device, an auxiliary lighting device (e.g., an LED) and a tactile feedback device (e.g., a vibration motor), etc. The above-mentioned display device includes but is not limited to a liquid crystal display, a light emitting diode, a display and a plasma display. In some optional embodiments, the display device may be a touch screen.

[0171] The embodiment of the present invention also provides a computer-readable storage medium. The method according to the embodiment of the present invention can be implemented in hardware, firmware, or can be implemented as a computer code that can be recorded in a storage medium, or can be implemented as a computer code that is originally stored in a remote storage medium or a non-temporary machine-readable storage medium and will be stored in a local storage medium through a network download, so that the method described herein can be stored in such software processing on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. Among them, the storage medium can be a magnetic disk, an optical disk, a read-only storage memory, a random access memory, a flash memory, a hard disk or a solid-state hard disk, etc.; further, the storage medium can also include a combination of the above types of memories. It can be understood that a computer, a processor, a microprocessor controller, or programmable hardware includes a storage component that can store or receive software or computer code. When the software or computer code is accessed and executed by a computer, a processor, or hardware, the method shown in the above embodiment is implemented.

[0172] A part of the present invention may be applied as a computer program product, such as a computer program instruction, which, when executed by a computer, can call or provide the method and / or technical solution according to the present invention through the operation of the computer. Those skilled in the art should understand that the existence of the computer program instruction in a computer-readable medium includes, but is not limited to, a source file, an executable file, an installation package file, etc., and accordingly, the way in which the computer program instruction is executed by the computer includes, but is not limited to: the computer directly executes the instruction, or the computer compiles the instruction and then executes the corresponding compiled program, or the computer reads and executes the instruction, or the computer reads and installs the instruction and then executes the corresponding installed program. Here, the computer-readable medium may be any available computer-readable storage medium or communication medium accessible to the computer.

[0173] Although the embodiments of the present invention have been described in conjunction with the accompanying drawings, those skilled in the art may make various modifications and variations without departing from the spirit and scope of the present invention, and such modifications and variations are all within the scope defined by the appended claims.

Claims

1. A vehicle defogging method, characterized in that: The method comprises: Acquire the associated data of the target vehicle; wherein the associated data of the target vehicle includes at least one of the in-vehicle environment data and the out-vehicle environment data; Determining whether the target vehicle needs to be defogged according to the associated data of the target vehicle; If the target vehicle needs to be defogged, the target vehicle is controlled to be defogged.

2. The vehicle defogging method according to claim 1, characterized in that: The in-vehicle environment data of the target vehicle includes: in-vehicle temperature data and in-vehicle humidity data; the out-vehicle environment data includes: out-vehicle temperature data and out-vehicle humidity data; and determining whether the target vehicle needs to be defogged according to the associated data of the target vehicle includes: Determining a temperature difference between the vehicle exterior temperature data and the vehicle interior temperature data according to the vehicle exterior temperature data and the vehicle interior temperature data; Determine the relative humidity outside the vehicle according to the maximum humidity corresponding to the humidity data outside the vehicle and the temperature data outside the vehicle; Determining the relative humidity in the vehicle according to the maximum humidity corresponding to the humidity data in the vehicle and the temperature data in the vehicle; If at least one of the relative humidity outside the vehicle and the relative humidity inside the vehicle is greater than the relative humidity threshold, and the temperature difference is greater than the temperature difference threshold, it is determined that the target vehicle needs to be defogged.

3. The vehicle defogging method according to claim 1, characterized in that: The vehicle exterior environment data includes rainfall data; and determining whether the target vehicle needs to be defogged based on the associated data of the target vehicle, including: when the rainfall data is not less than a rainfall threshold, determining that the target vehicle needs to be defogged.

4. The vehicle defogging method according to claim 1, characterized in that: The associated data of the target vehicle further includes: first image data to be tested of a glass member of the target vehicle, wherein determining whether the target vehicle needs to be defogged according to the associated data of the target vehicle includes: Determine a first defogger judgment result according to the first image data to be tested of the glass member of the target vehicle, the annotated image data corresponding to the first image data to be tested, and the target fogging threshold; wherein the annotated image data corresponding to the first image data to be tested is the image data of the glass member of the target vehicle collected when the target vehicle does not need to be defogged; According to the first defogger judgment result, it is determined whether the target vehicle needs to be defogged.

5. The vehicle defogging method according to claim 4, characterized in that: The determining of a first defogger judgment result according to the first image data to be tested of the glass member of the target vehicle, the annotated image data corresponding to the first image data to be tested, and the target fogging threshold value includes: Determine a first grayscale gradient component in a horizontal direction and a second grayscale gradient component in a vertical direction for each pixel in the first image data to be tested; Determine the gradient amplitude corresponding to each pixel point according to the first grayscale gradient component and the second grayscale gradient component; Determine the grayscale gradient mean corresponding to the first image data to be tested according to the gradient amplitude corresponding to each pixel point; A first defog judgment result is determined according to the ratio of the grayscale gradient mean to the target grayscale gradient and the target fogging threshold; wherein the target grayscale gradient is calculated from the annotated image data corresponding to the first image data to be tested.

6. The vehicle defogging method according to claim 5, characterized in that: Determining whether the target vehicle needs to be defogged according to the first defog judgment result includes: Using the pre-trained defogging judgment model, and according to the associated data, obtaining a second defogging judgment result; Whether the target vehicle needs to be defogged is determined according to the first defogger judgment result and the second defogger judgment result.

7. The vehicle defogging method according to claim 6, characterized in that: The method further comprises: Inputting the second to-be-tested image data of the glass member of the target vehicle into a pre-trained defogger judgment model to obtain a third defogger judgment result; The search operation of the target fogging threshold is repeatedly performed until the target fogging threshold is determined. The search operation of the target fogging threshold includes: Determine a fourth defogging judgment result targeted by the search operation according to the second image data to be tested of the glass member of the target vehicle, the annotated image data corresponding to the second image data to be tested, and the candidate fogging threshold targeted by the search operation; When the third defogging judgment result and the fourth defogging judgment result are different, determining the candidate fogging threshold for the next search operation according to the preset value and the candidate fogging threshold for the search operation; When the third defogging judgment result and the fourth defogging judgment result are the same, the candidate fogging threshold targeted by the search operation is determined as the target fogging threshold.

8. The vehicle defogging method according to claim 6, characterized in that: The method further comprises: Inputting the associated data into a pre-trained defogging judgment model to obtain a predicted defogging probability corresponding to the associated data, wherein the predicted defogging probability indicates the probability of needing to perform defogging under the conditions indicated by the associated data; Determining a fifth defogging judgment result corresponding to the associated data according to a comparison result between the predicted defogging probability corresponding to the associated data and a defogging probability threshold; When the fifth defog judgment result corresponding to the associated data is different from the labeled result of the associated data, the parameters of the pre-trained defog judgment model are updated.

9. The vehicle defogging method according to claim 1, characterized in that: The step of determining whether the target vehicle needs to be defogged according to the associated data of the target vehicle comprises: Based on the associated data of the target vehicle, determining the fogging judgment parameter corresponding to the associated data of the target vehicle; wherein the fogging judgment parameter includes a temperature parameter and a humidity parameter, and the associated data of the target vehicle is data that changes in real time; According to the comparison result of the fogging judgment parameter and the associated data of the target vehicle, it is determined whether the target vehicle needs to be defogged.

10. The vehicle defogging method according to claim 4, characterized in that: The method further comprises: The image data collected when the target vehicle meets the preset conditions is used as the annotated image data corresponding to the first image data to be tested; wherein the preset conditions include: the current weather is without fog and precipitation, the visibility of the glass parts of the target vehicle is greater than the visibility threshold, the relative humidity is lower than the relative humidity threshold, and the defog function of the target vehicle is not turned on.

11. The vehicle defogging method according to any one of claims 1 to 10, characterized in that: The associated data also includes: at least one of the own vehicle data and other vehicle data; The vehicle data includes at least one of the seasonal data of the target vehicle, the regional location of the target vehicle, and the speed data of the target vehicle; The other vehicle data includes at least one of the season data of the other vehicles, the regional location of the other vehicles, and the speed data of the other vehicles.

12. A vehicle defogger, characterized in that: The device comprises: An acquisition module, used to acquire the associated data of the target vehicle; wherein the associated data of the target vehicle includes at least one of the in-vehicle environment data and the out-vehicle environment data; A determination module, used to determine whether the target vehicle needs to be defogged according to the associated data of the target vehicle; The control module is used to control the target vehicle to perform defog if the target vehicle needs to be defogged.

13. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a computer to execute the vehicle defog method according to any one of claims 1 to 11.

14. A vehicle, characterized in that: include: A memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the vehicle defogger method according to any one of claims 1 to 11 by executing the computer instructions.

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