Vehicle lamp intelligent imaging optimization method and system suitable for complex environment and storage medium

Abstract

The invention discloses a vehicle lamp intelligent imaging optimization method and system suitable for a complex environment and a storage medium. The method comprises the steps that LED array parameters are initialized according to requirements of a vehicle lamp projection system; calculating a projection objective field angle according to the vehicle lamp mounting position and the projection range; calculating projection parameters according to the LED array parameters; calculating technical parameters of front and rear groups of the projection objective according to the field angle of the projection objective and the projection parameters; selecting vehicle lamp projection objective system initial structures with similar structures according to the technical parameters of the front and rear projection objective groups; according to the object plane inclination angle and / or the lens curvature radius and / or the thickness parameter, optimal configuration is carried out on the initial structure of the vehicle lamp projection objective lens system to obtain an optimized optical system structure; and obtaining a vehicle lamp imaging image suitable for a complex environment according to the optimized optical system structure. The problem that the imaging quality of the vehicle lamp is declined in a cliff mode when the optical axis is inclined in a complex environment is solved.

Description

Technical Field

[0001] The present invention belongs to the technical field of vehicle lamps, and in particular relates to a vehicle lamp intelligent imaging optimization method, system and storage medium suitable for complex environments. Background Art

[0002] In recent years, with the rapid development of automotive electronic technology, intelligent headlight systems have gradually become popular. These systems can adjust lighting parameters in real time through sensors and processors to cope with complex and changing driving environments. Although the imaging functions of existing intelligent headlight systems are becoming more and more abundant, the imaging quality still needs to be improved.

[0003] In order to improve the image quality, the current solution is to use high-resolution and high-brightness LED modules or use more LED modules. Although this solution can effectively improve the image quality in general scenarios, when facing more complex environmental scenarios, especially when the optical axis tilts in certain scenarios, the image quality of the headlights of these solutions will drop drastically, affecting the user experience of smart headlights.

[0004] In order to solve the problem that the imaging quality of existing intelligent headlights in complex environments drops drastically when the optical axis is tilted, an intelligent imaging optimization method, system and storage medium for headlights in complex environments are proposed. Summary of the invention

[0005] The present invention proposes an intelligent imaging optimization device, method, system and storage medium for vehicle lights suitable for complex environments, so as to at least solve the problem in the related art that the imaging quality of vehicle lights in complex environments drops drastically when the optical axis is tilted.

[0006] According to an embodiment of the present invention, a vehicle headlight intelligent imaging optimization method applicable to complex environments is provided, comprising the steps of:

[0007] Initialize LED array parameters according to the requirements of the car light projection system;

[0008] Calculate the field of view angle of the projection lens according to the installation position and projection range of the headlight;

[0009] Calculate projection parameters according to LED array parameters;

[0010] Calculate the technical parameters of the front and rear groups of the projection objective lens according to the projection objective lens field angle and projection parameters;

[0011] According to the technical parameters of the front and rear groups of the projection lens, the initial structure of the headlight projection lens system with similar structure is selected;

[0012] The initial structure of the vehicle lamp projection lens system is optimally configured according to the object plane inclination angle and / or the lens curvature radius and / or thickness parameters to obtain an optimized optical system structure;

[0013] According to the optimized optical system structure, the headlight imaging image suitable for complex environments is obtained.

[0014] Optionally, the initializing the LED array parameters according to the requirements of the vehicle light projection system comprises the steps of:

[0015] Calculate the basic size parameters of the LED array according to the system volume requirements of the vehicle light projection system;

[0016] Calculate basic brightness parameters of the LED array according to the required projection area and / or projection distance and / or the required basic image plane illumination;

[0017] Calculate the basic pixel parameters of the LED array according to the required image resolution;

[0018] Initialize the LED array parameters according to the LED array basic size parameters, LED array basic brightness parameters and LED array basic pixel parameters.

[0019] Optionally, the step of calculating the field of view angle of the projection objective lens according to the installation position of the vehicle lamp and the projection range comprises the following steps:

[0020] Construct the projection system space coordinate system;

[0021] Construct the geometric relationship between the lamp installation position and the projection range;

[0022] The field of view angle of the projection lens in the YOZ plane and the field of view angle in the XOY plane are determined according to the geometric relationship between the installation position of the headlight and the projection range, which is the field of view angle of the projection lens.

[0023] Optionally, the calculating of projection parameters according to LED array parameters comprises the steps of:

[0024] Calculate the focal length based on the diagonal length of the LED array, the projection distance, and the field of view of the projection lens;

[0025] Calculate the F number of the projection objective lens based on LED pixel parameters, light brightness parameters and image plane illumination;

[0026] The projection parameters are obtained according to the focal length and the F number of the projection objective lens.

[0027] Optionally, the calculating of the technical parameters of the front and rear groups of the projection objective lens according to the field angle of the projection objective lens and the projection parameters comprises the steps of:

[0028] Calculate the focal length of the front and rear lens groups of the projection objective according to the image height and the field angle of the chief ray;

[0029] Calculate the distance between the front and rear lens groups according to the focal length of the front and rear lens groups of the projection objective lens;

[0030] Calculate the F number of the front and rear groups of the projection objective lens according to the F number of the projection objective lens;

[0031] The technical parameters of the front and rear groups of the projection objective lens are obtained according to the focal length of the front and rear groups of the projection objective lens, the distance between the front and rear groups of the projection objective lens, and the F number of the front and rear groups of the projection objective lens.

[0032] Optionally, the selecting of an initial structure of a vehicle lamp projection objective lens system with a similar structure according to technical parameters of the front and rear groups of projection objective lenses comprises the steps of:

[0033] Obtain technical parameters of the front and rear groups of the projection objective lens of each selected vehicle lamp projection objective lens system structure;

[0034] The focal length weight value is calculated according to the influence of the focal length of the front and rear lens groups of the projection objective on the imaging quality;

[0035] Calculate the spacing weight value according to the influence of the spacing between the front and rear lens groups on the imaging quality;

[0036] Calculate the F-number weight value according to the influence of the F-number of the front and rear groups of the projection objective on the imaging quality;

[0037] The structural similarity is calculated according to the difference between the focal length weight value, the spacing weight value, the F number weight value and the technical parameters of the required front and rear groups of the projection objective lens and the initial structure related parameters of each selected vehicle lamp projection objective lens system;

[0038] Among the candidate vehicle headlight projection lens system structures, the one with the greatest structural similarity is selected as the initial structure of the vehicle headlight projection lens system.

[0039] Optionally, the step of optimizing the initial structure of the vehicle light projection objective lens system according to the object plane inclination angle and / or the lens curvature radius and / or thickness parameters to obtain an optimized optical system structure comprises the following steps:

[0040] Setting the object plane of the system as an inclined plane and taking the object plane inclination angle and / or the lens curvature radius and / or thickness parameters as variables;

[0041] Calculating a quality evaluation function according to the relationship between the object plane tilt angle and / or the lens curvature radius and / or thickness parameters and the imaging quality;

[0042] Calculate the maximum value of the quality evaluation function according to the variation range of each parameter and thereby obtain the optimal object plane inclination angle and / or lens curvature radius and / or thickness parameter;

[0043] The optimized optical system structure is obtained by adjusting the initial structure of the vehicle lamp projection objective lens system according to the optimal object plane tilt angle and / or lens curvature radius and / or thickness parameters.

[0044] Optionally, obtaining the headlight imaging image suitable for complex environments according to the optimized optical system structure is to obtain the headlight imaging image suitable for complex environments by optimizing the modulation transfer function algorithm using a genetic algorithm based on the optimized optical system structure.

[0045] According to another embodiment of the present invention, a computer-readable storage medium is provided, which stores a computer program for electronic data exchange, wherein the computer program enables a computer to execute the above method.

[0046] According to another embodiment of the present invention, there is provided a vehicle light intelligent imaging optimization system applicable to complex environments, comprising:

[0047] Optical components;

[0048] Data collector;

[0049] Optical system optimization module;

[0050] Memory;

[0051] and one or more programs, wherein the one or more programs are stored in a memory and configured to be executed by the optical system optimization module, the programs causing a computer to perform the above method.

[0052] The advantages of the vehicle light intelligent imaging optimization device, method, system and storage medium applicable to complex environments of the present invention are:

[0053] (1) Compared with traditional headlight imaging technology solutions, the design that combines object plane tilt with optical structure optimization can effectively reduce the serious degradation of imaging quality caused by optical axis tilt in the design of headlight projection optical system, and improve the overall imaging quality of smart headlights in various complex environments.

[0054] (2) The structural similarity is calculated based on the difference between the focal length weight value, the spacing weight value, the F-number weight value and the technical parameters of the front and rear groups of the required projection objective lens and the parameters related to the initial structure of each candidate headlight projection objective lens system, and the initial structure of the headlight projection objective lens system with a similar structure is selected. Compared with the traditional technical solution of headlight projection imaging, the initial structure of the headlight projection objective lens system with better parameters can be effectively selected, thereby reducing the time required for subsequent modulation.

[0055] (3) A quality evaluation function is calculated based on the relationship between the object plane tilt angle and / or the lens curvature radius and / or the thickness parameters and the imaging quality. The optimal object plane tilt angle and / or the lens curvature radius and / or the thickness parameters are obtained according to the quality evaluation function and the structure of the vehicle light projection lens system is adjusted accordingly. Compared with the traditional vehicle light imaging technology solutions, the precise adjustment of the vehicle light projection lens system structure can be effectively achieved. The real-time optimization and adjustment of the optical system can significantly improve the imaging quality of the vehicle light in various complex environments, thereby making up for the defects of the existing vehicle light projection system. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] Figure 1 is a flow chart of a vehicle light intelligent imaging optimization method applicable to complex environments according to an embodiment of the present invention;

[0057] Figure 2 is a flow chart of step S01 of the vehicle light intelligent imaging optimization method according to an embodiment of the present invention;

[0058] Figure 3 is a flow chart of step S02 of the vehicle light intelligent imaging optimization method according to an embodiment of the present invention;

[0059] Figure 4 is a flow chart of step S03 of the vehicle light intelligent imaging optimization method according to an embodiment of the present invention;

[0060] Figure 5 is a flow chart of step S04 of the vehicle light intelligent imaging optimization method according to an embodiment of the present invention;

[0061] Figure 6 is a flow chart of step S05 of the vehicle light intelligent imaging optimization method according to an embodiment of the present invention;

[0062] Figure 7 is a flow chart of step S06 of the vehicle light intelligent imaging optimization method according to an embodiment of the present invention;

[0063] Figure 8 It is a structural schematic diagram of a vehicle light intelligent imaging optimization system applicable to complex environments according to an embodiment of the present invention. DETAILED DESCRIPTION

[0064] The present invention is described in detail below in conjunction with specific embodiments. The following embodiments will help those skilled in the art to further understand the invention, but are not intended to limit the present invention in any form. It should be noted that, for those of ordinary skill in the art, several changes and improvements can be made without departing from the concept of the present invention. These all belong to the protection scope of the present invention.

[0065] The vehicle lights described in this embodiment include headlights, taillights, etc. of motor vehicles or non-motor vehicles.

[0066] According to an embodiment of the present invention, a method for optimizing intelligent imaging of vehicle lights applicable to complex environments is provided, and the flow chart is as follows: Figure 1 As shown, including:

[0067] Step S01, initializing LED array parameters according to the requirements of the vehicle light projection system;

[0068] Step S02, calculating the field angle of the projection lens according to the installation position of the vehicle lamp and the projection range;

[0069] Step S03, calculating projection parameters according to LED array parameters;

[0070] Step S04, calculating the technical parameters of the front and rear groups of the projection objective lens according to the projection objective lens field angle and projection parameters;

[0071] Step S05, selecting an initial structure of a vehicle lamp projection objective lens system with a similar structure according to the technical parameters of the front and rear groups of the projection objective lens;

[0072] Step S06, optimizing the initial structure of the vehicle light projection objective lens system according to the object plane inclination angle and / or the lens curvature radius and / or the thickness parameter to obtain an optimized optical system structure;

[0073] Step S07: obtaining a vehicle headlight imaging image suitable for complex environments according to the optimized optical system structure.

[0074] This embodiment considers the influence of various complex environments on the imaging quality of the intelligent headlight projection system, calculates the structural similarity according to the technical parameters of the front and rear groups of the headlight projection objective lens, selects the initial structure of the headlight projection objective lens system with similar structure, optimizes the initial structure of the headlight projection objective lens system according to the relationship between the object plane inclination angle and / or the lens curvature radius and / or thickness parameters and the imaging quality, and obtains the headlight imaging image suitable for complex environments according to the optimized optical system structure. The real-time optimization and adjustment of the optical system of this embodiment can significantly improve the imaging quality of the headlight in various complex environments and make up for the defects of the existing headlight projection system.

[0075] In an exemplary embodiment, the step S01 is to initialize the LED array parameters according to the requirements of the vehicle light projection system, as shown in the flowchart. Figure 2 As shown, the steps include:

[0076] Step S011, calculating basic size parameters of the LED array according to the system volume requirements of the vehicle light projection system;

[0077] Step S012, calculating basic brightness parameters of the LED array according to the required projection area and / or projection distance and / or the required basic image plane illumination;

[0078] Step S013, calculating basic pixel parameters of the LED array according to the required image resolution;

[0079] Step S014: Initialize the LED array parameters according to the LED array basic size parameters, the LED array basic brightness parameters and the LED array basic pixel parameters.

[0080] In this embodiment, the basic size parameters of the LED array are calculated according to the system volume requirements of the vehicle light projection system, which includes the length, width, thickness and other parameters of the LED array.

[0081] The method of calculating the basic brightness parameters of the LED array according to the required projection area and / or projection distance and / or the required basic image plane illumination comprises the following steps:

[0082] Preset the lower limit reference value of LED array brightness parameter;

[0083] The brightness parameter adjustment value is calculated according to the correlation between the required projection area and / or projection distance and / or the required basic image plane illumination and the required projection capability, including: calculating the brightness parameter adjustment value according to the positive correlation between the required projection area and the required projection capability (i.e., the projection area and the brightness parameter adjustment value are positively correlated), calculating the brightness parameter adjustment value according to the positive correlation between the projection distance and the required projection capability (i.e., the projection distance and the brightness parameter adjustment value are positively correlated), calculating the brightness parameter adjustment value according to the positive correlation between the required basic image plane illumination and the required projection capability (i.e., the required basic The brightness parameter adjustment value is calculated according to the required projection area and the projection distance, the brightness parameter adjustment value is calculated according to the required projection area and the required basic image plane illumination, the brightness parameter adjustment value is calculated according to the required projection distance and the required basic image plane illumination, and the brightness parameter adjustment value is calculated according to the required projection area and the required basic image plane illumination. Any one of the brightness parameter adjustment values ​​is calculated according to the required projection area and the projection distance and the required basic image plane illumination, which are positively correlated with the brightness parameter adjustment value.

[0084] The basic brightness parameter of the LED array is calculated according to the product or weighted sum of the preset LED array brightness parameter lower limit reference value and the brightness parameter adjustment value.

[0085] The calculation of the basic pixel parameters of the LED array according to the required image resolution is to calculate the basic pixel parameters of the LED array according to the relationship between the image resolution, the pixel size and the magnification. The basic pixel parameters of the LED array include parameters such as the pixel size and the magnification.

[0086] Initializing the LED array parameters according to the basic size parameters of the LED array, the basic brightness parameters of the LED array and the basic pixel parameters of the LED array is to initialize the LED array parameters according to the basic size parameters of the LED array, the basic brightness parameters of the LED array and the basic pixel parameters of the LED array obtained by the above calculations.

[0087] In an exemplary embodiment, the step S02, calculating the field angle of the projection lens according to the installation position of the vehicle lamp and the projection range, is as follows: Figure 3 As shown, the steps include:

[0088] Step S021, constructing a projection system space coordinate system;

[0089] Step S022: constructing a geometric relationship between the installation position of the headlight and the projection range;

[0090] Step S023, determining the field angle of the projection lens in the YOZ plane and the field angle of the projection lens in the XOY plane according to the geometric relationship between the installation position of the vehicle lamp and the projection range, that is, the field angle of the projection lens.

[0091] In this embodiment, an XYZ space coordinate system is constructed according to the field of view of the projection objective lens; the geometric relationship between the headlight installation position and the projection range is established according to the distance and height difference between the headlight installation position and the projection range; the field of view angle of the projection objective lens in the YOZ plane and the field of view angle in the XOY plane are determined according to the geometric relationship between the headlight installation position and the projection range, which is the field of view angle of the projection objective lens.

[0092] In an exemplary embodiment, the step S03, calculating the projection parameters according to the LED array parameters, is as follows: Figure 4 As shown, the steps include:

[0093] Step S031, calculating the focal length according to the diagonal length of the LED array, the projection distance and the field angle of the projection objective lens;

[0094] Step S032, calculating the F number of the projection objective lens according to the LED pixel parameters, the light brightness parameters and the image plane illumination;

[0095] Step S033, obtaining projection parameters according to the focal length and the F number of the projection objective lens.

[0096] In this embodiment, the focal length is calculated according to the size of the LED array and the field of view of the projection lens by the following formula:

[0097] (1)

[0098] in, is the focal length, y is the diagonal length of the array, is the diagonal length of the projected image, is the projection distance;

[0099] The Nyquist frequency and F number of the projection lens are calculated based on the LED pixel size, brightness parameters and image plane illumination; the F number is calculated by the following formula:

[0100] (2)

[0101] in, is the illumination at the center of the image plane of the optical system, L is the brightness of the LED array, is the transmittance of the optical system, is the magnification.

[0102] In an exemplary embodiment, the step S04, calculating the technical parameters of the front and rear groups of the projection objective lens according to the projection objective lens field angle and projection parameters, is as follows: Figure 5 As shown, the steps include:

[0103] Step S041, calculating the focal length of the front and rear lens groups of the projection objective lens according to the image height and the main ray field angle;

[0104] Step S042, calculating the distance between the front and rear lens groups according to the focal lengths of the front and rear lens groups of the projection objective lens;

[0105] Step S043, calculating the F number of the front and rear groups of the projection objective lens according to the F number of the projection objective lens;

[0106] Step S044, obtaining technical parameters of the front and rear groups of the projection objective lens according to the focal lengths of the front and rear groups of the projection objective lens, the distance between the front and rear groups of the projection objective lens, and the F number of the front and rear groups of the projection objective lens.

[0107] In this embodiment, the focal length of the front and rear lens groups of the projection objective lens is calculated according to the image height and the main ray field angle, and the calculation formula is:

[0108] (3)

[0109] The distance between the front and rear lenses is calculated based on the focal length of the front and rear lenses of the projection objective lens. The calculation formula is:

[0110] (4)

[0111] The F number of the front and rear groups of the projection objective is calculated based on the F number of the projection objective. The calculation formula is:

[0112] (5)

[0113] in, For image height, is the main ray field angle, and d is the distance between the front and rear lens groups.

[0114] In an exemplary embodiment, the step S05 selects an initial structure of a vehicle lamp projection lens system with a similar structure according to the technical parameters of the front and rear groups of the projection lens, and the flow chart is as follows: Figure 6 As shown, the steps include:

[0115] Step S051, obtaining technical parameters of the front and rear groups of the projection objective lens of each selected vehicle lamp projection objective lens system structure;

[0116] Step S052, calculating the focal length weight value according to the influence of the focal length of the front and rear lens groups of the projection objective lens on the imaging quality;

[0117] Step S053, calculating the distance weight value according to the influence of the distance between the front and rear lens groups on the imaging quality;

[0118] Step S054, calculating the F number weight value according to the influence of the F number of the front and rear groups of the projection objective lens on the imaging quality;

[0119] Step S055, calculating the structural similarity according to the focal length weight value, the spacing weight value, the F number weight value and the difference between the technical parameters of the required front and rear groups of the projection objective lens and the initial structure related parameters of each selected vehicle lamp projection objective lens system;

[0120] Step S056: selecting the one with the greatest structural similarity from among the candidate vehicle lamp projection lens system structures as the initial structure of the vehicle lamp projection lens system.

[0121] In this embodiment, a candidate vehicle lamp projection objective lens system structure suitable for vehicle model design is selected from a vehicle lamp projection objective lens system structure library, and technical parameters of the front and rear groups of projection objective lenses of each candidate vehicle lamp projection objective lens system structure are obtained.

[0122] The calculation of the focal length weight value according to the influence of the focal length of the front and rear lens groups of the projection objective on the imaging quality is to test and simulate the influence ratio of the unit change of the focal length of the front and rear lens groups of the projection objective on the imaging quality and calculate the focal length weight value according to the positive correlation between the influence ratio value and the weight value, which is represented by the variable a;

[0123] The calculation of the spacing weight value according to the influence of the front and rear lens groups spacing on the imaging quality is to test and simulate the influence ratio of the unit change of the front and rear lens groups spacing on the imaging quality of the projection objective lens and calculate the spacing weight value according to the positive correlation between the influence ratio value and the weight value, which is represented by the variable b;

[0124] The calculation of the F-number weight value according to the influence of the F-number of the front and rear groups of the projection objective lens on the imaging quality is to test and simulate the influence ratio of the unit change of the F-number of the front and rear groups of the projection objective lens on the imaging quality and calculate the F-number weight value according to the positive correlation between the influence ratio value and the weight value, which is represented by the variable c;

[0125] The structural similarity is calculated based on the difference between the focal length weight value, the spacing weight value, the F number weight value and the technical parameters of the required front and rear projection lens groups and the initial structure related parameters of each selected vehicle lamp projection lens system. The structural similarity is calculated based on the sum of the difference between each technical parameter and the product of each weight value. The focal length difference is recorded as , the spacing difference is recorded as , the F-number difference is recorded as , the structural similarity is recorded as p, then calculate , where e1, e2, e3, and e4 are the calculation coefficients obtained through prior training.

[0126] The method of selecting the structure with the greatest structural similarity among the candidate headlight projection objective system structures as the initial structure of the headlight projection objective system is to sort the structural similarities of the candidate headlight projection objective system structures and the required technical parameters and select the projection objective system structure with the greatest structural similarity as the initial structure of the headlight projection objective system.

[0127] In an exemplary embodiment, the step S06 is to optimize the initial structure of the vehicle light projection lens system according to the object plane inclination angle and / or the lens curvature radius and / or the thickness parameter to obtain an optimized optical system structure, as shown in the flowchart. Figure 7 As shown, the steps include:

[0128] Step S061, setting the object plane of the system as an inclined plane and taking the object plane inclination angle and / or the lens curvature radius and / or thickness parameters as variables;

[0129] Step S062, calculating a quality evaluation function according to the relationship between the object plane tilt angle and / or the lens curvature radius and / or thickness parameters and the imaging quality;

[0130] Step S063, calculating the maximum value of the quality evaluation function according to the variation range of each parameter and thereby obtaining the optimal object plane inclination angle and / or lens curvature radius and / or thickness parameter;

[0131] Step S064, adjusting the initial structure of the vehicle lamp projection objective lens system according to the optimal object plane tilt angle and / or lens curvature radius and / or thickness parameters to obtain an optimized optical system structure.

[0132] In this embodiment, the object plane of the system is set as an inclined plane and the object plane inclination angle and / or the lens curvature radius and / or thickness parameters are used as variables.

[0133] The method of calculating the quality evaluation function according to the relationship between the object plane inclination angle and / or the lens curvature radius and / or the thickness parameter and the imaging quality comprises: calculating the functional relationship between the object plane inclination angle and the imaging quality evaluation value according to the imaging quality evaluation values ​​corresponding to different object plane inclination angles and obtaining the quality evaluation function thereby; calculating the functional relationship between the lens curvature radius and the imaging quality evaluation value according to the imaging quality evaluation values ​​corresponding to different lens curvature radii and obtaining the quality evaluation function thereby; calculating the functional relationship between the thickness parameter and the imaging quality evaluation value according to the imaging quality evaluation values ​​corresponding to different thickness parameters and obtaining the quality evaluation function thereby; calculating the functional relationship between the object plane inclination angle and the imaging quality evaluation value according to the imaging quality evaluation values ​​corresponding to different object plane inclination angles and calculating the functional relationship between the lens curvature radius and the imaging quality evaluation value according to the imaging quality evaluation values ​​corresponding to different lens curvature radii and obtaining the quality evaluation function thereby; calculating the functional relationship between the object plane inclination angle and the imaging quality evaluation value according to the imaging quality evaluation values ​​corresponding to different object plane inclination angles and obtaining the quality evaluation function thereby; ... The functional relationship between the object plane inclination angle and the imaging quality evaluation value is calculated according to the imaging quality evaluation value corresponding to different object plane inclination angles, and the functional relationship between the thickness parameter and the imaging quality evaluation value is calculated according to the imaging quality evaluation values ​​corresponding to different thickness parameters, thereby obtaining the quality evaluation function; the functional relationship between the lens curvature radius and the imaging quality evaluation value is calculated according to the imaging quality evaluation values ​​corresponding to different lens curvature radii, and the functional relationship between the thickness parameter and the imaging quality evaluation value is calculated according to the imaging quality evaluation values ​​corresponding to different thickness parameters, thereby obtaining the quality evaluation function; the functional relationship between the object plane inclination angle and the imaging quality evaluation value is calculated according to the imaging quality evaluation values ​​corresponding to different object plane inclination angles, and the functional relationship between the lens curvature radius and the imaging quality evaluation value is calculated according to the imaging quality evaluation values ​​corresponding to different lens curvature radii, and the functional relationship between the thickness parameter and the imaging quality evaluation value is calculated according to the imaging quality evaluation values ​​corresponding to different thickness parameters, thereby obtaining any item of the quality evaluation function.

[0134] Taking the LED array display chip as the image plane, the optical system is designed in the form of a telecentric optical path in TracePro to obtain the system imaging quality evaluation diagram, including the MTF diagram, distortion diagram and comparison curve. The imaging quality evaluation value is obtained according to the system imaging quality evaluation diagram, and the functional relationship between different variables and the imaging quality evaluation value is obtained according to the imaging quality evaluation diagram under different variables trained multiple times. That is, the functional relationship between the object plane inclination angle and the imaging quality evaluation value is calculated according to the imaging quality evaluation value corresponding to different object plane inclination angles, and the functional relationship between the lens curvature radius and the imaging quality evaluation value is calculated according to the imaging quality evaluation value corresponding to different lens curvature radii. The functional relationship of the imaging quality evaluation value is calculated according to the imaging quality evaluation values ​​corresponding to different thickness parameters, wherein the imaging quality evaluation value is represented by the variable m, the object plane inclination angle is represented by the variable x, the lens curvature radius is represented by the variable y, and the thickness parameter is represented by the variable z. The functional relationship between the object plane inclination angle and the imaging quality evaluation value is recorded as m=g(x), the functional relationship between the lens curvature radius and the imaging quality evaluation value is recorded as m=h(y), the functional relationship between the thickness parameter and the imaging quality evaluation value is recorded as m=p(z), and the quality evaluation function is recorded as m=Q(x,y,z).

[0135] Different implementations of calculating the quality evaluation function are shown in Examples A1 to A7. The functional relationship g(x) between the object plane tilt angle and the imaging quality evaluation value, the functional relationship h(y) between the lens curvature radius and the imaging quality evaluation value, and the functional relationship p(z) between the thickness parameter and the imaging quality evaluation value are obtained according to the method described in the above implementations.

[0136] Embodiment A1: Calculate the quality evaluation function according to the functional relationship between the object plane tilt angle and the imaging quality evaluation value.

[0137] In this embodiment, the LED array display chip is used as the image plane, and the optical system is designed in the form of a telecentric optical path in TracePro to obtain a system imaging quality evaluation diagram, and an imaging quality evaluation value is obtained based on the system imaging quality evaluation diagram. The functional relationship between the object plane tilt angle and the imaging quality evaluation value is obtained based on multiple training of the imaging quality evaluation diagram under different variables, expressed as m=g(x); the quality evaluation function Q(x,y,z) is calculated based on the positive correlation between the functional relationship between the object plane tilt angle and the imaging quality evaluation value and the quality evaluation function. In one embodiment, the quality evaluation function Q(x,y,z)=o1·g(x) is calculated. o2 +o3, where o1, o2 (o2>0), and o3 are calculation coefficients obtained through prior training.

[0138] Embodiment A2: Calculate the quality evaluation function according to the functional relationship between the lens curvature radius and the imaging quality evaluation value.

[0139] In this embodiment, the LED array display chip is used as the image plane, and the optical system is designed in the form of a telecentric optical path in TracePro to obtain a system imaging quality evaluation diagram, and an imaging quality evaluation value is obtained based on the system imaging quality evaluation diagram. The functional relationship between the lens curvature radius and the imaging quality evaluation value is obtained based on the imaging quality evaluation diagram under multiple trainings with different variables, expressed as m=h(y); the quality evaluation function Q(x,y,z) is calculated based on the positive correlation between the functional relationship between the lens curvature radius and the imaging quality evaluation value and the quality evaluation function. In one embodiment, the quality evaluation function Q(x,y,z)=o4·h(y) is calculated. o5 +o6, where o4, o5 (o5>0), and o6 are calculation coefficients obtained through prior training.

[0140] Embodiment A3: Calculate the quality evaluation function according to the functional relationship between the thickness parameter and the imaging quality evaluation value.

[0141] In this embodiment, the LED array display chip is used as the image plane, and the optical system is designed in the form of a telecentric optical path in TracePro to obtain a system imaging quality evaluation diagram, and an imaging quality evaluation value is obtained based on the system imaging quality evaluation diagram. The functional relationship between the thickness parameter and the imaging quality evaluation value is obtained based on the imaging quality evaluation diagram under multiple trainings with different variables, expressed as m=p(z); the quality evaluation function Q(x,y,z) is calculated based on the positive correlation between the functional relationship between the thickness parameter and the imaging quality evaluation value and the quality evaluation function. In one embodiment, the quality evaluation function Q(x,y,z)=o7·p(z) is calculated. o8 +o9, where o7, o8 (o8>0), and o9 are calculation coefficients obtained through prior training.

[0142] Embodiment A4: Calculate the quality evaluation function according to the functional relationship between the object plane tilt angle and the imaging quality evaluation value and the functional relationship between the lens curvature radius and the imaging quality evaluation value.

[0143] In this embodiment, the LED array display chip is used as the image plane, and the optical system is designed in the form of a telecentric optical path in TracePro to obtain a system imaging quality evaluation diagram, and an imaging quality evaluation value is obtained based on the system imaging quality evaluation diagram. The functional relationship g(x) between the object plane inclination angle and the imaging quality evaluation value and the functional relationship h(y) between the lens curvature radius and the imaging quality evaluation value are obtained based on multiple training of the imaging quality evaluation diagram under different variables; the quality evaluation function Q(x, y, z) is calculated based on the positive correlation between the functional relationship between the object plane inclination angle and the imaging quality evaluation value and the functional relationship between the lens curvature radius and the imaging quality evaluation value and the quality evaluation function. In one embodiment, the quality evaluation function Q(x, y, z) is calculated as o10·g(x) o11 +o12·h(y)o13 , where o10, o11 (o11>0), o12, o13 (o13>0) are calculation coefficients obtained by prior training. In another implementation, the calculation quality evaluation function Q(x,y,z)=o14·g(x) o15 ·h(y) o16 +o17, where o14, o15 (o15>0), o16 (o16>0), and o17 are calculation coefficients obtained through prior training.

[0144] Embodiment A5: Calculate the quality evaluation function according to the functional relationship between the object plane tilt angle and the imaging quality evaluation value and the functional relationship between the thickness parameter and the imaging quality evaluation value.

[0145] In this embodiment, the LED array display chip is used as the image plane, and the optical system is designed in the form of a telecentric optical path in TracePro to obtain a system imaging quality evaluation diagram, and an imaging quality evaluation value is obtained based on the system imaging quality evaluation diagram. The functional relationship g(x) between the object plane tilt angle and the imaging quality evaluation value and the functional relationship p(z) between the thickness parameter and the imaging quality evaluation value are obtained based on multiple training of the imaging quality evaluation diagram under different variables; the quality evaluation function Q(x, y, z) is calculated based on the positive correlation between the functional relationship between the object plane tilt angle and the imaging quality evaluation value and the functional relationship between the thickness parameter and the imaging quality evaluation value and the quality evaluation function. In one embodiment, the quality evaluation function Q(x, y, z) is calculated as o18·g(x) o19 +o20·p(z) o21 , where o18, o19 (o19>0), o20, o21 (o21>0) are calculation coefficients obtained by prior training. In another implementation, the calculation quality evaluation function Q(x,y,z)=o22·g(x) o23 p(z) o23 +o25, where o22, o23 (o23>0), o24 (o24>0), and o25 are calculation coefficients obtained through prior training.

[0146] Embodiment A6: Calculate the quality evaluation function according to the functional relationship between the lens curvature radius and the imaging quality evaluation value and the functional relationship between the thickness parameter and the imaging quality evaluation value.

[0147] In this embodiment, the LED array display chip is used as the image plane, and the optical system is designed in the form of a telecentric optical path in TracePro to obtain a system imaging quality evaluation diagram, and an imaging quality evaluation value is obtained based on the system imaging quality evaluation diagram. The functional relationship h(y) between the lens radius of curvature and the imaging quality evaluation value and the functional relationship p(z) between the thickness parameter and the imaging quality evaluation value are obtained based on multiple training of the imaging quality evaluation diagram under different variables; the quality evaluation function Q(x, y, z) is calculated based on the positive correlation between the functional relationship between the lens radius of curvature and the imaging quality evaluation value and the functional relationship between the thickness parameter and the imaging quality evaluation value and the quality evaluation function. In one embodiment, the quality evaluation function Q(x, y, z) is calculated as o26·h(y) o27 +o28·p(z) o29 , where o26, o27 (o27>0), o28, o29 (o29>0) are calculation coefficients obtained by prior training. In another implementation, the calculation quality evaluation function Q(x,y,z)=o30·h(y) o31 p(z) o32 +o33, where o30, o31 (o31>0), o32 (o32>0), and o33 are calculation coefficients obtained through prior training.

[0148] Embodiment A7: Calculate the quality evaluation function according to the functional relationship between the object plane tilt angle and the imaging quality evaluation value, the functional relationship between the lens curvature radius and the imaging quality evaluation value, and the functional relationship between the thickness parameter and the imaging quality evaluation value.

[0149] In this embodiment, the LED array display chip is used as the image plane, and the optical system is designed in the form of a telecentric optical path in TracePro to obtain a system imaging quality evaluation diagram, and an imaging quality evaluation value is obtained based on the system imaging quality evaluation diagram. The functional relationship g(x) between the object plane inclination angle and the imaging quality evaluation value, the functional relationship h(y) between the lens curvature radius and the imaging quality evaluation value, and the functional relationship p(z) between the thickness parameter and the imaging quality evaluation value are obtained based on multiple training of the imaging quality evaluation diagram under different variables; the quality evaluation function Q(x, y, z) is calculated based on the positive correlation between the functional relationship between the object plane inclination angle and the imaging quality evaluation value, the functional relationship between the lens curvature radius and the imaging quality evaluation value, and the functional relationship between the thickness parameter and the imaging quality evaluation value and the quality evaluation function. In one embodiment, the quality evaluation function Q(x, y, z) is calculated as o34·g(x) o35 +o36·h(y) o37 +o38·p(z) o39, where o34, o35 (o35>0), o36, o37 (o37>0), o38, o39 (o39>0) are calculation coefficients obtained by prior training. In another implementation, the calculation quality evaluation function Q(x,y,z)=o40·g(x) o41 ·h(y) o42 p(z) o43 +o44, where o40, o41 (o41>0), o42 (o42>0), o43 (o43>0), and o44 are calculation coefficients obtained through prior training.

[0150] The method of calculating the maximum value of the quality evaluation function according to the variation range of each parameter and thereby obtaining the optimal object plane inclination angle and / or lens curvature radius and / or thickness parameter is to calculate the maximum value of the quality evaluation function Q(x,y,z) according to the variation range of the parameter variables x and / or y and / or z contained in the quality evaluation function Q(x,y,z) calculated by the method described in any one of Embodiments A1 to A7 (pre-set according to the shape of the headlight and the usage scenario) and obtain the optimal object plane inclination angle x according to the values ​​of each variable corresponding to the maximum value. i and / or lens curvature radius y i and / or thickness parameter z i .

[0151] According to the optimal object plane tilt angle x i and / or lens curvature radius y i and / or thickness parameter z i The initial structure of the headlight projection lens system is adjusted to obtain an optimized optical system structure.

[0152] In an exemplary embodiment, the step S07 of obtaining a headlight imaging image suitable for a complex environment according to the optimized optical system structure is to obtain a headlight imaging image suitable for a complex environment by optimizing the modulation transfer function algorithm based on the optimized optical system structure. In this embodiment, a genetic algorithm is used on the basis of the optimized optical system structure to obtain a headlight imaging image suitable for a complex environment by optimizing the MTF modulation transfer function algorithm.

[0153] According to another embodiment of the present invention, a computer-readable storage medium is provided, which stores a computer program for electronic data exchange, wherein the computer program enables a computer to execute the intelligent imaging optimization method for vehicle lights applicable to complex environments as described in the above-mentioned embodiment.

[0154] According to another embodiment of the present invention, there is also provided a vehicle light intelligent imaging optimization system applicable to complex environments, the structural diagram of which is shown in FIG. Figure 8 As shown, including:

[0155] Optical components;

[0156] Data collector;

[0157] Optical system optimization module;

[0158] Memory;

[0159] and one or more programs, wherein the one or more programs are stored in a memory and are configured to be executed by the optical system optimization module, the programs causing a computer to execute the intelligent imaging optimization method for vehicle lights in complex environments as described in the above embodiment. The optical system optimization module includes an optical adjustment module and a processor.

[0160] The method according to the present invention described above may be implemented in hardware, firmware, or as software or computer code that can be stored in a recording medium (such as a CD ROM, RAM, floppy disk, hard disk or magneto-optical disk), or as computer code that is originally stored in a remote recording medium or a non-temporary machine-readable medium downloaded through a network and will be stored in a local recording medium, so that the method described herein can be stored in such software processing on a recording medium using a general-purpose computer, a special-purpose processor, or programmable or special-purpose hardware (such as an ASIC or FPGA). It is understood that a computer, a processor, a microprocessor controller, or programmable hardware includes a storage component (e.g., RAM, ROM, flash memory, etc.) that can store or receive software or computer code, and when the software or computer code is accessed and executed by a computer, a processor, or hardware, the method described herein is implemented. In addition, when a general-purpose computer accesses the code for implementing the processing shown herein, the execution of the code converts the general-purpose computer into a special-purpose computer for executing the processing shown herein.

[0161] Of course, those skilled in the art should realize that the above embodiments are only used to illustrate the present invention, and are not intended to limit the present invention. As long as they are within the scope of the present invention, any changes or modifications to the above embodiments will fall within the protection scope of the present invention.

Claims

1. A vehicle light intelligent imaging optimization method suitable for complex environments, characterized in that: include: Initialize LED array parameters according to the requirements of the car light projection system; Calculate the field of view angle of the projection lens according to the installation position and projection range of the headlight; Calculate projection parameters according to LED array parameters; Calculate the technical parameters of the front and rear groups of the projection objective lens according to the projection objective lens field angle and projection parameters; According to the technical parameters of the front and rear groups of the projection lens, the initial structure of the headlight projection lens system with similar structure is selected; The initial structure of the vehicle lamp projection lens system is optimally configured according to the object plane inclination angle and / or the lens curvature radius and / or thickness parameters to obtain an optimized optical system structure; According to the optimized optical system structure, the headlight imaging image suitable for complex environments is obtained.

2. The intelligent imaging optimization method for vehicle lights in complex environments according to claim 1, characterized in that: Initializing the LED array parameters according to the requirements of the vehicle light projection system comprises the following steps: Calculate the basic size parameters of the LED array according to the system volume requirements of the vehicle light projection system; Calculate basic brightness parameters of the LED array according to the required projection area and / or projection distance and / or the required basic image plane illumination; Calculate the basic pixel parameters of the LED array according to the required image resolution; Initialize the LED array parameters according to the LED array basic size parameters, LED array basic brightness parameters and LED array basic pixel parameters.

3. The intelligent imaging optimization method for vehicle lights in complex environments according to claim 2 is characterized in that: The method of calculating the field of view angle of the projection objective lens according to the installation position of the vehicle lamp and the projection range comprises the following steps: Construct the projection system space coordinate system; Construct the geometric relationship between the lamp installation position and the projection range; The field of view angle of the projection lens in the YOZ plane and the field of view angle in the XOY plane are determined according to the geometric relationship between the installation position of the headlight and the projection range, which is the field of view angle of the projection lens.

4. The intelligent imaging optimization method for vehicle lights in complex environments according to claim 3 is characterized in that: The method of calculating projection parameters according to LED array parameters comprises the following steps: Calculate the focal length based on the diagonal length of the LED array, the projection distance, and the field of view of the projection lens; Calculate the F number of the projection objective lens based on LED pixel parameters, light brightness parameters and image plane illumination; The projection parameters are obtained according to the focal length and the F number of the projection objective lens.

5. The intelligent imaging optimization method for vehicle lights in complex environments according to claim 4 is characterized in that: The method of calculating the technical parameters of the front and rear groups of the projection objective lens according to the projection objective lens field angle and projection parameters comprises the steps of: Calculate the focal length of the front and rear lens groups of the projection objective according to the image height and the field angle of the chief ray; Calculate the distance between the front and rear lens groups according to the focal length of the front and rear lens groups of the projection objective lens; Calculate the F number of the front and rear groups of the projection objective lens according to the F number of the projection objective lens; The technical parameters of the front and rear groups of the projection objective lens are obtained according to the focal length of the front and rear groups of the projection objective lens, the distance between the front and rear groups of the projection objective lens, and the F number of the front and rear groups of the projection objective lens.

6. The intelligent imaging optimization method for vehicle lights in complex environments according to claim 5 is characterized in that: The method of selecting an initial structure of a vehicle lamp projection objective lens system having a similar structure according to the technical parameters of the front and rear groups of projection objective lenses comprises the following steps: Obtain technical parameters of the front and rear groups of the projection objective lens of each selected vehicle lamp projection objective lens system structure; The focal length weight value is calculated according to the influence of the focal length of the front and rear lens groups of the projection objective on the imaging quality; Calculate the spacing weight value according to the influence of the spacing between the front and rear lens groups on the imaging quality; Calculate the F-number weight value according to the influence of the F-number of the front and rear groups of the projection objective on the imaging quality; The structural similarity is calculated according to the difference between the focal length weight value, the spacing weight value, the F number weight value and the technical parameters of the required front and rear groups of the projection objective lens and the initial structure related parameters of each selected vehicle lamp projection objective lens system; Among the candidate vehicle headlight projection lens system structures, the one with the greatest structural similarity is selected as the initial structure of the vehicle headlight projection lens system.

7. The intelligent imaging optimization method for vehicle lights in complex environments according to claim 6, characterized in that: The method of optimizing the initial structure of the vehicle light projection objective lens system according to the object plane inclination angle and / or the lens curvature radius and / or thickness parameters to obtain an optimized optical system structure comprises the following steps: Setting the object plane of the system as an inclined plane and taking the object plane inclination angle and / or the lens curvature radius and / or thickness parameters as variables; Calculating a quality evaluation function according to the relationship between the object plane tilt angle and / or the lens curvature radius and / or thickness parameters and the imaging quality; Calculate the maximum value of the quality evaluation function according to the variation range of each parameter and thereby obtain the optimal object plane inclination angle and / or lens curvature radius and / or thickness parameter; The optimized optical system structure is obtained by adjusting the initial structure of the vehicle lamp projection objective lens system according to the optimal object plane tilt angle and / or lens curvature radius and / or thickness parameters.

8. The vehicle headlight intelligent imaging optimization method applicable to complex environments according to claim 7, characterized in that: The method of obtaining the headlight imaging image suitable for complex environments according to the optimized optical system structure is to obtain the headlight imaging image suitable for complex environments by optimizing the modulation transfer function algorithm using a genetic algorithm based on the optimized optical system structure.

9. A computer-readable storage medium storing a computer program for electronic data exchange, wherein: The computer program enables a computer to execute the method according to any one of claims 1 to 8.

10. A vehicle light intelligent imaging optimization system suitable for complex environments, characterized in that: include: Optical components; Data collector; Optical system optimization module; Memory; and one or more programs, wherein the one or more programs are stored in a memory and configured to be executed by the optical system optimization module, the programs causing a computer to execute the method according to any one of claims 1-8.

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