Dynamic virtual image display distance verification method and system for human-machine interface

Abstract

The present invention provides a method and system for verifying the dynamic virtual image display distance of a human-machine interface. The method includes the following steps: establishing a test background image database; displaying a first test background image in the test background image database by a display component; projecting a first image to be measured onto a superimposing component through a human-machine interface module, wherein the first image to be measured corresponds to a first virtual image display distance to be measured, and the first virtual image display distance to be measured is the same as a first reference virtual image display distance corresponding to the first test background image; acquiring the first test background image and the first image to be measured through an image acquisition module; performing a reliability assessment on the first image to be measured and the first test background image through an identification module; and calculating a superposition rate for the first image to be measured and the first test background image through a processing module to verify the accuracy of the first virtual image display distance to be measured of the human-machine interface.

Description

Technical Field

[0001] The invention relates to a virtual image display distance verification method and system thereof, and in particular to a dynamic virtual image display distance verification method and system thereof for a human-machine interface. Background Art

[0002] Head-up display (HUD) is one of the most commonly used automotive devices by many users. For HUD, the accuracy of the image size at different virtual image display distances is related to the accuracy of the HUD. Current virtual image display distance measurement methods include focusing method, extrapolation method and triangulation method, but none of them can simultaneously meet the requirements of wide lens measurement distance range, fast measurement speed and low equipment requirements.

[0003] The focusing method technically uses the clarity of the acquired image as the basis for judging the distance of the virtual image display. However, the focusing method must use a shallow depth of field device to measure the distance of the virtual image display, so the focusing method has the disadvantage of a narrow lens distance measurement range. Furthermore, since the lens group used in the focusing method needs to adjust the focal length to confirm the focus, the speed of measuring the virtual image distance is reduced. In addition, since the focusing method has high requirements for the ability of the lens to resolve images and the number of lenses, different focal length lenses need to be replaced to correspond to different virtual image display distances, making it difficult to achieve dynamic and continuous measurement. That is, the measurement distance of the focusing method and the lens group need to correspond 1:1, which makes the equipment required to use the focusing method to measure the virtual image display distance of the head-up display also have high requirements.

[0004] The dead reckoning and triangulation methods use the relationship between the device distance and the measurement angle to calculate the image properties as the basis for the virtual image display distance measurement. However, the dead reckoning and triangulation methods have high requirements for the lens's ability to resolve images and the number of lenses, which makes the equipment required to use the dead reckoning and triangulation methods to measure the virtual image display distance of the head-up display higher. In addition, since the accuracy of the dead reckoning method is limited by the error of the image pixels, the accuracy of the dead reckoning method is low.

[0005] Therefore, how to provide a dynamic virtual image display distance verification method and system for a human-machine interface has become a topic that urgently needs to be studied. Summary of the invention

[0006] In view of the above problems, the present invention provides a method for verifying the dynamic virtual image display distance of a human-machine interface, comprising the following steps: establishing a test base map database, where the test base map database includes multiple test base maps, and the multiple test base maps respectively correspond to multiple reference virtual image display distances. Display the first test base map in the test base map database through a display component. Project a first to-be-tested image onto a superimposed image component through a human-machine interface module, where the first to-be-tested image corresponds to a first to-be-tested virtual image display distance, and the first to-be-tested virtual image display distance is the same as the first reference virtual image display distance corresponding to the first test base map. Obtain the first test base map and the first to-be-tested image through an image acquisition module. Perform a reliability assessment on the first to-be-tested image and the first test base map through an identification module. Perform a coincidence rate calculation on the first to-be-tested image and the first test base map through a processing module to verify the accuracy of the first to-be-tested virtual image display distance of the human-machine interface.

[0007] The present invention provides a system for verifying the dynamic virtual image display distance of a human-machine interface, comprising a test base map database, an image acquisition module, an identification module, and a processing module. The test base map database includes at least one test base map displayed at at least one reference virtual image display distance, and the test base map is used as a reference for verifying a to-be-tested image projected by a human-machine interface module of a human-machine interface at a to-be-tested virtual image display distance. The image acquisition module acquires the first test base map in at least one test base map in the test base map database, and acquires a first to-be-tested image projected by a human-machine interface module; wherein the first to-be-tested image corresponds to a first to-be-tested virtual image display distance, and the first to-be-tested virtual image display distance is the same as the first reference virtual image display distance of the first test base map. The identification module performs a reliability assessment on the first to-be-tested image and the first test base map, wherein the identification module performs a reliability assessment on the approximate degree of the sizes of the first to-be-tested image and the first test base map. The identification module performs a reliability assessment on the first to-be-tested image and the first test base map. The processing module performs a coincidence rate calculation on the first to-be-tested image and the first test base map to verify the accuracy of the first to-be-tested image projected by the human-machine interface module according to the first to-be-tested virtual image display distance.

[0008] As described above, the dynamic virtual image display distance verification method and system of the present invention for a human-machine interface pre-selects a corresponding virtual image display distance, establishes a test base map database, and can change the virtual image display distance information according to the dynamic road conditions objects to correspond to scenes at different distances. Through the evaluation of reliability and the judgment of the coincidence rate, different image information is superimposed and directly displayed on the human-machine interface, achieving the effect of quickly identifying images and further improving the stability of the human-machine interface in accuracy verification. In addition, by comparing the image to be measured with the test base map, no additional hardware device needs to be set up, and the virtual image display distance verification of the human-machine interface can be carried out without changing the lens, reducing the test cost, and enabling dynamic continuous measurement, further improving the verification efficiency and achieving the effect of automatic verification of the human-machine interface. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] Figure 1A It is a block diagram of the dynamic virtual image display distance verification system for the human-machine interface of the present invention;

[0010] Figure 1B It is a schematic diagram of the present invention for projecting the image to be measured and the test base map;

[0011] Figure 1C It is a schematic diagram of the present invention for the image acquisition module to measure the sizes of multiple test objects at multiple reference virtual image display distances;

[0012] Figure 2A It is a schematic diagram of the first reliability evaluation of the present invention;

[0013] Figure 2B It is a schematic diagram of the second reliability evaluation of the present invention;

[0014] Figures 3A to 3C It is a schematic diagram of the coincidence rate of the present invention; and

[0015] Figure 4 It is a flowchart of the dynamic virtual image display distance verification method for the human-machine interface of the present invention.

[0016] REFERENCE NUMERALS

[0017] 1: Dynamic virtual image display distance verification system for a human-machine interface

[0018] 11: Test base map database

[0019] 12: Image acquisition module

[0020] 13: Identification module

[0021] 14: Processing module

[0022] 15: Storage module

[0023] S10~S16: Steps

[0024] D: Reference virtual image display distance

[0025] Dn: Reference virtual image display distance

[0026] D1: Reference virtual image display distance

[0027] D2: Reference virtual image display distance

[0028] D3: Reference virtual image display distance

[0029] L: Virtual image display distance to be measured

[0030] L1: First virtual image display distance to be measured

[0031] An: Test object

[0032] A1: Test object

[0033] A2: Test object

[0034] A3: Test object

[0035] P1, P2, P3, P4: Feature vertices

[0036] P5, P6, P7, P8: Feature vertices

[0037] Q1, Q2, Q3, Q4: Vertices

[0038] T: Test base map

[0039] T1: Test base map

[0040] T2: Test base map

[0041] T3: Test base map

[0042] I: Image to be measured

[0043] I1: First image to be measured

[0044] M: Display component

[0045] S: Superimposed image component

[0046] O1: Object to be measured

[0047] O2: Object to be measured

[0048] H: Human - machine interface

[0049] H1: Human - machine interface module Specific implementation manner

[0050] First, it should be noted that the dynamic virtual image display distance verification method and system of the present invention for the human-machine interface can be used to perform factory regulations verification on the human-machine interface before leaving the factory, or alternatively, it can also be used to measure the factory regulations of the human-machine interface after it has left the factory. This is not limited in the present invention. In addition, the dynamic virtual image display distance verification method of the present invention for the human-machine interface takes the human-machine interface as the system to be tested to test and verify the accuracy of the dynamic virtual image display distance of the human-machine interface. In the implementation of the present invention, the human-machine interface includes a head-up display.

[0051] Please refer to Figure 1A and Figure 1B , Figure 1A which is a block diagram of the dynamic virtual image display distance verification system of the present invention for the human-machine interface. Figure 1B is a schematic diagram of the present invention for projecting a first test image according to a first virtual image display distance to be measured and projecting a first test background image according to a first reference virtual image display distance. The dynamic virtual image display distance verification system 1 of the human-machine interface includes a test background image database 11, an image acquisition module 12, an identification module 13, and a processing module 14. The test background image database 11 includes at least one test background image T displayed at at least one reference virtual image display distance D. The test background image T is used as a reference for verifying a first test image I projected by a human-machine interface module H1 of a human-machine interface H at a virtual image display distance L to be measured. As Figure 1B shown, the image acquisition module 12 acquires the first test background image T1 among multiple test background images T in the test background image database 11, and acquires a first test image I1 projected by the human-machine interface module H1, where the first test image I1 corresponds to a first virtual image display distance L1 to be measured, the first test background image T1 corresponds to a first reference virtual image display distance D1, and the first virtual image display distance L1 to be measured is equal to the first reference virtual image display distance D1. The identification module 13 performs a reliability assessment on the degree of size approximation between the first test image I1 and the first test background image T1. The processing module 14 calculates the coincidence rate for the first test image I1 and the first test background image T1 to verify the accuracy of the first test image I1 projected by the human-machine interface module H1 according to the first virtual image display distance L1 to be measured.

[0052] Please refer to Figure 1C, which is a schematic diagram of the present invention's image acquisition module measuring the sizes of multiple test objects corresponding to multiple reference virtual image display distances. The test base map database 11 acquires the first test object A1 in the first test base map T1 corresponding to the first reference virtual image display distance D1 through the image acquisition module 12, acquires the second test object A2 in the second test base map T2 corresponding to the second reference virtual image display distance D2, and acquires the third test object A3 in the third test base map T3 corresponding to the third reference virtual image display distance D3, and establishes the test base map database 11 based on the sizes of the first test object A1, the second test object A2, and the third test object A3. In other words, in this embodiment, it is generated based on the sizes of the test objects actually acquired by the image acquisition module 12 in the test base map database 11.

[0053] Please refer to Figure 1A and Figure 1C , in a first embodiment of the present invention, the dynamic virtual image display distance verification system 1 of the human-machine interface further includes a storage module 15 that stores a look-up table, and the look-up table is established based on the sizes of the first test object A1, the second test object A2, and the third test object A3 actually acquired by the image acquisition module 12.

[0054] In a second embodiment of the present invention, the test base map database 11 acquires the size of the first test object A1 in the first test base map T1 corresponding to the first reference virtual image display distance D1 through the image acquisition module 12, acquires the size of the second test object A2 in the second test base map T2 corresponding to the second reference virtual image display distance D2, and acquires the size of the third test object A3 in the third test base map T3 corresponding to the third reference virtual image display distance D3, and corresponds to and calculates the size ratio relationships of multiple test objects An (n = 1, 2, 3...) at multiple reference virtual image display distances D based on the ratio relationships between the size of the first test object A1 and the first reference virtual image display distance D1, between the size of the second test object A2 and the second reference virtual image display distance D2, and between the size of the third test object A3 and the third reference virtual image display distance D3, and establishes the test base map database 11 based on this ratio relationship.

[0055] It should be noted that, regardless of the above first embodiment or second embodiment, the quantity is only for illustrative purposes and is not used to limit the quantity acquired by the image acquisition module 12 of the present invention, but rather the number of verification executions (quantity acquired) can be based on the requirements for the accuracy of the virtual image display distance.

[0056] Such as Figure 1A and Figure 1BAs shown, the human-machine interface module H1 is disposed in the human-machine interface H. The human-machine interface H includes an optical axis (not shown). After the optical axis is calibrated, the test image I is projected onto the image superposition element S according to the test virtual image display distance L to be measured. Further, since the test image I is projected onto the image superposition element S through the human-machine interface module H1, therefore, before projecting the test image I, the optical axis of the human-machine interface H must be calibrated first to ensure that the test image I can be accurately projected onto the image superposition element S. In an embodiment of the present invention, the image superposition element S includes the windshield of a vehicle or the image superposition film of the human-machine interface H.

[0057] Please refer to Figure 2A and Figure 2B , which are schematic diagrams of the first reliability evaluation and the second reliability evaluation of the present invention. In Figure 2A , the identification module 13 identifies the positions of multiple feature vertices P1, P2, P3, P4 of the first test object O1 in the first test image I1 and the positions of multiple vertices Q1, Q2, Q3, Q4 of the first test object A1 in the first test base map T1, and a plurality of first distance values. And the processing module 14 determines the first reliability of the first test image I1 according to the magnitudes of the plurality of first distance values.

[0058] In Figure 2B , when Figure 2A the first reliability of the first test image I1 is unreliable, the second test image O2 is projected onto the image superposition element S through the human-machine interface module H1, and the second test image I2 and the first test base map T1 are acquired by the image acquisition module 12. And the identification module 13 identifies the positions of multiple feature vertices P5, P6, P7, P8 of the second test object O2 in the second test image I2 and the positions of multiple vertices Q1, Q2, Q3, Q4 of the first test object A1 in the first test base map T1, and a plurality of second distance values. And the processing module 14 determines the second reliability of the second test image T2 according to the plurality of second distance values, where the second test image T2 corresponds to the first test virtual image display distance L1, that is, at the same test virtual image display distance, different test images are re-acquired to perform the reliability evaluation of the test images.

[0059] As described above, in Figure 2A and Figure 2B the methods for the first reliability evaluation and the second reliability evaluation, in addition to using the magnitude of the distance value as the basis for the reliability level, it further includes the processing module 14 determining the magnitude of the gradient between each adjacent two feature vertices of the test object in the test image as the judgment criterion for the reliability level. Further, taking Figure 2AFor example, although the positions of the multiple characteristic vertices P1, P2, P3, P4 of the first object to be measured O1 are close to the positions of the multiple vertices Q1, Q2, Q3, Q4 of the first test object A1 in the first test base map T1, the characteristic vertices P1, P2, P3, P4 may be skewed. Therefore, by judging the gradient magnitude between two adjacent characteristic vertices P1, P2, P3, P4 on the first object to be measured O1, including the gradient in the X-axis direction and the gradient in the Y-axis direction. For example, the X-axis gradient and the Y-axis gradient between the characteristic vertex P1 and the characteristic vertex P2, the X-axis gradient and the Y-axis gradient between the characteristic vertex P1 and the characteristic vertex P3, the X-axis gradient and the Y-axis gradient between the characteristic vertex P2 and the characteristic vertex P4, and the X-axis gradient and the Y-axis gradient between the characteristic vertex P3 and the characteristic vertex P4. If the gradient is smaller, it means that the positions of two adjacent characteristic vertices P1, P2, P3, P4 on the first object to be measured O1 are closer to the shape of a square in the axial direction. Therefore, it can be further judged that the reliability of the first object to be measured O1 is higher; if the gradient is larger, it means that the positions of two adjacent characteristic vertices P1, P2, P3, P4 on the first object to be measured O1 are more deviated from the shape of a square in the axial direction. Therefore, it can be further judged that the reliability of the first object to be measured O1 is lower. In another case, taking Figure 2B as an example, although the positions of the multiple characteristic vertices P5, P6, P7, P8 of the second object to be measured O2 are far from the positions of the multiple vertices Q1, Q2, Q3, Q4 of the first test object A1 in the first test base map T1, by calculating the gradient magnitude between adjacent characteristic vertices P5, P6, P7, P8 and the multiple second distance values between each characteristic vertex P5, P6, P7, P8 and each vertex position Q1, Q2, Q3, Q4, it can be known that since the gradient between adjacent characteristic vertices is 0, it means that the positions of two adjacent characteristic vertices P5, P6, P7, P8 on the second object to be measured O2 are closer to the shape of a square in the axial direction. Therefore, the weight value of the gradient parameter is increased, and the weight value of the distance parameter is decreased, so that in the judgment of the overall reliability parameter of the gradient and the distance value, it can still be further judged that the reliability of the second object to be measured O2 is relatively high.

[0060] Please refer to Figures 3A to 3C , which is a schematic diagram of the superposition rate of the present invention. The superposition rate calculation is to calculate the area superposition ratio of the first object image to be measured I1 and the first test base map T1 by the processing module 14. The area superposition ratio is generated by calculating the overlapping part of the area of the first object to be measured O1 in the first object image to be measured I1 and the area of the first test object A1 in the first test base map T1, and then dividing it by the area of the first test object A1 in the first test base map T1.

[0061] Please refer to Figure 4, which is a flowchart of the method for verifying the dynamic virtual image display distance of the human-machine interface of the present invention. The method for verifying the dynamic virtual image display distance of the human-machine interface includes the following steps. In step S11, a test base map database 11 is established, where the test base map database 11 includes at least one test base map T corresponding to the display at at least one reference virtual image display distance D. In step S12, a display element M is used to display the first test base map T1 among at least one test base map T in the test base map database 11. In step S13, a human-machine interface module H1 projects a first image to be measured I1 onto the superposition element S, where the first image to be measured I1 is correspondingly displayed at the first virtual image display distance L1 to be measured, and the first virtual image display distance L1 to be measured is the same as the first reference virtual image display distance D1 corresponding to the first test base map T1. In step S14, an image acquisition module 12 acquires the first test base map T1 and the first image to be measured I1. In step S15, an identification module 13 performs a first reliability evaluation on the first image to be measured I1 and the first test base map T1. In step S16, a processing module 14 calculates the superposition rate for the first image to be measured I1 and the first test base map T1 to verify the accuracy of the first virtual image display distance L1 to be measured of the human-machine interface H, where the identification module 13 evaluates the reliability based on the approximate degree of the size of the first object to be measured O1 in the first image to be measured I1 and the first test object A1 in the first test base map T1. It should be noted that in the description of the present invention, the so-called "first" is only used to correspondingly illustrate the number of times that the method for verifying the dynamic virtual image display distance of the human-machine interface of the present case can be verified, but it is not limited to this, but can be verified more than once according to actual needs. That is, in the case where the test base map database 11 includes, for example, the first test base map T1, the second test base map T2, and the third test base map T3, 3 verifications can be performed respectively for 3 test base maps and 3 images to be measured to improve the accuracy of verifying the virtual image display distance L to be measured.

[0062] Please refer to again Figure 4 , the method for verifying the dynamic virtual image display distance of the human-machine interface further includes step S10 of calibrating the optical axis of the human-machine interface, so that the human-machine interface module H1 projects the image to be measured onto the superposition element S according to the virtual image display distance to be measured. Further, since in step S13, it is the human-machine interface module H1 that projects the first image to be measured I1 onto the superposition element S, therefore, the optical axis of the human-machine interface H must be calibrated before projecting the first image to be measured I1 to ensure that the first image to be measured I1 can be accurately projected onto the superposition element S. In an embodiment of the present invention, the superposition element S includes the windshield of a vehicle or the superposition film of the human-machine interface H.

[0063] Please refer to again Figure 1C, which is a schematic diagram of the imaging acquisition module of the present invention measuring the sizes of multiple test objects corresponding to multiple virtual image display distances. In step S11 of establishing the test base map database 11, it includes recording and acquiring the sizes of multiple test objects A1, A2, A3 corresponding to multiple reference virtual image display distances D1, D2, D3 through the imaging acquisition module 12, so as to generate multiple test base maps T1, T2, T3 according to the sizes of the multiple test objects A1, A2, A3. In a first embodiment of the present invention, the imaging acquisition module 12 is a photographic device, and through the photographic device, the actual sizes of the test objects A1, A2, A3 at different reference virtual image display distances D1, D2, D3 can be acquired, and the sizes of the test objects A1, A2, A3 are recorded as the test base maps T1, T2, T3. In addition, according to the sizes of the test objects A1, A2, A3 acquired by the imaging acquisition module 12, a lookup table for the test base maps T1, T2, T3 corresponding to different reference virtual image display distances D1, D2, D3 can be further established, and the table can be looked up through an algorithm to improve the calculation accuracy of subsequent steps.

[0064] In a second embodiment of the present invention, step S11 of establishing the test base map database includes acquiring the sizes of the test objects A1, A2, A3 corresponding to the reference virtual image display distances D1, D2, D3 through the imaging acquisition module 12, and the processing module 14 generates multiple test base maps T1, T2, T3 according to the proportional relationship between the sizes of the test objects A1, A2, A3 and the reference virtual image display distances D1, D2, D3. Further, in the first embodiment, the step of establishing the test base map database 11 is actually to record and acquire the actual test objects A1, A2, A3 of the test base maps T1, T2, T3 through the imaging acquisition module 12 at different reference virtual image display distances D1, D2, D3. In the second embodiment, based on the sizes of the test objects A1, A2, A3 recorded and acquired by the imaging acquisition module 12, the sizes of the test objects An (n = 1, 2, 3...) at other reference virtual image display distances Dn (n = 1, 2, 3...) are further deduced according to the proportion, the relationship between the object size and the display distance Dn is established according to this proportion, and the size of the test object An is recorded as the test base map. In other words, since the size of the object image presented is inversely proportional to the distance, the farther the distance, the smaller the object image, and the closer the distance, the larger the object image. Therefore, through this inverse relationship, a relational expression between the sizes of the test objects A1, A2, A3 and the reference virtual image display distances D1, D2, D3 can be established, and the test base map database 11 can be further established. And this relational expression can also be measured for the sizes of the test objects A1, A2, A3 displayed at different reference virtual image display distances D1, D2, D3 in the above manner, so as to verify whether the relational expression established in this embodiment is accurate according to the measured actual values.

[0065] After establishing the test base map database 11 through the above-mentioned step S11, in step S12, the virtual image display distance L to be verified is selected, and the corresponding reference virtual image display distance D is selected corresponding to the virtual image display distance L to be measured. From the test base map database 11, the test base map T corresponding to the reference virtual image display distance D is selected and boxed, and the test base map T is projected onto the display element M. In an embodiment of the present invention, the display element M includes a display screen or a projection screen.

[0066] Please refer to Figure 1B , which is a schematic diagram of projecting the image to be measured and the test base map of the present invention. In step S13 of the human-machine interface module H1 projecting the first image to be measured I1 onto the image superposition element S, it corresponds to step S12 of the display element M displaying the first test base map T1. The first image to be measured I1 and the first test base map T1 are simultaneously acquired by the image acquisition module 12, where the first virtual image display distance L1 is the same as the first reference virtual image display distance D1. In addition, in step 13, the virtual image display distance L to be measured can be selected by the user, and it is not limited to the verification of a single distance, but multiple virtual image display distances L to be measured can be verified according to the user's needs to improve the accuracy of the virtual image display distance projected by the human-machine interface H.

[0067] Please refer to again Figure 2A and Figure 2B, which is a schematic diagram of the first reliability assessment and the second reliability assessment of the present invention. In step S15 of the first reliability assessment, it includes identifying the positions of multiple feature vertices P1, P2, P3, P4 of the first object to be measured O1 in the first image to be measured I1 selected by the identification module 13, so as to determine whether the first object to be measured O1 in the first image to be measured I1 is square. In the verification method of the present invention, the processing module 14 calculates the conformity degree between the size of the first object to be measured O1 in the first image to be measured I1 and the size of the first test object A1 in the first test base map T1 corresponding to the first reference virtual image distance D1, and further verifies whether the size of the first image to be measured I1 projected by the human-machine interface H according to the first virtual image display distance L1 is accurate. Therefore, in step 15, it is necessary to first confirm the first reliability of the first image to be measured I1, which is to identify whether the image of the first object to be measured O1 selected in the first image to be measured I1 by the identification module 13 is square, so as to facilitate subsequent verification steps by comparing the first object to be measured O1 with the first test object A1 in the first test base map T1. Further, since the first test object A1 is square in shape, therefore, in step 15, the processing module 14 calculates the positions of the four feature vertices P1, P2, P3, P4 of the first object to be measured O1 corresponding to the four vertices Q1, Q2, Q3, Q4 of the first test object A1 in the first test base map T1 to generate multiple first distance values, and further determines whether the first object to be measured O1 is square. The calculation method is to calculate according to the first distance difference values between the positions of each feature vertex P1, P2, P3, P4 of the first object to be measured O1 and the positions of each vertex Q1, Q2, Q3, Q4 of the first test object A1 in the first test base map T1 respectively. The actual calculation is carried out by using the calculation formula of the distance between two points in the space coordinate in basic mathematics, that is, calculating the first distance difference values of P1-Q1, P2-Q2, P3-Q3 and P4-Q4. If the calculated first distance difference values between the vertices are smaller, it means that the first object to be measured O1 selected in the first image to be measured I1 conforms more to the square shape of the first test object A1, and the reliability of the first image to be measured I1 is higher. If the calculated first distance values are larger, it means that the first object to be measured O1 selected in the first image to be measured I1 does not conform to the square shape, that is, the reliability of the first image to be measured I1 is lower.

[0068] Please refer to Figure 2B, which is a schematic diagram of the second reliability evaluation of the present invention. In step S15 of the first reliability evaluation, it further includes step S151 of adjusting the step of obtaining the image I to be measured. When it is determined that the first object to be measured O1 is not approximately square, it indicates that the first reliability of the first image I1 to be measured is unreliable, and then the second reliability evaluation is performed. The human-machine interface module H1 projects the second image I2 to be measured onto the display element M, and the image acquisition module 12 readsjusts the positions of the four characteristic vertices P5, P6, P7, P8 of the second object O2 to be measured in the second image I2 to be measured and the positions of the four vertices Q1, Q2, Q3, Q4 of the first test object A1 in the first test base map T1. Then, the identification module 13 identifies the positions of the four characteristic vertices P5, P6, P7, P8 of the second object O2 to be measured framed in the second image I2 to be measured and the positions of the four vertices Q1, Q2, Q3, Q4 of the first test object A1 in the first test base map T1. And the processing module 14 calculates the multiple second distance values between the positions of the characteristic vertices P5, P6, P7, P8 of the second object O2 to be measured in the second image I2 to be measured and the multiple vertices Q1, Q2, Q3, Q4 of the first test object A1 in the first test base map T1 to determine whether the second object O2 to be measured is square. That is, when the identification module 13 identifies Figure 2A that the first object O1 to be measured in the first image I1 to be measured is not square, it may indicate that the error in obtaining the characteristic vertices P1, P2, P3, P4 of the first object O1 to be measured is relatively large, so it is necessary to re-obtain the characteristic vertices of the object. In addition, the acquisition of the characteristic vertices P1, P2, P3, P4 of the first object O1 to be measured in the first image I1 to be measured is automatically obtained by an image recognition algorithm to adjust the accuracy of the reliability evaluation. Furthermore, readjusting the system environment or the settings of the hardware, such as adjusting the configuration of the ambient light and the configuration of the position of the image acquisition module 12, etc., also helps to improve the accuracy of the reliability evaluation. In addition. In the step of reliability evaluation in step S15, it includes performing image performance measurement on the image I to be measured, including field curvature calculation, sharpness calculation, and chroma calculation, to further improve the accuracy of verifying the virtual image display distance L to be measured. Furthermore, the identification and acquisition of the characteristic vertices P1, P2, P3, P4 of the first object O1 to be measured in the first image I1 to be measured are determined according to the difference in pixel gray scale values between the first image I1 to be measured and the background. Those skilled in the art should understand the principle, which will not be elaborated here.

[0069] Please refer to Figures 3A to 3C, which is a schematic diagram for calculating the superposition rate of the present invention. In step S16, the processing module 14 calculates the superposition rate for the first image I1 to be measured and the first test base map T1. The calculation of the superposition rate is determined by the processing module 14 calculating the area superposition ratio of the first object O1 to be measured in the first image I1 to be measured and the first test object A1 in the first test base map T1. The lower the superposition rate, the greater the error between the display distance L1 of the first virtual image to be measured of the human-machine interface and the first reference virtual image display distance D1 (target value). Conversely, it means that the display distance L1 of the first virtual image to be measured is more accurate. As Figure 3A shown, only a part of the first image I1 to be measured overlaps with the first test base map T1, indicating a lower superposition rate. As Figure 3B shown, most of the first image I1 to be measured overlaps with the first test base map T1, indicating a higher superposition rate. As Figure 3C shown, the first image I1 to be measured almost completely overlaps with the first test base map T1, indicating the best superposition rate. The calculation formula for the superposition rate is as follows:

[0070] Superposition rate = Overlapping part of the area of the object to be measured in the image to be measured and the area of the test object in the test base map / Area of the test object

[0071] In step S16, the processing module 14 generates a verification result for the display distance L of the virtual image to be measured of the human-machine interface H according to the calculation result of the superposition rate to verify the accuracy of the display distance L of the virtual image to be measured of the human-machine interface H. For the verification result, for example, if the area superposition rate of the object in the image I to be measured and the object in the test base map T is 100%, it means that at the display distance L of the virtual image to be measured, for example, the display distance L of 10 meters, the credibility of the image I to be measured projected by the human-machine interface H (system to be measured) is 100%. If the area superposition rate of the object in the image I to be measured and the object in the test base map is 50%, it means that at the display distance L of the virtual image to be measured, the credibility of the image to be measured projected by the human-machine interface is 50%.

[0072] As mentioned above, whether it is the reliability evaluation in step S15 or the superposition rate calculation in step S16, the magnitude of the tolerance error calculated depends on the user's accuracy requirements for the human-machine interface H. That is, if the user requires a high accuracy of the virtual image display distance L for the human-machine interface H, then the reliability evaluation and the verification result of the superposition rate must have a small tolerance error. If the user does not require a high accuracy of the virtual image display distance L for the human-machine interface H, then a larger error can be tolerated for the reliability evaluation and the verification result of the superposition rate, and the tolerance error value is not limited in the present invention.

[0073] In addition, the dynamic virtual image display distance verification method of the present invention's human-machine interface is not limited to verification at the local end (the processing module of the dynamic virtual image display distance verification system of the human-machine interface), but the to-be-tested image I and the test base map T obtained by the image acquisition module 12 can also be transmitted to the cloud server for calculation together.

[0074] In summary, the dynamic virtual image display distance verification method and system of the present invention's human-machine interface pre-select the corresponding virtual image display distance, establish a test base map database, and can change the virtual image display distance information according to the dynamic road conditions objects to correspond to the scenery beyond different distances. By evaluating the reliability and judging the coincidence rate, different image information is superimposed and directly displayed on the human-machine interface, which can achieve the effect of quickly identifying images and further improve the stability of accuracy verification. In addition, by comparing the to-be-tested image with the test base map, no additional hardware device needs to be set up, and the virtual image display distance verification can be carried out without changing the lens, which can reduce the test cost, and can perform dynamic continuous measurement, further improving the verification efficiency and achieving the effect of automatic verification.

Claims

1. A method for verifying the display distance of a dynamic virtual image of a human-machine interface, characterized in that, It includes the following steps: Establish a test base map database; wherein the test base map database includes at least one test base map respectively displayed at at least one reference virtual image display distance; Display a first test base map among the at least one test base map by a display component; Project a first image to be measured onto a superimposing element through a human-machine interface module; wherein the first image to be measured corresponds to a first virtual image display distance to be measured, and the first virtual image display distance to be measured is the same as a first reference virtual image display distance of the first test base map; Obtain the first test base map and the first image to be measured through an image acquisition module; Perform a first reliability evaluation of the size approximation degree for the first image to be measured and the first test base map by an identification module; and Perform a first superposition rate calculation for the first image to be measured and the first test base map by a processing module to verify the accuracy of the first virtual image display distance to be measured of the human-machine interface module.

2. The dynamic virtual image display distance verification method for the human-machine interface according to claim 1, wherein The step of establishing the test base map database includes: obtaining a second test base map corresponding to a second reference virtual image display distance through the image acquisition module, and generating multiple test base maps according to the first test base map and the second test base map.

3. The method for verifying the dynamic virtual image display distance of the human-machine interface according to claim 2, wherein, The step of the first reliability evaluation includes: storing a look-up table through a storage module; wherein the look-up table is established according to the sizes of a first test object in the first test base map obtained by the image acquisition module and a second test object in the second test base map.

4. The dynamic virtual image display distance verification method for the human-machine interface according to claim 2, characterized in that The step of establishing the test base map database includes: obtaining a first test object of the first test base map corresponding to the first reference virtual image display distance through the image acquisition module, and obtaining a second test object of the second test base map corresponding to the second reference virtual image display distance, and generating a size ratio relationship of multiple test objects corresponding to multiple reference virtual image display distances according to between the first test object and the first reference virtual image display distance and according to between the second test object and the second reference virtual image display distance.

5. The method for verifying the dynamic virtual image display distance of the human-machine interface according to claim 1, characterized in that, It further includes: the step of correcting an optical axis of the human-machine interface, so that the human-machine interface module projects the image to be measured onto the superimposing element according to the first virtual image display distance to be measured.

6. The method for verifying the dynamic virtual image display distance of the human-machine interface according to claim 1, characterized in that, The step of the first reliability evaluation includes: identifying multiple first distance values of multiple characteristic vertex positions of a first object to be measured in the image to be measured and multiple vertex positions of a first test object in the test base map by the identification module, and judging the first reliability of the first image to be measured by the processing module according to the multiple first distance values.

7. The method for verifying the dynamic virtual image display distance of the human-machine interface according to claim 6, characterized in that, The steps of the first reliability evaluation further include: when the first reliability of the first image to be measured is unreliable, projecting a second image to be measured onto the image superposition element through the human-machine interface module, acquiring the second image to be measured and a second test background image through the image acquisition module, and identifying, through the identification module, a plurality of feature vertex positions of a second object to be measured in the second image to be measured and a plurality of second distance values of a plurality of vertex positions of a second test object in the second test background image, and judging, through the processing module, a second reliability of the second image to be measured according to the plurality of second distance values, wherein the second image to be measured corresponds to the first virtual image display distance to be measured.

8. The method for verifying the dynamic virtual image display distance of the human-machine interface according to claim 1, characterized in that, The steps of the first superposition rate calculation further include: calculating, through the processing module, an area superposition ratio of the first image to be measured and the first test background image.

9. The method for verifying the dynamic virtual image display distance of the human-machine interface according to claim 8, characterized in that, The area superposition ratio is generated by calculating a superposition part of an area of a first object to be measured in the first image to be measured and an area of a first test object in the first test background image and dividing the area of the first test object in the first test background image.

10. The dynamic virtual image display distance verification method for a human-machine interface according to claim 6, wherein The steps of the first reliability evaluation include: further calculating, through the processing module, a plurality of first axial gradient values and a plurality of second axial gradient values for adjacent feature vertices of the first object to be measured in the first image to be measured, and performing the first reliability evaluation according to the plurality of first axial gradient values and the plurality of second axial gradient values, wherein when the plurality of first axial gradient values and the plurality of second axial gradient values are smaller, and the plurality of first distance values of the plurality of vertex positions are smaller, the processing module judges that the first image to be measured has a high first reliability.

11. A dynamic virtual image display distance verification system for a human-machine interface, characterized in that, Include: A test background image database, including a plurality of test background images respectively displayed at a plurality of reference virtual image display distances; An image acquisition module, acquiring a first test background image in the test background image database and acquiring a first image to be measured projected by a human-machine interface module; wherein the first image to be measured corresponds to a first virtual image display distance to be measured, and the first virtual image display distance to be measured is the same as a first reference virtual image display distance of the first test background image; An identification module, performing a first reliability evaluation on the first image to be measured and the first test background image; and A processing module, performing a superposition rate calculation on the first image to be measured and the first test background image to verify an accuracy of the first virtual image display distance of the human-machine interface module; Wherein the identification module performs the first reliability evaluation on a size approximation degree of the first image to be measured and the first test background image.

12. The dynamic virtual image display distance verification system for the human-machine interface according to claim 11, wherein, The test background image database acquires a second test background image corresponding to a second reference virtual image display distance through the image acquisition module, and generates the plurality of test background images according to the first test background image and the second test background image.

13. The dynamic virtual image display distance verification system for the human-machine interface according to claim 12, characterized in that, The invention further comprises a storage module for storing a lookup table, wherein the lookup table is established according to the sizes of a first test object in the first test base map and a second test object in the second test base map acquired by the image acquisition module.

14. The dynamic virtual image display distance verification system for a human-machine interface according to claim 12, characterized in that, The test base map database obtains a first test object corresponding to the first test base map at the first reference virtual image display distance, and obtains a second test object corresponding to the second test base map at the second reference virtual image display distance through the image acquisition module, and generates a size ratio relationship corresponding to multiple test objects at multiple reference virtual image display distances based on the relationship between the first test object and the first reference virtual image display distance and based on the relationship between the second test object and the second reference virtual image display distance.

15. The dynamic virtual image display distance verification system for the human-machine interface according to claim 11, wherein The human-machine interface module is disposed in a human-machine interface, and the human-machine interface includes an optical axis. After the optical axis is calibrated, the first image to be measured is projected onto a stacking element according to the first virtual image display distance to be measured.

16. The dynamic virtual image display distance verification system for a human-machine interface according to claim 11, wherein The recognition module identifies multiple first distance values ​​between multiple feature vertex positions of a first object to be tested in the first image to be tested and multiple vertex positions of a first object to be tested in the first test base map, and uses the processing module to determine the first reliability of the first image to be tested based on the multiple first distance values.

17. The dynamic virtual image display distance verification system for the human-machine interface according to claim 16, wherein When the first reliability of the first image to be tested is unreliable, a second image to be tested is projected onto the stacking element by the human-machine interface module, and the second image to be tested and a second test base map are acquired by the image acquisition module, and a plurality of second distance values ​​between a plurality of feature vertex positions of a second object to be tested in the second image to be tested and a plurality of vertex positions of a second object to be tested in the second test base map are identified by the recognition module, and a second reliability of the second image to be tested is determined by the processing module according to the plurality of second distance values, wherein the second image to be tested corresponds to the first virtual image display distance to be tested.

18. The dynamic virtual image display distance verification system for a human-machine interface according to claim 11, characterized in that, The overlap ratio calculation is performed by calculating, by the processing module, an overlap ratio of the first image to be tested and the first test background image.

19. The dynamic virtual image display distance verification system for the human-machine interface according to claim 18, characterized in that, The area overlap ratio is generated by calculating an overlapped portion of an area of ​​a first test object in the first test image and an area of ​​a first test object in the first test background image and dividing it by the area of ​​the first test object in the first test background image.

20. The dynamic virtual image display distance verification system for a human-machine interface according to claim 16, wherein The processing module further calculates multiple first axial gradient values ​​and multiple second axial gradient values ​​for adjacent feature vertices of the first object to be tested in the first image to be tested, and performs the first reliability evaluation based on the multiple first axial gradient values ​​and the multiple second axial gradient values, wherein when the multiple first axial gradient values ​​and the multiple second axial gradient values ​​are smaller and the multiple first distance values ​​of the multiple vertex positions are smaller, the processing module judges that the first image to be tested has a high first reliability.

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