An adaptive LED headlight control strategy optimization method based on environmental adaptation

Abstract

The present invention discloses an adaptive LED headlight control strategy optimization method based on environmental adaptation, which relates to the field of headlights, including: establishing a model of the influence of personnel distribution on the vehicle body suspension system; obtaining the distribution of personnel in the vehicle, calculating the angle between the lighting direction of the LED headlight and the horizontal plane as a characteristic angle; adjusting the lighting direction of the LED headlight using an angle adjustment device according to the characteristic angle; establishing a light attenuation model for fog, snow and rain; obtaining a light intensity attenuation coefficient; establishing a light reflection model for snow, obtaining a light intensity reflection coefficient; and calculating the target light intensity. By establishing a model of the influence of personnel distribution on the vehicle body suspension system, establishing a light attenuation model for fog, snow and rain, and establishing a light reflection model for snow, sufficient lighting is achieved with a lower light intensity, so that the lighting distance is sufficient for the brake reaction, thereby achieving an energy-saving effect, and ensuring that the headlight is always level, so that the lighting distance will not be shortened.

Description

Technical Field

[0001] The present invention relates to the field of vehicle lights, and in particular to an adaptive LED vehicle light control strategy optimization method based on environment adaptation. Background Art

[0002] LED automotive lights refer to the use of LED technology for both interior and exterior lighting. External lighting equipment involves thermal limits and EMC issues, as well as many complex standards for unloading load testing. LEDs have a sturdy structure and are not easily affected by vibration, and the light output brightness will not drop significantly during use.

[0003] The vehicle usage environment includes not only the external environment but also the internal environment. The internal environment is mainly composed of the positions of people. In actual use, due to the different load positions of the vehicle, the suspension will adjust the body according to the load position, thereby changing the lighting angle of the LED headlights, causing the lighting distance of the LED headlights to change, affecting the braking reaction time. In addition, due to the large number of influencing factors in the external environment, it is difficult to determine the most energy-saving light intensity, which may consume more energy. Summary of the invention

[0004] In order to solve the above technical problems, a method for optimizing an adaptive LED headlight control strategy based on environmental adaptation is provided. This technical solution solves the problems raised in the above background technology.

[0005] In order to achieve the above purpose, the technical solution adopted by the present invention is:

[0006] An adaptive LED headlight control strategy optimization method based on environmental adaptation includes:

[0007] Establish a model for the impact of occupant distribution on the vehicle body suspension system;

[0008] At least one camera device is arranged in the vehicle, wherein the camera angle of one camera device is a bird's-eye view, and the camera device is used to identify the occupants in the vehicle to obtain the distribution of the occupants in the vehicle, and based on the distribution of the occupants in the vehicle and the influence model of the occupant distribution on the vehicle body suspension system, the angle between the illumination direction of the LED headlight and the horizontal plane is calculated as the characteristic angle;

[0009] An angle adjustment device is provided at the connection between the LED lamp and the vehicle body, and the lighting direction of the LED lamp is adjusted using the angle adjustment device according to the characteristic angle;

[0010] Based on big data, obtain the safe braking distance for normal vehicle driving, and based on historical data, obtain the characteristic light intensity of the lighting distance as the safe braking distance;

[0011] Model the attenuation of light by fog, snow, and rain;

[0012] Identify the weather conditions to obtain the real-time weather conditions. When there is fog, snow or rain in the real-time weather conditions, input the real-time weather conditions into the attenuation model of fog, snow and rain to obtain the light intensity attenuation coefficient. When there is no fog, snow or rain in the real-time weather conditions, the light intensity attenuation coefficient is set to 1.

[0013] A light reflection model of snow is established. When there is snowfall in the driving environment, the snow coverage is identified to obtain the snow coverage ratio. Based on the snow coverage ratio and the light reflection model of snow, the light intensity reflection coefficient is obtained. When there is no snowfall in the driving environment, the light intensity reflection coefficient is set to 0.

[0014] The light source in the environment is identified to obtain the ambient light intensity. Based on the light intensity attenuation coefficient and the light intensity reflection coefficient, the target light intensity is calculated and the lighting intensity of the LED headlight is adjusted to the target light intensity.

[0015] Preferably, the establishment of the influence model of personnel distribution on the vehicle body suspension system comprises the following steps:

[0016] The vehicle interior image acquired by the camera device with a camera angle of view being a bird's-eye view is used as the target vehicle interior image;

[0017] Coordinate modeling in the target vehicle interior image;

[0018] Uniformly select at least one sampling point in the vehicle body as an action point, and obtain the coordinates of the action point in the target vehicle interior image;

[0019] Obtain the upper load limit of the vehicle, divide the upper load limit of the vehicle at equal intervals, and obtain at least one load point;

[0020] Randomly pair the value of the load point with the coordinates of the action point to obtain at least one test data group;

[0021] Under the conditions of the test data group, the inclination angle of the vehicle body is obtained, and the test data group is paired with the inclination angle and fitted to obtain a suspension fitting function, in which the value of the load point and the coordinates of the action point are independent variables, and the inclination angle of the vehicle body is the dependent variable. It is stipulated that when the front of the vehicle is biased upward, the inclination angle of the vehicle body is positive, and when the front of the vehicle is biased downward, the inclination angle of the vehicle body is negative.

[0022] Preferably, the step of using a camera device to identify people in the vehicle and obtaining the distribution of people in the vehicle comprises the following steps:

[0023] A miniature doll is arranged in the vehicle, the volume of the miniature doll is a characteristic volume, and at least one camera device is used to capture a picture of the vehicle interior to obtain at least one vehicle interior image;

[0024] identifying a first occupied area of ​​the miniature figure in the in-vehicle image, and averaging at least one first occupied area to obtain a first average area;

[0025] Identifying a second occupied area of ​​a person in the vehicle in the vehicle interior image, and averaging at least one second occupied area to obtain a second average area;

[0026] Based on big data, human body density is obtained and the total weight of the people in the car is calculated using the weight formula;

[0027] The overall outline of the occupant is identified in the target in-vehicle image, at least one sampling point is selected at the edge of the overall outline, and the horizontal coordinate and the vertical coordinate of the at least one sampling point are fitted to obtain a profile fitting function, wherein the horizontal coordinate of the sampling point is an independent variable and the vertical coordinate of the sampling point is a dependent variable;

[0028] Using the center of gravity formula, the coordinates of the overall center of gravity of the occupants of the vehicle are calculated, wherein the coordinates of the overall center of gravity are composed of the abscissa of the overall center of gravity and the ordinate of the overall center of gravity;

[0029] The total weight of the people in the car and the coordinates of the overall center of gravity are used as the distribution of people in the car;

[0030] The weight formula is as follows: ,

[0031] Where A is the total weight of the people in the car, a is the second average area, b is the first average area, c is the characteristic volume, d is the human body density, and g is the acceleration of gravity;

[0032] The center of gravity formula is as follows: ,

[0033] Among them, X is the abscissa of the overall centroid, Y is the ordinate of the overall centroid, the area of ​​the double integral is the area enclosed by the profile fitting function, x is the abscissa of the point in the area enclosed by the profile fitting function, and y is the ordinate of the point in the area enclosed by the profile fitting function.

[0034] Preferably, the calculating and obtaining the angle between the lighting direction of the LED headlight and the horizontal plane as the characteristic angle comprises the following steps:

[0035] Substitute the total weight of the occupants and the coordinates of the overall center of gravity into the suspension fitting function to obtain the characteristic angle.

[0036] Preferably, the establishment of the attenuation model of fog, snow and rain on light comprises the following steps:

[0037] The position at which the distance from the LED light is equal to the safe braking distance is taken as the characteristic position;

[0038] Obtaining a density range of fog, dividing the density range of fog at equal intervals, and obtaining at least one first identification point;

[0039] Under the condition that the density of fog is equal to the value at the first identification point, the LED headlight emits light with a characteristic light intensity to obtain a first intensity at the characteristic position;

[0040] The first intensity is divided by the characteristic light intensity to obtain a first coefficient, and the value at the first identification point is paired with the first coefficient and fitted to obtain a first fitting function;

[0041] Acquire a snowfall density range, divide the snowfall density range into equal intervals, and obtain at least one second identification point;

[0042] Under the condition that the density of snowfall is equal to the value at the second identification point, the LED headlight emits light at a characteristic light intensity to obtain a second intensity at the characteristic position;

[0043] The second intensity is divided by the characteristic light intensity to obtain a second coefficient, and the value at the second identification point is paired and fitted with the second coefficient to obtain a second fitting function;

[0044] Acquire a rainfall density range, divide the rainfall density range into equal intervals, and obtain at least one third identification point;

[0045] Under the condition that the density of rainfall is equal to the value at the third identification point, the LED headlight emits light at a characteristic light intensity to obtain a third intensity at the characteristic position;

[0046] The third intensity is divided by the characteristic light intensity to obtain a third coefficient, and the value at the third identification point is paired and fitted with the third coefficient to obtain a third fitting function;

[0047] The first fitting function, the second fitting function and the third fitting function are used as the attenuation models of light caused by fog, snow and rain.

[0048] Preferably, the step of identifying the weather conditions to obtain the real-time weather conditions comprises the following steps:

[0049] Get the image outside the vehicle and determine whether there is fog, snow or rain in the image outside the vehicle. If not, the real-time weather condition is normal;

[0050] If so, identify the proportion of fog droplets, snow or rain in the image outside the vehicle as the area proportion;

[0051] Use the proportional transformation formula to calculate the volume percentage;

[0052] When fog appears in the image outside the car, the real-time weather condition is foggy, and the actual density of fog is equal to the volume ratio multiplied by the density of water;

[0053] When snow appears in the image outside the car, the real-time weather condition is snowy, and the actual density of snowfall is equal to the volume percentage multiplied by the density of accumulated snow;

[0054] When rain appears in the image outside the car, the real-time weather condition is rainy, and the actual density of rainfall is equal to the volume percentage multiplied by the density of water;

[0055] The proportional transformation formula is as follows: ,

[0056] Among them, B is the volume ratio and e is the area ratio.

[0057] Preferably, the step of inputting the real-time weather conditions into the light attenuation model of fog, snow and rain to obtain the light intensity attenuation coefficient comprises the following steps:

[0058] When the real-time weather condition is foggy, the actual density of fog is substituted into the first fitting function to obtain the light intensity attenuation coefficient;

[0059] When the real-time weather condition is snowy, the actual snowfall density is substituted into the second fitting function to obtain the light intensity attenuation coefficient;

[0060] When the real-time weather condition is rainy, the actual rainfall density is substituted into the third fitting function to obtain the light intensity attenuation coefficient.

[0061] Preferably, the step of establishing the light reflection model of snow comprises the following steps:

[0062] Obtain the light intensity reflected by the characteristic light intensity when the environment is fully covered by snow as the calibration intensity;

[0063] The calibrated intensity is divided by the characteristic light intensity to obtain the characteristic reflection coefficient;

[0064] A reflection fitting function is formed and used as a light reflection model of the snow. The reflection fitting function is equal to hj, where h is the snow coverage ratio and j is the characteristic reflection coefficient.

[0065] Preferably, obtaining the light intensity reflection coefficient based on the snow coverage ratio and the light reflection model of the snow comprises the following steps:

[0066] Substitute the snow cover ratio into the reflectance fitting function to obtain the light intensity reflectance coefficient.

[0067] Preferably, the step of calculating the target light intensity based on the light intensity attenuation coefficient and the light intensity reflection coefficient comprises the following steps:

[0068] When the ambient light intensity is not less than the characteristic light intensity, the target light intensity is 0;

[0069] When the ambient light intensity is less than the characteristic light intensity, the characteristic light intensity minus the ambient light intensity is obtained to obtain the light intensity to be compensated;

[0070] Use the comprehensive formula of light intensity to calculate the target light intensity;

[0071] The comprehensive formula for light intensity is as follows: ,

[0072] Among them, C is the target light intensity, k is the light intensity to be compensated, m is the light intensity attenuation coefficient, and n is the light intensity reflection coefficient.

[0073] Compared with the prior art, the present invention has the following beneficial effects:

[0074] By establishing a model for the impact of personnel distribution on the vehicle body suspension system, a model for the attenuation of light by fog, snow and rain, and a model for light reflection on snow, the target light intensity is calculated. The target light intensity obtained is the optimal result of comprehensive environmental factors. Under the influence of various weather factors, sufficient lighting can still be achieved with a lower light intensity, so that the lighting distance is sufficient for braking reaction, thereby achieving energy-saving effects. At the same time, the impact of vehicle body load on the lighting angle of the headlights is predicted and adjusted accordingly to ensure that the headlights are always level, so that the lighting distance is not shortened, and sufficient braking reaction time is reserved. BRIEF DESCRIPTION OF THE DRAWINGS

[0075] Figure 1 It is a flow chart of the method for optimizing the adaptive LED vehicle light control strategy based on environmental adaptation of the present invention;

[0076] Figure 2 A schematic diagram of a flow chart of establishing a model of the influence of personnel distribution on a vehicle body suspension system according to the present invention;

[0077] Figure 3 A schematic diagram of a process of using a camera device to identify people in a vehicle and obtain the distribution of people in the vehicle according to the present invention;

[0078] Figure 4 A schematic diagram of a process for establishing a light attenuation model of fog, snow and rain according to the present invention;

[0079] Figure 5 A schematic diagram of a process of identifying weather conditions and obtaining real-time weather conditions according to the present invention;

[0080] Figure 6 A schematic diagram of a flow chart of the present invention for inputting real-time weather conditions into the light attenuation model of fog, snow and rain to obtain the light intensity attenuation coefficient;

[0081] Figure 7 A schematic diagram of the process of establishing a light reflection model of snow in the present invention;

[0082] Figure 8 The present invention is a flow chart of calculating the target light intensity based on the light intensity attenuation coefficient and the light intensity reflection coefficient. DETAILED DESCRIPTION

[0083] The following description is used to disclose the present invention so that those skilled in the art can implement the present invention. The preferred embodiments described below are only examples, and those skilled in the art may think of other obvious variations.

[0084] Reference Figure 1 As shown, an adaptive LED headlight control strategy optimization method based on environmental adaptation includes:

[0085] Establish a model for the impact of occupant distribution on the vehicle body suspension system;

[0086] At least one camera device is arranged in the vehicle, wherein the camera angle of one camera device is a bird's-eye view, and the camera device is used to identify the occupants in the vehicle to obtain the distribution of the occupants in the vehicle, and based on the distribution of the occupants in the vehicle and the influence model of the occupant distribution on the vehicle body suspension system, the angle between the illumination direction of the LED headlight and the horizontal plane is calculated as the characteristic angle;

[0087] An angle adjustment device is provided at the connection between the LED lamp and the vehicle body, and the lighting direction of the LED lamp is adjusted using the angle adjustment device according to the characteristic angle;

[0088] Based on big data, obtain the safe braking distance for normal vehicle driving, and based on historical data, obtain the characteristic light intensity of the lighting distance as the safe braking distance;

[0089] Model the attenuation of light by fog, snow, and rain;

[0090] Identify the weather conditions to obtain the real-time weather conditions. When there is fog, snow or rain in the real-time weather conditions, input the real-time weather conditions into the attenuation model of fog, snow and rain to obtain the light intensity attenuation coefficient. When there is no fog, snow or rain in the real-time weather conditions, the light intensity attenuation coefficient is set to 1.

[0091] A light reflection model of snow is established. When there is snowfall in the driving environment, the snow coverage is identified to obtain the snow coverage ratio. Based on the snow coverage ratio and the light reflection model of snow, the light intensity reflection coefficient is obtained. When there is no snowfall in the driving environment, the light intensity reflection coefficient is set to 0.

[0092] The light source in the environment is identified to obtain the ambient light intensity. Based on the light intensity attenuation coefficient and the light intensity reflection coefficient, the target light intensity is calculated and the lighting intensity of the LED headlight is adjusted to the target light intensity.

[0093] The weight of people in the vehicle is different, and their sitting postures are also different. In particular, the position of people in the back seat changes greatly, which will cause the body of the vehicle to tilt significantly, and the headlights will also tilt accordingly, which will cause the lighting direction to change. When the headlights tilt downward, the lighting distance will be shortened, and when the headlights tilt upward, the lighting distance will also be shortened because the light projected onto the road is reduced. Therefore, it is necessary to predict the tilt of the body caused by the load and adjust the headlights according to the prediction results;

[0094] In addition, fog, snow or rain will have different effects on light intensity, and corresponding models can be established to estimate this situation. However, when using the model, it is necessary to estimate the amount of fog, snow or rain, that is, the density of fog, snow or rain. The density here is the density of fog, snow or rain before it falls to the ground. At the same time, the situation of snow is more complicated, because although snowfall will weaken the light intensity, the snow will also reflect and strengthen the light. Therefore, the situation of snowfall needs to be handled differently;

[0095] Therefore, a series of steps are provided in this solution to solve the above problems in a targeted manner.

[0096] Reference Figure 2 As shown in FIG. 1 , establishing a model for the influence of occupant distribution on the vehicle body suspension system includes the following steps:

[0097] The vehicle interior image acquired by the camera device with a camera angle of view being a bird's-eye view is used as the target vehicle interior image;

[0098] Coordinate modeling in the target vehicle interior image;

[0099] Uniformly select at least one sampling point in the vehicle body as an action point, and obtain the coordinates of the action point in the target vehicle interior image;

[0100] Obtain the upper load limit of the vehicle, divide the upper load limit of the vehicle at equal intervals, and obtain at least one load point;

[0101] Randomly pair the value of the load point with the coordinates of the action point to obtain at least one test data group;

[0102] Under the conditions of the test data group, the inclination angle of the vehicle body is obtained, and the test data group is paired with the inclination angle and fitted to obtain a suspension fitting function, in which the value of the load point and the coordinates of the action point are independent variables, and the inclination angle of the vehicle body is the dependent variable. It is stipulated that when the front of the vehicle is biased upward, the inclination angle of the vehicle body is positive, and when the front of the vehicle is biased downward, the inclination angle of the vehicle body is negative.

[0103] Under the conditions of the test data set, it means that the load at the point of action in the test data set is equal to the value of the load point in the test data set;

[0104] According to physical knowledge, the situation of multiple objects can be simplified as the weight concentrated on the center of gravity of multiple objects. Therefore, in the model of the influence of personnel distribution on the vehicle body suspension system, the possible load at each point of action is sampled and fitted. Thus, the inclination of the vehicle body under various load conditions can be estimated according to the suspension fitting function. In the actual personnel load situation, multiple people can be simplified into a point mass, and the weight of multiple people can be set on the point mass. Thus, the model of the influence of personnel distribution on the vehicle body suspension system can be used to predict the inclination of the vehicle body.

[0105] Reference Figure 3 As shown, using a camera device to identify people in a car and obtaining the distribution of people in the car includes the following steps:

[0106] A miniature doll is arranged in the vehicle, the volume of the miniature doll is a characteristic volume, and at least one camera device is used to capture a picture of the vehicle interior to obtain at least one vehicle interior image;

[0107] identifying a first occupied area of ​​the miniature figure in the in-vehicle image, and averaging at least one first occupied area to obtain a first average area;

[0108] Identifying a second occupied area of ​​a person in the vehicle in the vehicle interior image, and averaging at least one second occupied area to obtain a second average area;

[0109] Based on big data, human body density is obtained and the total weight of the people in the car is calculated using the weight formula;

[0110] The overall outline of the occupant is identified in the target in-vehicle image, at least one sampling point is selected at the edge of the overall outline, and the horizontal coordinate and the vertical coordinate of the at least one sampling point are fitted to obtain a profile fitting function, wherein the horizontal coordinate of the sampling point is an independent variable and the vertical coordinate of the sampling point is a dependent variable;

[0111] Using the center of gravity formula, the coordinates of the overall center of gravity of the occupants of the vehicle are calculated, wherein the coordinates of the overall center of gravity are composed of the abscissa of the overall center of gravity and the ordinate of the overall center of gravity;

[0112] The total weight of the people in the car and the coordinates of the overall center of gravity are used as the distribution of people in the car;

[0113] The weight formula is as follows: ,

[0114] Among them, A is the total weight of the people in the car, a is the second average area, b is the first average area, c is the characteristic volume, d is the body density, and g is the gravitational acceleration; the center of gravity formula is as follows: ,

[0115] Among them, X is the abscissa of the overall centroid, Y is the ordinate of the overall centroid, the area of ​​the double integral is the area enclosed by the profile fitting function, x is the abscissa of the point in the area enclosed by the profile fitting function, and y is the ordinate of the point in the area enclosed by the profile fitting function.

[0116] When using an in-car image taken at one angle for identification, there may be errors in the identification. Therefore, the identification results of the in-car images taken at multiple angles are integrated to eliminate the errors. In order to use the impact model of occupant distribution on the vehicle body suspension system, it is necessary to obtain the coordinates of the total weight of the occupants in the car and the overall center of gravity. The proportions of the body parts of the miniature dolls and the occupants in the car are consistent, and there is only a difference in size. From the geometric relationship, we know that the ratio of the second average area to the first average area is equal to the square of the ratio of the height of the occupant to the height of the miniature doll, and the ratio of the volume of the occupant to the characteristic volume of the miniature doll is equal to the cube of the ratio of the height of the occupant to the height of the miniature doll. Therefore, the volume of the occupant can be solved by this relationship, and then the total weight of the occupants in the car can be calculated. As for the center of gravity, it can be calculated according to the center of gravity formula, which is knowledge in higher mathematics.

[0117] Calculating the angle between the LED headlight illumination direction and the horizontal plane as a characteristic angle includes the following steps:

[0118] Substitute the total weight of the occupants and the coordinates of the overall center of gravity into the suspension fitting function to obtain the characteristic angle.

[0119] Since the positive and negative characteristic angles are regulated, the angle of the LED headlights can be adjusted according to the regulations.

[0120] Reference Figure 4 As shown in the figure, establishing the attenuation model of fog, snow and rain on light includes the following steps:

[0121] The position at which the distance from the LED light is equal to the safe braking distance is taken as the characteristic position;

[0122] Obtaining a density range of fog, dividing the density range of fog at equal intervals, and obtaining at least one first identification point;

[0123] Under the condition that the density of fog is equal to the value at the first identification point, the LED headlight emits light with a characteristic light intensity to obtain a first intensity at the characteristic position;

[0124] The first intensity is divided by the characteristic light intensity to obtain a first coefficient, and the value at the first identification point is paired with the first coefficient and fitted to obtain a first fitting function;

[0125] Acquire a snowfall density range, divide the snowfall density range into equal intervals, and obtain at least one second identification point;

[0126] Under the condition that the density of snowfall is equal to the value at the second identification point, the LED headlight emits light at a characteristic light intensity to obtain a second intensity at the characteristic position;

[0127] The second intensity is divided by the characteristic light intensity to obtain a second coefficient, and the value at the second identification point is paired and fitted with the second coefficient to obtain a second fitting function;

[0128] Acquire a rainfall density range, divide the rainfall density range into equal intervals, and obtain at least one third identification point;

[0129] Under the condition that the density of rainfall is equal to the value at the third identification point, the LED headlight emits light at a characteristic light intensity to obtain a third intensity at the characteristic position;

[0130] The third intensity is divided by the characteristic light intensity to obtain a third coefficient, and the value at the third identification point is paired and fitted with the third coefficient to obtain a third fitting function;

[0131] The first fitting function, the second fitting function and the third fitting function are used as the attenuation models of light caused by fog, snow and rain.

[0132] The purpose of the light attenuation model of fog, snow and rain is to estimate the attenuation at the characteristic position, because the distance between the characteristic position and the headlight is equal to the safe braking distance. As long as the lighting is sufficient to see the objects within the safe braking distance, the safe braking distance is sufficient for the driver to react in time. Since fog, snow and rain have different effects on light attenuation, it is necessary to distinguish and establish a model.

[0133] Reference Figure 5 As shown, identifying the weather conditions and obtaining the real-time weather conditions includes the following steps:

[0134] Get the image outside the vehicle and determine whether there is fog, snow or rain in the image outside the vehicle. If not, the real-time weather condition is normal;

[0135] If so, identify the proportion of fog droplets, snow or rain in the image outside the vehicle as the area proportion;

[0136] Use the proportional transformation formula to calculate the volume percentage;

[0137] When fog appears in the image outside the car, the real-time weather condition is foggy, and the actual density of fog is equal to the volume ratio multiplied by the density of water;

[0138] When snow appears in the image outside the car, the real-time weather condition is snowy, and the actual density of snowfall is equal to the volume percentage multiplied by the density of accumulated snow;

[0139] Here, the actual density of snowfall is the density of snow floating in the air, which is the dispersed density, while the density of accumulated snow is the density of snow covering the ground, which are different;

[0140] When rain appears in the image outside the car, the real-time weather condition is rainy, and the actual density of rainfall is equal to the volume percentage multiplied by the density of water;

[0141] The proportional transformation formula is as follows: ,

[0142] Among them, B is the volume ratio and e is the area ratio.

[0143] During identification, it is necessary to obtain the actual density of fog, the actual density of snowfall or the actual density of rainfall, and then use the attenuation model of fog, snow and rain to obtain the light intensity attenuation coefficient. During identification, taking fog droplets as an example, fog droplets are actually water droplets. Therefore, the area ratio of fog droplets is identified, but since the area ratio of fog droplets is proportional to the square of the width of the fog droplets, and the volume ratio of fog droplets is proportional to the cube of the width of the fog droplets, the proportional transformation formula can be obtained through this relationship to calculate the volume ratio. Density is equal to mass divided by volume, and the actual density of fog is equal to the unit volume, the volume ratio multiplied by the density of water divided by the unit volume, that is, the volume ratio multiplied by the density of water.

[0144] Reference Figure 6 As shown, the real-time weather conditions are input into the light attenuation model of fog, snow and rain to obtain the light intensity attenuation coefficient, which includes the following steps:

[0145] When the real-time weather condition is foggy, the actual density of fog is substituted into the first fitting function to obtain the light intensity attenuation coefficient;

[0146] When the real-time weather condition is snowy, the actual snowfall density is substituted into the second fitting function to obtain the light intensity attenuation coefficient;

[0147] When the real-time weather condition is rainy, the actual rainfall density is substituted into the third fitting function to obtain the light intensity attenuation coefficient.

[0148] Reference Figure 7 As shown in the figure, establishing the light reflection model of snow includes the following steps:

[0149] Obtain the light intensity reflected by the characteristic light intensity when the environment is fully covered by snow as the calibration intensity;

[0150] The calibrated intensity is divided by the characteristic light intensity to obtain the characteristic reflection coefficient;

[0151] A reflection fitting function is formed and used as a light reflection model of the snow. The reflection fitting function is equal to hj, where h is the snow coverage ratio and j is the characteristic reflection coefficient.

[0152] The reflection of light needs to be considered in the case of snow. Since the reflection of snow is only related to the area ratio of snow, the characteristic reflection coefficient can be calculated first, and then the corresponding reflection coefficient can be calculated according to the snow coverage ratio.

[0153] Based on the snow coverage ratio and the light reflection model of the snow, obtaining the light intensity reflection coefficient includes the following steps: substituting the snow coverage ratio into the reflection fitting function to obtain the light intensity reflection coefficient.

[0154] Reference Figure 8 As shown, based on the light intensity attenuation coefficient and the light intensity reflection coefficient, calculating the target light intensity includes the following steps:

[0155] When the ambient light intensity is not less than the characteristic light intensity, the target light intensity is 0;

[0156] When the ambient light intensity is less than the characteristic light intensity, the characteristic light intensity minus the ambient light intensity is obtained to obtain the light intensity to be compensated;

[0157] Use the comprehensive formula of light intensity to calculate the target light intensity;

[0158] The comprehensive formula for light intensity is as follows: ,

[0159] Among them, C is the target light intensity, k is the light intensity to be compensated, m is the light intensity attenuation coefficient, and n is the light intensity reflection coefficient.

[0160] Here, the situation where the ambient light intensity is not less than the characteristic light intensity is during the day or when the brightness is relatively high, such as when there are street lights. In these situations, there is no need for headlights to illuminate, but for situations where the brightness is insufficient, headlights are required to compensate. When there is no snowfall, n is 0. The result calculated by the comprehensive formula for light intensity can ensure that when the headlights illuminate according to the target light intensity, the light intensity of the headlights at the characteristic position is the light intensity to be compensated, which is superimposed with the ambient light intensity to obtain the characteristic light intensity. When there is snowfall, the reflection situation needs to be considered. Since snow hardly absorbs light, although the light emitted by the headlights is scattered, the light intensity reflected on the snow can be approximately regarded as the target light intensity. Therefore, the target light intensity calculated according to the comprehensive formula for light intensity is multiplied and superimposed with the light intensity attenuation coefficient and the light intensity reflection coefficient respectively, to obtain the light intensity to be compensated. This shows that when the light intensity of the headlights is the target light intensity, the light intensity of the headlights at the characteristic position that is reflected by the snow and weakened by snowfall is the light intensity to be compensated, which is superimposed with the ambient light intensity to obtain the characteristic light intensity. Therefore, it meets the lighting needs and is also the critical light intensity with less energy consumption.

[0161] Furthermore, the present solution also proposes a storage medium on which a computer-readable program is stored. When the computer-readable program is called, the above-mentioned adaptive LED vehicle light control strategy optimization method based on environmental adaptation is executed.

[0162] It is understandable that the storage medium may be a magnetic medium, such as a floppy disk, a hard disk, or a magnetic tape; an optical medium, such as a DVD; or a semiconductor medium, such as a solid state drive (SSD).

[0163] In summary, the advantages of the present invention are: by establishing a model for the impact of personnel distribution on the vehicle body suspension system, establishing a light attenuation model for fog, snow and rain, and establishing a light reflection model for snow, the target light intensity is calculated, and the obtained target light intensity is the optimal result of comprehensive various environmental factors. Under the influence of various weather factors, sufficient lighting can still be achieved with a lower light intensity, so that the lighting distance is sufficient for braking reaction, thereby achieving energy-saving effects. At the same time, the impact of the vehicle body load on the lighting angle of the headlights is predicted, and corresponding adjustments are made to ensure that the headlights are always level, so that the lighting distance will not be shortened, and sufficient braking reaction time is reserved.

[0164] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions only describe the principles of the present invention. The present invention may be subject to various changes and improvements without departing from the spirit and scope of the present invention. These changes and improvements fall within the scope of the present invention. The scope of protection claimed by the present invention is defined by the attached claims and their equivalents.

Claims

1. An adaptive LED headlight control strategy optimization method based on environmental adaptation, characterized in that: include: Establish a model for the impact of occupant distribution on the vehicle body suspension system; At least one camera device is arranged in the vehicle, wherein the camera angle of one camera device is a bird's-eye view, and the camera device is used to identify the occupants in the vehicle to obtain the distribution of the occupants in the vehicle, and based on the distribution of the occupants in the vehicle and the influence model of the occupant distribution on the vehicle body suspension system, the angle between the illumination direction of the LED headlight and the horizontal plane is calculated as the characteristic angle; An angle adjustment device is provided at the connection between the LED lamp and the vehicle body, and the lighting direction of the LED lamp is adjusted using the angle adjustment device according to the characteristic angle; Based on big data, obtain the safe braking distance for normal vehicle driving, and based on historical data, obtain the characteristic light intensity of the lighting distance as the safe braking distance; Model the attenuation of light by fog, snow, and rain; Identify the weather conditions to obtain the real-time weather conditions. When there is fog, snow or rain in the real-time weather conditions, input the real-time weather conditions into the attenuation model of fog, snow and rain to obtain the light intensity attenuation coefficient. When there is no fog, snow or rain in the real-time weather conditions, the light intensity attenuation coefficient is set to 1. A light reflection model of snow is established. When there is snowfall in the driving environment, the snow coverage is identified to obtain the snow coverage ratio. Based on the snow coverage ratio and the light reflection model of snow, the light intensity reflection coefficient is obtained. When there is no snowfall in the driving environment, the light intensity reflection coefficient is set to 0. The light source in the environment is identified to obtain the ambient light intensity. Based on the light intensity attenuation coefficient and the light intensity reflection coefficient, the target light intensity is calculated and the lighting intensity of the LED headlight is adjusted to the target light intensity.

2. The method for optimizing the adaptive LED vehicle light control strategy based on environmental adaptation according to claim 1, characterized in that: The establishment of the influence model of personnel distribution on the vehicle body suspension system comprises the following steps: The vehicle interior image acquired by the camera device with a camera angle of view being a bird's-eye view is used as the target vehicle interior image; Coordinate modeling in the target vehicle interior image; Uniformly select at least one sampling point in the vehicle body as an action point, and obtain the coordinates of the action point in the target vehicle interior image; Obtain the upper load limit of the vehicle, divide the upper load limit of the vehicle at equal intervals, and obtain at least one load point; Randomly pair the value of the load point with the coordinates of the action point to obtain at least one test data group; Under the conditions of the test data group, the inclination angle of the vehicle body is obtained, and the test data group is paired with the inclination angle and fitted to obtain a suspension fitting function, in which the value of the load point and the coordinates of the action point are independent variables, and the inclination angle of the vehicle body is the dependent variable. It is stipulated that when the front of the vehicle is biased upward, the inclination angle of the vehicle body is positive, and when the front of the vehicle is biased downward, the inclination angle of the vehicle body is negative.

3. The method for optimizing the adaptive LED vehicle light control strategy based on environmental adaptation according to claim 2, characterized in that: The method of using a camera device to identify people in the vehicle and obtain the distribution of people in the vehicle comprises the following steps: A miniature doll is arranged in the vehicle, the volume of the miniature doll is a characteristic volume, and at least one camera device is used to capture a picture of the vehicle interior to obtain at least one vehicle interior image; identifying a first occupied area of ​​the miniature figure in the in-vehicle image, and averaging at least one first occupied area to obtain a first average area; Identifying a second occupied area of ​​a person in the vehicle in the vehicle interior image, and averaging at least one second occupied area to obtain a second average area; Based on big data, human body density is obtained and the total weight of the people in the car is calculated using the weight formula; The overall outline of the occupant is identified in the target in-vehicle image, at least one sampling point is selected at the edge of the overall outline, and the horizontal coordinate and the vertical coordinate of the at least one sampling point are fitted to obtain a profile fitting function, wherein the horizontal coordinate of the sampling point is an independent variable and the vertical coordinate of the sampling point is a dependent variable; Using the center of gravity formula, the coordinates of the overall center of gravity of the occupants of the vehicle are calculated, wherein the coordinates of the overall center of gravity are composed of the abscissa of the overall center of gravity and the ordinate of the overall center of gravity; The total weight of the people in the car and the coordinates of the overall center of gravity are used as the distribution of people in the car; The weight formula is as follows: , Where A is the total weight of the people in the car, a is the second average area, b is the first average area, c is the characteristic volume, d is the human body density, and g is the acceleration of gravity; The center of gravity formula is as follows: , Among them, X is the abscissa of the overall centroid, Y is the ordinate of the overall centroid, the area of ​​the double integral is the area enclosed by the profile fitting function, x is the abscissa of the point in the area enclosed by the profile fitting function, and y is the ordinate of the point in the area enclosed by the profile fitting function.

4. The method for optimizing the adaptive LED vehicle light control strategy based on environmental adaptation according to claim 3, characterized in that: The calculation to obtain the angle between the LED headlight illumination direction and the horizontal plane as a characteristic angle includes the following steps: Substitute the total weight of the occupants and the coordinates of the overall center of gravity into the suspension fitting function to obtain the characteristic angle.

5. The method for optimizing the adaptive LED vehicle light control strategy based on environmental adaptation according to claim 4, characterized in that: The establishment of the attenuation model of fog, snow and rain for light comprises the following steps: The position at which the distance from the LED light is equal to the safe braking distance is taken as the characteristic position; Obtaining a density range of fog, dividing the density range of fog at equal intervals, and obtaining at least one first identification point; Under the condition that the density of fog is equal to the value at the first identification point, the LED headlight emits light with a characteristic light intensity to obtain a first intensity at the characteristic position; The first intensity is divided by the characteristic light intensity to obtain a first coefficient, and the value at the first identification point is paired with the first coefficient and fitted to obtain a first fitting function; Acquire a snowfall density range, divide the snowfall density range into equal intervals, and obtain at least one second identification point; Under the condition that the density of snowfall is equal to the value at the second identification point, the LED headlight emits light at a characteristic light intensity to obtain a second intensity at the characteristic position; The second intensity is divided by the characteristic light intensity to obtain a second coefficient, and the value at the second identification point is paired and fitted with the second coefficient to obtain a second fitting function; Acquire a rainfall density range, divide the rainfall density range into equal intervals, and obtain at least one third identification point; Under the condition that the density of rainfall is equal to the value at the third identification point, the LED headlight emits light at a characteristic light intensity to obtain a third intensity at the characteristic position; The third intensity is divided by the characteristic light intensity to obtain a third coefficient, and the value at the third identification point is paired and fitted with the third coefficient to obtain a third fitting function; The first fitting function, the second fitting function and the third fitting function are used as the attenuation models of light caused by fog, snow and rain.

6. The method for optimizing the adaptive LED vehicle light control strategy based on environmental adaptation according to claim 5, characterized in that: The identification of weather conditions to obtain real-time weather conditions includes the following steps: Get the image outside the vehicle and determine whether there is fog, snow or rain in the image outside the vehicle. If not, the real-time weather condition is normal; If so, identify the proportion of fog droplets, snow or rain in the image outside the vehicle as the area proportion; Use the ratio transformation formula to calculate the volume percentage; When fog appears in the image outside the car, the real-time weather condition is foggy, and the actual density of fog is equal to the volume ratio multiplied by the density of water; When snow appears in the image outside the car, the real-time weather condition is snowy, and the actual density of snowfall is equal to the volume percentage multiplied by the density of accumulated snow; When rain appears in the image outside the car, the real-time weather condition is rainy, and the actual density of rainfall is equal to the volume percentage multiplied by the density of water; The proportional transformation formula is as follows: , Among them, B is the volume percentage and e is the area percentage.

7. The method for optimizing the adaptive LED vehicle light control strategy based on environmental adaptation according to claim 6, characterized in that: The step of inputting the real-time weather conditions into the attenuation model of fog, snow and rain to obtain the light intensity attenuation coefficient comprises the following steps: When the real-time weather condition is foggy, the actual density of fog is substituted into the first fitting function to obtain the light intensity attenuation coefficient; When the real-time weather condition is snowy, the actual snowfall density is substituted into the second fitting function to obtain the light intensity attenuation coefficient; When the real-time weather condition is rainy, the actual rainfall density is substituted into the third fitting function to obtain the light intensity attenuation coefficient.

8. The method for optimizing the adaptive LED vehicle light control strategy based on environmental adaptation according to claim 7, characterized in that: The step of establishing the light reflection model of snow comprises the following steps: Obtain the light intensity reflected by the characteristic light intensity when the environment is fully covered by snow as the calibration intensity; The calibrated intensity is divided by the characteristic light intensity to obtain the characteristic reflection coefficient; A reflection fitting function is formed and used as a light reflection model of the snow. The reflection fitting function is equal to hj, where h is the snow coverage ratio and j is the characteristic reflection coefficient.

9. The method for optimizing the adaptive LED vehicle light control strategy based on environmental adaptation according to claim 8, characterized in that: The step of obtaining the light intensity reflection coefficient based on the snow coverage ratio and the light reflection model of the snow comprises the following steps: Substitute the snow cover ratio into the reflectance fitting function to obtain the light intensity reflectance coefficient.

10. The method for optimizing the adaptive LED vehicle light control strategy based on environmental adaptation according to claim 9, characterized in that: The step of calculating the target light intensity based on the light intensity attenuation coefficient and the light intensity reflection coefficient comprises the following steps: When the ambient light intensity is not less than the characteristic light intensity, the target light intensity is 0; When the ambient light intensity is less than the characteristic light intensity, the characteristic light intensity minus the ambient light intensity is obtained to obtain the light intensity to be compensated; Use the comprehensive formula of light intensity to calculate the target light intensity; The comprehensive formula of light intensity is as follows: , Among them, C is the target light intensity, k is the light intensity to be compensated, m is the light intensity attenuation coefficient, and n is the light intensity reflection coefficient.

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