Vehicle control method, vehicle control device and vehicle

Abstract

The invention provides a vehicle control method, a vehicle control device and a vehicle, and relates to the technical field of vehicle control. The method comprises the following steps: acquiring a target image of a front windshield of a vehicle; based on the target image of the front windshield, the target proportion of a target area in the front windshield is obtained, and the target area refers to the area where fog on the front windshield is located; determining a fog risk level based on the target proportion and the environmental data of the vehicle; and controlling the vehicle to execute a defogging strategy based on the fog risk level. According to the method, the accuracy of fogging judgment can be improved, so that efficient defogging is realized.

Description

Technical Field

[0001] This application relates to the field of vehicle control technology, and more specifically, to a method for controlling a vehicle, a device for controlling a vehicle, and a vehicle in the field of vehicle control technology. Background Technology

[0002] When driving in winter or rainy weather, fog often appears on the windshield. The fog may form on the inside or outside of the windshield, and both can affect the driver's visibility due to scattering light.

[0003] Existing defogging methods rely solely on sensor data to predict the timing of fogging, aiming to prevent windshield fogging in advance. However, judging fogging based on data from a single sensor can lead to missed or false alarms, resulting in low accuracy in fogging detection. This can cause incorrect defogging strategies to be implemented or defogging to be delayed.

[0004] Therefore, improving the accuracy of fogging detection to achieve efficient defogging is an urgent problem to be solved. Summary of the Invention

[0005] This application provides a method for controlling a vehicle, a device for controlling a vehicle, and a vehicle. The method can improve the accuracy of fogging determination, thereby achieving efficient defogging.

[0006] Firstly, a method for controlling a vehicle is provided, the method comprising: Acquire a target image of the vehicle's windshield; Based on the target image of the windshield, the target area in the windshield is obtained, where the target area refers to the area where the fog is located on the windshield. The fog risk level is determined based on the target proportion and vehicle environmental data; Based on the fog risk level, control the vehicle to execute a defogging strategy.

[0007] In the above technical solution, based on the acquired target image of the vehicle's windshield, the target proportion of the fogged area within the windshield is determined. Then, combining this target proportion with environmental data of the vehicle's surroundings, the fog risk level is determined, thereby controlling the vehicle to execute a defogging strategy and achieving windshield defogging. Compared to existing technologies that determine the risk level solely based on sensor data, which are prone to misjudgments and omissions, this solution improves the accuracy of fogging assessment by simultaneously combining the target image of the windshield and the vehicle's environmental data for a two-dimensional fog risk level determination. This allows for dynamic matching of the corresponding defogging strategy based on a more accurate fog risk level, preventing improper defogging from affecting driving safety and improving windshield defogging efficiency.

[0008] In conjunction with the first aspect, in some possible implementation methods, the fog risk level is determined based on the target proportion and vehicle environmental data, including: When the target percentage is within the first range, the initial risk level is determined based on the vehicle's environmental data. The first range refers to the range that is greater than or equal to the first percentage threshold and less than the second percentage threshold. The fog risk level is determined based on the first range and the initial risk level.

[0009] In the above technical solution, when the target proportion of the fogged area is within a first range that is greater than or equal to a first proportion threshold and less than a second proportion threshold, the corresponding initial risk level is determined based on the vehicle's environmental data. Then, based on the first range and the initial risk level, the fog risk level is determined. This solution uses the target proportion being within the first range as a prerequisite for judgment, combines vehicle environmental data to derive the initial risk level, and finally determines the fog risk level. Through a multi-dimensional and hierarchical fog risk level determination logic, the accuracy of fog risk level determination can be further improved, thereby providing a reliable decision-making basis for the vehicle's automatic defogging safety control strategy and improving the vehicle's defogging efficiency.

[0010] In combination with the first aspect and the above implementation methods, in some possible implementations, the method further includes: When the target percentage falls within the second range, the risk level corresponding to the second range will be determined as the fog risk level. The second range refers to the range that is less than the first percentage threshold, or the second range refers to the range that is greater than or equal to the second percentage threshold.

[0011] In the above technical solution, this solution uses a segmented and hierarchical judgment logic to determine the target proportion of fog on the windshield. When the target proportion is less than a first proportion threshold or greater than or equal to a second proportion threshold, the risk level corresponding to the second range is directly used as the final fog risk level. This ensures that in fog-free conditions (less than the first proportion threshold) and heavy fog conditions (greater than or equal to the second proportion threshold), the fog risk level directly corresponds to the risk level indicated by the target image, reflecting the actual fogging state and improving the accuracy of the fog risk level. This provides a reliable decision-making basis for the vehicle's automatic defogging safety control strategy, ensuring driving safety.

[0012] Combining the first aspect and the above implementation methods, in some possible implementation methods, the fog risk level is determined based on the first scope and the initial risk level, including: When the initial risk level is the first risk level or the second risk level, the risk level corresponding to the first range is determined as the fog risk level; When the initial risk level is the third or fourth risk level, the target level is determined as the fog risk level. The target level refers to the risk level that is higher than the risk level corresponding to the first range by a preset level. Among them, the fourth risk level is higher than the third risk level, the third risk level is higher than the second risk level, and the second risk level is higher than the first risk level.

[0013] In the above technical solution, when the target proportion is within the first range, if the initial risk level is a relatively high fourth or third risk level, a preset level is added to the risk level corresponding to the first range to obtain the fog risk level. This avoids using the risk level corresponding to the first range for defogging when the fogging situation worsens in an environment with a high fogging risk, as the low intervention intensity would prevent timely response to the fogging intensification. When the initial risk level is a relatively low second or first risk level, the risk level corresponding to the first range is used to ensure the matching degree between the fog risk level and the actual fogging situation, achieving accurate dynamic assessment of the fog risk level and further improving defogging efficiency.

[0014] Combining the first aspect and the above-mentioned implementation methods, in some possible implementation methods, environmental data includes the vehicle's external ambient temperature, external ambient humidity, and windshield temperature. Based on the vehicle's environmental data, an initial risk level is determined, including: Determine the dew point temperature outside the vehicle based on the ambient temperature and humidity outside the vehicle. The initial risk level of the windshield is determined based on the dew point temperature and the glass temperature.

[0015] In the above technical solution, when the target proportion of fog on the windshield is within the first range, the dew point temperature is calculated by combining the ambient temperature and humidity outside the vehicle, and the initial risk level is determined based on the dew point temperature and the glass temperature. This ensures that the initial risk level can accurately reflect the degree of influence of the current environment on the fogging state of the windshield. Furthermore, a more accurate fog risk level can be determined based on the initial risk level, thereby improving the accuracy of the vehicle's defogging strategy and improving defogging efficiency.

[0016] Combining the first aspect and the above-mentioned implementation methods, in some possible implementation methods, the environmental data also includes the in-vehicle ambient temperature. Based on the dew point temperature and glass temperature, the initial risk level of the windshield is determined, including: The initial risk value of the windshield is determined based on the dew point temperature and the glass temperature. Based on the vehicle interior temperature, vehicle exterior temperature, vehicle exterior humidity, and glass temperature, the initial risk value is corrected to obtain the corrected initial risk value. The initial risk level of the windshield is determined based on the revised initial risk value.

[0017] In the above technical solution, the initial risk value of the windshield is obtained by combining the dew point temperature of the external environment and the glass temperature of the windshield. Then, the initial risk value is corrected by multiple parameters, including the ambient temperature inside the vehicle, the temperature and humidity outside the vehicle, and the glass temperature. Based on the corrected risk value, the initial risk level is determined, which realizes a multi-dimensional quantitative assessment of the risk of windshield fogging, improves the accuracy of the initial risk level determination and the fit between the initial risk level and the actual environment.

[0018] In conjunction with the first aspect and the above implementation methods, in some possible implementation methods, environmental data also include solar radiation values ​​and rainfall, and the methods also include: The target compensation coefficient is determined based on solar radiation, rainfall, and vehicle speed. Based on the revised initial risk value, the initial risk level of the windshield is determined, including: The initial risk level of the windshield is determined based on the revised initial risk value and the target compensation coefficient.

[0019] In the above technical solution, based on the correction of the initial risk value by combining the temperature and humidity inside and outside the vehicle, glass temperature and dew point temperature, the target compensation coefficient is further introduced by the solar radiation value, rainfall and vehicle speed. The corrected initial risk value is then recalibrated using the target compensation coefficient to determine the initial risk level. This allows the initial risk level to be dynamically updated in real time with the changes in the vehicle's driving environment, achieving a precise match between the initial risk level determination and the actual driving scenario. This improves the accuracy of fog risk level determination, thereby determining a more accurate defogging strategy to achieve efficient defogging.

[0020] Combining the first aspect and the above implementation methods, in some possible implementation methods, based on the target image of the windshield, the target area's proportion in the windshield is obtained, including: Input the target image of the windshield into the target model; The target area is used to obtain the target proportion in the windshield through the target model.

[0021] In the above technical solution, by inputting the target image of the windshield into the target model to directly obtain the proportion data of the fog target area, the fog coverage of the windshield is quickly, objectively and accurately quantified, avoiding the subjectivity and error of manual judgment, providing a reliable and unified quantitative basis for the subsequent stratification of fog risk level, and further improving the accuracy of fog risk level.

[0022] Secondly, a device for controlling a vehicle is provided, the device comprising: The acquisition module is used to acquire a target image of the vehicle's windshield; The processing module is used to obtain the target area's proportion in the windshield based on the target image, where the target area refers to the area where fog is located on the windshield; determine the fog risk level based on the target proportion and the vehicle's environmental data; and control the vehicle to execute a defogging strategy based on the fog risk level.

[0023] In conjunction with the second aspect, in some possible implementations, the processing module is also used to determine an initial risk level based on the vehicle's environmental data when the target proportion is within a first range, wherein the first range refers to a range that is greater than or equal to a first proportion threshold and less than a second proportion threshold; and to determine the fog risk level based on the first range and the initial risk level.

[0024] In combination with the second aspect and the above implementation methods, in some possible implementation methods, the processing module is also used to determine the risk level corresponding to the second range as the fog risk level when the target proportion is within the second range; wherein, the second range refers to the range that is less than the first proportion threshold, or the second range refers to the range that is greater than or equal to the second proportion threshold.

[0025] In conjunction with the second aspect and the above implementation methods, in some possible implementation methods, the processing module is further configured to determine the risk level corresponding to the first range as the fog risk level when the initial risk level is the first risk level or the second risk level; and to determine the target level as the fog risk level when the initial risk level is the third risk level or the fourth risk level, wherein the target level refers to a risk level that is higher than the risk level corresponding to the first range by a preset level; wherein the fourth risk level is higher than the third risk level, and the third risk level is higher than the second risk level, and the second risk level is higher than the first risk level.

[0026] In combination with the second aspect and the above implementation methods, in some possible implementation methods, the environmental data includes the outside ambient temperature, the outside ambient humidity, and the windshield glass temperature. The processing module is also used to determine the dew point temperature outside the vehicle based on the outside ambient temperature and the outside ambient humidity; and to determine the initial risk level of the windshield based on the dew point temperature and the glass temperature.

[0027] In conjunction with the second aspect and the above implementation methods, in some possible implementation methods, the environmental data also includes the in-vehicle ambient temperature. The processing module is also used to determine the initial risk value of the windshield based on the dew point temperature and the glass temperature; to correct the initial risk value based on the in-vehicle ambient temperature, the outside ambient temperature, the outside ambient humidity, and the glass temperature, to obtain a corrected initial risk value; and to determine the initial risk level of the windshield based on the corrected initial risk value.

[0028] In conjunction with the second aspect and the above implementation methods, in some possible implementation methods, the environmental data also includes solar radiation value and rainfall. The processing module is also used to determine the target compensation coefficient based on solar radiation value, rainfall and vehicle speed; and to determine the initial risk level of the windshield based on the corrected initial risk value, including: determining the initial risk level of the windshield based on the corrected initial risk value and the target compensation coefficient.

[0029] In combination with the second aspect and the above implementation methods, in some possible implementation methods, the processing module is also used to input the target image of the windshield into the target model; and through the target model, to obtain the target proportion of the target area in the windshield.

[0030] Thirdly, a vehicle is provided, including a memory and a processor. The memory is used to store executable program code, and the processor is used to call and run the executable program code from the memory, causing the vehicle to perform the methods described in the first aspect or any possible implementation thereof.

[0031] Fourthly, a computer program product is provided, comprising: computer program code, which, when run on a computer, causes the computer to perform the methods described in the first aspect or any possible implementation thereof.

[0032] Fifthly, a computer-readable storage medium is provided that stores computer program code, which, when executed on a computer, causes the computer to perform the methods described in the first aspect or any possible implementation thereof. Attached Figure Description

[0033] Figure 1 This is a schematic diagram of a scenario provided in an embodiment of this application; Figure 2 This is a schematic flowchart illustrating a method for controlling a vehicle according to an embodiment of this application; Figure 3 This is a schematic flowchart illustrating another method for controlling a vehicle provided in an embodiment of this application; Figure 4 This is a schematic diagram of the structure of a vehicle control device provided in an embodiment of this application; Figure 5 This is a schematic diagram of the structure of a vehicle provided in an embodiment of this application. Detailed Implementation

[0034] The technical solutions in this application will be clearly and thoroughly described below with reference to the accompanying drawings. In the description of the embodiments of this application, unless otherwise stated, " / " means "or," for example, A / B can mean A or B. "And / or" in the text is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Furthermore, in the description of the embodiments of this application, "multiple" refers to two or more than two.

[0035] Hereinafter, the terms "first" and "second" are used for descriptive purposes only and should not be construed as implying or suggesting relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature.

[0036] Figure 1 This is a schematic diagram of a scenario provided in an embodiment of this application. For example... Figure 1 As shown, during vehicle operation, fog can form on the windshield in certain scenarios, obstructing the driver's view and affecting driving safety. For example, in winter or cold weather, the outside temperature is low, and the windshield temperature also becomes low. Inside the car, the humidity increases due to people breathing, damp items (such as umbrellas), and moisture evaporating from the heater. When this warm, humid air from inside the car comes into contact with the cooler inside of the windshield, the water vapor condenses, forming fog on the inside. In summer or rainy weather, the outside humidity is high. If the air conditioning is on and the temperature is low, the windshield temperature will be lowered by the air conditioning. In this case, the warm, humid air from outside will come into contact with the cooler outside of the glass, forming fog on the outside.

[0037] Current defogging technologies rely solely on sensor data combined with formulas to determine whether a vehicle's windshield is fogged. The risk level calculated using these formulas is time-dependent, making it difficult to pinpoint the actual fogging timing. This leads to missed detections, low accuracy in fogging detection, delayed or erroneous execution of defogging strategies, and low defogging efficiency, ultimately impacting driving safety.

[0038] In view of the problems existing in the prior art, this application provides a method for controlling a vehicle, a device for controlling a vehicle, and a vehicle. The method determines the target proportion of the fogged area in the windshield based on an acquired target image of the vehicle's windshield; then, combining the target proportion with environmental data of the vehicle's surroundings, it determines the fog risk level of the windshield; finally, based on the fog risk level, it controls the vehicle to execute a corresponding defogging strategy. This method, by simultaneously combining image analysis and environmental data collection to determine the final fog risk level, can improve the accuracy of fogging determination, thereby improving defogging efficiency and avoiding impacts on vehicle driving safety.

[0039] The following is combined Figures 2 to 3 The method for controlling a vehicle provided in the embodiments of this application will be described in detail.

[0040] Figure 2 This is a schematic flowchart illustrating a method for controlling a vehicle according to an embodiment of this application. It should be understood that this method can be applied to a vehicle; or, to a processor in a vehicle; or, to a chip in a processor integrated into a vehicle.

[0041] For example, such as Figure 2 As shown, the method 200 includes: S201, Acquire the target image of the vehicle's windshield.

[0042] For example, images of the vehicle's windshield are captured in real time using a camera device located inside the vehicle's cabin or outside the vehicle to obtain a target image of the windshield. Simultaneously, environmental data of the vehicle's surroundings is acquired.

[0043] S202, Based on the target image of the windshield, obtain the target area proportion in the windshield, where the target area refers to the area where the fog is located on the windshield.

[0044] For example, after acquiring a target image of the windshield, the target proportion of the fogged area on the windshield can be determined based on the target image. The target proportion reflects the current degree of fogging on the windshield.

[0045] In one implementation, the process of obtaining the target area's proportion within the windshield based on the target image can specifically include: Input the target image of the windshield into the target model; The target area is used to obtain the target proportion in the windshield through the target model.

[0046] For example, before applying the method provided in this application embodiment, a pre-trained model (e.g., a classification model) can be trained using various windshield images with different fog ratios and their corresponding ratio labels to obtain a trained target model. During the application of this method, the target image of the windshield acquired in real time can be input into the target model. By analyzing and processing the target image through the target model, the target ratio of the target region in the windshield can be obtained.

[0047] In another implementation, the proportion of the area where fog is located can also be represented by a hierarchy.

[0048] For example, the sample images may include windshield images in a fog-free state (target percentage of 0), a light fog state (e.g., target percentage of 1%-20%), a moderate fog state (e.g., target percentage of 21%-70%), and a heavy fog state (e.g., target percentage of 71%-100%). Correspondingly, the level label for a fog-free windshield image is 0, for a light fog state it is 1, for a moderate fog state it is 2, and for a heavy fog state it is 3. Inputting these sample images and their corresponding level labels into a pre-trained model yields a trained target model; this target model can then determine the level of the target percentage indicated by the acquired windshield image.

[0049] Optionally, the trained target model can be stored locally on the vehicle or on a cloud server. In one implementation, the target model can be saved as an Open Neural Network Exchange (ONNX) model, enabling lightweight deployment locally on the vehicle. During application, the ONNX model can be called via C++ code to predict the target's proportion.

[0050] In this embodiment of the application, by inputting the target image of the windshield into the target model to directly obtain the proportion data of the fog target area, the fog coverage of the windshield is quickly, objectively and accurately quantified, avoiding the subjectivity and error of manual judgment, providing a reliable and unified quantitative basis for the subsequent stratification of fog risk level, and further improving the accuracy of fog risk level.

[0051] In another implementation, feature extraction can be performed on the target image, and the real-time acquired image features can be compared with the preset label images. If the similarity is high (e.g., higher than 80%), the proportion corresponding to the acquired target image can be determined as the label proportion corresponding to the image with higher similarity.

[0052] S203 determines the fog risk level based on the target proportion and vehicle environmental data.

[0053] The fog risk level indicates the urgency of defogging the windshield. A higher fog risk level indicates a greater urgency to defog the windshield, while a lower fog risk level indicates a less urgency.

[0054] For example, environmental data about the vehicle's surroundings can reflect whether the current environment meets the conditions for fogging. After determining the target proportion of the fogged area based on the target image of the windshield, the current fogging risk level of the windshield can be determined by combining the target proportion with the environmental data of the vehicle's current environment.

[0055] In one implementation, the process of determining the fog risk level based on the target proportion and vehicle environmental data may include: When the target percentage is within the first range, the initial risk level is determined based on the vehicle's environmental data. The first range refers to the range that is greater than or equal to the first percentage threshold and less than the second percentage threshold. The fog risk level is determined based on the first range and the initial risk level.

[0056] For example, when the target proportion of the fog area indicated by the target image on the windshield is within a first range, that is, the target proportion is greater than or equal to the first proportion threshold (e.g., 1%) and the target proportion is less than the second proportion threshold (e.g., 70%), the initial risk level of the windshield fogging risk can be determined based on the environmental data of the vehicle's environment; then, by combining the first range and the initial risk level, the final fog risk level can be determined.

[0057] In one implementation, the first range can be divided into two sub-ranges: a second sub-range and a third sub-range. The second sub-range indicates a percentage greater than or equal to the first percentage threshold and less than the third percentage threshold (e.g., 1% to 20%). The third sub-range indicates a percentage greater than or equal to the third percentage threshold and less than the second percentage threshold (e.g., 20% to 70%). The risk level corresponding to the second sub-range is level two (also known as low fog level); the risk level corresponding to the third sub-range is level three (also known as medium fog level). The first percentage threshold is less than the third percentage threshold, and the third percentage threshold is less than the second percentage threshold.

[0058] In this embodiment, when the target proportion of the foggy area is within a first range that is greater than or equal to a first proportion threshold and less than a second proportion threshold, the corresponding initial risk level is determined based on the vehicle's environmental data. Then, based on the first range and the initial risk level, the fog risk level is determined. This solution uses the target proportion being within the first range as a prerequisite, derives the initial risk level from vehicle environmental data, and finally determines the fog risk level. Through a multi-dimensional and hierarchical fog risk level determination logic, the accuracy of fog risk level determination can be further improved, thereby providing a reliable decision-making basis for the vehicle's automatic defogging safety control strategy and improving the vehicle's defogging efficiency.

[0059] In one implementation, the process of determining the fog risk level based on the first range and the initial risk level may specifically include: When the initial risk level is the first risk level or the second risk level, the risk level corresponding to the first range is determined as the fog risk level; When the initial risk level is the third or fourth risk level, the target level is determined as the fog risk level. The target level refers to the risk level that is higher than the risk level corresponding to the first range by a preset level. Among them, the risk of fogging of the windshield indicated by the fourth risk level is higher than that indicated by the third risk level, and the risk of fogging of the windshield indicated by the third risk level is higher than that indicated by the second risk level, and the risk of fogging of the windshield indicated by the second risk level is higher than that indicated by the first risk level.

[0060] For example, the initial risk level is obtained based on environmental data of the vehicle's surroundings. When the initial risk level is either the first risk level (also known as the no-fog risk level) or the second risk level (also known as the low risk level), it indicates a low risk of fogging on the vehicle's windshield. In this case, the likelihood of fogging worsening is also low. Therefore, the risk level corresponding to the first range can be directly determined as the fog risk level. The first range includes a second sub-range and a third sub-range. When the target percentage is within the second sub-range, the risk level corresponding to the second sub-range is level two, and the current fog risk level is determined as level two. When the target percentage is within the third sub-range, the risk level corresponding to the third sub-range is level three, and the current fog risk level is determined as level three. Level two indicates a low level of urgency for defogging the windshield; level three indicates a medium level of urgency for defogging the windshield.

[0061] For example, when the initial risk level corresponding to the environmental data is the third risk level (also known as the medium risk level) or the fourth risk level (also known as the high risk level), it indicates a high risk of fogging on the vehicle's windshield. In this case, the likelihood of further fogging is also high. Therefore, the fog risk level is determined to be a preset level higher than the risk level corresponding to the first range. The preset level can be one level, meaning the fog risk level is determined to be one level higher than the risk level corresponding to the first range. For example, the first range includes a second sub-range and a third sub-range. When the target percentage is in the second sub-range, the risk level corresponding to the second sub-range is the second level, so the current fog risk level is determined to be the third level, which is higher than the second level. When the target percentage is in the third sub-range, the risk level corresponding to the third sub-range is the third level, so the current fog risk level is determined to be the fourth level, which is higher than the third level. The fourth level indicates the highest urgency for defogging the windshield.

[0062] In this embodiment, when the target percentage is within a first range, if the initial risk level is a relatively high fourth or third risk level, a preset level is added to the risk level corresponding to the first range to obtain a fog risk level. This avoids using the risk level corresponding to the first range for defogging when the fogging situation worsens in an environment with a high fogging risk, as the low intervention intensity would prevent timely response to fog aggravation. When the initial risk level is a relatively low second or first risk level, the risk level corresponding to the first range is used to ensure the matching degree between the fog risk level and the actual fogging situation, achieving accurate dynamic assessment of the fog risk level and further improving defogging efficiency.

[0063] In one implementation, the environmental data includes the outside temperature, outside humidity, and windshield temperature. The process of determining the initial risk level based on the vehicle's environmental data may specifically include: Determine the dew point temperature outside the vehicle based on the ambient temperature and humidity outside the vehicle. The initial risk level of the windshield is determined based on the dew point temperature and the glass temperature.

[0064] For example, in determining the initial risk level of windshield fogging, the dew point temperature outside the vehicle can be determined by combining the vehicle's outside ambient temperature and humidity; then, based on the outside dew point temperature and the windshield's glass temperature, the initial risk level of the windshield can be predicted.

[0065] For example, the process of determining the dew point temperature can be found in the following formulas 1 and 2: (Formula 1) (Formula 2) In Formula 1 and Formula 2, A temperature function used to represent the saturated vapor pressure; The value is used to represent the dew point temperature outside the vehicle; T represents the ambient temperature outside the vehicle; RH represents the ambient humidity outside the vehicle; a and b are empirical constants, where a can take the value 17.27 and b can take the value 237.7.

[0066] For example, based on Formula 1 above, a temperature function can be calculated by combining the ambient temperature and humidity outside the vehicle. Then, by combining the temperature function, the ambient temperature, and the ambient humidity outside the vehicle, the dew point temperature outside the vehicle can be calculated. Finally, by combining the dew point temperature and the windshield temperature, the initial risk level can be calculated.

[0067] Optionally, the ambient temperature and humidity outside the vehicle can be collected by sensors installed in the vehicle, or determined by obtaining publicly available weather forecast information.

[0068] In this embodiment, when the target proportion of fog on the windshield is within a first range, the dew point temperature is calculated by combining the ambient temperature and humidity outside the vehicle, and the initial risk level is determined based on the dew point temperature and the glass temperature. This ensures that the initial risk level can accurately reflect the degree of influence of the current environment on the fogging state of the windshield. Furthermore, a more accurate fog risk level can be determined based on the initial risk level, thereby improving the accuracy of the vehicle's defogging strategy and improving defogging efficiency.

[0069] In one implementation, the environmental data also includes the in-vehicle ambient temperature. The process of determining the initial risk level of the windshield based on the dew point temperature and glass temperature may include: The initial risk value of the windshield is determined based on the dew point temperature and the glass temperature. Based on the vehicle interior temperature, vehicle exterior temperature, vehicle exterior humidity, and glass temperature, the initial risk value is corrected to obtain the corrected initial risk value. The initial risk level of the windshield is determined based on the revised initial risk value.

[0070] For example, an initial risk value (also known as an initial risk index) for the windshield is determined based on the current dew point temperature outside the vehicle and the windshield temperature. The greater the temperature difference between the glass and the ambient temperature, the higher the risk of fogging. To ensure the accuracy of the risk level, the initial risk value needs to be corrected by considering the interior and exterior ambient temperatures, the exterior humidity, and the windshield temperature. The initial risk level can then be determined based on the corrected initial risk value.

[0071] For example, the process of determining the initial risk value of the windshield based on dew point temperature and glass temperature can be found in the following formula three: (Formula 3) In Formula 3, Used to represent the initial risk value; Used to indicate the dew point temperature outside the vehicle; Used to indicate the temperature of the windshield.

[0072] In one implementation, the process of correcting the initial risk value based on the vehicle interior temperature, vehicle exterior temperature, vehicle exterior humidity, and glass temperature to obtain the corrected initial risk value may include the following steps: Step 1: Based on the ambient temperature outside the vehicle and the temperature of the windshield, obtain the first correction coefficient, where the temperature of the windshield is the outer temperature of the windshield.

[0073] For example, the calculation method for the first correction factor can be found in Formula 4 below: (Formula 4) In Formula 4, This is the first correction factor; Used to indicate the ambient temperature outside the vehicle; Used to indicate the temperature of the outside of the windshield; Used to indicate a reference temperature. It can take values ​​such as 37℃.

[0074] For example, the greater the temperature difference between the outside temperature and the glass temperature, the higher the risk level of fogging on the outside of the windshield. By combining the outside temperature and the glass temperature, a first correction factor can be determined to correct the initial risk value.

[0075] Step 2: Based on the in-vehicle ambient temperature, the outside ambient temperature, and the outside ambient humidity, obtain the second correction coefficient.

[0076] For example, the calculation method for the second correction factor can be found in Formula 5 below: (Formula 5) In Formula 5, This is the second correction factor; Used to indicate the ambient temperature inside the vehicle; RH is used to indicate the ambient temperature outside the vehicle; RH is used to indicate the humidity outside the vehicle. Used to indicate a reference temperature. It can take values ​​such as 37℃.

[0077] For example, the difference between the interior and exterior temperatures can also affect the risk of fogging on the windshield; by combining the interior temperature, exterior temperature, and exterior humidity, a second correction factor can be determined to adjust the initial risk value.

[0078] Step 3: Based on the first and second correction coefficients, the initial risk value is corrected to obtain the corrected initial risk value.

[0079] For example, the process of determining the initial risk value can be found in the following formula six: (Formula Six) In Formula Six, Used to represent the initial risk value; Used to represent the corrected initial risk value; This is the first correction factor; This is the second correction factor.

[0080] For example, after determining the correction factor, the corrected initial risk value can be determined by combining the correction factor and the initial risk value; then, the corresponding initial risk level can be determined based on the corrected initial risk value.

[0081] In this embodiment, the initial risk value of the windshield is obtained by combining the dew point temperature of the external environment and the glass temperature of the windshield. Then, the initial risk value is corrected by multiple parameters, including the ambient temperature inside the vehicle, the temperature and humidity outside the vehicle, and the glass temperature. Based on the corrected risk value, the initial risk level is determined, thereby realizing a multi-dimensional quantitative assessment of the risk of windshield fogging, improving the accuracy of the initial risk level determination and the fit between the initial risk level and the actual environment.

[0082] In one implementation, the environmental data also includes solar radiation and rainfall, and the method further includes: The target compensation coefficient is determined based on solar radiation, rainfall, and vehicle speed. The process of determining the initial risk level of the windshield based on the revised initial risk value may include: The initial risk level of the windshield is determined based on the revised initial risk value and the target compensation coefficient.

[0083] For example, during vehicle operation, the amount of solar radiation received by the vehicle, rainfall, and vehicle speed all have varying degrees of impact on the risk of fogging of the windshield. After determining the corrected initial risk value, a target compensation coefficient can be determined by combining the solar radiation value, rainfall, and vehicle speed. Combining the target compensation coefficient with the corrected initial risk value yields the initial risk level. The target compensation coefficient can include the solar compensation coefficient corresponding to solar radiation, the rainfall compensation coefficient corresponding to rainfall, and the vehicle speed compensation coefficient corresponding to vehicle speed.

[0084] In one implementation, a weight dictionary is pre-defined for different solar radiation values, different rainfall amounts, and different driving speeds. Based on the weight dictionary and the current solar radiation value, rainfall amount, and driving speed, the degree of influence of environmental data on the fog risk level under the current operating conditions can be determined, thereby determining the final initial risk level.

[0085] For example, there is a corresponding solar radiation value and a solar compensation coefficient. The solar radiation value can include the solar radiation received from the driver's seat and the solar radiation received from the passenger's seat. Based on the solar radiation value, the corresponding weight can be determined from a weight dictionary. Sunlight heats the windshield, raising its temperature and making it more difficult for it to fall below the dew point temperature, thus inhibiting water vapor condensation; therefore, the solar radiation value and the solar radiation weight are negatively correlated. The solar compensation coefficient can then be obtained through weighted averaging. The process of determining the solar compensation coefficient based on the solar radiation value can be seen in the following formula seven: (Formula 7) In Formula 7, Used to represent the solar compensation coefficient; Used to indicate the solar radiation value for the driver's seat; The solar radiation weight value used to represent the driver's seat position; Used to indicate the solar radiation level for the passenger seat; This is used to represent the solar radiation weight value for the passenger seat.

[0086] For example, rainfall amounts correspond to rainfall compensation coefficients. Based on the weight dictionary corresponding to rainfall amounts, the rainfall compensation coefficient corresponding to the current rainfall amount can be retrieved. The greater the rainfall, the higher the humidity outside the car. The humidity inside the car is also more likely to rise when the door is opened or the air conditioner is used. At the same time, rainwater may adhere to the glass, lowering its temperature and accelerating water vapor condensation. Therefore, rainfall and rainfall compensation coefficient are positively correlated.

[0087] For example, the vehicle speed compensation coefficient can be determined based on the vehicle's current speed. The process of determining the vehicle speed compensation coefficient can be found in Formula 8 below: (Formula 8) In Formula 8, Used to represent the vehicle speed compensation coefficient; V is used to represent the vehicle's speed; min() is used to indicate the speed at which the vehicle travels. The value is taken as the minimum between 1 and 0, to avoid deviations in risk compensation due to excessive vehicle speed.

[0088] For example, after determining the compensation coefficients corresponding to different parameters, the initial risk value after compensation is obtained by combining the compensation coefficients and the initial risk value. The process for determining the initial risk value after compensation can be found in Formula Nine below: (Formula Nine) In Formula Nine, Used to represent the initial risk value after compensation; Used to represent the corrected initial risk value; Used to represent the solar compensation coefficient; Used to represent the rainfall compensation coefficient; Used to represent the vehicle speed compensation coefficient.

[0089] For example, after determining the initial risk value of the windshield by combining vehicle environmental data and driving speed, the current initial risk percentage can be determined based on the initial risk value. The process for determining the initial risk percentage can be found in the following formula ten: (Formula 10) In Formula 10, Used to indicate the initial percentage of risk; Used to represent the initial risk value after compensation; Used to represent the maximum risk value under all operating conditions; Used to represent the minimum risk value under all operating conditions.

[0090] For example, after determining the initial risk percentage, the final initial risk level can be obtained based on the initial risk percentage. For instance, when the initial risk percentage is 0%, the initial risk level is determined to be the first risk level, indicating that there is currently no risk of fogging; when the initial risk percentage is in the range of 1% to 30%, the initial risk level is determined to be the second risk level, indicating that there is currently a low risk of fogging; when the initial risk percentage is in the range of 31% to 70%, the initial risk level is determined to be the third risk level, indicating that there is currently a moderate risk of fogging; and when the initial risk percentage is in the range of 71% to 100%, the initial risk level is determined to be the fourth risk level, indicating that there is currently a high risk of fogging.

[0091] In this embodiment, based on the correction of the initial risk value by combining the temperature and humidity inside and outside the vehicle, glass temperature, and dew point temperature, the target compensation coefficient is further introduced by the solar radiation value, rainfall, and vehicle speed. The corrected initial risk value is then recalibrated using the target compensation coefficient to determine the initial risk level. This allows the initial risk level to be dynamically updated in real time with changes in the vehicle's driving environment, achieving a precise match between the initial risk level determination and the actual driving scenario. This improves the accuracy of fog risk level determination, thereby determining a more accurate defogging strategy to achieve efficient defogging.

[0092] In one implementation, when the target percentage falls within the second range, the risk level corresponding to the second range is determined as the fog risk level. The second range refers to the range that is less than the first percentage threshold, or the second range refers to the range that is greater than or equal to the second percentage threshold.

[0093] For example, when the target proportion of the fog area indicated by the target image on the windshield is within the second range, that is, the target proportion is less than the first proportion threshold, or the target proportion is greater than or equal to the second proportion threshold, the risk level corresponding to the second range can be directly determined as the final fog risk level.

[0094] For example, the second range may include two sub-ranges, including a third sub-range and a fourth sub-range. The third sub-range indicates the range less than the first percentage threshold (e.g., less than 1%), and the fourth sub-range indicates the range greater than or equal to the second percentage threshold (e.g., greater than or equal to 70%). When the target percentage is within the third sub-range, the risk level corresponding to the third sub-range is Level 1 (also known as the no-fog level), meaning the current fog risk level is determined to be Level 1. When the target percentage is within the fourth sub-range, the risk level corresponding to the fourth sub-range is Level 4 (also known as the heavy fog level), meaning the current fog risk level is determined to be Level 4. Specifically, Level 1 indicates that there is no need for defrosting the windshield; Level 4 indicates that the urgency of defrosting the windshield is the highest.

[0095] In this embodiment, the solution uses a segmented and hierarchical judgment logic to determine the target proportion of fog on the windshield. When the target proportion is less than a first proportion threshold or greater than or equal to a second proportion threshold, the risk level corresponding to the second range is directly used as the final fog risk level. This ensures that the fog risk level directly corresponds to the risk level indicated by the target image in both fog-free (less than the first proportion threshold) and heavy fog (greater than or equal to the second proportion threshold) states, reflecting the actual fogging state and improving the accuracy of the fog risk level. This provides a reliable decision-making basis for the vehicle's automatic defogging safety control strategy, ensuring driving safety.

[0096] S204 controls the vehicle to execute a defogging strategy based on the fog risk level.

[0097] Among them, the fog risk level is positively correlated with the intensity of fog removal strategy intervention.

[0098] For example, after determining the current fog risk level of the windshield by combining the target image of the windshield and the vehicle's environmental data, a corresponding defogging strategy can be determined based on the fog risk level, and the vehicle can be controlled to execute the defogging strategy.

[0099] In one implementation, when the fog risk level is Level 1, no vehicle control is applied. When the fog risk level is Level 2, or low risk, mild intervention strategies to prevent fogging can be implemented. These can include: turning on the air conditioning in external circulation mode to introduce dry outside air and reduce interior humidity; moderately increasing the air conditioning fan speed to direct airflow towards the windshield; and, if it is winter, adjusting the air conditioning temperature slightly higher than the interior temperature to avoid excessive temperature differences on the glass. When the fog risk level is Level 3, or medium risk, active defogging strategies for rapid relief can be implemented. These can include: if it is summer, turning on the air conditioning in cooling and external circulation mode to reduce air humidity while simultaneously introducing dry air; if it is winter, turning on the air conditioning in heating and external circulation mode, and raising the temperature, for example, to above 25°C, to direct a large airflow directly at the windshield to accelerate evaporation of moisture from the glass surface; for vehicles with a dedicated windshield defogging button, the button can be briefly activated; or the windshield wipers can be turned on to defog. When the fog risk level is Level 4, which is a high-risk level, a strategy of strong intervention and multiple measures for emergency defogging is required. Specifically, this may include: turning on the windshield defroster button to achieve defogging by forcing the maximum airflow and corresponding mode; simultaneously opening the window gaps for ventilation to quickly exchange the high-humidity air inside the vehicle; and in winter, simultaneously turning on the windshield heating function to increase the glass temperature and inhibit water vapor condensation.

[0100] It should be noted that the above defogging strategies are only illustrative examples, and the embodiments of this application do not specifically limit the defogging strategies corresponding to each fog risk level.

[0101] In summary, in this embodiment, based on the acquired target image of the vehicle's windshield, the target proportion of the fogged area within the windshield is determined. Then, combining this target proportion with environmental data of the vehicle's surroundings, the fog risk level is determined, thereby controlling the vehicle to execute a defogging strategy and achieving windshield defogging. Compared to existing technologies that determine risk levels solely based on sensor data, which are prone to misjudgments and omissions, this solution improves the accuracy of fogging assessment by simultaneously combining the target image of the windshield and the vehicle's environmental data for a two-dimensional fog risk level determination. This allows for dynamic matching of the corresponding defogging strategy based on a more accurate fog risk level, preventing improper defogging from affecting driving safety and improving windshield defogging efficiency.

[0102] Figure 3 This is a schematic flowchart illustrating another method for controlling a vehicle provided in an embodiment of this application. It should be understood that this method can be applied to a vehicle; or, to a processor in a vehicle; or, to a chip in a processor integrated into a vehicle.

[0103] For example, such as Figure 3 As shown, the method 300 includes: S301, acquires a target image of the vehicle's windshield and environmental data of the vehicle.

[0104] The environmental data includes the outside temperature, outside humidity, windshield temperature, inside temperature, solar radiation, and rainfall.

[0105] For example, during vehicle operation, target images of the windshield and environmental data of the vehicle's surroundings are collected and detected.

[0106] S302, input the target image of the windshield into the target model.

[0107] The target model is used to analyze the proportion of the fog area indicated in the image of the windshield on the windshield.

[0108] For example, after obtaining the target image of the windshield, the target image is input into the target model for analysis and processing.

[0109] S303, through the target model, obtains the target area's proportion in the windshield.

[0110] The target area refers to the area on the windshield where the fog is located.

[0111] For example, by analyzing the target image of the windshield using a target model, the proportion of the fogged area in the windshield can be obtained.

[0112] Alternatively, the implementation methods of S302 to S303 can be found in [reference needed]. Figure 2 The relevant description of S202 is not repeated here in the embodiments of this application.

[0113] S304, determine if the target percentage is within the first range. If yes, proceed to S305; otherwise, proceed to S311.

[0114] The first range refers to the range that is greater than or equal to the first percentage threshold and less than the second percentage threshold. A target percentage within the first range indicates that the windshield is in a low-level or medium-level fogging state.

[0115] For example, after obtaining the target proportion output by the target model, it is determined whether the target proportion is within the first range, that is, whether the target proportion is between the first proportion threshold and the second proportion threshold.

[0116] S305 determines the dew point temperature outside the vehicle based on the ambient temperature and humidity outside the vehicle.

[0117] For example, if the target percentage is within the first range, the dew point temperature outside the vehicle is determined by further combining the ambient temperature and humidity outside the vehicle.

[0118] S306, based on dew point temperature and glass temperature, determines the initial risk value of the windshield.

[0119] For example, after obtaining the dew point temperature outside the vehicle, an initial risk value for the windshield is determined by combining the dew point temperature with the windshield's glass temperature. The higher the initial risk value, the greater the risk of the windshield fogging.

[0120] S307, based on the ambient temperature inside the vehicle, the ambient temperature outside the vehicle, the humidity outside the vehicle, and the glass temperature, corrects the initial risk value to obtain the corrected initial risk value.

[0121] For example, the initial risk value is corrected by combining the ambient temperature inside the vehicle, the ambient temperature and humidity outside the vehicle, and the glass temperature, resulting in a corrected initial risk value that is more consistent with the environment in which the vehicle is located.

[0122] S308 determines the target compensation coefficient based on solar radiation, rainfall, and vehicle speed.

[0123] For example, after correcting the initial risk value, the target compensation coefficient for the impact of the above parameters on the fogging state of the windshield is determined by further combining the current solar radiation value, rainfall and vehicle speed.

[0124] S309, based on the revised initial risk value and target compensation coefficient, determines the initial risk level of the windshield.

[0125] For example, by combining the corrected initial risk value and the target compensation coefficient, the initial risk level of the windshield can be obtained.

[0126] S310, based on the first range and initial risk level, determines the fog risk level.

[0127] For example, after determining the initial risk level, the final fog risk level can be determined together with the initial risk level and the first range.

[0128] For example, when the initial risk level is the first risk level (also known as the no-fog risk level) or the second risk level (also known as the low risk level), it indicates that the risk of fogging on the windshield of the vehicle is low. At this time, the possibility of the fogging situation worsening is also low. Therefore, the risk level corresponding to the first range can be directly determined as the fog risk level.

[0129] For example, when the initial risk level corresponding to the environmental data is the third risk level (also known as the medium risk level) or the fourth risk level (also known as the high risk level), it indicates that the risk of fogging on the vehicle's windshield is relatively high. At this time, the possibility of the fogging situation further aggravating is also relatively high. Therefore, the fog risk level is determined to be a risk level that is higher than the risk level corresponding to the first range by a preset level. The preset level can be level one.

[0130] S311, the risk level corresponding to the second range is determined as the fog risk level.

[0131] The second range refers to the range that is less than the first percentage threshold, or the second range refers to the range that is greater than or equal to the second percentage threshold.

[0132] For example, if the target percentage is determined to be outside the first range, the risk level corresponding to the second range is directly determined as the final fog risk level.

[0133] For example, when the target percentage is less than the first percentage threshold, the fog risk level is determined to be Level 1 to indicate that there is no need for defogging at present; when the target percentage is greater than or equal to the second percentage threshold, the fog risk level is determined to be Level 4 to indicate that the urgency of defogging is the highest.

[0134] Alternatively, the implementation methods of S304 to S311 can be found in [reference needed]. Figure 2 The relevant description of S203 is not repeated here in the embodiments of this application.

[0135] S312 controls the vehicle to execute a defogging strategy based on the fog risk level.

[0136] Among them, the degree of intervention of the fog removal strategy is positively correlated with the fog risk level.

[0137] For example, the fog risk level can reflect the urgency of the windshield defogging. After determining the corresponding fog risk level based on the acquired target image and vehicle environmental data, a corresponding defogging strategy is determined based on the fog risk level, and the vehicle is controlled to execute it.

[0138] Alternatively, the implementation of S312 can be found in [reference needed]. Figure 2 The relevant description of S204 is not repeated here in the embodiments of this application.

[0139] In summary, in this embodiment, the proportion of foggy areas on the windshield is quantified by a target model. Combined with multi-dimensional environmental data such as vehicle interior and exterior temperature and humidity, glass temperature, and solar radiation, and through dew point calculation, risk value correction, compensation calibration, and hierarchical logic of interval and grade judgment, a precise dynamic assessment of fog risk level is achieved. This avoids the bias of single-dimensional judgment and human subjective error, and can adapt to different driving scenarios to adjust the accuracy of risk judgment, avoiding the problem of missed or false reports of fogging conditions. Ultimately, it provides a reliable decision-making basis for vehicle defogging strategies and effectively ensures driving visibility safety.

[0140] The above text combined Figures 1 to 3 The method for controlling a vehicle provided in the embodiments of this application has been described in detail; the following will be combined with Figure 4 and Figure 5 The apparatus embodiments of this application are described in detail below. It should be understood that the apparatus in the embodiments of this application can perform the various methods described in the foregoing embodiments of this application, that is, the specific working processes of the various products described below can be referred to the corresponding processes in the foregoing method embodiments.

[0141] Figure 4 This is a schematic diagram of a device for controlling a vehicle provided in an embodiment of this application.

[0142] For example, such as Figure 4 As shown, the device 400 includes: The acquisition module 401 is used to acquire a target image of the vehicle's windshield; The processing module 402 is used to obtain the target area proportion in the windshield based on the target image of the windshield, wherein the target area refers to the area where the fog is located on the windshield; determine the fog risk level based on the target proportion and the vehicle's environmental data; and control the vehicle to execute a defogging strategy based on the fog risk level.

[0143] In one possible implementation, the processing module 402 is further configured to determine an initial risk level based on the vehicle's environmental data when the target proportion is within a first range, wherein the first range refers to a range that is greater than or equal to a first proportion threshold and less than a second proportion threshold; and to determine the fog risk level based on the first range and the initial risk level.

[0144] In one possible implementation, the processing module 402 is further configured to determine the risk level corresponding to the second range as the fog risk level when the target proportion is within the second range; wherein, the second range refers to the range that is less than the first proportion threshold, or the second range refers to the range that is greater than or equal to the second proportion threshold.

[0145] In one possible implementation, the processing module 402 is further configured to determine the risk level corresponding to the first range as the fog risk level when the initial risk level is the first risk level or the second risk level; and to determine the target level as the fog risk level when the initial risk level is the third risk level or the fourth risk level, wherein the target level refers to a risk level that is higher than the risk level corresponding to the first range by a preset level; wherein the fourth risk level is higher than the third risk level, and the third risk level is higher than the second risk level, and the second risk level is higher than the first risk level.

[0146] In one possible implementation, the environmental data includes the outside ambient temperature, the outside ambient humidity, and the windshield glass temperature. The processing module 402 is also used to determine the dew point temperature outside the vehicle based on the outside ambient temperature and the outside ambient humidity; and to determine the initial risk level of the windshield based on the dew point temperature and the glass temperature.

[0147] In one possible implementation, the environmental data also includes the in-vehicle ambient temperature. The processing module 402 is further used to determine the initial risk value of the windshield based on the dew point temperature and the glass temperature; to correct the initial risk value based on the in-vehicle ambient temperature, the outside ambient temperature, the outside ambient humidity, and the glass temperature to obtain a corrected initial risk value; and to determine the initial risk level of the windshield based on the corrected initial risk value.

[0148] In one possible implementation, the environmental data also includes solar radiation value and rainfall. The processing module 402 is further used to determine the target compensation coefficient based on the solar radiation value, rainfall and vehicle speed; and to determine the initial risk level of the windshield based on the corrected initial risk value, including: determining the initial risk level of the windshield based on the corrected initial risk value and the target compensation coefficient.

[0149] In one possible implementation, the processing module 402 is further configured to input the target image of the windshield into the target model; and obtain the target proportion of the target area in the windshield through the target model.

[0150] It should be noted that the aforementioned vehicle control devices are embodied in the form of functional units. The term "module" here can be implemented in software and / or hardware, without specific limitations.

[0151] For example, a "module" can be a software program, a hardware circuit, or a combination of both that implements the above functions. The hardware circuit may include an application-specific integrated circuit (ASIC), electronic circuits, a processor (e.g., a shared processor, a proprietary processor, or a group processor) and memory for executing one or more software or firmware programs, integrated logic circuits, and / or other suitable components that support the described functions.

[0152] Therefore, the units of the various examples described in the embodiments of this application can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0153] Figure 5 This is a schematic diagram of the structure of a vehicle provided in an embodiment of this application.

[0154] For example, such as Figure 5 As shown, the vehicle 500 includes a memory 501 and a processor 502, wherein the memory 501 stores executable program code 503, and the processor 502 is used to call and execute the executable program code 503 to perform a method for controlling the vehicle.

[0155] Furthermore, embodiments of this application also protect an apparatus that may include a memory and a processor, wherein the memory stores executable program code, and the processor is used to call and execute the executable program code to perform a method for controlling a vehicle provided in embodiments of this application.

[0156] This embodiment can divide the device into functional modules based on the above method example. For example, each module can correspond to a separate function, or two or more functions can be integrated into one processing module. The integrated module can be implemented in hardware. It should be noted that the module division in this embodiment is illustrative and only represents one logical functional division. In actual implementation, there may be other division methods.

[0157] When the functional modules are divided according to their respective functions, the device may also include a processing module, a control module, etc. It should be noted that all relevant content of each step involved in the above method embodiments can be referenced from the functional descriptions of the corresponding functional modules, and will not be repeated here.

[0158] It should be understood that the device provided in this embodiment is used to execute the above-described method for controlling a vehicle, and therefore can achieve the same effect as the above-described implementation method.

[0159] When using an integrated unit, the device may include a processing module and a storage module. When the device is applied to a vehicle, the processing module can be used to control and manage the vehicle's movements. The storage module can be used to support the vehicle in executing relevant program code.

[0160] The processing module may be a processor or a controller, which can implement or execute the various exemplary logic blocks, modules, and circuits described in conjunction with the disclosure of this application. The processor may also be a combination of functions that implement computing capabilities, such as a combination of one or more microprocessors, a combination of digital signal processing (DSP) and a microprocessor, etc., and the storage module may be a memory.

[0161] In addition, the device provided in the embodiments of this application may specifically be a chip, component or module. The chip may include a connected processor and a memory. The memory is used to store instructions. When the processor calls and executes the instructions, the chip can execute a method for controlling a vehicle provided in the above embodiments.

[0162] This embodiment also provides a computer-readable storage medium storing computer program code. When the computer program code is run on a computer, the computer executes the above-described related method steps to implement a method for controlling a vehicle provided in the above embodiment.

[0163] The computer-readable storage medium may include, but is not limited to, any type of disk, including floppy disks, optical disks, Digital Video Discs (DVDs), Compact Disc Read-Only Memory (CD-ROMs), microdrives, and magneto-optical disks, read-only memory (ROMs), random access memory (RAMs), erasable programmable read-only memory (EPROMs), electrically erasable programmable read-only memory (EEPROMs), dynamic random access memory (DRAMs), video random access memory (VRAMs), flash memory devices, magnetic cards or optical cards, nanosystems (including molecular memory ICs), or any type of medium or device suitable for storing instructions and / or data.

[0164] This embodiment also provides a computer program product that, when run on a computer, causes the computer to perform the aforementioned steps to implement a method for controlling a vehicle provided in the above embodiment.

[0165] In this embodiment, the device, computer-readable storage medium, computer program product, or chip are all used to execute the corresponding methods provided above. Therefore, the beneficial effects they can achieve can be referred to the beneficial effects in the corresponding methods provided above, and will not be repeated here.

[0166] Through the above description of the embodiments, those skilled in the art will understand that, for the sake of convenience and brevity, only the division of the above functional modules is used as an example. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above.

[0167] In the embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another device, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0168] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for controlling a vehicle, characterized in that, The method includes: Acquire a target image of the vehicle's windshield; Based on the target image of the windshield, the target proportion of the target area in the windshield is obtained, wherein the target area refers to the area where the fog is located on the windshield; Based on the target percentage and the vehicle's environmental data, the fog risk level is determined; Based on the fog risk level, the vehicle is controlled to execute a defogging strategy.

2. The method according to claim 1, characterized in that, The determination of fog risk level based on the target proportion and the vehicle's environmental data includes: When the target percentage is within a first range, an initial risk level is determined based on the vehicle's environmental data, wherein the first range refers to a range that is greater than or equal to a first percentage threshold and less than a second percentage threshold. The fog risk level is determined based on the first range and the initial risk level.

3. The method according to claim 2, characterized in that, The method further includes: When the target percentage is within the second range, the risk level corresponding to the second range is determined as the fog risk level; Wherein, the second range refers to the range that is less than the first percentage threshold, or the second range refers to the range that is greater than or equal to the second percentage threshold.

4. The method according to claim 2, characterized in that, Determining the fog risk level based on the first range and the initial risk level includes: When the initial risk level is the first risk level or the second risk level, the risk level corresponding to the first range is determined as the fog risk level; When the initial risk level is the third or fourth risk level, the target level is determined as the fog risk level, whereby the target level is a risk level that is a preset level higher than the risk level corresponding to the first range. Wherein, the fourth risk level is higher than the third risk level, the third risk level is higher than the second risk level, and the second risk level is higher than the first risk level.

5. The method according to claim 2, characterized in that, The environmental data includes the outside temperature, outside humidity, and the temperature of the windshield. Determining the initial risk level based on the vehicle's environmental data includes: Based on the ambient temperature and humidity outside the vehicle, the dew point temperature outside the vehicle is determined. The initial risk level of the windshield is determined based on the dew point temperature and the glass temperature.

6. The method according to claim 5, characterized in that, The environmental data also includes the in-vehicle ambient temperature. The determination of the initial risk level of the windshield based on the dew point temperature and the glass temperature includes: Based on the dew point temperature and the glass temperature, determine the initial risk value of the windshield; Based on the in-vehicle ambient temperature, the outside ambient temperature, the outside ambient humidity, and the glass temperature, the initial risk value is corrected to obtain a corrected initial risk value. Based on the revised initial risk value, the initial risk level of the windshield is determined.

7. The method according to claim 6, characterized in that, The environmental data also includes solar radiation values ​​and rainfall, and the method further includes: The target compensation coefficient is determined based on the solar radiation value, the rainfall, and the vehicle's speed. Determining the initial risk level of the windshield based on the corrected initial risk value includes: The initial risk level of the windshield is determined based on the corrected initial risk value and the target compensation coefficient.

8. The method according to any one of claims 1 to 7, characterized in that, The process of obtaining the target area's proportion within the windshield based on the target image of the windshield includes: Input the target image of the windshield into the target model; The target area is obtained as a percentage of the windshield by the target model.

9. A device for controlling a vehicle, characterized in that, The device includes: The acquisition module is used to acquire a target image of the vehicle's windshield; The processing module is configured to obtain the target proportion of the target area in the windshield based on the target image of the windshield, wherein the target area refers to the area where the fog is located on the windshield; determine the fog risk level based on the target proportion and the vehicle's environmental data; and control the vehicle to execute a defogging strategy based on the fog risk level.

10. A vehicle, characterized in that, The vehicles include: Memory, used to store executable program code; A processor for calling and running the executable program code from the memory, causing the vehicle to perform the method as described in any one of claims 1 to 8.

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