Vehicle control method and vehicle

Abstract

The invention provides a vehicle control method and a vehicle. The method is applied to the technical field of intelligent driving. The control method of the vehicle comprises the steps that whether defogging is conducted on a vehicle window or not is determined based on the fog concentration and the vehicle window temperature of the vehicle window in the vehicle; when it is determined that demisting is conducted on the vehicle window, a target strategy is determined based on the mist concentration of the vehicle window, and the target strategy is used for determining the demisting strength of demisting; determining target demisting intensity based on the target strategy; and controlling the vehicle to perform automatic demisting at the target demisting intensity. According to the method, the demisting efficiency of the vehicle window can be improved, and driving safety is ensured.

Description

Technical Field

[0001] This application relates to the field of intelligent driving technology, and more specifically, to a vehicle control method and a vehicle. Background Technology

[0002] With the increasing prevalence of vehicles, more and more users are using them as an essential tool for travel. As the windows through which drivers and passengers observe road conditions, car windows are crucial to driving safety and the overall driving experience.

[0003] In related technologies, the probability of vehicle fogging is determined by assessing the temperature and humidity differences between the inside and outside of the vehicle, and then deciding whether to automatically activate the vehicle's defogger. However, by the time the defogger is activated, the fogged area may already be obstructing the driver's view, and the inefficiency of the defogger can pose a safety hazard. Therefore, improving the efficiency of vehicle window defoggers to ensure driving safety has become an urgent problem to be solved. Summary of the Invention

[0004] This application provides a vehicle control method and a vehicle. The vehicle control method determines a target strategy based on the fog concentration of the vehicle windows, avoiding the problems of low defogging efficiency and energy waste caused by a fixed defogging intensity. Furthermore, by dynamically adjusting the target defogging intensity based on the fog concentration of the vehicle windows, the method achieves precise adjustment of the defogging intensity as needed, improving the defogging efficiency of the windows and thus ensuring the clarity of vision for drivers and passengers, thereby ensuring driving safety.

[0005] Firstly, a vehicle control method is provided, the vehicle control method including: Based on the fog concentration and window temperature of the vehicle windows, it is determined whether to defog the windows; when it is determined to defog the windows, a target strategy is determined based on the fog concentration of the windows, and the target strategy is used to determine the defogging intensity; based on the target strategy, a target defogging intensity is determined; and the vehicle is controlled to automatically defog at the target defogging intensity.

[0006] The above-mentioned technical solution determines whether to defog the windows based on the fog concentration and temperature of the vehicle windows. When it is determined that the windows should be defogged, a target strategy is determined based on the fog concentration, and a target defogging intensity is determined based on the target strategy for automatic defogging. Compared with defogging with a fixed defogging intensity, this application determines the target strategy based on the fog concentration of the windows, and then dynamically determines the target defogging intensity. This avoids the problems of low defogging efficiency and energy waste caused by a fixed defogging intensity, and achieves the purpose of accurately adjusting the target defogging intensity as needed, thereby improving the defogging efficiency of the windows and ensuring driving safety.

[0007] In conjunction with the first aspect, in some possible implementations, the fog concentration includes the regional fog concentration of multiple sub-regions within the vehicle window; determining the target strategy based on the fog concentration of the vehicle window includes: Based on the regional fog concentration of the multiple sub-regions, a target parameter value is determined, wherein the target parameter value is the degree of influence of the regional fog concentration of the multiple sub-regions on visibility; based on the target parameter value, the target strategy is determined.

[0008] The above technical solution determines target parameter values ​​based on the regional fog concentration in multiple sub-regions of the vehicle window, and then determines a target strategy based on the target parameter values. The target parameter values ​​are the degree of influence of the regional fog concentration in multiple sub-regions on visibility. By understanding the degree of influence of regional fog concentration on visibility, the accuracy of the target strategy can be ensured, thus achieving the goal of dynamically adjusting the target defogging intensity according to the target strategy.

[0009] In combination with the first aspect and the above implementation methods, in some possible implementation methods, determining the target strategy based on the target parameter value includes: Determine whether the target parameter value is less than a first preset threshold; when the target parameter value is less than the first preset threshold, determine the first preset strategy as the target strategy, the first preset strategy being determined based on the target parameter value, the regional fog concentration of the multiple sub-regions, and the window temperature; when the target parameter value is greater than or equal to the first preset threshold, determine the second preset strategy as the target strategy, the second preset strategy being determined based on a preset defogging intensity, the regional fog concentration of the multiple sub-regions, and the window temperature.

[0010] The above technical solution determines the first preset strategy as the target strategy when the target parameter value is less than the first preset threshold, and determines the second preset strategy as the target strategy when the target parameter value is greater than or equal to the first preset threshold. By combining the regional fog concentration of multiple sub-regions to determine the target parameter value, and then dynamically adjusting the target strategy according to the target parameter value, the accuracy of the target strategy can be improved.

[0011] In combination with the first aspect and the above implementation methods, in some possible implementation methods, determining the target parameter value based on the regional fog concentration of the multiple sub-regions includes: Based on the regional fog concentration of the multiple sub-regions, the maximum fog concentration is determined; based on the maximum fog concentration, the target parameter value is determined.

[0012] The above technical solution determines the maximum fog concentration based on the regional fog concentration in multiple sub-regions of the vehicle window, and then determines the target parameter value based on the maximum fog concentration. Since the target parameter value is the degree of influence of regional fog concentration on visibility, and vehicle window fogging starts from a local area, determining the target parameter value by using the maximum fog concentration ensures the accuracy of the target parameter value by ensuring the influence of local fog concentration on the vehicle window.

[0013] In combination with the first aspect and the above implementation methods, in some possible implementation methods, determining the target parameter value based on the maximum fog concentration includes: Based on weather data of the vehicle's location, a fog concentration threshold is determined, which is negatively correlated with the severity of the weather. When the maximum fog concentration is greater than the fog concentration threshold, a second preset threshold is determined as the target parameter value. When the maximum fog concentration is less than or equal to the fog concentration threshold, a third preset threshold is determined as the target parameter value, which is less than the second preset threshold.

[0014] The above technical solution determines the fog concentration threshold based on weather data of the vehicle's location, and determines the target parameter value based on the fog concentration threshold and the maximum fog concentration; by fusing the maximum fog concentration with weather data, the fog concentration threshold is determined, thereby determining the target defogging intensity, which can ensure the safety of the user's vision in severe weather.

[0015] In combination with the first aspect and the above implementation methods, in some possible implementation methods, the target defogging intensity is used to indicate the defogging intensity of the target actuator; determining the target defogging intensity based on the target strategy includes: Based on the target strategy and the target actuator, the target defogging intensity of the target actuator is determined; wherein, there is a correspondence between the target actuator and the target defogging intensity.

[0016] The above technical solution determines the target defogging intensity of the target actuator through the target strategy and the target actuator. There is a corresponding relationship between the target actuator and the target defogging intensity. When the target strategy is determined, the target defogging intensity of the target actuator can be determined only according to the target strategy, which can reduce the amount of calculation to determine the target defogging intensity and thus improve the efficiency of determining the target defogging intensity.

[0017] In conjunction with the first aspect and the above implementation methods, in some possible implementation methods, when the target actuator includes a heating wire, a sprayer, and a blower, the target demisting intensity includes the heating power of the heating wire, the spray volume of the demisting agent sprayed by the sprayer, and the directional wind speed of the blower; determining the target demisting intensity of the target actuator based on the target strategy and the target actuator includes: When the target strategy is the first preset strategy, the heating power is determined based on the average fog concentration and the target parameter value, the spraying amount is determined based on the average fog concentration and the window temperature, and the turning wind speed is determined based on the target parameter value. The average fog concentration is used to indicate the average value of the regional fog concentration of the multiple sub-regions. When the target strategy is the second preset strategy, the preset power is determined as the heating power, the preset wind speed is determined as the turning wind speed, and the spraying amount is determined based on the average fog concentration and the window temperature.

[0018] In the above technical solution, when the target actuator includes a heating wire, a sprayer, and a blower, the target defogging intensity includes the heating power of the heating wire, the spray volume of the defogging agent sprayed by the sprayer, and the directional wind speed of the blower. By measuring the fog concentration and temperature of the vehicle window, the target actuator for defogging the vehicle window is determined, and the target defogging intensity corresponding to the target actuator is determined. The target defogging intensity can be dynamically determined based on the fog concentration of the vehicle window, thereby ensuring the accuracy of the target defogging intensity.

[0019] In combination with the first aspect and the above implementation methods, in some possible implementation methods, the fog concentration includes the regional fog concentration of multiple sub-regions in the window; determining whether to defog the window based on the fog concentration and window temperature in the vehicle includes: Based on the regional fog concentration of the multiple sub-regions, a fog concentration gradient is determined; based on the regional fog concentration of each sub-region, the window temperature, the light intensity of the window, and the fog concentration gradient, it is determined whether there is a target actuator for defogging the window; when there is no target actuator, it is determined that there is no need to defog the window; when there is a target actuator, it is determined that the window should be defogged.

[0020] The above technical solution determines whether a target actuator for defogging the windows exists based on the regional fog concentration, window temperature, window light intensity, and fog concentration gradient in each of the multiple sub-regions. When no target actuator exists, it is determined that defogging is not necessary; when a target actuator exists, it is determined that defogging is performed. By combining high-dimensional dynamic parameters, the target actuator for defogging the windows is automatically adjusted, which avoids the problems of false triggering, response lag, or over-defogging caused by traditional methods, thereby improving the accuracy and intelligence of defogging control.

[0021] In combination with the first aspect and the above implementation methods, in some possible implementations, the method further includes: The global fog concentration of the vehicle window is obtained; when the global fog concentration is greater than a preset concentration threshold, the vehicle window is divided into multiple sub-regions based on a first preset size; when the global fog concentration is less than or equal to the preset concentration threshold, the vehicle window is divided into multiple sub-regions based on a second preset size, wherein the second preset size is greater than the first preset size.

[0022] The above technical solution involves dividing the window into multiple sub-regions based on a first preset size when the global fog concentration exceeds a preset threshold; and dividing the window into multiple sub-regions based on a second preset size when the global fog concentration is less than or equal to the preset threshold. The second preset size differs from the first preset size. This dynamic division of the window based on the global fog concentration allows for the determination of the defogging intensity based on the fog concentration of each dynamically divided sub-region, thus achieving adaptive defogging.

[0023] Secondly, a vehicle control device is provided, the vehicle control device comprising: The system includes a judgment module for determining whether to defog the vehicle windows based on the fog concentration and temperature of the windows; a determination module for determining a target strategy based on the fog concentration of the windows when it is determined to defog the windows, wherein the target strategy is used to determine the defogging intensity; a calculation module for determining a target defogging intensity based on the target strategy; and a control module for controlling the vehicle to automatically defog at the target defogging intensity.

[0024] In conjunction with the second aspect, in some possible implementations, the fog concentration includes the regional fog concentration of multiple sub-regions within the vehicle window; the determining module is specifically used for: Based on the regional fog concentration of the multiple sub-regions, a target parameter value is determined, wherein the target parameter value is the degree of influence of the regional fog concentration of the multiple sub-regions on visibility; based on the target parameter value, the target strategy is determined.

[0025] Combining the second aspect and the above implementation methods, in some possible implementation methods, the module is specifically used for: Determine whether the target parameter value is less than a first preset threshold; when the target parameter value is less than the first preset threshold, determine the first preset strategy as the target strategy, the first preset strategy being determined based on the target parameter value, the regional fog concentration of the multiple sub-regions, and the window temperature; when the target parameter value is greater than or equal to the first preset threshold, determine the second preset strategy as the target strategy, the second preset strategy being determined based on a preset defogging intensity, the regional fog concentration of the multiple sub-regions, and the window temperature.

[0026] Combining the second aspect and the above implementation methods, in some possible implementation methods, the module is specifically used for: Based on the regional fog concentration of the multiple sub-regions, the maximum fog concentration is determined; based on the maximum fog concentration, the target parameter value is determined.

[0027] Combining the second aspect and the above implementation methods, in some possible implementation methods, the module is specifically used for: Based on weather data of the vehicle's location, a fog concentration threshold is determined, which is negatively correlated with the severity of the weather. When the maximum fog concentration is greater than the fog concentration threshold, a second preset threshold is determined as the target parameter value. When the maximum fog concentration is less than or equal to the fog concentration threshold, a third preset threshold is determined as the target parameter value, which is less than the second preset threshold.

[0028] In combination with the second aspect and the above implementation methods, in some possible implementation methods, the target defogging intensity is used to indicate the defogging intensity of the target actuator; the calculation module is specifically used for: Based on the target strategy and the target actuator, the target defogging intensity of the target actuator is determined; wherein, there is a correspondence between the target actuator and the target defogging intensity.

[0029] In conjunction with the second aspect and the above implementation methods, in some possible implementations, when the target actuator includes a heating wire, a sprayer, and a blower, the target demisting intensity includes the heating power of the heating wire, the spray volume of the demisting agent sprayed by the sprayer, and the directional wind speed of the blower; the calculation module is specifically used for: When the target strategy is the first preset strategy, the heating power is determined based on the average fog concentration and the target parameter value, the spraying amount is determined based on the average fog concentration and the window temperature, and the turning wind speed is determined based on the target parameter value. The average fog concentration is used to indicate the average value of the regional fog concentration of the multiple sub-regions. When the target strategy is the second preset strategy, the preset power is determined as the heating power, the preset wind speed is determined as the turning wind speed, and the spraying amount is determined based on the average fog concentration and the window temperature.

[0030] Combining the second aspect and the above implementation methods, in some possible implementations, the fog concentration includes the regional fog concentration of multiple sub-regions in the vehicle window; the judgment module is specifically used for: Based on the regional fog concentration of the multiple sub-regions, a fog concentration gradient is determined; based on the regional fog concentration of each sub-region, the window temperature, the light intensity of the window, and the fog concentration gradient, it is determined whether there is a target actuator for defogging the window; when there is no target actuator, it is determined that there is no need to defog the window; when there is a target actuator, it is determined that the window should be defogged.

[0031] Combining the second aspect and the above implementation methods, in some possible implementations, the judgment module is specifically used for: The global fog concentration of the vehicle window is obtained; when the global fog concentration is greater than a preset concentration threshold, the vehicle window is divided into multiple sub-regions based on a first preset size; when the global fog concentration is less than or equal to the preset concentration threshold, the vehicle window is divided into multiple sub-regions based on a second preset size, wherein the second preset size is greater than the first preset size.

[0032] Thirdly, a vehicle is provided, including a memory and a processor, wherein 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 vehicle control method of the first aspect or any possible implementation thereof.

[0033] Fourthly, a computer-readable storage medium is provided that stores computer program code, which, when executed on a computer, causes the computer to perform the vehicle control method of the first aspect or any possible implementation thereof.

[0034] Fifthly, a computer program product is provided, comprising: computer program code, which, when executed on a computer, causes the computer to perform the vehicle control method described in the first aspect or any possible implementation thereof. Attached Figure Description

[0035] Figure 1 This is a schematic diagram of a vehicle control method provided in an embodiment of this application; Figure 2 This is a schematic diagram of the framework of a vehicle control system provided in an embodiment of this application; Figure 3 This is a schematic flowchart of a vehicle control method provided in an embodiment of this application; Figure 4 This is a schematic flowchart illustrating a data synchronization method provided in an embodiment of this application; Figure 5This is a schematic flowchart of another vehicle control method provided in an embodiment of this application; Figure 6 This is a schematic diagram of the structure of a vehicle control device provided in an embodiment of this application; Figure 7 This is a schematic diagram of the structure of a vehicle provided in an embodiment of this application. Detailed Implementation

[0036] 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.

[0037] 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.

[0038] Figure 1 This is a schematic diagram of a vehicle control method provided in an embodiment of this application.

[0039] For example, such as Figure 1 As shown, vehicle 100 includes a windshield 110, a driver's side window 120, and a rear windshield 130. When any window of vehicle 100 (windshield 110, driver's side window 120, and rear windshield 130) fogs up while the vehicle 100 is in motion, it needs to be defogged. Common defogging methods include automatic defogging and manual defogging.

[0040] During manual defogging, when the driver perceives that the fog on the windows is obstructing their vision, the driver manually activates the defogging function. This method of manually activating the defogging function is delayed and seriously affects driving safety.

[0041] During automatic defogging, the system determines whether to activate the defogging function based on the temperature and humidity differences between the inside and outside of the vehicle. When activation is determined, the defogging function is automatically activated. However, because the defogging intensity is consistently the same each time it is automatically activated, this may result in wasted resources or insufficient defogging power. Furthermore, since window fogging often starts locally, by the time the defogging function is activated based on the combined humidity and temperature differences, the fogged area may have already obstructed the driver's vision, posing a safety hazard.

[0042] In view of this, this application proposes a vehicle control method and a vehicle, which determines the fog concentration of the vehicle windows by dividing and identifying them, and determines whether to defog the windows based on the fog concentration. When it is determined that the windows should be defogged, a target strategy is determined based on the fog concentration, and then a target defogging intensity is determined to automatically defog the windows. Since different strategies determine the target defogging intensity in different ways, determining the target strategy by the fog concentration of the windows can avoid the problems of low defogging efficiency and energy waste caused by a fixed defogging intensity. Furthermore, by dynamically adjusting the corresponding target defogging intensity by the fog concentration of the windows, the purpose of accurately adjusting the defogging intensity as needed is achieved, which can improve the defogging efficiency of the windows, thereby ensuring the clarity of the driver's and passengers' vision and ensuring driving safety.

[0043] Figure 2 This is a schematic diagram of the framework of a vehicle control system provided in an embodiment of this application.

[0044] For example, such as Figure 2 As shown, the vehicle control system 200 includes a data acquisition layer 210, a data processing layer 220, a decision-making layer 230, and an execution layer 240. The data acquisition layer 210 includes multimodal sensors to collect vehicle weather data and window parameters. Optionally, the multimodal sensors include, but are not limited to, infrared cameras, gravity sensors, ambient light sensors, temperature and humidity sensors, and speed sensors. Weather data includes, but is not limited to, rainfall and snowfall. Window parameters include, but are not limited to, window fog concentration, overall fog concentration, window temperature, window tilt angle, and window illuminance.

[0045] The data processing layer 220 acquires weather data and window parameters from the data acquisition layer 210, and determines the regional fog concentration, window temperature, window illumination intensity, and fog concentration gradient of multiple sub-regions of the window. It then inputs these data into a defogging model deployed in the data processing layer 220 to determine if a target actuator exists for defogging the window. If no target actuator exists, defogging is not required; if a target actuator exists, defogging is performed. The target actuator includes at least one of a heating wire, a sprayer, and a blower. Further, a target strategy is determined based on the regional fog concentration of the multiple sub-regions.

[0046] The decision layer 230 obtains the target policy and target executor from the data processing layer 220, and determines the target defogging intensity of the target executor based on the target policy and target executor, and sends the target defogging intensity to the execution layer 240. The execution layer 240 controls the target executor in the vehicle to automatically defog the windows at the target defogging intensity.

[0047] Figure 3 This is a schematic flowchart of a vehicle control method provided in an embodiment of this application.

[0048] For example, Figure 3 The vehicle control method shown can be executed by the vehicle's overall controller or chip. Alternatively, the vehicle's overall controller or chip can send vehicle parameters to the cloud, determine the target defogging intensity through the cloud, and then control the vehicle to automatically defog at the target intensity via the overall controller or chip.

[0049] S310 determines whether to defog the windows based on the fog concentration and window temperature in the vehicle.

[0050] For example, the decision to defog the windows is determined by the fog concentration and window temperature. Specifically, if the fog concentration is greater than a preset fog concentration and the window temperature is greater than a preset temperature, defogging is performed. If the fog concentration is less than or equal to the preset fog concentration, or the window temperature is less than or equal to the preset temperature, defogging is not required.

[0051] For example, the fog concentration of a vehicle window includes the regional fog concentration of multiple sub-regions within the window, which indicates the fog concentration corresponding to each sub-region. Specifically, an infrared camera captures an image of the vehicle window, and the image is divided into multiple sub-regions. The brightness channel of each sub-region is extracted, representing local brightness and darkness information. Based on the local brightness and darkness information and a fog mask, pixels with fog are identified. Then, the ratio of foggy pixels to the number of pixels in the window area is used to determine the fog percentage of the window, which is the initial fog concentration. Further, an environmental sensor collects the light intensity of the window, with the light intensity range being greater than 0 and less than 100 klux. The initial fog concentration is then corrected using the light intensity to obtain the regional fog concentration of each sub-region.

[0052] For example, the expression for regional fog concentration can be represented as follows: ; in, This represents the fog concentration in the i-th region, where the fog concentration ranges from 0 to 100%. This represents the initial fog concentration in the i-th region. This indicates the light intensity through the vehicle window. Correcting the initial fog concentration using light intensity avoids interference from light intensity during infrared imaging, ensuring accurate determination of the fog concentration in the area.

[0053] The initial temperature of the car window is collected by a temperature and humidity sensor, and the window tilt angle is collected by a gravity sensor. The initial temperature is then corrected using the window tilt angle to obtain the final window temperature. For example, the expression for window temperature can be as follows: ; in, Indicates the car window temperature. Indicates the initial temperature of the car window. This indicates the window tilt angle. By adjusting the window tilt angle, the initial temperature of the window can be corrected, compensating for temperature distribution deviations caused by gravity tilt angle, and ensuring the accuracy of window temperature readings.

[0054] In one example, based on the regional fog concentrations of multiple sub-regions, the highest fog concentration among these sub-regions is selected. It is then determined whether the highest fog concentration is greater than a preset fog concentration. If the highest fog concentration is greater than the preset fog concentration, defogging of the windows is initiated, and the target actuators are identified as heating wires, sprayers, and blowers. If the highest fog concentration is less than or equal to the preset fog concentration, defogging of the windows is not required.

[0055] Optionally, the preset fog concentration can be 70%, 65%, 75%, etc. The preset fog concentration can be determined according to the actual situation, and no specific limitation is made here.

[0056] In another example, a fog concentration gradient is determined based on the regional fog concentration in multiple sub-regions. Then, based on the regional fog concentration in each sub-region, the window temperature, the window's illuminance, and the fog concentration gradient, it is determined whether a target actuator exists for defogging the windows. If no target actuator exists, it is determined that defogging the windows is unnecessary; if a target actuator exists, it is determined that defogging the windows will be performed.

[0057] By analyzing the regional fog concentrations across multiple sub-regions, a fog concentration gradient is determined. Fog concentration indicates the change in regional fog concentration per unit time. The fog concentration gradient reflects whether fog is rapidly increasing (positive and large gradient), slowly dissipating (negative and small gradient), or approaching stability (gradient close to 0). For example, the fog concentration gradient can be expressed as follows: ; in, Indicates the fog concentration gradient. Indicates the sampling interval duration. This represents the fog concentration in the i-th sub-region at the current sampling time. This represents the fog concentration in the i-th sub-region at the last sampling time.

[0058] The state vector of the input defogging model is constructed by considering the regional fog concentration, window temperature, window illumination intensity, and fog concentration gradient within each of the multiple sub-regions. The state vector can be expressed as follows: ; Where s represents the state vector. This indicates the fog concentration in the first sub-area of ​​the car window. This indicates the fog concentration in the second sub-region of the car window. This represents the fog concentration in the nth sub-region of the car window. Indicates the car window temperature. Indicates the light intensity through the car window. The fog concentration gradient represents the fog concentration of multiple sub-regions.

[0059] Input the state vector s into the defogging model and output the defogging action, which indicates whether there is a target actuator for defogging the windows. When the defogging action is empty, it is determined that there is no target actuator for defogging the windows, so there is no need to defog the windows. When the defogging action is not empty, it is determined that there is a target actuator for defogging the windows, so the windows are defogged.

[0060] Optionally, the target actuator includes at least one of a heating wire, a sprayer, and a blower. The heating wire is used to heat the window, the sprayer is used to spray anti-fogging agent onto the window, and the blower is used to blow air onto the window. Furthermore, the blowing direction of the blower can be determined based on the area's fog concentration. For example, the maximum fog concentration in the area is determined, and the target blowing direction is determined based on the maximum fog concentration, and the blower is controlled to blow air in the target direction.

[0061] The defogging model is a learning model (e.g., Q-Value Learning Model, Q-learning model), which is based on reinforcement learning (RL) algorithms. The core of the Q-learning model is to learn a state-action function (called the Q-function or Q-table). The Q-function is used to evaluate the long-term value of performing each action in a given state, thereby finding the optimal policy to maximize the cumulative reward.

[0062] Based on the regional fog concentration of each sub-region Car window temperature The light intensity L and the fog concentration gradient ▽G are used to construct a state vector s. This state vector s is then input into a Q-learning model to obtain an action vector a. Action vector a indicates whether a defogging action is performed, and if so, the actions to be performed include heating with a heating wire, spraying defogging agent with a sprayer, and blowing air with a blower. Specifically, the state vector is input into the Q-learning model, and the Q-function in the model determines the candidate actions corresponding to the state vector. The action with the highest cumulative reward among the candidate actions is then selected as the defogging action, thereby defogging the vehicle's windows.

[0063] Furthermore, after defogging the car window, the immediate reward for the state vector s and action vector a is determined. The expression for the immediate reward can be represented as follows: ; in, This represents the immediate reward between the state vector s and the action vector a. For the response item in the reward function, This represents the energy efficiency term in the reward function. This represents the penalty term in the reward function. k represents the fog adjustment parameter, and t represents the defogging time, obtained from the execution feedback. This indicates the instantaneous power consumption of the air conditioner. The fixed power is 120W. 'n' represents the number of sprayers in the vehicle. This represents the amount of defrost consumed by the i-th sprayer. It should be noted that when there is only one sprayer in the vehicle, then n is 1. That is, 0.1*C.

[0064] When an immediate reward is provided, the Q-function is optimized using that reward. The optimized function can be represented as follows: ; in, This represents the long-term reward of updating the left-hand state vector and action vector in the Q-function through the expression on the right. Represents the state vector in the Q function With action vectors Corresponding long-term rewards, This represents the immediate reward of the state vector s and the defogging action a. . This represents the value that maximizes the long-term reward among all possible actions in the next state, signifying the maximum long-term gain obtainable from that state. Furthermore, by continuously optimizing the Q-function using the methods described above, the accuracy of the model's output parameters can be improved.

[0065] The above technical solution determines whether a target actuator for defogging the windows exists based on the regional fog concentration, window temperature, window light intensity, and fog concentration gradient in each of the multiple sub-regions. When no target actuator exists, it is determined that defogging is not necessary; when a target actuator exists, it is determined that defogging is performed. By combining high-dimensional dynamic parameters, the target actuator for defogging the windows is automatically adjusted, which avoids the problems of false triggering, response lag, or over-defogging caused by traditional methods, thereby improving the accuracy and intelligence of defogging control.

[0066] For example, "windows" indicates which windows in the vehicle require defrosting. Examples include the windshield, rear windshield, driver's side window, and passenger side window. Of course, it could also refer to all windows in the vehicle. Multiple sub-regions are created by dividing the windows into sections.

[0067] In one example, the car window is divided into multiple sub-regions based on a preset size. Alternatively, the required region values ​​can be determined, and the car window can be divided according to these values; for example, if the region value is 4, the car window can be directly divided into 4 sub-regions.

[0068] Optionally, the preset size can be 8cm*8cm, or 9cm*9cm, etc. The preset size can be determined according to the actual situation, and no specific limitation is made here.

[0069] In another example, the global fog concentration of the car window is obtained. When the global fog concentration is greater than a preset concentration threshold, the car window is divided into multiple sub-regions according to a first preset size. When the global fog concentration is less than or equal to the preset concentration threshold, the car window is divided into multiple sub-regions according to a second preset size, where the second preset size is greater than the first preset size.

[0070] Optionally, the global fog concentration is used to indicate the overall fog concentration of the vehicle window, and the global fog concentration is determined by the vehicle window image. The method for determining the global fog concentration can refer to the determination method in the foregoing disclosed embodiments, and will not be repeated here.

[0071] Optionally, the preset concentration threshold can be 40%, 35%, 45%, etc. Preferably, the preset concentration threshold is 40%.

[0072] For example, the first preset size can be 2cm*2cm, 3cm*3cm, etc.; the second preset size can be 5cm*5cm, 6cm*6cm, etc., and the second preset size is larger than the first preset size. Preferably, the first preset size is 2cm*2cm and the second preset size is 5cm*5cm.

[0073] For example, if the global fog concentration is 65%, which is greater than the preset concentration threshold of 40%, the window is divided into multiple sub-regions based on the first preset size of 2cm*2cm. Alternatively, if the global fog concentration is 35%, which is less than the preset concentration threshold of 40%, the window is divided into multiple sub-regions based on the second preset size of 5cm*5cm.

[0074] The above technical solution involves dividing the window into multiple sub-regions based on a first preset size when the global fog concentration exceeds a preset threshold; and dividing the window into multiple sub-regions based on a second preset size when the global fog concentration is less than or equal to the preset threshold. The second preset size differs from the first preset size. This dynamic division of the window based on the global fog concentration allows for the determination of the defogging intensity based on the fog concentration of each dynamically divided sub-region, thus achieving adaptive defogging.

[0075] S320: When it is determined that the windows should be defogged, a target strategy is determined based on the fog concentration of the windows.

[0076] It should be noted that the target strategy is used to determine the defogging intensity. Different strategies have different target algorithms for determining the defogging intensity, so different strategies determine different target defogging intensities.

[0077] For example, the fog concentration includes the regional fog concentration of multiple sub-regions in the vehicle window. Based on the regional fog concentration of multiple sub-regions in the vehicle window, a target parameter value is determined, and based on the target parameter value, a target strategy is determined.

[0078] Optionally, the target parameter value is the degree of influence of regional fog concentration on visibility in multiple sub-regions. Determining the target strategy through the target parameter value can ensure the adaptability of the target strategy to the defogging scenario.

[0079] The above technical solution determines target parameter values ​​based on the regional fog concentration in multiple sub-regions of the vehicle window, and then determines a target strategy based on the target parameter values. The target parameter values ​​are the degree of influence of the regional fog concentration in multiple sub-regions on visibility. By understanding the degree of influence of regional fog concentration on visibility, the accuracy of the target strategy can be ensured, thus achieving the goal of dynamically adjusting the target defogging intensity according to the target strategy.

[0080] In one example, based on the regional fog concentration of multiple sub-regions, the average fog concentration is determined, and it is determined whether the average fog concentration is greater than a preset fog concentration. When the average fog concentration is greater than the preset fog concentration, the first preset parameter value is determined as the target parameter value; when the average fog concentration is less than or equal to the preset fog concentration, the second preset parameter value is determined as the target parameter value.

[0081] In another example, a maximum fog concentration is determined based on the regional fog concentration in multiple sub-regions of the window. The maximum fog concentration is used to indicate the fog concentration with the highest value among the regional fog concentrations. And a target parameter value is determined based on the maximum fog concentration.

[0082] The maximum fog concentration is selected from multiple sub-regions, and it is determined whether the maximum fog concentration is greater than a preset fog concentration. When the maximum fog concentration is greater than the preset fog concentration, a second preset threshold is determined as the target parameter value; when the maximum fog concentration is less than or equal to the preset fog concentration, a third preset threshold is determined as the target parameter value.

[0083] Optionally, the second preset threshold can be 0.5, 0.55, 0.45, etc. Preferably, the second preset threshold is 0.5.

[0084] Optionally, the third preset threshold can be 0.1, 0.15, 0.05, etc. Preferably, the second preset threshold is 0.1.

[0085] The aforementioned technical solution determines the maximum fog concentration based on the regional fog concentration in multiple sub-regions of the vehicle window, and then determines the target parameter value based on the maximum fog concentration. Since the target parameter value represents the degree of impact of regional fog concentration on visibility, and vehicle window fogging starts in a localized area, determining the target parameter value by using the maximum fog concentration ensures the accuracy of the target parameter value by controlling the impact of fog concentration in localized areas on the vehicle window. Furthermore, during driving, dynamically determining the target defogging intensity using the target parameter value improves defogging efficiency, reducing it from 6.8 seconds to 2.7 seconds.

[0086] To improve the accuracy of target parameter values, weather data can also be used to determine them.

[0087] For example, based on weather data of the vehicle's location, a fog concentration threshold is determined, which is negatively correlated with the severity of the weather. When the maximum fog concentration is greater than the fog concentration threshold, a second preset threshold is determined as the target parameter value. When the maximum fog concentration is less than or equal to the fog concentration threshold, a third preset threshold is determined as the target parameter value, which is less than the second preset threshold.

[0088] Optionally, weather data may include rainfall, snowfall, etc. Preferably, the weather data is rainfall.

[0089] For example, a fog concentration threshold can be determined based on the rainfall at the vehicle's location and a preset fog concentration. The expression for the fog concentration threshold can be given as follows: ; Where f represents the fog concentration threshold, 70% represents the preset fog concentration, and p represents the rainfall. By adjusting the fog concentration threshold in conjunction with rainfall, the false trigger rate can be reduced from 22% to 2.3%, thus improving defogging efficiency.

[0090] Given a defined fog concentration threshold, determine whether the maximum fog concentration exceeds the threshold. If the maximum fog concentration exceeds the threshold, set the second preset threshold of 0.5 as the target parameter value; if the maximum fog concentration is less than or equal to the threshold, set the third preset threshold of 0.1 as the target parameter value.

[0091] The above technical solution determines the fog concentration threshold based on weather data of the vehicle's location, and determines the target parameter value based on the fog concentration threshold and the maximum fog concentration; by fusing the maximum fog concentration with weather data, the fog concentration threshold is determined, thereby determining the target defogging intensity, which can ensure the safety of the user's vision in severe weather.

[0092] Furthermore, given the target parameter values, the target strategy is determined based on those values.

[0093] For example, it is determined whether the target parameter value is less than a first preset threshold; when the target parameter value is less than the first preset threshold, the first preset strategy is determined as the target strategy, which is determined based on the target parameter value, the regional fog concentration of multiple sub-regions and the window temperature; when the target parameter value is greater than or equal to the first preset threshold, the second preset strategy is determined as the target strategy, which is determined based on the preset defogging intensity, the regional fog concentration of multiple sub-regions and the window temperature.

[0094] Optionally, the first preset threshold can be 0.5, 0.45, 0.55, etc. Preferably, the first preset threshold is 0.5.

[0095] For example, if the first preset strategy and the second preset strategy are different, then the target defogging intensity determined by the first prediction strategy and the second preset strategy will be different. The first preset strategy is determined based on the target parameter value, the regional fog concentration of the multiple sub-regions, and the window temperature. The expression of the first prediction strategy can be expressed as follows: ; Where P represents the heating power of the heating wire in the first preset strategy, G represents the average fog concentration in the region, and k represents the target parameter value. Based on the average fog concentration and the target parameter value, candidate power is calculated, and the minimum value between the candidate power and 100 is selected as the heating power of the heating wire.

[0096] ; in, This indicates the amount of anti-misting agent sprayed by the sprayer in the first preset strategy. G represents the window temperature, and G represents the average fog concentration in the area.

[0097] ; Where v represents the blower speed in the first preset strategy, and k represents the target parameter value. The target defogging intensity is determined through the target strategy and the target actuator, thereby controlling the vehicle to automatically defog.

[0098] In the second prediction strategy, the heating power of the heating wire is a preset power, the preset wind speed is determined as the wind speed of the blower, and the expression for the spraying amount of demister agent by the sprayer can be expressed as follows: ; in, This indicates the amount of anti-misting agent sprayed by the sprayer in the second preset strategy. G represents the window temperature, and G represents the average fog concentration in the area.

[0099] Optionally, the preset power is the maximum power; of course, the preset power can be determined according to the actual situation, and no specific limitation is made here.

[0100] Optionally, the preset wind speed can be 3.6 m / s, 3.7 m / s, 3.8 m / s, etc. Preferably, the preset wind speed is 3.8 m / s.

[0101] The above technical solution determines the first preset strategy as the target strategy when the target parameter value is less than the first preset threshold, and determines the second preset strategy as the target strategy when the target parameter value is greater than or equal to the first preset threshold. By combining the regional fog concentration of multiple sub-regions to determine the target parameter value, and then dynamically adjusting the target strategy according to the target parameter value, the accuracy of the target strategy can be improved.

[0102] S330 determines the target defogging intensity based on the target strategy.

[0103] Optionally, the target demisting intensity is used to indicate the demisting intensity of the target actuator. The target actuator includes at least one of a heating wire, a sprayer, and a blower.

[0104] For example, based on the target strategy and the target actuator, the target defogging intensity of the target actuator is determined. The target defogging intensity indicates the strength of defogging the vehicle window; the higher the target defogging intensity, the stronger the defogging effect on the vehicle window. There is a correspondence between the target actuator and the target defogging intensity. Specifically, when the target actuator includes a heating wire, the target defogging intensity includes the heating power; when the target actuator includes a sprayer, the target defogging intensity includes the amount of defogging agent sprayed by the sprayer; when the target actuator includes a blower, the target defogging intensity includes the blowing speed of the blower.

[0105] The above technical solution determines the target defogging intensity of the target actuator through the target strategy and the target actuator. There is a corresponding relationship between the target actuator and the target defogging intensity. When the target strategy is determined, the target defogging intensity of the target actuator can be determined only according to the target strategy, which can reduce the amount of calculation to determine the target defogging intensity and thus improve the efficiency of determining the target defogging intensity.

[0106] In one example, when the target actuator includes at least one of a heating wire, a sprayer, and a blower, the target demisting intensity includes at least one of heating power, the amount of demisting agent sprayed by the sprayer, and the directional airflow speed of the blower. For example, if the target actuator includes a heating wire and a blower, the target demisting intensity includes heating power and directional airflow speed.

[0107] In another example, when the target actuator includes a heating wire, a sprayer, and a blower, the target demisting intensity includes the heating power of the heating wire, the spray volume of the demisting agent sprayed by the sprayer, and the turning speed of the blower. When the target strategy is a first preset strategy, the heating power is determined based on the average fog concentration and the target parameter value, the spray volume is determined based on the average fog concentration and the window temperature, and the turning speed is determined based on the target parameter value. The average fog concentration is used to indicate the average value of the regional fog concentration in multiple sub-regions. When the target strategy is a second preset strategy, the preset power is determined as the heating power, the preset speed is determined as the turning speed, and the spray volume is determined based on the average fog concentration and the window temperature.

[0108] Optionally, when the target strategy is the first preset strategy, the heating power, spray volume and turning wind speed can be determined according to the expression in the first preset strategy in the aforementioned disclosed embodiments, which will not be repeated here.

[0109] Optionally, when the target strategy is to achieve the preset strategy, the heating power, spray volume and turning wind speed can be determined according to the determination method in the second preset strategy in the aforementioned disclosed embodiments, which will not be repeated here.

[0110] In the above technical solution, when the target actuator includes a heating wire, a sprayer, and a blower, the target defogging intensity includes the heating power of the heating wire, the spray volume of the defogging agent sprayed by the sprayer, and the directional wind speed of the blower. By measuring the fog concentration and temperature of the vehicle window, the target actuator for defogging the vehicle window is determined, and the target defogging intensity corresponding to the target actuator is determined. The target defogging intensity can be dynamically determined based on the fog concentration of the vehicle window, thereby ensuring the accuracy of the target defogging intensity.

[0111] S340 controls the vehicle to automatically defog at a target defogging intensity.

[0112] For example, when a target defogging intensity is determined, a target actuator in the vehicle is controlled to automatically defog at the target defogging intensity. For instance, when the target actuator includes a heating wire, the target defogging intensity includes heating power; when the target actuator includes a heating wire and a blower, the target defogging intensity includes heating power and steering airflow speed; when the target actuator includes a heating wire, a sprayer, and a blower, the target defogging intensity includes the heating power of the heating wire, the spray volume of defogging agent sprayed by the sprayer, and the steering airflow speed of the blower.

[0113] The above-mentioned technical solution determines whether to defog the windows based on the fog concentration and temperature of the vehicle windows. When it is determined that the windows should be defogged, a target strategy is determined based on the fog concentration, and a target defogging intensity is determined based on the target strategy for automatic defogging. Compared with defogging with a fixed defogging intensity, this application determines the target strategy based on the fog concentration of the windows, and then dynamically determines the target defogging intensity. This avoids the problems of low defogging efficiency and energy waste caused by a fixed defogging intensity, and achieves the purpose of accurately adjusting the target defogging intensity as needed, thereby improving the defogging efficiency of the windows and ensuring driving safety.

[0114] Figure 4 This is a schematic flowchart of a data synchronization method provided in an embodiment of this application.

[0115] For example, in this application, the vehicle can be vehicle A or vehicle B, and vehicle A and vehicle B are different vehicles.

[0116] For example, the data synchronization method in this embodiment can be applied to a federated server, which includes a cloud server and a global model.

[0117] S410, Vehicle A sends encrypted gradient data to the cloud server.

[0118] For example, vehicle A optimizes the defogging model (Q-learning model) using its own vehicle data, encrypts the optimization gradient to obtain encrypted gradient data, and sends the encrypted gradient data to the cloud server.

[0119] S420, Vehicle B sends encrypted gradient data to the cloud server.

[0120] For example, vehicle B optimizes the defogging model using its own vehicle data, encrypts the optimization gradient to obtain encrypted gradient data, and sends the encrypted gradient data to the cloud server.

[0121] In the S430, the cloud server securely aggregates data from multiple vehicles to obtain fused gradient data.

[0122] For example, when a cloud server receives multiple encrypted gradient data sets, it filters these sets to obtain filtered encrypted gradient data. This filtered data is then securely aggregated to obtain fused gradient data. Since the fused gradient data is obtained through cloud server filtering, it ensures the data is secure. For instance, vehicle data can be filtered based on the reliability of each vehicle among multiple vehicles. Encrypted gradient data with lower reliability is removed, and the more reliable data is securely aggregated to obtain the fused gradient data.

[0123] Optionally, the cloud server does not need to decrypt the encrypted gradient data. Instead, it filters the encrypted gradient data sent by the vehicles based on the reliability of each vehicle and aggregates the filtered data to obtain the fused gradient data, which is then encrypted.

[0124] S440, the cloud server sends fused gradient data to the global model.

[0125] For example, when the cloud server processes multiple encrypted gradient data to obtain fused gradient data, it sends the fused gradient data to the global model to update the policy library.

[0126] S450, the global model updates the policy library based on the fused gradient data to obtain the target policy library.

[0127] For example, upon receiving fused gradient data, the global model updates the policy library based on the fused gradient data to obtain the target policy library. Alternatively, upon receiving fused gradient data, the global model decrypts the fused gradient data to obtain decrypted data, and then updates the policy library using the decrypted data to obtain the target policy library. For instance, based on the decrypted data, the average gradient data of the vehicles is determined, and the data in the policy library is updated using the average gradient data to obtain the target policy library.

[0128] Optionally, if the policy library is a Q-function, then updating the policy library means updating the long-term reward in the Q-function accordingly.

[0129] S460, the global model synchronizes the target policy library with all vehicles.

[0130] For example, when the global model receives the target policy library, it sends the target policy library to all vehicles. Upon receiving the target policy library (Q-function), a vehicle can either use the target policy library directly or perform a weighted processing of the target policy library and its own Q-function to update its respective vehicle's model parameters.

[0131] The above technical solution optimizes the vehicle defogging model through a federated optimization method, which can avoid the problem of insufficient training samples for a single vehicle leading to poor model prediction ability, and thus ensure the prediction effect of the defogging model.

[0132] Figure 5 This is a schematic flowchart of another vehicle control method provided in the embodiments of this application.

[0133] For example, Figure 5 The vehicle control method shown can be executed by the vehicle controller or chip in the vehicle.

[0134] For example, such as Figure 5 As shown, the vehicle control method 500 includes steps S510-S580.

[0135] S510 acquires the global fog concentration, window temperature, and light intensity of the vehicle's windows.

[0136] For example, an infrared camera captures images of the vehicle windows, and the global fog concentration is obtained based on these images. The initial temperature of the windows is obtained using a temperature and humidity sensor; the window tilt angle is obtained using a gravity sensor; and the light intensity is obtained using an ambient light sensor.

[0137] The vehicle window image is segmented to obtain the window region, and the luminance channel of the window region is extracted. The luminance channel represents local brightness and darkness information. Based on the local brightness and darkness information and a fog mask, pixels containing fog are identified. Then, the ratio of fog-containing pixels to the number of pixels in the window region is used to determine the fog percentage of the window, i.e., the initial fog concentration. Further, the initial fog concentration is corrected by light intensity to obtain the global fog concentration. Correcting the initial fog concentration by light intensity avoids interference caused by light intensity in infrared imaging, improving the accuracy of the global fog concentration determination.

[0138] Given the initial temperature and tilt angle of the car window, the initial temperature is corrected using the tilt angle to obtain the final window temperature. Window temperature is positively correlated with window tilt angle; correcting the initial temperature using the tilt angle compensates for temperature distribution deviations caused by gravity tilt, thus ensuring the accuracy of the window temperature readings.

[0139] The S520 divides the windows based on the global fog concentration, resulting in multiple sub-regions of the windows.

[0140] For example, given a determined global fog concentration, the window is divided into multiple sub-regions based on this global fog concentration. Specifically, it is determined whether the global fog concentration is greater than a preset concentration threshold. When the global fog concentration is greater than the preset threshold, a first preset size is determined as the sub-region size, and the window is divided into multiple sub-regions based on this first preset size. When the global fog concentration is less than or equal to the preset concentration threshold, a second preset size is determined as the sub-region size, and the window is divided into multiple sub-regions based on this second preset size. The first preset size is smaller than the second preset size.

[0141] Optionally, the preset concentration threshold can be 40%, 45%, 35%, etc. The preset concentration threshold can be determined according to the actual situation, and no specific limitation is made here.

[0142] For example, the global fog concentration is the percentage of fog on the car window. The system determines whether the global fog concentration is greater than 40% (a preset concentration threshold). When the global fog concentration is greater than 40%, the car window is divided into multiple sub-regions based on a 2cm x 2cm sub-region size (a first preset size). When the global fog concentration is less than or equal to 40%, the car window is divided into multiple sub-regions based on a 5cm x 5cm sub-region size (a second preset size).

[0143] S530 determines the regional fog concentration of each sub-region based on the window image and multiple sub-regions, and obtains the fog concentration gradient based on the regional fog concentration.

[0144] For example, when multiple sub-regions of the vehicle window are determined, the vehicle window image is segmented according to the positions of the multiple sub-regions to obtain sub-region images corresponding to each sub-region, and the fog concentration corresponding to each sub-region is determined respectively. Optionally, the method for determining the regional fog concentration can refer to the method for determining the global fog concentration in the aforementioned disclosed embodiments, and will not be repeated here.

[0145] Given the regional fog concentration in each sub-region, the historical fog concentration and sampling interval of each sub-region at the previous sampling time are obtained. Based on the regional fog concentration, historical fog concentration, and sampling interval of each sub-region, the fog concentration gradient is determined. The fog concentration gradient is used to indicate the rate of change or the degree of change in fog concentration in each sub-region of the vehicle window per unit time.

[0146] S540: Based on the regional fog concentration, window temperature, light intensity, fog concentration gradient and defogging model of each sub-region, determine whether to defog the window; if not, execute S510; if yes, execute S550.

[0147] For example, the defogging model is a reinforcement learning model that can determine whether to defog the vehicle windows. Specifically, the fog concentration of each sub-region, the window temperature, the light intensity, and the fog concentration gradient are input into the defogging model to determine whether a target actuator exists for defogging the vehicle windows. If no target actuator exists, it is determined that defogging is not necessary; if a target actuator exists, it is determined that defogging should be performed. The target actuator includes at least one of a heating wire, a sprayer, and a blower.

[0148] When there is no need to defog the windows, the system continues to acquire the vehicle's global fog concentration, window temperature, and light intensity, and determines whether to defog the windows based on these parameters.

[0149] S550 determines target parameter values ​​based on the regional fog concentration in multiple sub-regions.

[0150] For example, based on the regional fog concentration in multiple sub-regions of the vehicle window, a maximum fog concentration is determined, which indicates the fog concentration with the highest value among the regional fog concentrations; and a target parameter value is determined based on the maximum fog concentration.

[0151] Specifically, based on weather data of the vehicle's location, a fog concentration threshold is determined, which is negatively correlated with the severity of the weather. The maximum fog concentration is selected from the regional fog concentrations of multiple sub-regions, and it is determined whether the maximum fog concentration exceeds the fog concentration threshold. When the maximum fog concentration exceeds the fog concentration threshold, a second preset threshold is set as the target parameter value; when the maximum fog concentration is less than or equal to the fog concentration threshold, a third preset threshold is set as the target parameter value.

[0152] Optionally, the second preset threshold can be determined in accordance with the determination method in the aforementioned disclosed embodiments, and will not be repeated here.

[0153] Optionally, the third preset threshold can be determined in accordance with the determination method in the aforementioned disclosed embodiments, and will not be repeated here.

[0154] S560, determine whether the target parameter value is less than the first preset threshold; if yes, execute S570; if no, execute S580.

[0155] For example, it is determined whether the target parameter value is less than a first preset threshold; when the target parameter value is less than the first preset threshold, the first preset strategy is determined as the target strategy; when the target parameter value is greater than or equal to the first preset threshold, the second preset strategy is determined as the target strategy.

[0156] S570 determines the target defogging intensity based on the first preset strategy and the target actuator.

[0157] For example, based on the first preset strategy and the target actuator, a target defogging intensity of the target actuator is determined. The target defogging intensity indicates the intensity of window defogging; the higher the target defogging intensity, the stronger the defogging effect on the window. There is a correspondence between the target actuator and the target defogging intensity. Specifically, when the target actuator includes a heating wire, the target defogging intensity includes the heating power; when the target actuator includes a sprayer, the target defogging intensity includes the amount of defogging agent sprayed by the sprayer; when the target actuator includes a blower, the target defogging intensity includes the blowing speed of the blower.

[0158] Optionally, the heating power, spray volume, and directional wind speed can be determined according to the expression in the first preset strategy in the aforementioned disclosed embodiments, which will not be elaborated here.

[0159] S580 determines the target defogging intensity based on the second preset strategy and the target actuator.

[0160] For example, based on the second strategy and the target actuator, the target defogging intensity of the target actuator is determined, and there is a corresponding relationship between the target actuator and the target defogging intensity.

[0161] Optionally, the heating power, spray volume, and directional wind speed can be determined according to the second preset strategy in the aforementioned disclosed embodiments, which will not be repeated here.

[0162] S590 controls the vehicle to defog the windows to the target defogging intensity of the target actuator.

[0163] For example, given a target defogging intensity, the vehicle is controlled to defog the windows at the target defogging intensity of the target actuator.

[0164] For example, if the overall fog concentration is 65% and the current rainfall is 0%, and the overall fog concentration is greater than 40%, the car window is divided into 2cm*2cm sub-regions, and the fog concentration in each sub-region is determined. Based on the fog concentration in each sub-region, the window temperature, light intensity, and fog concentration gradient, the defogging mechanism is determined, and the target actuators for defogging are identified as the blower and the spray rate. The spray rate is set to 0.28 m / s, and the blower speed to 3.2 m / s. Controlling the blower speed to 3.2 m / s and the spray rate to 0.28 m / s enables automatic defogging of the car window.

[0165] The aforementioned technical solution divides the vehicle window into multiple sub-regions and determines whether to defog the window by combining the regional fog concentration, window temperature, light intensity, and fog concentration gradient of these sub-regions. This allows for a more precise determination of whether to defog the window, thus ensuring the timeliness of defogging. Furthermore, by dynamically adjusting target parameter values ​​based on rainfall, the solution ensures that the target parameter values ​​are adapted to actual conditions. This allows for dynamic adjustment of the target defogging intensity according to actual needs, improving the efficiency of window defogging and ensuring clear visibility for drivers and passengers, thereby enhancing driving safety.

[0166] It should be understood that the above examples are provided to help those skilled in the art understand the embodiments of this application, and are not intended to limit the embodiments of this application to the specific values ​​or scenarios illustrated. Those skilled in the art can obviously make various equivalent modifications or changes based on the above examples, and such modifications or changes also fall within the scope of the embodiments of this application.

[0167] The above text combined Figures 1 to 5 The vehicle control method provided in the embodiments of this application is described in detail below; the following will be combined with Figure 6 and Figure 7 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.

[0168] Figure 6 This is a schematic diagram of the structure of a vehicle control device provided in an embodiment of this application.

[0169] For example, such as Figure 6 As shown, the vehicle control device 600 includes: Judgment module 610: Used to determine whether to defog the windows based on the fog concentration and window temperature in the vehicle; Determining module 620: When it is determined that the vehicle window should be defogging, it determines the target strategy based on the fog concentration of the vehicle window. The target strategy is used to determine the defogging intensity. Calculation module 630: used to determine the target defogging intensity based on the target strategy; Control module 640: Used to control the vehicle to automatically defog at the target defog intensity.

[0170] Optionally, as an embodiment, the fog concentration includes the regional fog concentration of multiple sub-regions in the vehicle window; the determining module 620 is specifically used for: Based on the regional fog concentration in multiple sub-regions, target parameter values ​​are determined. These target parameter values ​​represent the degree of impact of the regional fog concentration in multiple sub-regions on visibility. Based on these target parameter values, target strategies are then determined.

[0171] Optionally, as an embodiment, the determining module 620 is specifically used for: Determine whether the target parameter value is less than a first preset threshold; when the target parameter value is less than the first preset threshold, the first preset strategy is determined as the target strategy, which is determined based on the target parameter value, the regional fog concentration of multiple sub-regions, and the window temperature; when the target parameter value is greater than or equal to the first preset threshold, the second preset strategy is determined as the target strategy, which is determined based on the preset defogging intensity, the regional fog concentration of multiple sub-regions, and the window temperature.

[0172] Optionally, as an embodiment, the determining module 620 is specifically used for: Based on the regional fog concentration in multiple sub-regions, the maximum fog concentration is determined; based on the maximum fog concentration, the target parameter value is determined.

[0173] Optionally, as an embodiment, the determining module 620 is specifically used for: Based on weather data of the vehicle's location, a fog concentration threshold is determined, which is negatively correlated with the severity of the weather. When the maximum fog concentration is greater than the fog concentration threshold, a second preset threshold is set as the target parameter value. When the maximum fog concentration is less than or equal to the fog concentration threshold, a third preset threshold is set as the target parameter value, which is less than the second preset threshold.

[0174] Optionally, as an embodiment, the target defogging intensity is used to indicate the defogging intensity of the target actuator; the calculation module 630 is specifically used for: Based on the target strategy and the target actuator, the target defogging intensity of the target actuator is determined; there is a corresponding relationship between the target actuator and the target defogging intensity.

[0175] Optionally, as an embodiment, when the target actuator includes a heating wire, a sprayer, and a blower, the target demisting intensity includes the heating power of the heating wire, the spray volume of the demisting agent sprayed by the sprayer, and the directional wind speed of the blower; the calculation module 630 is specifically used for: When the target strategy is the first preset strategy, the heating power is determined based on the average fog concentration and the target parameter value, the spray volume is determined based on the average fog concentration and the window temperature, and the turning wind speed is determined based on the target parameter value. The average fog concentration is used to indicate the average value of the regional fog concentration of multiple sub-areas. When the target strategy is the second preset strategy, the preset power is determined as the heating power, the preset wind speed is determined as the turning wind speed, and the spray volume is determined based on the average fog concentration and the window temperature.

[0176] Optionally, as an embodiment, the fog concentration includes the regional fog concentration of multiple sub-regions in the vehicle window; the determination module 610 is specifically used for: Based on the regional fog concentration in multiple sub-regions, a fog concentration gradient is determined. Based on the regional fog concentration in each sub-region, the window temperature, the light intensity of the window, and the fog concentration gradient, it is determined whether there is a target actuator for defogging the window. If there is no target actuator, it is determined that there is no need to defog the window. If there is a target actuator, it is determined that the window should be defogged.

[0177] Optionally, as an embodiment, the determination module 610 is specifically used for: Obtain the global fog concentration of the vehicle window; when the global fog concentration is greater than a preset concentration threshold, divide the vehicle window into multiple sub-regions based on a first preset size; when the global fog concentration is less than or equal to the preset concentration threshold, divide the vehicle window into multiple sub-regions based on a second preset size, wherein the second preset size is greater than the first preset size.

[0178] It should be noted that the control device 600 of the aforementioned vehicle is embodied in the form of a functional unit. The term "module" here can be implemented in software and / or hardware, without specific limitations.

[0179] 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 circuitry, 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 circuitry, and / or other suitable components that support the described functions.

[0180] 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.

[0181] Figure 7 This is a schematic diagram of the structure of a vehicle provided in an embodiment of this application.

[0182] For example, vehicle 700 and Figure 1 Vehicle 100 in the text refers to the same vehicle.

[0183] For example, such as Figure 7 As shown, the vehicle 700 includes a memory 710 and a processor 720, wherein the memory 710 stores executable program code 730, and the processor 720 is used to call and execute the executable program code 730 to perform a vehicle control method.

[0184] For example, the memory 710 can be used to store related programs of the vehicle control method provided in the embodiments of this application; the processor 720 can call the related programs of the vehicle control method stored in the memory 710 to execute the vehicle control method of the embodiments of this application; for example, based on the fog concentration and window temperature of the vehicle windows, it is determined whether to defog the windows; when it is determined to defog the windows, a target strategy is determined based on the fog concentration of the windows, and the target strategy is used to determine the defogging intensity; based on the target strategy, a target defogging intensity is determined; and the vehicle is controlled to automatically defog at the target defogging intensity.

[0185] 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.

[0186] When each functional module is divided according to its corresponding function, the device may further include a judgment module, a determination module, a calculation module, and a control module. It should be noted that all relevant content regarding the steps involved in the above method embodiments can be referenced from the functional descriptions of the corresponding functional modules, and will not be repeated here.

[0187] It should be understood that the device provided in this embodiment is used to execute the above-described vehicle control method, and therefore can achieve the same effect as the above-described implementation method.

[0188] 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.

[0189] The processing module may be a processor or a controller, which can implement or execute various exemplary logic blocks, modules, and circuits shown 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.

[0190] 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 vehicle control method provided in the above embodiments.

[0191] This application also provides a computer-readable storage medium storing computer program code. When the computer program code is run on a computer, it causes the computer to execute the above-described related method steps to implement a vehicle control method provided in the above embodiments. 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 media or device suitable for storing instructions and / or data.

[0192] This application also provides a computer program product that, when run on a computer, causes the computer to perform the aforementioned related steps to implement a vehicle control method provided in the above embodiments.

[0193] The vehicle, computer-readable storage medium, computer program product or chip provided in this application are all used to execute the corresponding methods provided above. Therefore, the beneficial effects that can be achieved can be referred to the beneficial effects of the corresponding methods provided above, and will not be repeated here.

[0194] 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.

[0195] 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.

[0196] 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: Based on the fog concentration and window temperature in the vehicle, determine whether to defog the windows; When it is determined that the vehicle window should be defogged, a target strategy is determined based on the fog concentration of the vehicle window. The target strategy is used to determine the defogging intensity. Based on the target strategy, the target defogging intensity is determined; The vehicle is controlled to automatically defog at the target defog intensity.

2. The method according to claim 1, characterized in that, The fog concentration includes the regional fog concentration of multiple sub-regions within the vehicle window; determining the target strategy based on the fog concentration of the vehicle window includes: Based on the regional fog concentration of the multiple sub-regions, a target parameter value is determined, wherein the target parameter value is the degree of influence of the regional fog concentration of the multiple sub-regions on the line of sight; The target strategy is determined based on the target parameter value.

3. The method according to claim 2, characterized in that, Determining the target strategy based on the target parameter value includes: Determine whether the target parameter value is less than a first preset threshold; When the target parameter value is less than the first preset threshold, the first preset strategy is determined as the target strategy. The first preset strategy is determined based on the target parameter value, the regional fog concentration of the multiple sub-regions, and the window temperature. When the target parameter value is greater than or equal to the first preset threshold, the second preset strategy is determined as the target strategy. The second preset strategy is determined based on the preset defogging intensity, the regional fog concentration of the multiple sub-regions, and the window temperature.

4. The method according to claim 2, characterized in that, The determination of target parameter values ​​based on the regional fog concentration of the multiple sub-regions includes: The maximum fog concentration is determined based on the regional fog concentration in the multiple sub-regions; The target parameter value is determined based on the maximum fog concentration.

5. The method according to claim 4, characterized in that, Determining the target parameter value based on the maximum fog concentration includes: Based on weather data of the vehicle's location, a fog concentration threshold is determined, which is negatively correlated with the severity of the weather. When the maximum fog concentration is greater than the fog concentration threshold, the second preset threshold is determined as the target parameter value; When the maximum fog concentration is less than or equal to the fog concentration threshold, the third preset threshold is determined as the target parameter value, and the third preset threshold is less than the second preset threshold.

6. The method according to claim 2, characterized in that, The target defogging intensity is used to indicate the defogging intensity of the target actuator; determining the target defogging intensity based on the target strategy includes: Based on the target strategy and the target actuator, the target defogging intensity of the target actuator is determined; There is a corresponding relationship between the target actuator and the target defogging intensity.

7. The method according to claim 6, characterized in that, When the target actuator includes a heating wire, a sprayer, and a blower, the target demisting intensity includes the heating power of the heating wire, the spraying amount of demisting agent sprayed by the sprayer, and the directional wind speed of the blower. The step of determining the target defogging intensity of the target actuator based on the target strategy and the target actuator includes: When the target strategy is the first preset strategy, the heating power is determined based on the average fog concentration and the target parameter value, the spray volume is determined based on the average fog concentration and the window temperature, and the turning wind speed is determined based on the target parameter value. The average fog concentration is used to indicate the average value of the regional fog concentration of the multiple sub-regions. When the target strategy is the second preset strategy, the preset power is determined as the heating power, the preset wind speed is determined as the turning wind speed, and the spraying amount is determined based on the average fog concentration and the window temperature.

8. The method according to claim 1, characterized in that, The fog concentration includes the regional fog concentration of multiple sub-regions within the vehicle window; determining whether to defog the vehicle window based on the fog concentration and window temperature includes: Based on the regional fog concentration in the multiple sub-regions, a fog concentration gradient is determined; Based on the regional fog concentration of each sub-region in the plurality of sub-regions, the window temperature, the light intensity of the window and the fog concentration gradient, it is determined whether there is a target actuator for defogging the window; When the target actuator is not present, it is determined that there is no need to defog the window; When the target actuator is present, it is determined that the window will be defogged.

9. The method according to any one of claims 2 to 8, characterized in that, The method further includes: Obtain the global fog concentration of the vehicle window; When the global fog concentration is greater than a preset concentration threshold, the vehicle window is divided based on a first preset size to obtain the multiple sub-regions; When the global fog concentration is less than or equal to a preset concentration threshold, the window is divided into multiple sub-regions based on a second preset size, wherein the second preset size is greater than the first preset size.

10. A vehicle, characterized in that, The vehicles include: Memory, used to store executable program code; A processor is configured to call and run the executable program code from the memory, causing the vehicle to perform the vehicle control method as described in any one of claims 1 to 9.

Images