A vehicle-mounted HUD dust detection and dust removal method

Abstract

This invention discloses a method for dust detection and removal in a vehicle-mounted head-up display (HUD). After the vehicle is powered on, the air quality is monitored in real time. If the air quality meets the set conditions, the HUD projects a calibration image. The brightness of several feature points on the calibration image is detected, and the brightness of these feature points determines whether the HUD mask needs dust removal. If dust removal is required, a dust removal device is activated to remove the dust, after which the HUD projects normally. If dust removal is not required, the HUD projects normally. This invention can automatically detect and remove dust from the HUD mask, is easy to operate, and will not scratch the surface of the HUD mask.

Description

Technical Field

[0001] This invention belongs to the field of automotive technology, specifically relating to a method for dust detection and removal in a vehicle-mounted HUD. Background Technology

[0002] With the development of automotive intelligence, visual applications are increasingly used in vehicle models, and more and more models are equipped with HUDs (Head-Up Displays). HUDs are located in the recessed area in front of the dashboard, and the HUD cover (dust cover) easily accumulates dust, affecting the image quality. Currently, dust is removed manually using tools, but because the HUD is positioned relatively far forward, manual cleaning is not only inconvenient but also easily scratches the outer surface of the HUD cover, thus affecting the projection effect. Summary of the Invention

[0003] The purpose of this invention is to address the shortcomings of the aforementioned background technology and provide a method for dust detection and removal in vehicle-mounted HUDs.

[0004] The technical solution adopted in this invention is: a method for dust detection and removal in a vehicle-mounted HUD.

[0005] After the vehicle is powered on, the air quality is monitored in real time. If the air quality meets the set conditions, the HUD projection calibration screen is used to detect the brightness of several feature points on the calibration screen. Based on the brightness of several feature points, it is determined whether the HUD mask needs to be cleaned.

[0006] If dust removal is required, the dust removal equipment will be controlled to perform dust removal, and the HUD will project normally after dust removal; if dust removal is not required, the HUD will project normally.

[0007] Furthermore, air quality is detected by collecting PM2.5 concentration data. If the PM2.5 concentration is less than or equal to the set concentration, the air quality is determined to meet the set conditions; otherwise, the air quality does not meet the set conditions.

[0008] Furthermore, if the air quality does not meet the set conditions, the system checks whether this is the first time the vehicle has been powered on that day. If so, it controls the dust removal equipment to work and remove dust. After the dust removal takes three set times, the HUD will project normally. If not, the HUD will project normally.

[0009] Furthermore, the brightness of several feature points on the detection calibration screen includes:

[0010] Based on the standard image being divided into equal parts, several feature points are selected, and the brightness of the feature points is determined according to their RGB values.

[0011] Furthermore, the brightness of the feature points is determined using the following formula:

[0012] L = (Cmax + Cmin) / 2

[0013] Cmax = max(R', G', B')

[0014] Cmin = min(R', G', B'),

[0015] R' = R / R0,

[0016] G' = G / G0,

[0017] B' = B / B0,

[0018] Where L is the brightness of the feature point, Cmax and Cmin are the maximum and minimum values ​​of the three normalized channels, R', G', and B' are the normalized red, green, and blue channels of the feature point, R, G, and B are the red, green, and blue channels of the feature point, and R0, G0, and B0 are the red, green, and blue channels of any point on the standard image.

[0019] Furthermore, the step of determining whether the HUD mask needs dust removal based on the brightness of several feature points includes:

[0020] Compare the brightness of each feature point with the standard brightness value. If the brightness of m feature points satisfies Li ≤ a*L0... n is the number of feature points. If the value is rounded up, it indicates that dust removal is required; otherwise, dust removal is not required. Here, Li is the brightness of the i-th feature point, a is the first proportional coefficient, and L0 is the standard brightness value.

[0021] Furthermore, the dust removal equipment is controlled to perform dust removal operations, including the following steps:

[0022] a. Control the dust removal equipment to operate for the first set time, detect the brightness of several feature points, and proceed to step b or c;

[0023] b. If the brightness of m feature points satisfies Li>b*L0, then the dust removal work is completed and the HUD projects normally.

[0024] c. If the brightness of m feature points satisfies Li≤b*L0, then after the second set time of dust removal operation, the brightness of several feature points will be detected again, and step b or d will be performed.

[0025] d. If the brightness of m feature points satisfies Li≤b*L0, then the alert status position 1 will be set, and the HUD will be projected normally.

[0026] in, n is the number of feature points. This indicates rounding up, where Li is the brightness of the i-th feature point, b is the second proportionality coefficient, and L0 is the standard brightness value.

[0027] Furthermore, before testing the air quality, first check the alert status.

[0028] If the reminder status is set to 1, when the vehicle speed is less than or equal to the set speed, the dust removal equipment will operate for the fourth set time. Then, the HUD will project a calibration image and detect the brightness of several feature points on the calibration image. If the brightness of m feature points satisfies Li ≤ b * L0, a pop-up window will remind the customer that there is dust accumulation on the HUD mask and they should clean it manually. The HUD will then project normally. n is the number of feature points. This indicates rounding up, where Li is the brightness of the i-th feature point, b is the second proportionality coefficient, and L0 is the standard brightness value.

[0029] If the status is 0, then air quality is being checked.

[0030] Furthermore, when the vehicle is powered off, the dust removal equipment is controlled to perform dust removal for the fifth set time.

[0031] Furthermore, the dust removal equipment is a dust removal fan installed inside the HUD, with the air outlet of the dust removal fan facing the surface of the HUD mask.

[0032] The beneficial effects of this invention are as follows:

[0033] This invention, under conditions of good air quality, controls the HUD projection calibration image and detects the brightness of several feature points on the calibration image. Based on the brightness of these feature points, it determines whether there is dust on the HUD mask, and thus whether dust removal is needed. If dust removal is required, a designated dust removal device is used to remove the dust. This method can automatically detect and remove dust from the HUD mask, is not only easy to operate but also will not scratch the surface of the HUD mask. Furthermore, it automatically performs dust detection and removal every time the vehicle is powered on, eliminating the need for manual operation and increasing efficiency. In addition, this invention performs dust detection under good air quality conditions, resulting in more accurate detection results.

[0034] This invention performs dust removal directly when the air quality is poor and the vehicle is powered on for the first time that day, without needing to detect the dust level, thus reducing operational complexity.

[0035] This invention controls the HUD projection calibration image, uses a specific method to determine the brightness of several feature points on the calibration image, and compares the brightness of several feature points with the standard brightness value to determine whether there is dust on the HUD mask. It has the advantages of simple feature point brightness determination method, high reliability, and easy dust detection.

[0036] When the present invention determines that the HUD mask needs dust removal, if the dust removal effect is still not achieved after multiple dust removals, it will set a reminder position and perform dust removal directly after the vehicle is powered on again, and display a pop-up reminder to the customer that "the HUD mask has dust accumulation, please clean it manually", without the need for repeated dust detection. The logic is reasonable and the operation is simpler.

[0037] This invention incorporates a dust removal device inside the HUD. The air vent of the dust removal device is aimed at the surface of the HUD mask, and the dust is automatically removed by electric suction or blowing. The dust removal effect is good and will not damage the surface of the HUD mask. Attached Figure Description

[0038] Figure 1 This is a flowchart of the present invention.

[0039] Figure 2 This is a schematic diagram of the equipment distribution of the dust detection and dust removal system of the present invention.

[0040] Figure 3 This is a schematic diagram showing the distribution of the nine feature points selected in this invention.

[0041] Figure 4 This is a schematic diagram illustrating the principle of the dust detection and removal system of the present invention. Detailed Implementation

[0042] The specific embodiments of the present invention will be further described below with reference to the accompanying drawings. It should be noted that these descriptions are for the purpose of aiding understanding the present invention, but do not constitute a limitation thereof. Furthermore, the technical features involved in the various embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0043] like Figure 1 As shown, this invention provides a method for dust detection and removal in a vehicle-mounted HUD. After the vehicle is powered on, the air quality is monitored in real time. If the air quality meets the set conditions, the HUD projects a calibration image. The brightness of several feature points on the calibration image is detected, and the HUD mask is judged based on the brightness of these feature points to determine whether dust removal is needed (i.e., the degree of dust coverage). If dust removal is needed, the dust removal equipment is controlled to work and remove the dust. After dust removal, the HUD projects normally. If dust removal is not needed, the HUD projects normally. This invention can automatically detect whether there is dust on the HUD mask when the air quality is good, and can automatically remove the dust. It is not only easy to operate, but also will not scratch the surface of the HUD mask. Moreover, it automatically detects and removes dust every time the vehicle is powered on, eliminating the need for manual operation and increasing efficiency.

[0044] It is understandable that the above-mentioned air quality detection is achieved by collecting the concentration of PM2.5 in the air, and the detected concentration is the outdoor PM2.5 concentration, that is, the PM2.5 concentration outside the vehicle. When the PM2.5 concentration is less than or equal to a set concentration, the set concentration can be set at 7.5 μg / m³. 3 If the air quality meets the set conditions (i.e., is relatively good), then dust detection on the HUD mask can be performed. Otherwise, if the air quality is poor, indicating heavy smog or dust, dust detection is not very meaningful, and dust removal or normal projection can be performed directly. PM2.5 concentration can be detected using PM2.5 concentration sensors placed on the vehicle surface.

[0045] It is understandable that air quality can be measured in ways other than just PM2.5 concentration. Other methods can be used, such as measuring nitrogen oxides or sulfide concentrations in the air. Any factor related to air quality can be used to measure air quality.

[0046] like Figure 2 As shown, the components of a HUD system generally include a PGU (Power Container Unit), a convex mirror, a concave mirror, a HUD mask, and a HUD controller. The HUD controller is not shown in the figure. To implement dust detection and removal methods, this invention also includes an ADAS (Advanced Driver Assistance System) camera and dust removal equipment. The ADAS camera is mounted on the rearview mirror and located on its front side for easy image acquisition from the windshield. During normal projection, the HUD controller, PGU, convex mirror, and concave mirror work together to project driving information onto the windshield. When dust detection is required, the HUD controller controls the PGU to emit a calibration image, which is then projected onto the windshield via the convex and concave mirrors. The ADAS camera captures the image (GRB format) corresponding to the calibration image and transmits it to the HUD controller. The HUD controller performs feature point sampling and brightness comparison to determine whether dust removal is necessary. Because of dust on the HUD mask, the standard image projected onto the windshield is not completely consistent with the image captured by the ADAS camera. Therefore, by comparing the brightness of feature points on the captured image, it can be determined whether there is dust on the HUD mask.

[0047] The calibration screen mentioned above is generally a pure white screen, as a pure white screen can better highlight dust on the panel, making it easier to identify dust after brightness measurement. The calibration screen is not limited to a pure white screen; it can also be a solid color screen of other light colors.

[0048] Understandably, after receiving the image corresponding to the calibration screen, the HUD controller collects several feature points from the image. The number of feature points is between 9 and 16, preferably 9, 12, or 16. The selection criterion is to select several feature points based on evenly dividing the standard screen. For example, if 9 feature points are selected, the 9 feature points are evenly distributed on the image in three rows and three columns. Figure 3 As shown, Figure 3 The feature point sizes in the image are for illustrative purposes only. Each row of three feature points evenly divides the image width; that is, the width between the top-left feature point and the left edge of the image, the width between the top-right feature point and the right edge of the image, and the width X between adjacent horizontal feature points are all the same, and each is 1 / 4 of the image width. Similarly, each column of three feature points evenly divides the image height; that is, the width between the top-left feature point and the top edge of the image, the width between the bottom-left feature point and the bottom edge of the image, and the width Y between adjacent vertical feature points are all the same, and each is 1 / 4 of the image height. If 12 feature points are selected, they will be evenly distributed in three rows and four columns on the image; if 16 feature points are selected, they will be evenly distributed in four rows and four columns on the image.

[0049] Understandably, after selecting feature points, the brightness of each feature point is determined based on its RGB values. Taking nine feature points as an example, the brightness levels of the nine feature points are L1, L2, L3, L4…L9, respectively. The specific formula for calculating the brightness of each feature point is as follows:

[0050] L = (Cmax + Cmin) / 2

[0051] Cmax = max(R', G', B')

[0052] Cmin = min(R', G', B'),

[0053] R' = R / R0,

[0054] G' = G / G0,

[0055] B' = B / B0,

[0056] Where L represents the brightness of the feature point, Cmax and Cmin represent the maximum and minimum normalized color values ​​of the three channels, R', G', and B' represent the normalized red, green, and blue channel colors of the feature point, R, G, and B represent the red, green, and blue channel colors of the feature point, and R0, G0, and B0 represent the red, green, and blue channel colors of any point on the standard image. When the standard image is a pure white image, R0, G0, and B0 are each 255.

[0057] After confirming the brightness of several feature points, the brightness of each feature point is compared with the standard brightness value to determine whether the HUD mask needs dust removal. Because the brightness of the projected image varies under different light intensities, meaning the brightness of the feature points also varies, the current light intensity (or ambient brightness) must be detected simultaneously during feature point acquisition and brightness detection. This ensures that the final determined brightness of the feature points corresponds to the current light intensity. When comparing feature point brightness, the standard brightness value for the corresponding light intensity is found based on the current light intensity, and then compared. Detecting the current light intensity can be achieved using a brightness sensor installed on the vehicle.

[0058] The standard luminance values ​​for different light intensities need to be calibrated in advance. The specific steps for calibrating the standard luminance values ​​for different light intensities are as follows:

[0059] 1. Discretely test luminance values ​​under different light intensities: Based on the set total light intensity range, divide the area into multiple sub-ranges. The interval between tests varies depending on the sub-range; smaller intervals for lower light intensities and larger intervals for higher light intensities. For example, to test luminance values ​​in the 10-40000 Lux range, tests are performed every 20 Lux for 10-200 Lux, every 100 Lux for 200-1000 Lux, every 1000 Lux for 1000-20000 Lux, and every 2000 Lux for 20000-40000 Lux. Light intensity is obtained using a light intensity sensor.

[0060] 2. Before testing, check the vehicle HUD mask to ensure it is clean and tidy. Conduct the test according to the standards defined in step 1 (i.e., project a standard image under different light intensities and collect the brightness of several feature points). Take 5-10 photos (images) for each test, calculate the brightness value according to the brightness formula mentioned above, and finally calculate the average brightness of the 5-10 photos as the standard brightness value under the current light intensity.

[0061] 3. Fit the luminance data obtained from steps 1 and 2 under different light intensities to form a curve of luminance versus standard luminance value. In subsequent actual driving, find the current standard luminance value by using the current light intensity (linear interpolation).

[0062] Understandably, after acquiring the brightness of several feature points, a judgment is made based on the set criteria to determine whether the HUD mask needs dust removal. The specific judgment process is as follows:

[0063] The brightness value of each feature point obtained above is compared with the standard brightness value under the corresponding illumination intensity. If the brightness of m feature points meets the first preset condition (i.e., less than or equal to the first set value), it is determined that dust removal is required, indicating that the dust coverage is high. n is the number of feature points. This indicates rounding up. When n is even, n / 2 is an integer; when n is odd, n / 2 has a decimal point and needs to be rounded up. This means the number of feature points meeting the preset conditions must be at least half the number of feature points collected. If the brightness of m feature points does not meet the first preset condition, it is determined that dust removal is not necessary, and the HUD can directly project normally (projecting driving information).

[0064] It is understandable that the first preset condition is set according to actual needs. In this embodiment, the first preset condition is Li≤a*L0, where Li is the brightness of the i-th feature point, a is the first proportional coefficient, which takes a value of 0.8-0.95, preferably 0.9, and L0 is the standard brightness value. That is, if the brightness of m feature points is less than or equal to a*L0, it means that there is dust on the HUD mask and it needs to be cleaned.

[0065] When the brightness of several feature points indicates that the HUD mask needs dust removal, the dust removal process should be carried out according to the following steps:

[0066] a. The HUD controller controls the dust removal equipment to operate for a first set time. The first set time is set according to actual needs. In this embodiment, the first set time can be set to 2-10 minutes, preferably 3 minutes or 5 minutes. After the dust removal equipment operates for 3 minutes, the ADAS camera captures the image corresponding to the projected calibration screen again. The HUD controller detects the brightness of several feature points. If the brightness values ​​of m feature points still meet the second preset condition (i.e., less than or equal to the second set value, and the feature point brightness value differs significantly from the standard brightness value), it is determined that the dust removal does not meet the requirements or the effect is not good, and dust removal needs to be performed again. If the brightness values ​​of m feature points do not meet the second preset condition, it indicates that the dust removal is relatively successful, and normal HUD projection can be performed directly.

[0067] b. If the first dust removal fails to achieve the preset effect, control the dust removal equipment to operate for a second preset time. The second preset time is shorter than the first preset time, generally selected as 10s-2min, preferably 15s, 20s, or 30s. After the second dust removal, the brightness of several feature points is detected in the same way. If m feature points still meet the second preset condition after the second dust removal, it indicates that the second dust removal effect is still unsatisfactory, and stubborn dust on the HUD mask cannot be removed by the dust removal equipment. In this case, dust removal will not be performed again, and normal projection will proceed. At the same time, the reminder status position 1 will be set, and the dust removal work will end until the vehicle is powered off. If the second dust removal achieves the preset effect (i.e., the brightness values ​​of m feature points do not meet the second preset condition, and the feature point brightness values ​​are close to the standard brightness values), then normal projection will proceed directly.

[0068] It is understandable that the second preset condition is different from the first preset condition. Under normal circumstances, the brightness of the feature points will change after the first dust removal. Therefore, the second preset value is set to be greater than the first preset value. That is, the second preset condition is Li≤b*L0, where b is the second proportional coefficient and b>a. The value of b is in the range of 0.9-0.98, and the preferred value is 0.95.

[0069] In some embodiments, since the preset effect cannot be achieved after multiple dust removals, a reminder status position is set. Therefore, after the vehicle is powered on, before the initial air quality test, the reminder status position can be detected to determine if there is stubborn dust on the HUD mask that cannot be cleaned. If reminder status position 1 is detected, it means that there is dust on the HUD mask that cannot be cleaned by the dust removal equipment. At this time, dust removal can be performed directly without further air quality testing, because even if the air quality is tested, the dust removal will still not be effective. Therefore, the detection of the reminder status position can effectively reduce the complexity of dust removal.

[0070] If the detection alert status is set to 1, since the vehicle has just been powered on, its speed will inevitably be less than or equal to the set speed. Therefore, the dust removal equipment will directly perform a dust removal operation for the fourth set time, preferably 15s or 30s. After dust removal, the standard image will be projected again, and the brightness of several feature points will be detected to determine if manual dust cleaning is necessary. If the brightness values ​​of m feature points still meet the second preset condition, it indicates that stubborn dust still exists on the HUD mask. A pop-up reminder will then appear on the vehicle display screen, reminding the customer that "dust has accumulated on the HUD mask; please clean it manually." Alternatively, a special prompt sound can be set to remind the customer.

[0071] If the detection alert status bit is not set to 1 (i.e., set to 0), then proceed directly with air quality detection and dust removal as described above.

[0072] Understandably, since the HUD cannot simultaneously perform feature point brightness detection on the projected standard image after dust removal, normal projection cannot be performed at the same time. Therefore, when there is dust that the dust removal equipment cannot remove (i.e., alert status position 1), and dust removal and projection detection are required, the vehicle speed needs to be controlled within a certain range. That is, the operation can only be performed when the vehicle speed is below a certain value to ensure driving safety. In this embodiment, the set vehicle speed is generally 2-10 km / h, preferably 3 km / h or 5 km / h, to ensure that the dust removal operation is carried out under relatively safe conditions.

[0073] In some embodiments, when the vehicle is powered on and the air quality is detected to be poor, a determination is made as to whether this is the first power-on of the day. If it is the first power-on, the dust removal equipment is directly controlled to perform dust removal for a third set time. After dust removal, the HUD projects normally. This scheme enables automatic dust removal after the vehicle is powered on when the air quality is poor. If it is determined that this is not the first power-on of the day, it means that the HUD mask has already been cleaned of dust before, and therefore, dust removal is not required again, further simplifying the dust removal operation.

[0074] It is understandable that, since it is the first dust removal of the day, the third setting time is generally longer than the second and fourth setting times mentioned above. The third setting time is generally 30 seconds to 5 minutes, preferably 1 minute.

[0075] In some embodiments, a dust removal process can be set to be performed when the vehicle is powered off. The fifth set dust removal time is determined based on actual needs, typically set to 15 seconds or 30 seconds. Performing dust detection when the vehicle is powered on and dust removal once when powered off ensures that the HUD mask remains relatively clean.

[0076] In some embodiments, the dust removal equipment may be a dust removal fan or dust removal blower installed inside the HUD system; the figure is for illustrative purposes only. Because the HUD mask is recessed, multiple through holes can be provided on one or more sides of the mask. The air outlet of the dust removal fan aligns with these through holes, thus aiming at the surface of the HUD mask. The dust removal fan is controlled by the HUD controller, which activates it when dust removal is required, cleaning the dust from the surface of the HUD mask by blowing or suction to keep it clean.

[0077] To achieve the aforementioned dust detection and removal methods, this invention also provides a HUD dust detection and removal system, such as... Figure 4As shown, the system includes a HUD system, dust removal equipment, an ADAS camera, an air quality sensor, and a light intensity sensor. The HUD system comprises a PGU (Power Container Unit), a convex mirror, a concave mirror, a HUD mask, and a HUD controller. During normal projection, the HUD controller controls the PGU to project driving information onto the windshield after passing through the convex and concave mirrors and the HUD mask. During dust detection, the HUD controller controls the PGU to project a standard image onto the windshield after passing through the convex and concave mirrors and the HUD mask. The ADAS camera captures the image corresponding to the standard image, the air quality sensor detects air quality, and the light intensity sensor detects light intensity. The HUD controller uses the image corresponding to the standard image, air quality, and light intensity to determine whether dust removal is needed. If dust removal is required, it controls the dust removal equipment to operate. This system effectively detects and removes dust from the HUD mask, has a simple structure, and is easy to implement.

[0078] It should be understood that the specific order or hierarchy of steps in the disclosed process is an example of an exemplary method. Based on design preferences, it should be understood that the specific order or hierarchy of steps in the process may be rearranged without departing from the scope of this disclosure. The appended method claims provide elements of various steps in an exemplary order and are not intended to limit the scope to the specific order or hierarchy described.

[0079] To make the description of this disclosure more detailed and complete, illustrative descriptions of the embodiments and specific examples of the present invention have been provided above; however, this is not the only form of implementing or utilizing the specific examples of the present invention. The embodiments cover the features of multiple specific examples and the method steps and their order for constructing and operating these specific examples. However, other specific examples may also be used to achieve the same or equivalent functions and order of steps.

[0080] Through the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein can be implemented by software or by combining software with necessary hardware. Therefore, the technical solutions according to the embodiments of this application can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (such as a CD-ROM, USB flash drive, external hard drive, etc.) or on a network, including several instructions to cause a computing device (such as a personal computer, server, touch terminal, or network device, etc.) to execute the method according to the embodiments of this application.

[0081] The above are merely specific embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Contents not described in detail in this specification belong to prior art known to those skilled in the art.

Claims

1. A method for dust detection and removal in a vehicle-mounted HUD, characterized in that: After the vehicle is powered on, the air quality is monitored in real time. If the air quality meets the set conditions, the HUD projection calibration screen is used to detect the brightness of several feature points on the calibration screen. Based on the brightness of several feature points, it is determined whether the HUD mask needs to be cleaned. If dust removal is required, control the dust removal equipment to perform dust removal. After dust removal, the HUD will project normally. If dust removal is not required, the HUD will project normally. Air quality is determined by collecting the concentration of PM2.5 in the air. If the concentration of PM2.5 is less than or equal to the set concentration, the air quality is determined to meet the set conditions; otherwise, the air quality does not meet the set conditions. If the air quality does not meet the set conditions, the system will check whether this is the first time the vehicle has been powered on that day. If so, the system will control the dust removal equipment to work and remove dust. The HUD will be projected normally after the third set time of dust removal. If not, the HUD will be projected normally.

2. The method for dust detection and removal in a vehicle-mounted HUD according to claim 1, characterized in that, The brightness of several feature points on the detection calibration screen includes: Several feature points are selected based on the bisector of the calibration image, and the brightness of the feature points is determined according to their RGB values.

3. The method for dust detection and removal in a vehicle-mounted HUD according to claim 2, characterized in that, The brightness of a feature point is determined using the following formula: L = (Cmax + Cmin) / 2 ， Cmax = max(R',G',B') ， Cmin = min(R',G',B') ， R' = R / R 0， G' = G / G 0， B' = B / B 0， in, L The brightness of the feature points, Cmax, Cmin These represent the maximum and minimum values ​​of the normalized red, green, and blue channels, respectively. R'、G'、B' These represent the colors of the red, green, and blue channels of the normalized feature points. R, G, B These represent the colors of the red, green, and blue channels of the feature points, respectively. R 0 、G 0 、B 0 represents the color of the red, green, and blue channels at any point on the calibration screen.

4. The vehicle-mounted HUD dust detection and removal method according to claim 3, characterized in that, The step of determining whether the HUD mask needs dust removal based on the brightness of several feature points includes: The brightness of each feature point is compared with the standard brightness value. If the brightness of m feature points satisfies Li≤a*L0, m≥⌈n / 2⌉, where n is the number of feature points and ⌈⌉ represents rounding up, then dust removal is required; otherwise, dust removal is not required. Here, Li is the brightness of the i-th feature point, a is the first proportionality coefficient, and L0 is the standard brightness value.

5. The vehicle-mounted HUD dust detection and removal method according to claim 3, characterized in that, The dust removal equipment is controlled to perform dust removal operations, including the following steps: a. Control the dust removal equipment to operate for the first set time, detect the brightness of several feature points, and proceed to step b or c; b. If the brightness of m feature points satisfies Li>b*L0, then the dust removal work is completed and the HUD projects normally. c. If the brightness of m feature points satisfies Li≤b*L0, then after the second set time of dust removal operation, the brightness of several feature points will be detected again, and step b or d will be performed. d. If the brightness of m feature points satisfies Li≤b*L0, then the alert status position 1 will be set, and the HUD will be projected normally. Where m≥⌈n / 2⌉, n is the number of feature points, ⌈ ⌉ indicates rounding up, Li is the brightness of the i-th feature point, b is the second proportionality coefficient, and L0 is the standard brightness value.

6. The vehicle-mounted HUD dust detection and removal method according to claim 5, characterized in that, Before checking the air quality, check the alert status. If the reminder status is set to 1, when the vehicle speed is less than or equal to the set speed, the dust removal equipment will be controlled to work for the fourth set time. Then, the HUD will project a calibration screen and detect the brightness of several feature points on the calibration screen. If the brightness of m feature points satisfies Li≤b*L0, a pop-up window will remind the customer that there is dust accumulation on the HUD mask and that they should clean it manually. The HUD will then project normally. Where m≥⌈n / 2⌉, n is the number of feature points, ⌈ ⌉ means rounding up, Li is the brightness of the i-th feature point, b is the second proportional coefficient, and L0 is the standard brightness value. If the status is 0, then air quality is being checked.

7. The method for dust detection and removal in a vehicle-mounted HUD according to claim 1, characterized in that: When the vehicle is powered off, the dust removal equipment is controlled to operate for the fifth set time for dust removal.

8. The method for dust detection and removal in a vehicle-mounted HUD according to claim 1, characterized in that: The dust removal equipment is a dust removal fan installed inside the HUD, with the fan's air outlet facing the surface of the HUD mask.

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