Vehicle-mounted display screen brightness intelligent adjustment method and device, electronic equipment and storage medium

Abstract

The invention is suitable for the technical field of vehicles, and provides a vehicle-mounted display screen brightness intelligent adjustment method and device, electronic equipment and a storage medium, and the method comprises the steps: obtaining the ambient light intensity of a vehicle; obtaining eye features of the vehicle driver; calculating the visual comfort of the driver according to the ambient light intensity and the eye features; and if the visual comfort level is lower than a preset visual comfort level threshold value, triggering brightness adaptive adjustment of the vehicle-mounted display screen. According to the scheme, a driver does not need to manually adjust the brightness, the brightness of the vehicle-mounted display screen can be intelligently adjusted, and driving safety is ensured while effective display of the display screen is guaranteed.

Description

Technical Field

[0001] The present application relates to the technical field of vehicles, and particularly to an intelligent brightness adjustment method, device, electronic device and storage medium for an in-vehicle display screen. Background Art

[0002] With the continuous improvement of the intelligence level of automobiles, in-vehicle display screens have become an indispensable key part of vehicle central control systems and are widely used in many aspects such as navigation systems, in-vehicle entertainment systems, and vehicle status monitoring. In-vehicle display screens provide drivers with rich information and a convenient operation interface, greatly enhancing the driving experience.

[0003] In actual vehicle driving scenarios, the brightness of in-vehicle display screens plays a crucial role during driving. An overly bright display screen will produce dazzling light at night or in a dimly lit environment, interfering with the driver's line of sight and distracting attention; while an overly dim display screen may cause the driver to have difficulty seeing the screen content, especially in complex road conditions when frequently checking navigation or vehicle status information, it is easy to cause danger due to blocked sight. Appropriate brightness can ensure that the driver clearly obtains information, makes the attention more concentrated, and thus improves driving safety. If the brightness is inappropriate, whether it is too bright or too dim, it will have a negative impact on driving behavior. However, for drivers, manually adjusting the brightness of in-vehicle display screens will pose certain safety hazards.

[0004] In view of this, how to intelligently adjust the brightness of in-vehicle display screens, ensure the effective display of the display screen while ensuring driving safety, is a problem that needs to be considered currently. Summary of the Invention

[0005] Embodiments of the present application provide an intelligent brightness adjustment method, device, electronic device and storage medium for an in-vehicle display screen, which can intelligently adjust the brightness of the in-vehicle display screen without the driver manually adjusting the brightness, ensure the effective display of the display screen while ensuring driving safety.

[0006] In a first aspect, embodiments of the present application provide an intelligent brightness adjustment method for an in-vehicle display screen, including:

[0007] Obtaining the ambient light intensity of the vehicle;

[0008] Obtaining the eye characteristics of the vehicle driver;

[0009] Calculating the visual comfort level of the driver according to the ambient light intensity and the eye characteristics;

[0010] If the visual comfort level is lower than a preset visual comfort level threshold, triggering the adaptive brightness adjustment of the in-vehicle display screen.

[0011] In a possible implementation of the first aspect, the steps for adaptively adjusting the brightness of the in-vehicle display screen include:

[0012] Obtain the predicted brightness of the display screen of the in-vehicle display;

[0013] Calculate a fitness score based on the ambient light intensity, the visual comfort level, and the predicted brightness;

[0014] Obtain the base brightness of the in-vehicle display screen, and determine the target brightness based on the base brightness and the fitness score;

[0015] Adjust the brightness of the in-vehicle display screen based on the target brightness.

[0016] In a possible implementation of the first aspect, the steps for obtaining the predicted brightness of the display screen of the in-vehicle display include:

[0017] Obtain the real-time brightness of the display screen of the in-vehicle display;

[0018] Input the real-time brightness into a pre-trained picture brightness prediction model to obtain the predicted brightness of the display screen of the in-vehicle display;

[0019] Wherein, the picture brightness prediction model is a neural network model for predicting the brightness of a display screen.

[0020] In a possible implementation of the first aspect, the steps for obtaining the base brightness of the in-vehicle display screen include:

[0021] Take the actual display brightness of the in-vehicle display screen obtained by brightness sensors internally embedded at the four corners or the screen edge of the in-vehicle display screen as the base brightness;

[0022] Alternatively, obtain the historical preferred brightness as the base brightness.

[0023] In a possible implementation of the first aspect, the steps for calculating a fitness score based on the ambient light intensity, the visual comfort level, and the predicted brightness include:

[0024] Determine the ambient light score corresponding to the ambient light intensity according to a preset ambient light scoring rule;

[0025] Determine the predicted brightness score corresponding to the predicted brightness according to a preset predicted brightness scoring rule;

[0026] Calculate the fitness score based on the ambient light score and ambient light weight, the visual comfort level and comfort weight, and the predicted brightness score and predicted brightness weight.

[0027] In a possible implementation of the first aspect, the eye features include pupil diameter and blink frequency; the step of calculating the visual comfort of the driver according to the ambient light intensity and the eye features includes:

[0028] Input the ambient light intensity, the pupil diameter, and the blink frequency into a pre-trained Bayesian network model;

[0029] Through the Bayesian network model, determine the posterior probability of the visual comfort according to the corresponding conditional probability table;

[0030] Determine the visual comfort according to the posterior probability.

[0031] In a possible implementation of the first aspect, the step of obtaining the ambient light intensity of the vehicle includes:

[0032] Collect the external ambient light and the internal ambient light of the vehicle;

[0033] Fuse the external ambient light and the internal ambient light, and determine the ambient light intensity according to the fusion result.

[0034] In a second aspect, an embodiment of the present application provides an in-vehicle display brightness intelligent adjustment device, including:

[0035] An ambient light acquisition unit for acquiring the ambient light intensity of the vehicle;

[0036] A feature acquisition unit for acquiring the eye features of the vehicle driver;

[0037] A comfort calculation unit for calculating the visual comfort of the driver according to the ambient light intensity and the eye features;

[0038] A brightness adjustment unit for triggering the adaptive adjustment of the brightness of the in-vehicle display if the visual comfort is lower than a preset visual comfort threshold.

[0039] In a third aspect, an embodiment of the present application provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the in-vehicle display brightness intelligent adjustment method described in the first aspect above is implemented.

[0040] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium. The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the in-vehicle display brightness intelligent adjustment method described in the first aspect above is implemented.

[0041] Fifth aspect, an embodiment of the present application provides a computer program product. When the computer program product runs on an electronic device, it causes the electronic device to execute the intelligent adjustment method for the brightness of the in-vehicle display screen as described in the first aspect above.

[0042] In the embodiment of the present application, by obtaining the ambient light intensity of the vehicle and the eye characteristics of the vehicle driver, and then calculating the visual comfort level of the driver according to the ambient light intensity and the eye characteristics, taking the eye characteristics of the driver into consideration provides a certain basis for personalized brightness adjustment. If the visual comfort level is lower than the preset visual comfort threshold, the brightness adaptive adjustment of the in-vehicle display screen is triggered. The solution of the present application can sense the driver's adaptation degree to the current brightness in real time. When the comfort level is poor, there is no need for the driver to manually adjust the brightness, and the brightness adaptive adjustment is automatically triggered to realize the intelligent adjustment of the brightness of the in-vehicle display screen. Compared with manual adjustment, it avoids the safety hazards brought by manual operation during driving by the driver, ensures the effective display of the display screen while ensuring driving safety. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following will briefly introduce the drawings required for use in the embodiments or the description of the prior art. Obviously, the following drawings are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0044] Figure 1 is the implementation flowchart of the intelligent adjustment method for the brightness of the in-vehicle display screen provided by the embodiment of the present application;

[0045] Figure 2 is a specific implementation flowchart of step S103 in the intelligent adjustment method for the brightness of the in-vehicle display screen provided by the embodiment of the present application;

[0046] Figure 3 is a specific implementation flowchart of the brightness adaptive adjustment of the in-vehicle display screen in the intelligent adjustment method for the brightness of the in-vehicle display screen provided by the embodiment of the present application;

[0047] Figure 4 is a specific implementation flowchart of obtaining the predicted brightness of the display screen of the in-vehicle display screen in the intelligent adjustment method for the brightness of the in-vehicle display screen provided by the embodiment of the present application;

[0048] Figure 5 is a specific implementation flowchart of calculating the fitness score in the intelligent adjustment method for the brightness of the in-vehicle display screen provided by the embodiment of the present application;

[0049] Figure 6 is the structural block diagram of the intelligent adjustment device for the brightness of the in-vehicle display screen provided by the embodiment of the present application;

[0050] Figure 7 It is a schematic diagram of an electronic device provided by an embodiment of the present application. Detailed implementation manners

[0051] In the following description, for the purpose of illustration rather than limitation, specific details such as specific system architectures, technologies, etc. are presented to thoroughly understand the embodiments of the present application. However, those skilled in the art should clearly understand that the present application can also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to avoid unnecessary details from interfering with the description of the present application.

[0052] It should be understood that when used in the specification of the present application and the appended claims, the term "comprising" indicates the presence of the described features, wholes, steps, operations, elements, and / or components, but does not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components, and / or their combinations.

[0053] It should also be understood that the term "and / or" used in the specification of the present application and the appended claims refers to any combination and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0054] As used in the specification of the present application and the appended claims, the term "if" can be interpreted as "when", "once", "in response to determining", or "in response to detecting" according to the context. Similarly, the phrase "if determined" or "if [the described condition or event] is detected" can be interpreted as meaning "once determined", "in response to determining", "once [the described condition or event] is detected", or "in response to detecting [the described condition or event]" according to the context.

[0055] In addition, in the description of the specification of the present application and the appended claims, the terms "first", "second", "third", etc. are only used for distinguishing descriptions and cannot be understood as indicating or implying relative importance.

[0056] References to "one embodiment" or "some embodiments" in the description of this application mean that a particular feature, structure, or characteristic described in connection with the embodiment is included in one or more embodiments of this application. Thus, statements such as "in one embodiment", "in some embodiments", "in other some embodiments", "in still other embodiments", etc. that appear in different places in this specification do not necessarily all refer to the same embodiment, but mean "one or more but not all embodiments", unless otherwise specifically emphasized. The terms "comprising", "including", "having" and their variants all mean "including but not limited to", unless otherwise specifically emphasized.

[0057] By way of example and not limitation, the intelligent brightness adjustment method for in-vehicle displays provided in the embodiments of this application can be applied to electronic devices that need to perform display brightness control for various types of data, and specifically can include in-vehicle display devices, mobile phones, tablet computers, laptop computers, ultra-mobile personal computers (UMPCs), desktop computers, etc. The embodiments of this application do not impose any restrictions on the specific types of electronic devices.

[0058] Figure 1 The implementation process of the intelligent brightness adjustment method for in-vehicle displays provided in the embodiments of this application is shown. This method process includes steps S101 to S104. The specific implementation principles of each step are as follows:

[0059] Step S101: Obtain the ambient light intensity of the vehicle.

[0060] The ambient light intensity is the combined light intensity of the external ambient light and the internal ambient light of the vehicle. In the embodiments of this application, the external ambient light and the internal ambient light of the vehicle are collected, the external ambient light and the internal ambient light are combined, and the ambient light intensity is determined according to the combination result.

[0061] The external ambient light and the internal ambient light of the vehicle are collected by sensors installed at different positions of the vehicle. In the embodiments of this application, the external ambient light and the internal ambient light of the vehicle can be collected according to a specified collection frequency (such as 0.1 second / time). Data is sampled 10 times per second to cope with rapid light changes (such as sudden light changes when entering or exiting a tunnel). The collected external ambient light and internal ambient light can be transmitted to the main control processing unit of the electronic device through communication interfaces such as CAN bus or I 2 C, spi, etc., to ensure low latency and high real-time performance of the transmission.

[0062] The external ambient light sensor can be installed at the front of the vehicle to detect the light intensity of the vehicle's external environment; all external ambient light sensors have a wide-angle detection ability and can cover the light changes in front of the vehicle. The external ambient light sensor can select high-sensitivity optical components, such as photodiodes or linear photoresistors (LDRs), to improve its response ability in complex light environments.

[0063] The internal ambient light sensor can be arranged at a suitable position inside the vehicle, such as above the in-vehicle display or in the middle of the roof, to capture the light intensity of the vehicle's internal ambient light. The internal ambient light sensor can select components with a spectral response range of 400 - 700nm to better capture the visible light changes perceived by the human eye.

[0064] Fuse the collected external ambient light and internal ambient light to determine the final ambient light intensity.

[0065] In some possible implementation manners, the way to fuse the external ambient light and the internal ambient light can be weighted average. Different weights are set according to different light environments and the actual situation of the vehicle. For example, when driving during the day, the weight of the external ambient light is higher, while in scenarios such as at night or when entering a tunnel, the weight of the internal ambient light may be adjusted accordingly. The specific weight setting can be determined according to the historical driving trajectory data of the vehicle.

[0066] In some possible implementation manners, use a Kalman filter to fuse the vehicle's external ambient light and the vehicle's internal ambient light. Initialize the parameters of the Kalman filter, including the initial estimate of the state variable, the covariance matrix, etc. At each sampling moment, input the data collected by the external ambient light sensor and the internal ambient light sensor into the Kalman filter. The Kalman filter predicts the current state using the state transition equation based on the previous state estimate and the current observation data, then calculates the error between the predicted value and the observed value through the observation equation, and then updates the state estimate and the covariance matrix according to the error to obtain the fusion result of the internal and external ambient light intensities. Fusing the external ambient light and the internal ambient light through the Kalman filter can obtain a smoother and more accurate fusion result.

[0067] During the vehicle's driving process, it faces diverse lighting scenarios, such as strong sunlight outdoors on a sunny day, weak light in a tunnel, and the interior lights being on at night. By fusing the internal and external ambient light intensities, these complex lighting changes can be comprehensively captured, accurately reflecting the actual lighting environment where the vehicle is located, providing an accurate basis for adjusting the brightness of the in-vehicle display, and effectively improving the user experience.

[0068] Step S102: Obtain the eye features of the vehicle driver.

[0069] Eye features mainly include pupil diameter and blink frequency. In the embodiments of the present application, image acquisition devices such as sensors or cameras installed inside the vehicle, such as the driver behavior perception camera above the dashboard or the center console, can be used to capture the driver's eye images in real time. This image acquisition device has the ability to capture high-definition images in low-light environments and is not easily affected by ambient light. Through image recognition and analysis techniques, pupil diameter and blink frequency information are extracted from the eye images.

[0070] In some possible implementation manners, the image acquisition device preprocesses the captured eye and face images and then extracts eye features. Among them, the preprocessing includes grayscale conversion and histogram equalization processing to enhance the image contrast. Based on the preprocessed eye images, the eye features of the driver are located. The 68 facial feature point location algorithm in the Dlib open source library can be used to extract the feature points around the driver's eyes (such as the upper and lower eyelids, and the left and right eye corners). The pupil position is calculated by the following formula (1):

[0071]

[0072] where x left represents the x coordinate of the left eye corner, and x right represents the x coordinate of the right eye corner, y top represents the y coordinate of the upper eyelid, and y bottom represents the y coordinate of the lower eyelid. The pupil diameter is determined based on the pupil position.

[0073] In some possible implementation manners, the eye features also include the distance between the upper and lower eyelids. Whether the eyes are closed is judged according to the distance between the upper and lower eyelids. When the distance between the upper and lower eyelids is lower than a preset threshold (for example, 0.2), it is judged that the eyes are in a closed state. Within a specific time window (for example, 60 seconds), the ratio of the eye closing time is calculated: PERCLOS = (T closed / T total ) × 100%. When PERCLOS is higher than 30%, it can be judged that the driver is in a fatigued state.

[0074] The brightness requirements of different drivers in different states may vary. By detecting the fatigued state, a more personalized brightness adjustment strategy can be provided for each driver to improve the user experience. If the system judges that the driver is inattentive or sleepy (such as a high PERCLOS value), the display brightness is appropriately reduced, which can reduce the interference of information display and enable the driver to focus more attention on the road, thereby improving driving safety.

[0075] Step S103: Calculate the visual comfort of the driver according to the ambient light intensity and the eye features.

[0076] Under different ambient light intensities, the driver's demand for the brightness of the in-vehicle display screen also varies. In the embodiments of the present application, by comprehensively considering the ambient light intensity and the driver's eye characteristics to calculate the visual comfort level, it is possible to timely understand the comfort level of the driver when viewing the in-vehicle display screen in the current environment. When the visual comfort level is not good, the brightness of the in-vehicle display screen can be adjusted in a timely manner, enabling the driver to view the screen information more clearly, reducing the obstruction of the line of sight or distraction of attention caused by inappropriate screen brightness, thereby reducing the driving risk and improving driving safety.

[0077] As a possible implementation manner of the present application, Figure 2 Fig. 5 shows a specific implementation process of step S103 in the intelligent adjustment method for the brightness of the in-vehicle display screen provided by the embodiments of the present application, which is described in detail as follows:

[0078] A1: Input the ambient light intensity, the pupil diameter, and the blink frequency into the pre-trained Bayesian network model.

[0079] A2: Determine the posterior probability of the visual comfort level through the Bayesian network model according to the corresponding conditional probability table.

[0080] A3: Determine the visual comfort level according to the posterior probability. The maximum value of the visual comfort level is defined as 1, and the minimum value is -1.

[0081] In the embodiments of the present application, the obtained ambient light intensity, pupil diameter, and blink frequency are input into the pre-trained Bayesian network model. The parent nodes of the Bayesian network model are the ambient light intensity, the pupil diameter, and the blink frequency, and the child node is the visual comfort level. During the pre-training process, a large amount of experimental data is used to collect the visual comfort feedback data of the driver under different combinations of ambient light intensity, pupil diameter, and blink frequency. Based on these data, the conditional probability table corresponding to the Bayesian network model is constructed. The posterior probability of the visual comfort level is calculated through the Bayesian formula, and the calculation formula is as follows:

[0082]

[0083] Among them, P(comfort level|ambient light, pupil diameter, blink frequency) represents the posterior probability of the driver's visual comfort level under the condition of given ambient light, pupil diameter, and blink frequency, and is used to judge whether the current screen brightness meets the driver's visual comfort requirements.

[0084] P(comfort level) represents the prior probability that the driver feels visually comfortable under normal circumstances. This is an initial fixed value, which is statistically obtained based on the driver's preference data or historical data.

[0085] P(Ambient light | Comfort) represents the conditional probability of the current ambient light intensity when the driver feels comfortable. This reflects the possible distribution of ambient light intensity when the driver feels comfortable and can be obtained by statistical analysis based on the driver's preference data or historical data.

[0086] P(Pupil diameter | Comfort) represents the conditional probability of the pupil diameter when the driver feels comfortable. This reflects the possible range of the pupil diameter when the driver is in a visually comfortable state, which may be affected by brightness adaptation and fatigue level, and can be obtained by statistical analysis based on the driver's preference data or historical data.

[0087] P(Blinking frequency | Comfort) represents the conditional probability of the blinking frequency when the driver feels comfortable. This is usually related to the driver's visual burden or fatigue state and is a physiological indicator reflecting the driver's visual comfort level, which can be obtained by statistical analysis based on the driver's preference data or historical data.

[0088] P(Ambient light, Pupil diameter, Blinking frequency) represents the joint probability, which is the probability that a specific ambient light intensity, pupil diameter, and blinking frequency occur simultaneously. This is a normalization term used to ensure that the calculated posterior probability is a valid probability value and can be obtained by statistical analysis based on the driver's preference data or historical data.

[0089] In this embodiment, P(Comfort), P(Ambient light | Comfort), P(Pupil diameter | Comfort), P(Blinking frequency | Comfort), and P(Ambient light, Pupil diameter, Blinking frequency) can be obtained by looking up in the conditional probability table.

[0090] Exemplarily, the current ambient light intensity is 200 lux (dim environment, such as entering a tunnel), the pupil diameter is 6 mm (pupil dilation, dark adaptation), and the blinking frequency is 15 times per minute. The conditional probability table in the Bayesian network is obtained by statistical analysis based on the driver's preference data or historical data. By looking up in the conditional probability table, we get: P(Comfort) = 0.7; P(Ambient light = 200 lux | Comfort) = 0.8; P(Pupil diameter = 6 mm | Comfort) = 0.9; P(Blinking frequency = 15 | Comfort) = 0.6; the joint probability P(Ambient light = 200 lux, Pupil diameter = 6 mm, Blinking frequency = 15) = 0.4, which is the probability that the three conditions of ambient light, pupil diameter, and blinking frequency occur simultaneously; P(Comfort | Ambient light = 200 lux, Pupil diameter = 6 mm, Blinking frequency = 15) = (0.7 * 0.8 * 0.9 * 0.6) / 0.4 = 0.756. That is, the posterior probability of the driver's visual comfort is 0.756, indicating that under the current conditions of ambient light, pupil diameter, and blinking frequency, the comfort level of the driver feeling visually comfortable is 0.756, which is close to 1. This means that the current screen brightness is relatively appropriate and does not require major adjustment.

[0091] In some possible embodiments, corresponding visual comfort levels are set for different posterior probability intervals. For example, it is set that the posterior probability in the interval of 0 - 0.2 corresponds to "extremely uncomfortable", 0.2 - 0.4 corresponds to "uncomfortable", 0.4 - 0.6 corresponds to "average", 0.6 - 0.8 corresponds to "comfortable", and 0.8 - 1 corresponds to "very comfortable". By judging the interval where the posterior probability is located, the visual comfort level of the driver is determined according to the value corresponding to the interval.

[0092] In some possible embodiments, a weight value is assigned to each possible visual comfort state, and then a weighted average calculation is performed based on the posterior probability and these weight values. For example, there are three states of visual comfort: "comfortable", "average", and "uncomfortable", and the corresponding weights are 1, 0.5, and 0 respectively. The posterior probabilities are P 1 , P 2 , P 3 , then the visual comfort level can be calculated according to P 1 ×1 + P 2 ×0.5 + P 3 ×0, and the result obtained can be a continuous value, representing different degrees of visual comfort.

[0093] Step S104: If the visual comfort level is lower than the preset visual comfort threshold, trigger the brightness adaptive adjustment of the in - vehicle display screen.

[0094] If the calculated visual comfort level is lower than the preset visual comfort threshold, trigger the brightness adaptive adjustment of the in - vehicle display screen. The preset visual comfort threshold can be determined based on a large number of experiments and actual driving tests. By evaluating the comfort levels of different drivers in different environments and eye states, a reasonable preset visual comfort threshold can be determined.

[0095] Exemplarily, when the visual comfort level is lower than 0.3, the system determines that the "brightness is too high" or "too low", and triggers the brightness adaptive adjustment of the in - vehicle display screen.

[0096] Figure 3 Shows a specific implementation process of the brightness adaptive adjustment of the in - vehicle display screen in the in - vehicle display screen brightness intelligent adjustment method provided by the embodiment of the present application, which is described in detail as follows:

[0097] Step S201: Obtain the predicted brightness of the display screen of the in - vehicle display.

[0098] As a possible implementation manner of the present application, Figure 4 Shows a specific implementation process of obtaining the predicted brightness of the display screen of the in - vehicle display in the in - vehicle display screen brightness intelligent adjustment method provided by the embodiment of the present application, which is described in detail as follows:

[0099] B1: Obtain the real-time brightness of the display screen of the vehicle-mounted display.

[0100] B2: Input the real-time brightness into the pre-trained display screen brightness prediction model to obtain the predicted brightness of the display screen of the vehicle-mounted display. Wherein, the display screen brightness prediction model is a neural network model for predicting the brightness of the display screen.

[0101] In the embodiments of the present application, the brightness of the real-time collected display screen is input into the display screen brightness prediction model to predict the future brightness change.

[0102] In some possible implementation manners, the display screen brightness prediction model is constructed and trained based on the LSTM model. The display screen brightness prediction model includes an input layer, a hidden layer, and an output layer. When training this model, a large amount of historical display screen brightness data is collected as the training set and the validation set, and the training set and the validation set are divided according to the ratio of 8:2. The historical brightness sequence with a time step of TTT is used as the input (such as the brightness data of 20 frames of historical display screens), the hidden layer uses one or more layers of LSTM units, each layer contains 50-100 neurons, and the brightness data of subsequent historical screens is used as the output for training, and the output layer is trained to predict the brightness information of the next frame or multiple frames of the display screen. The mean square error (MSE) can be used as the loss function during the training process. The Adam optimizer is used, and the learning rate is set to 0.001. When the training and validation meet the training conditions, the pre-trained display screen brightness prediction model is obtained.

[0103] By predicting the brightness of the display screen, the brightness of the display screen can be adjusted in advance to match the ambient light intensity and the visual comfort of the driver. This can avoid problems such as visual fatigue, glare, or unclear vision caused by too high or too low display screen brightness, thereby improving the visual comfort of the driver and reducing the discomfort during driving.

[0104] Step S202: Calculate a fitness score according to the ambient light intensity, the visual comfort, and the predicted brightness.

[0105] As a possible implementation manner of the present application, Figure 5 Illustrates a specific implementation process of calculating a fitness score according to the ambient light intensity, the visual comfort, and the predicted brightness in the vehicle-mounted display screen brightness intelligent adjustment method provided by the embodiments of the present application, which is described in detail as follows:

[0106] C1: Determine the ambient light score corresponding to the ambient light intensity according to a preset ambient light scoring rule. For example, divide the ambient light intensity into different levels, and different scores correspond to different light intensity ranges. For example, a low light environment (0 - 100 lux) corresponds to a lower score, and a high light environment (above 1000 lux) corresponds to a higher score.

[0107] In some possible implementation manners, the ambient light score = 2×(1 - ambient light intensity / maximum light intensity) - 1, where the maximum value of the ambient light score is limited to 1 and the minimum value is -1. The maximum light intensity is an initialized preset value.

[0108] C2: Determine the predicted brightness score corresponding to the predicted brightness according to a preset predicted brightness scoring rule.

[0109] In some possible implementation manners, the predicted brightness score = (predicted brightness - current brightness) / maximum brightness change value. The maximum brightness change value is an initial preset value and can be personalized according to the driver's needs.

[0110] C3: Calculate the fitness score according to the ambient light score and ambient light weight, the visual comfort and comfort weight, and the predicted brightness score and predicted brightness weight.

[0111] Exemplarily, the ambient light weight w1 = 0.5, the comfort weight w2 = 0.3, and the predicted brightness weight w3 = 0.2. The fitness score = w1×ambient light score + w2×visual comfort + w3×predicted brightness score.

[0112] In some possible implementation manners, adjust the ambient light weight, comfort weight, and predicted brightness weight according to the driving environment (such as day, night, tunnel). For example, when driving at night, increase the comfort weight and decrease the ambient light weight.

[0113] Step S203: Obtain the base brightness of the in-vehicle display screen, and determine the target brightness according to the base brightness and the fitness score.

[0114] In the embodiments of the present application, the target brightness = base brightness × (1 + fitness score).

[0115] Among them, the actual display brightness of the in-vehicle display screen obtained by the brightness sensors embedded at the four corners or the screen edge of the in-vehicle display screen can be used as the base brightness. In some possible implementation manners, obtain the historical preferred brightness as the base brightness. The historical preferred brightness is stored in the storage unit of the vehicle system and can be formed by statistical analysis based on the brightness records manually adjusted by the driver in different environments and driving scenarios. For example, according to the driver's previous brightness adjustment history in scenarios such as day, night, and entering a tunnel, summarize the preferred brightness values of the driver in different scenarios.

[0116] Step S204: Adjust the brightness of the in-vehicle display screen based on the target brightness.

[0117] In the embodiment of the present application, through the brightness adjustment interface of the in-vehicle display screen, the target brightness is input as an adjustment instruction to achieve the adjustment of the brightness of the in-vehicle display screen. The brightness adjustment circuit and software interface of the in-vehicle display screen itself can be used to transfer the target brightness value to the corresponding adjustment module, enabling it to directly adjust the brightness of the display screen according to this value without manual adjustment.

[0118] In a possible implementation manner, after performing brightness adaptive adjustment, the eye characteristics of the driver are monitored, and visual discomfort detection is performed based on the eye characteristics to check whether there are situations such as a sharp change in pupil diameter and an increase in blink frequency. If visual discomfort is detected, then according to the detection result, the brightness is reduced or increased according to the specified brightness. Among them, if the current brightness of the in-vehicle display screen is higher than the specified brightness, the brightness of the in-vehicle display screen is reduced to the specified brightness; conversely, if the current brightness of the in-vehicle display screen is lower than the specified brightness, the brightness of the in-vehicle display screen is increased to the specified brightness. The specified brightness can be determined according to the historical preferred brightness in the driving scenario.

[0119] As can be seen from the above, in the embodiment of the present application, by obtaining the ambient light intensity of the vehicle and the eye characteristics of the vehicle driver, and then calculating the visual comfort of the driver according to the ambient light intensity and the eye characteristics, taking the eye characteristics of the driver into consideration provides a certain basis for personalized brightness adjustment. If the visual comfort is lower than the preset visual comfort threshold, then the brightness adaptive adjustment of the in-vehicle display screen is triggered. The solution of the present application can real-time sense the adaptation degree of the driver to the current brightness. When the comfort is poor, without the driver manually adjusting the brightness, the brightness adaptive adjustment is automatically triggered to achieve intelligent adjustment of the brightness of the in-vehicle display screen. Compared with manual adjustment, it avoids the safety hazards brought by manual operation during driving by the driver, ensuring the effective display of the display screen while ensuring driving safety.

[0120] It should be understood that the magnitudes of the sequence numbers of the steps in the above embodiments do not mean the order of execution. The execution order of each process should be determined according to its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of the present application.

[0121] Corresponding to the method for intelligent adjustment of the brightness of the in-vehicle display screen described in the above embodiments, Figure 6 The structural block diagram of the device for intelligent adjustment of the brightness of the in-vehicle display screen provided by the embodiment of the present application is shown. For the sake of convenience of description, only the parts related to the embodiment of the present application are shown.

[0122] Refer to Figure 6, the in-vehicle display brightness intelligent adjustment device includes: an ambient light acquisition unit 61, a feature acquisition unit 62, a comfort level calculation unit 63, and a brightness adjustment unit 64, where:

[0123] The ambient light acquisition unit 61 is used to acquire the ambient light intensity of the vehicle;

[0124] The feature acquisition unit 62 is used to acquire the eye features of the vehicle driver;

[0125] The comfort level calculation unit 63 is used to calculate the visual comfort level of the driver according to the ambient light intensity and the eye features;

[0126] The brightness adjustment unit 64 is used to trigger the adaptive adjustment of the brightness of the in-vehicle display if the visual comfort level is lower than a preset visual comfort level threshold.

[0127] As a possible implementation manner of the present application, the comfort level calculation unit 63 includes:

[0128] A predicted brightness acquisition module, which is used to acquire the predicted brightness of the display screen of the in-vehicle display;

[0129] A fitness score calculation module, which is used to calculate a fitness score according to the ambient light intensity, the visual comfort level, and the predicted brightness;

[0130] A target brightness determination module, which is used to acquire the basic brightness of the in-vehicle display and determine the target brightness according to the basic brightness and the fitness score;

[0131] A brightness adjustment module, which is used to adjust the brightness of the in-vehicle display based on the target brightness.

[0132] As a possible implementation manner of the present application, the predicted brightness acquisition module is specifically used for:

[0133] Acquire the real-time brightness of the display screen of the in-vehicle display;

[0134] Input the real-time brightness into a pre-trained display screen brightness prediction model to obtain the predicted brightness of the display screen of the in-vehicle display;

[0135] Among them, the display screen brightness prediction model is a neural network model for predicting the brightness of the display screen.

[0136] As a possible implementation manner of the present application, the step of acquiring the basic brightness of the in-vehicle display includes:

[0137] Use the actual display brightness of the in-vehicle display obtained by the brightness sensors internally embedded at the four corners or the screen edge of the in-vehicle display as the basic brightness;

[0138] Alternatively, obtain the historical preferred brightness as the base brightness.

[0139] As a possible implementation manner of this application, the fitness score calculation module is specifically configured to:

[0140] Determine the ambient light score corresponding to the ambient light intensity according to a preset ambient light scoring rule;

[0141] Determine the predicted brightness score corresponding to the predicted brightness according to a preset predicted brightness scoring rule;

[0142] Calculate the fitness score according to the ambient light score and ambient light weight, the visual comfort and comfort weight, and the predicted brightness score and predicted brightness weight.

[0143] As a possible implementation manner of this application, the eye feature includes pupil diameter and blink frequency; the comfort calculation unit 63 is specifically configured to:

[0144] Input the ambient light intensity, the pupil diameter, and the blink frequency into a pre-trained Bayesian network model;

[0145] Determine the posterior probability of the visual comfort through the Bayesian network model according to the corresponding conditional probability table;

[0146] Determine the visual comfort according to the posterior probability.

[0147] As a possible implementation manner of this application, the ambient light acquisition unit 61 includes:

[0148] An ambient light collection module, configured to collect the external ambient light and internal ambient light of the vehicle;

[0149] A fusion determination module, configured to fuse the external ambient light and the internal ambient light, and determine the ambient light intensity according to the fusion result.

[0150] As can be seen from the above, in the embodiments of this application, by obtaining the ambient light intensity of the vehicle and the eye features of the vehicle driver, and then calculating the visual comfort of the driver according to the ambient light intensity and the eye features, taking the eye features of the driver into consideration provides a certain basis for personalized brightness adjustment. If the visual comfort is lower than a preset visual comfort threshold, the brightness adaptive adjustment of the in-vehicle display screen is triggered. The solution of this application can sense the adaptation degree of the driver to the current brightness in real time. When the comfort is poor, the brightness adaptive adjustment is automatically triggered without the driver manually adjusting the brightness, realizing the intelligent adjustment of the in-vehicle display screen brightness. Compared with manual adjustment, it avoids the safety hazards brought by manual operation during driving by the driver, ensures the effective display of the display screen while ensuring driving safety.

[0151] It should be noted that for the information interaction, execution process, etc. between the above-mentioned devices / units, since they are based on the same concept as the method embodiments of this application, for their specific functions and the technical effects brought, reference can be specifically made to the method embodiment part, and details will not be elaborated here.

[0152] The embodiments of this application also provide a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, it implements the steps of any one of the vehicle-mounted display brightness intelligent adjustment methods as Figures 1 to 5 represented.

[0153] The embodiments of this application also provide an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps of any one of the vehicle-mounted display brightness intelligent adjustment methods as Figures 1 to 5 represented.

[0154] The embodiments of this application also provide a computer program product. When the computer program product runs on an electronic device, it enables the electronic device to execute and implement the steps of any one of the vehicle-mounted display brightness intelligent adjustment methods as Figures 1 to 5 represented.

[0155] Figure 7 is a schematic diagram of an electronic device provided by an embodiment of this application. As Figure 7 shown, the electronic device 7 of this embodiment includes: a processor 70, a memory 71, and a computer program 72 stored in the memory 71 and executable on the processor 70. When the processor 70 executes the computer program 72, it implements the steps in the above-mentioned various vehicle-mounted display brightness intelligent adjustment method embodiments, such as Figure 1 the steps S101 to S104 shown. Or, when the processor 70 executes the computer program 72, it implements the functions of each module / unit in the above-mentioned device embodiments, such as Figure 6 the functions of the units 61 to 64 shown.

[0156] Exemplarily, the computer program 72 can be divided into one or more modules / units. The one or more modules / units are stored in the memory 71 and executed by the processor 70 to complete this application. The one or more modules / units can be a series of computer-readable instruction segments capable of performing specific functions, and these instruction segments are used to describe the execution process of the computer program 72 in the electronic device 7.

[0157] The electronic device 7 may include, but is not limited to, a processor 70 and a memory 71. Those skilled in the art can understand thatFigure 7 This is only an example of the electronic device 7 and does not constitute a limitation on the electronic device 7. It may include more or fewer components than those shown in the figure, or combine some components, or different components. For example, the electronic device 7 may further include an input / output device, a network access device, a bus, etc.

[0158] The processor 70 may be a central processing unit (CPU), or may also be other general-purpose processors, digital signal processors (DSPs), application specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0159] The memory 71 may be an internal storage unit of the electronic device 7, such as the hard disk or memory of the electronic device 7. The memory 71 may also be an external storage device of the electronic device 7, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc., equipped on the electronic device 7. Further, the memory 71 may also include both the internal storage unit and the external storage device of the electronic device 7. The memory 71 is used to store the computer program and other programs and data required by the electronic device 7. The memory 71 may also be used to temporarily store data that has been output or is to be output.

[0160] It should be noted that for the information interaction, execution process, etc. between the above-mentioned devices / units, since they are based on the same concept as the method embodiment of the present application, for their specific functions and the technical effects brought, please refer to the method embodiment part specifically, and details will not be elaborated here.

[0161] Those skilled in the art can clearly understand that, for the convenience and conciseness of description, only the above division of each functional unit and module is used as an example. In actual applications, the above functions can be allocated to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. Each functional unit and module in the embodiment can be integrated into a processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit. The above integrated unit can be implemented in the form of hardware or in the form of a software functional unit. In addition, the specific names of each functional unit and module are only for the convenience of mutual distinction and do not limit the protection scope of this application. The specific working process of the units and modules in the above system can refer to the corresponding process in the foregoing method embodiment and will not be repeated here.

[0162] If the above integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such an understanding, to implement all or part of the processes in the above method embodiments of this application, a computer program can be used to instruct relevant hardware to complete. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by a processor, the steps of the above method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in the form of source code, object code, executable file or some intermediate form, etc. The computer-readable medium can at least include: any entity or device that can carry the computer program code to the device / terminal device, recording medium, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electrical carrier signal, telecommunication signal, and software distribution medium. For example, a USB flash drive, a mobile hard disk, a magnetic disk or an optical disc, etc. In some jurisdictions, according to legislation and patent practice, the computer-readable medium cannot be an electrical carrier signal and a telecommunication signal.

[0163] In the above embodiments, the descriptions of each embodiment have their own emphases. For the parts not detailed or recorded in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0164] The above-described embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements on some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. A method for intelligently adjusting the brightness of a vehicle display screen, characterized in that: include: Get the vehicle's ambient light intensity; Acquiring eye features of the vehicle driver; Calculating the visual comfort of the driver according to the ambient light intensity and the eye characteristics; If the visual comfort is lower than a preset visual comfort threshold, the brightness of the vehicle display screen is adaptively adjusted.

2. The method according to claim 1, characterized in that The step of adaptively adjusting the brightness of the vehicle-mounted display screen comprises: Get the predicted brightness of the display screen on the vehicle; Calculating a fitness score according to the ambient light intensity, the visual comfort and the predicted brightness; Obtaining a basic brightness of the vehicle-mounted display screen, and determining a target brightness according to the basic brightness and the fitness score; The brightness of the vehicle display screen is adjusted based on the target brightness.

3. The method according to claim 2, characterized in that The step of obtaining the predicted brightness of the display screen of the vehicle-mounted display screen comprises: Get the real-time brightness of the vehicle display screen; Inputting the real-time brightness into a pre-trained picture brightness prediction model to obtain the predicted brightness of the picture displayed on the vehicle display screen; The picture brightness prediction model is a neural network model used to predict the brightness of the displayed picture.

4. The method according to claim 2, characterized in that: The step of obtaining the basic brightness of the vehicle-mounted display screen includes: The actual display brightness of the vehicle display screen obtained by the brightness sensor embedded in the four corners or the edge of the screen of the vehicle display screen is used as the basic brightness; Alternatively, obtain the historical preferred brightness as the base brightness.

5. The method according to claim 2, characterized in that: The step of calculating the fitness score according to the ambient light intensity, the visual comfort and the predicted brightness comprises: Determine the ambient light score corresponding to the ambient light intensity according to a preset ambient light scoring rule; Determining a predicted brightness score corresponding to the predicted brightness according to a preset predicted brightness scoring rule; A fitness score is calculated based on the ambient light score and the ambient light weight, the visual comfort and the comfort weight, and the predicted brightness score and the predicted brightness weight.

6. The method according to any one of claims 1 to 5, characterized in that: The eye features include pupil diameter and blinking frequency; the step of calculating the driver's visual comfort according to the ambient light intensity and the eye features includes: Inputting the ambient light intensity, the pupil diameter and the blinking frequency into the pre-trained Bayesian network model; Determining the posterior probability of the visual comfort by using the Bayesian network model according to the corresponding conditional probability table; The visual comfort is determined according to the posterior probability.

7. The method according to any one of claims 1 to 5, characterized in that: The step of obtaining the ambient light intensity of the vehicle comprises: Collect the external and internal ambient light of the vehicle; The external ambient light is fused with the internal ambient light, and the ambient light intensity is determined according to the fusion result.

8. An intelligent brightness adjustment device for a vehicle display screen, characterized in that: include: An ambient light acquisition unit, used to acquire the ambient light intensity of the vehicle; A feature acquisition unit, used to acquire eye features of the vehicle driver; a comfort calculation unit, configured to calculate the visual comfort of the driver according to the ambient light intensity and the eye characteristics; A brightness adjustment unit is used to trigger adaptive brightness adjustment of the vehicle display screen if the visual comfort level is lower than a preset visual comfort level threshold.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the method for intelligently adjusting the brightness of a vehicle display screen as described in any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the method for intelligently adjusting the brightness of a vehicle display screen as described in any one of claims 1 to 7 is implemented.

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