HUD sunlight backflow dynamic protection system and method based on multi-source perception

Abstract

The invention provides an HUD sunlight backward flow dynamic protection system and method based on multi-source perception, and the method comprises the steps: determining the incident angle of the sun relative to a windshield, predicting the position of a reflection light spot according to the incident angle of the sun relative to the windshield, and obtaining the temperature of each assembly position in an HUD and the internal environment temperature of the HUD in real time, and according to the position of the reflected light spot, the temperature of the position of each component in the HUD and the internal environment temperature of the HUD, the sunlight backward flow risk level of the components in the HUD is judged, and according to the sunlight backward flow risk level of the components in the HUD, the brightness attenuation of the HUD and the movement of a rotating mirror are controlled, so that the dynamic protection of sensitive components in the HUD is realized. According to the system and the method, astronomical-level sun position prediction and micro-scale temperature field analysis are combined, the problem of misjudgment of a single signal is solved, the accident rate of backward sunlight damage is reduced, the protection efficiency is improved, a hierarchical response mechanism is formulated, and user experience decline caused by excessive protection is avoided.

Description

Technical Field

[0001] This invention relates to the field of head-up display technology, and in particular to a dynamic protection system for backlighting in a head-up display (HUD) based on multi-source sensing, and a dynamic protection method for backlighting in a head-up display based on multi-source sensing. Background Technology

[0002] Sunlight reversion (or solar load) in augmented reality head-up displays (AR-HUDs) refers to the phenomenon where strong sunlight shines on the HUD's reflective optical system (especially the windshield or combiner), and after reflection, the light is accidentally focused or intensely shines on sensitive components inside the HUD (such as the projection unit, image source, or brightness sensor).

[0003] Its core principles can be summarized as follows:

[0004] 1. Optical Path Design of HUD: The basic working principle of HUD is to project light emitted from an image source (usually a miniature display such as TFT or DLP) through a series of optical elements (mirrors, lenses) onto the windshield or a dedicated combiner. The windshield or combiner acts as a partial reflector, reflecting some of the light to the driver's eyes to form a virtual image; at the same time, it allows most of the light to pass through, allowing the driver to see the real scene outside the vehicle. This reflective surface is essentially concave (to magnify the virtual image and make it appear farther away), which lays the groundwork for focusing sunlight.

[0005] 2. Sunlight incidence: When the sun is at a specific angle in front of the vehicle (usually at a low angle, such as in the morning or evening), strong parallel sunlight beams will shine into the vehicle's windshield (i.e., the reflective surface of the HUD) in a specific direction.

[0006] 3. Unexpected Reflections and Focusing: Due to the concave curvature of the windshield or assemblies and the presence of partially reflective coatings, not all incident sunlight will pass through. Some sunlight will be reflected back. This concave reflector may focus or strongly converge the reflected sunlight into the optical path inside the HUD system. This focal point or high-energy area may very well fall on the image generation unit (PGU) inside the HUD (such as the LCD panel or DLP chip), the projection lens, or the ambient light sensor responsible for automatically adjusting brightness.

[0007] Sunlight focused inside the HUD can cause the following problems:

[0008] 1. Image source damage (screen burn-in): Focused sunlight spots can generate extremely high local temperatures (similar to using a magnifying glass to touch paper), which may permanently damage delicate, heat-sensitive components such as the LCD liquid crystal layer, DLP micromirrors, or color filters.

[0009] 2. Sensor failure / misjudgment: Strong light shining directly on the ambient light sensor will cause the sensor to saturate and incorrectly judge that the ambient light is extremely dark (because it is "blinded"), which may lead to incorrectly lowering the HUD brightness so that it cannot be seen at all in strong light, or conversely, incorrectly triggering the maximum brightness.

[0010] 3. Aging / deformation of optical components: Long-term or strong sunlight exposure will accelerate the aging, deformation or yellowing of internal plastic lenses, adhesives and other materials.

[0011] 4. Stray light interference: Even if the image is not focused, strong reflected light entering the optical path will create stray light, reducing image contrast and clarity.

[0012] Existing HUD designs employ various measures to prevent or mitigate backflow of sunlight:

[0013] 1. Optical design optimization: Carefully design the curvature of the reflective surface, the layout of optical components and the optical path to avoid the focal point falling on sensitive parts under common solar incidence angles.

[0014] 2. Light shield / aperture: Set up physical light shielding structures at key locations to block stray light at specific angles from entering the internal light path.

[0015] 3. Selective reflective coating: Optimize the reflective coating characteristics of the windshield or combination unit so that it mainly reflects light of a specific wavelength emitted by the HUD projection unit, while having a lower reflectivity to light of other angles or wavelengths (such as sunlight).

[0016] 4. Internal light-absorbing material: Black, high-light-absorbing material is used on the inner wall of the HUD housing to absorb stray light entering the system.

[0017] 5. Thermal Management: Add heat dissipation design to areas where high temperatures may be generated (such as near the image source).

[0018] 6. Sensor location optimization: Place the ambient light sensor in a location where it is not easily exposed to direct sunlight.

[0019] In summary, the essence of HUD sunlight backflow is that strong external sunlight is accidentally reflected and focused onto the internal sensitive elements of the HUD by the concave mirror used to reflect the virtual image, leading to potential device overheating damage, sensor failure, or performance degradation. This is a significant environmental reliability challenge in HUD design, requiring careful optical, mechanical, and materials design to address.

[0020] While optical design and material optimization are key to addressing sunlight backflow in HUD (Head-Up Display) design, both have significant limitations. These limitations directly impact HUD performance, reliability, and cost, especially in the era of AR-HUDs that prioritize larger field of view (FOV) and longer virtual image distance (VID).

[0021] I. Limitations of Optical Design

[0022] 1. Physical constraints of surface geometry

[0023] Uncontrollable windshield curvature:

[0024] The curvature of a car windshield is determined by aerodynamics and safety regulations and cannot be optimized separately for a HUD. Its fixed curvature may cause sunlight to be reflected and focused on sensitive areas (such as the PGU), and designers can only passively adjust the internal light path to avoid this.

[0025] Manufacturing errors of aspherical / freeform mirrors:

[0026] The complex curved mirrors used to correct optical paths are difficult to manufacture with nanometer-level precision. Even minute curvature deviations can cause the sunlight's focal point to shift, potentially aligning it with sensitive components.

[0027] 2. Conflict between field of view (FOV) and sunlight backflow

[0028] Increased FOV exacerbates the risk of backflow:

[0029] A larger field of view (FOV) requires a wider beam angle, resulting in sunlight potentially entering the light path from more directions. For example, when the FOV is expanded from 5°×2° to 10°×5°, the area at risk of sunlight entering the light increases fourfold.

[0030] Side effects of increased virtual image distance (VID):

[0031] Longer VID (e.g., 15m or more) requires stronger optical magnification to further amplify the focusing effect of the concave mirror, making it easier to concentrate sunlight energy.

[0032] 3. Spatial limitations of optical path layout

[0033] PGU location cannot be completely isolated:

[0034] Due to the limited depth of the instrument panel, the PGU is often forced to be placed in the "danger zone" of the sunlight reflection path. Even with a multi-stage reflection and folding optical path, it may still be hit by sunlight reflected a second time.

[0035] The contradiction of light blocking by baffles:

[0036] While adding a light shield can block stray light, it will also cut off the effective display light, reduce the brightness of the image edges, or cause vignetting.

[0037] II. Limitations of Material Optimization

[0038] 1. Physical bottlenecks of coating technology

[0039] Spectral conflicts in narrow-band coatings:

[0040] HUDs need to reflect visible light from the PGU (e.g., 450–650 nm), but the peak intensity of sunlight is precisely in the 500–600 nm band. Excessively suppressing the reflectivity in this band will simultaneously weaken the brightness of the HUD image.

[0041] Typical data: The optimal coating can only reduce solar reflectance to 8–12%, while PGU light reflectance needs to be >25%.

[0042] Wide-temperature-range performance degradation:

[0043] The coating may crack or shift its refractive index within the automotive-grade temperature range of -40℃ to 85℃, resulting in the failure of its reflective properties.

[0044] 2. The efficiency ceiling of light-absorbing materials

[0045] The trade-off between absorbance and space occupancy:

[0046] High-absorbency materials (such as carbon nanotube coatings) require a certain thickness to absorb more than 95% of stray light, but the internal space of a HUD is typically <100mm. 3 It is difficult to deploy.

[0047] Heat saturation risk:

[0048] The light-absorbing material converts light energy into heat energy. Under continuous backflow of sunlight, it may exceed the heat dissipation limit (e.g., >120℃) and become a heat source to bake the PGU.

[0049] 3. Applicability limitations of polarization schemes

[0050] The polarization characteristics of windshields are uncontrollable.

[0051] The stress distribution in ordinary windshields can cause random changes in polarization state, and the polarization component of reflected sunlight may still enter the system.

[0052] Cost and compatibility:

[0053] A custom-designed polarization PGU+ polarization reflective layer is required, which increases the cost by more than 30%, and interference with polarized sunglasses can cause image loss.

[0054] It is evident that purely physical protection solutions have many shortcomings, and there is an urgent need for better protection solutions that differ from purely physical protection. Summary of the Invention

[0055] In view of the above problems, the present invention is proposed to provide a dynamic protection system for HUD sunlight backflow based on multi-source sensing and a dynamic protection method for HUD sunlight backflow based on multi-source sensing, which overcomes or at least partially solves the above problems.

[0056] This invention provides a dynamic protection system for backflow sunlight from a HUD based on multi-source sensing, comprising:

[0057] The solar position prediction module is used to determine the angle of incidence of the sun relative to the windshield and predict the position of the reflected light spot based on the angle of incidence of the sun relative to the windshield.

[0058] Temperature monitoring array module is used to acquire the temperature of each component inside the HUD and the ambient temperature inside the HUD in real time;

[0059] The risk decision module is used to determine the risk level of sunlight backflow into the internal components of the HUD based on the location of the reflected light spot, the temperature of each component inside the HUD, and the ambient temperature inside the HUD.

[0060] The dynamic actuator module is used to control the brightness decay and mirror movement of the HUD based on the risk level of sunlight backflow into the internal components of the HUD.

[0061] Optionally, the solar position prediction module is used for:

[0062] Based on real-time vehicle pose and real-time time, astronomical algorithms are used to calculate the angle of incidence of the sun relative to the windshield, establish a local coordinate system for the windshield, and project the vector of the angle of incidence of the sun relative to the windshield onto the windshield plane to predict the position of the reflected light spot.

[0063] or,

[0064] Based on the real-time vehicle location and time, the incident angle of the sun relative to the windshield is queried from the pre-stored table of future sun position data. A local coordinate system of the windshield is established, and the vector of the incident angle of the sun relative to the windshield is projected onto the windshield plane to predict the position of the reflected light spot. The pre-stored table of future sun position data is obtained by pre-calculating and storing the incident angle of the vehicle relative to the windshield at different times and positions in the future.

[0065] Optionally, the risk decision module is used for:

[0066] Calculate the temperature difference between the temperature of each sensitive component inside the HUD and the ambient temperature inside the HUD;

[0067] Determine whether the reflected light spot falls on the sensitive component inside the HUD based on the position of the reflected light spot.

[0068] Based on the preset multi-level solar backflow risk response strategy, the temperature difference between the temperature of each sensitive component inside the HUD and the temperature of the HUD's internal environment, and whether the reflected light spot falls on the sensitive component inside the HUD, the solar backflow risk level of the sensitive component is determined.

[0069] Optionally, the temperature monitoring array module further includes a temperature compensation unit, which is used for:

[0070] Real-time humidity, real-time air pressure, measured temperature of each component inside the HUD, and measured temperature of the HUD's internal environment are collected. Based on the real-time humidity and real-time air pressure, a pre-calibrated temperature compensation model is used to calculate the compensation values ​​of the measured temperature of each component inside the HUD and the measured temperature of the HUD's internal environment, so as to obtain the true temperature of each component inside the HUD and the true temperature of the HUD's internal environment.

[0071] or,

[0072] Real-time humidity, real-time air pressure, measured temperature of each component inside the HUD, and measured temperature of the HUD's internal environment are collected. Based on the real-time humidity and real-time air pressure, the temperature compensation value corresponding to the real-time humidity and real-time air pressure is found from a preset temperature compensation table to obtain the true temperature of each component inside the HUD and the true temperature of the HUD's internal environment. The preset temperature compensation table is obtained in advance through calibration experiments simulating different combinations of humidity and air pressure.

[0073] Optionally, the risk decision module is further configured to:

[0074] Determine whether the temperature difference between the temperature of each sensitive component inside the HUD and the ambient temperature inside the HUD is greater than the preset temperature rise threshold.

[0075] If not, then there is no risk of sunlight backflow.

[0076] If so, and the reflected light spot falls on the sensitive component inside the HUD, then there is a risk of solar backflow. Based on the first correspondence between temperature difference and solar backflow risk level in the preset multi-level solar backflow risk level response strategy, and the temperature difference between the temperature of each sensitive component inside the HUD and the temperature of the HUD's internal environment, the solar backflow risk level corresponding to the sensitive component is determined.

[0077] If so, and the reflected light spot does not fall on the sensitive components inside the HUD, then there is no risk of solar backflow. The system then enters the local temperature gradient detection phase. Based on the second correspondence between temperature difference and solar backflow risk level in the preset multi-level solar backflow risk level response strategy, and the temperature difference between the temperature of each sensitive component inside the HUD and the temperature of the HUD's internal environment, the solar backflow risk level corresponding to the sensitive component is determined. The temperature difference in the second correspondence is greater than the temperature difference in the first correspondence.

[0078] This invention also provides a dynamic protection method for HUD sunlight backflow based on multi-source sensing, the method comprising:

[0079] Determine the angle of incidence of the sun relative to the windshield, and predict the location of the reflected light spot based on the angle of incidence of the sun relative to the windshield.

[0080] Real-time acquisition of the temperature of each component inside the HUD and the ambient temperature inside the HUD;

[0081] Based on the location of the reflected light spot, the temperature of each component inside the HUD, and the ambient temperature inside the HUD, the risk level of sunlight backflow into the internal components of the HUD is determined.

[0082] Based on the risk level of sunlight backflow into the internal components of the HUD, control the HUD brightness decay and mirror rotation.

[0083] Optionally, the angle of incidence of the sun relative to the windshield is determined, and the location of the reflected light spot is predicted based on the angle of incidence of the sun relative to the windshield, including:

[0084] Based on real-time vehicle pose and real-time time, astronomical algorithms are used to calculate the angle of incidence of the sun relative to the windshield, establish a local coordinate system for the windshield, and project the vector of the angle of incidence of the sun relative to the windshield onto the windshield plane to predict the position of the reflected light spot.

[0085] or,

[0086] Based on the real-time vehicle location and time, the incident angle of the sun relative to the windshield is queried from the pre-stored table of future sun position data. A local coordinate system of the windshield is established, and the vector of the incident angle of the sun relative to the windshield is projected onto the windshield plane to predict the position of the reflected light spot. The pre-stored table of future sun position data is obtained by pre-calculating and storing the incident angle of the vehicle relative to the windshield at different times and positions in the future.

[0087] Optionally, the risk level of solar backflow into the internal components of the HUD is determined based on the location of the reflected light spot, the temperature of each component inside the HUD, and the ambient temperature inside the HUD, including:

[0088] Calculate the temperature difference between the temperature of each sensitive component inside the HUD and the ambient temperature inside the HUD;

[0089] Determine whether the reflected light spot falls on the sensitive component inside the HUD based on the position of the reflected light spot.

[0090] Based on the preset multi-level solar backflow risk response strategy, the temperature difference between the temperature of each sensitive component inside the HUD and the temperature of the HUD's internal environment, and whether the reflected light spot falls on the sensitive component inside the HUD, the solar backflow risk level of the sensitive component is determined.

[0091] Optionally, the temperature of each component inside the HUD and the ambient temperature inside the HUD can be acquired in real time, including:

[0092] Real-time humidity, real-time air pressure, measured temperature of each component inside the HUD, and measured temperature of the HUD's internal environment are collected. Based on the real-time humidity and real-time air pressure, a pre-calibrated temperature compensation model is used to calculate the compensation values ​​of the measured temperature of each component inside the HUD and the measured temperature of the HUD's internal environment, so as to obtain the true temperature of each component inside the HUD and the true temperature of the HUD's internal environment.

[0093] or,

[0094] Real-time humidity, real-time air pressure, measured temperature of each component inside the HUD, and measured temperature of the HUD's internal environment are collected. Based on the real-time humidity and real-time air pressure, the temperature compensation value corresponding to the real-time humidity and real-time air pressure is found from a preset temperature compensation table to obtain the true temperature of each component inside the HUD and the true temperature of the HUD's internal environment. The preset temperature compensation table is obtained in advance through calibration experiments simulating different combinations of humidity and air pressure.

[0095] Optionally, based on a preset multi-level solar backflow risk response strategy, the temperature difference between the temperature of each sensitive component inside the HUD and the ambient temperature inside the HUD, and whether the reflected light spot falls on the sensitive component inside the HUD, the solar backflow risk level of the sensitive component is determined, including:

[0096] Determine whether the temperature difference between the temperature of each sensitive component inside the HUD and the ambient temperature inside the HUD is greater than the preset temperature rise threshold.

[0097] If not, then there is no risk of sunlight backflow.

[0098] If so, and the reflected light spot falls on the sensitive component inside the HUD, then there is a risk of solar backflow. Based on the first correspondence between temperature difference and solar backflow risk level in the preset multi-level solar backflow risk level response strategy, and the temperature difference between the temperature of each sensitive component inside the HUD and the temperature of the HUD's internal environment, the solar backflow risk level corresponding to the sensitive component is determined.

[0099] If so, and the reflected light spot does not fall on the sensitive components inside the HUD, then there is no risk of solar backflow. The system then enters the local temperature gradient detection phase. Based on the second correspondence between temperature difference and solar backflow risk level in the preset multi-level solar backflow risk level response strategy, and the temperature difference between the temperature of each sensitive component inside the HUD and the temperature of the HUD's internal environment, the solar backflow risk level corresponding to the sensitive component is determined. The temperature difference in the second correspondence is greater than the temperature difference in the first correspondence.

[0100] This invention has the following advantages:

[0101] This invention presents a dynamic protection system and method for HUD (Head-Up Display) solar backflow protection based on multi-source sensing. It determines the angle of incidence of the sun relative to the windshield, predicts the position of the reflected light spot based on this angle, and acquires the real-time temperature of each component inside the HUD and the internal ambient temperature. Furthermore, based on the reflected light spot position, the temperature of each component, and the internal ambient temperature, it determines the solar backflow risk level of the internal components. According to this risk level, it controls the HUD brightness decay and mirror rotation to achieve dynamic protection of sensitive internal components. This system and method combine astronomical solar position prediction with microscale temperature field analysis to solve the problem of misjudgment based on a single signal, reduce the accident rate of solar backflow damage, improve protection efficiency, and establish a graded response mechanism to avoid user experience degradation caused by over-protection. In addition, the dual-channel verification mechanism can meet functional safety certification requirements and is less expensive than purely physical protection solutions. Attached Figure Description

[0102] Figure 1 This is a structural block diagram of the HUD dynamic protection system for backflow of sunlight based on multi-source sensing provided in an embodiment of the present invention;

[0103] Figure 2 This is a flowchart of the astronomical algorithm provided by the present invention;

[0104] Figure 3 This is a flowchart of the solar position calculation provided in an embodiment of the present invention;

[0105] Figure 4 This is a flowchart of the solar backflow risk assessment provided in an embodiment of the present invention;

[0106] Figure 5 This is a flowchart of the steps of the HUD dynamic protection method for backflow of sunlight based on multi-source sensing provided in the embodiments of the present invention. Detailed Implementation

[0107] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0108] Reference Figure 1 The diagram illustrates the structural block diagram of the HUD dynamic protection system for backflow prevention based on multi-source sensing provided in an embodiment of the present invention, which may specifically include the following modules:

[0109] The solar position prediction module is used to determine the angle of incidence of the sun relative to the windshield and predict the position of the reflected light spot based on the angle of incidence of the sun relative to the windshield.

[0110] Temperature monitoring array module is used to acquire the temperature of each component inside the HUD and the ambient temperature inside the HUD in real time;

[0111] The risk decision module is used to determine the risk level of sunlight backflow into the internal components of the HUD based on the location of the reflected light spot, the temperature of each component inside the HUD, and the ambient temperature inside the HUD.

[0112] The dynamic actuator module is used to control the brightness decay and mirror movement of the HUD based on the risk level of sunlight backflow into the internal components of the HUD.

[0113] In an optional embodiment of the present invention, the risk decision module is used for:

[0114] Calculate the temperature difference between the temperature of each sensitive component inside the HUD and the ambient temperature inside the HUD;

[0115] Determine whether the reflected light spot falls on the sensitive component inside the HUD based on the position of the reflected light spot.

[0116] Based on the preset multi-level solar backflow risk response strategy, the temperature difference between the temperature of each sensitive component inside the HUD and the temperature of the HUD's internal environment, and whether the reflected light spot falls on the sensitive component inside the HUD, the solar backflow risk level of the sensitive component is determined.

[0117] In an optional embodiment of the present invention, the risk decision module is further configured to:

[0118] Determine whether the temperature difference between the temperature of each sensitive component inside the HUD and the ambient temperature inside the HUD is greater than the preset temperature rise threshold.

[0119] If not, then there is no risk of sunlight backflow.

[0120] If so, and the reflected light spot falls on the sensitive component inside the HUD, then there is a risk of solar backflow. Based on the first correspondence between temperature difference and solar backflow risk level in the preset multi-level solar backflow risk level response strategy, and the temperature difference between the temperature of each sensitive component inside the HUD and the temperature of the HUD's internal environment, the solar backflow risk level corresponding to the sensitive component is determined.

[0121] If so, and the reflected light spot does not fall on the sensitive components inside the HUD, then there is no risk of solar backflow. The system then enters the local temperature gradient detection phase. Based on the second correspondence between temperature difference and solar backflow risk level in the preset multi-level solar backflow risk level response strategy, and the temperature difference between the temperature of each sensitive component inside the HUD and the temperature of the HUD's internal environment, the solar backflow risk level corresponding to the sensitive component is determined. The temperature difference in the second correspondence is greater than the temperature difference in the first correspondence.

[0122] This invention is a dynamic protection system for backflow sunlight from a HUD based on multi-source sensing, and its system architecture is as follows: Figure 1 As shown, the components are described below:

[0123] Solar position prediction module: Based on the vehicle's GPS coordinates, IMU sensor, and real-time clock, calculate the angle of incidence of the sun relative to the windshield;

[0124] Temperature monitoring array module: Distributed temperature sensors deployed at key nodes of the optical path inside the HUD (image generation unit surface, projection lens, and back of the folding mirror);

[0125] Risk Decision Module: Maps the solar incidence angle to the local coordinate system of the windshield. When the predicted reflected light spot covers the sensitive area and the temperature sensor detects an abnormal temperature rise, a graded response is triggered.

[0126] Dynamic actuator module: Controls HUD brightness decay and mirror rotation based on risk level.

[0127] Through the above system modules, a collaborative monitoring mechanism for HUD internal temperature and sunlight incident angle is established to achieve proactive defense against solar backflow through dynamic monitoring, verification, and protection.

[0128] The following is the specific design framework:

[0129] 1. Solar Position Prediction Module

[0130] Hardware configuration:

[0131] Vehicle GPS module (ublox F9K-0-001, positioning accuracy ±1.5m)

[0132] Three-axis electronic compass (TDKICM-42605, heading angle accuracy ±0.5°)

[0133] Real-time clock chip (MAXIM DS3231, time error ±2ppm)

[0134] Windshield mapping model calibration:

[0135] The windshield curvature data was collected using a laser scanner (Keyence LJ-V7080) on the final assembly line;

[0136] Establish a local coordinate system: the origin is the center of the HUD projection area, and the Z-axis is perpendicular to the glass and points outward.

[0137] The solar position prediction module first uses the vehicle's pose (GPS coordinates + IMU) and time information (UTC time + date) to perform a solar position prediction based on an astronomical algorithm. Figure 2 As shown in the diagram, the process of calculating the solar incidence angle using an astronomical algorithm based on vehicle coordinates, vehicle attitude, and time is as follows: The Julian day is calculated using time, followed by the calculation of the solar declination angle δ (in radians) using the Julian day. Simultaneously, the hour angle ω (in radians) is determined by combining the vehicle's geographical coordinates and time information. Then, relevant angle parameters are adjusted based on the vehicle's attitude information. Combining the solar declination angle δ, hour angle ω, and the vehicle's latitude, the altitude and azimuth angles of the sun relative to the vehicle are calculated using specific trigonometric functions and astronomical algorithm formulas, thus obtaining the current solar incidence angle in real time. Next, a local coordinate system is established on the windshield, and the solar incidence angle vector is projected onto the glass plane to predict the location of the reflected light spot (e.g., whether it covers the image generation unit area). The process is as follows: Figure 3 As shown.

[0138] 2. The temperature monitoring array module uses distributed temperature sensors deployed at key nodes inside the HUD (the surface of the image generation unit, the projection lens, and the back of the folding mirror) to obtain the real-time temperature at various locations inside the HUD.

[0139] For example, a temperature sensor can be deployed as follows:

[0140]

[0141]

[0142] 3. The risk decision module combines the predicted reflected light spot position output by the solar position prediction module, the real-time temperature of various locations inside the HUD output by the temperature monitoring array module, and the ambient temperature inside the HUD, and applies abnormal temperature judgment logic. Figure 4 As shown in the figure, the process is explained as follows:

[0143] A. Read the real-time temperature of each location inside the HUD, calculate the temperature difference (ΔT) between the surface of the image generation unit and the ambient temperature inside the HUD. If the temperature difference (ΔT) is less than the temperature rise judgment threshold, it is considered that there is no risk of solar backflow. If the temperature difference is greater than the temperature rise judgment threshold, it indicates that there is a possibility of abnormal temperature rise, and proceed to the next step of solar backflow judgment.

[0144] B. Read the predicted reflected light spot position. If the light spot position falls on the surface of the image generation unit, it is determined that sunlight backflow has occurred (θ_sun risk); if the light spot position does not fall on the surface of the image generation unit, it is determined that there is no risk of sunlight backflow (θ_sun no risk); proceed to local temperature rise gradient detection.

[0145] Based on the above judgment process, a tiered response strategy can be established as follows:

[0146] Risk level Triggering conditions Normal (L0) ΔT < 25℃ Warning (L1) ΔT = 25-30℃ and θ_sun risk or ΔT = 40-45℃ Light protection (L2) ΔT = 30-35℃ and θ_sun risk or ΔT = 45-50℃ Emergency Protection (L3) ΔT > 35℃ and θ_sun risk or ΔT > 50℃ Fault Lockout (L4) L3 continuously triggered for more than 5 minutes

[0147] The classification is explained as follows:

[0148] Normal (L0): No abnormality when the temperature difference ΔT is less than 25℃;

[0149] Warning (L1): When there is a risk of backflow of sunlight and the temperature difference ΔT is between 25-30℃, or when there is no backflow of sunlight and ΔT is between 40-45℃, the warning state is entered.

[0150] Light protection (L2): When there is a risk of backflow of sunlight and the temperature difference ΔT is between 30-35℃, or when there is no backflow of sunlight and ΔT is between 45-50℃, enter the light protection state;

[0151] Emergency Protection (L3): When there is a risk of backflow of sunlight and the temperature difference ΔT is higher than 35℃, or when there is no backflow of sunlight and ΔT is higher than 50℃, the emergency protection state is entered.

[0152] Fault Lockout (L4): If the emergency protection state lasts for more than 5 minutes, the system enters fault lockout mode.

[0153] In different scenarios and environments, the risk level, temperature rise judgment threshold, temperature difference value, and continuous triggering time in the graded response strategy can be set based on the actual situation.

[0154] 4. The dynamic execution mechanism module executes corresponding response actions based on the risk decision-making module's tiered response strategy, as follows:

[0155]

[0156] The response actions are described below:

[0157] Normal (L0): No action;

[0158] Warning (L1): Adjust HUD backlight brightness to 70% of full power; High temperature warning message.

[0159] Light protection (L2): Adjust the HUD backlight brightness to 50% of full power, and issue a high temperature warning.

[0160] Emergency Protection (L3): Adjust the HUD backlight brightness to 30% of full power, issue a high temperature warning, and sound an alarm.

[0161] Fault Lockout (L4): Disconnect the PGU power supply, completely turn off the backlight output, enter the fault lockout state, report the fault status to the system, prompt the system to record the fault information, and rotate the rotating mirror back to the park position to avoid the position of backlight;

[0162] In different scenarios and environments, the dynamic protective measures corresponding to the risk levels in the graded response strategy can be set based on the actual situation.

[0163] In an optional embodiment of the present invention, the solar position prediction module is used for:

[0164] Based on real-time vehicle pose and real-time time, astronomical algorithms are used to calculate the angle of incidence of the sun relative to the windshield, establish a local coordinate system for the windshield, and project the vector of the angle of incidence of the sun relative to the windshield onto the windshield plane to predict the position of the reflected light spot.

[0165] or,

[0166] Based on the real-time vehicle location and time, the incident angle of the sun relative to the windshield is queried from the pre-stored table of future sun position data. A local coordinate system of the windshield is established, and the vector of the incident angle of the sun relative to the windshield is projected onto the windshield plane to predict the position of the reflected light spot. The pre-stored table of future sun position data is obtained by pre-calculating and storing the incident angle of the vehicle relative to the windshield at different times and positions in the future.

[0167] In this embodiment, to address the constraints of low-computing-power embedded hardware, the PSA astronomical algorithm is simplified to a lookup table method, pre-storing annual or multi-year solar position data:

[0168] The core problem with the PSA astronomical algorithm (or similar high-precision astronomical algorithms) lies in its high computational complexity.

[0169] It involves a large number of complex calculations: calculating the eccentricity of Earth's orbit, perihelion angle, obliquity of the ecliptic, mean anomalous angle of the sun, solar longitude, right ascension, and declination. These calculations involve a large number of trigonometric functions (sin, cos, tan, arctan), square roots, iterations, and high-precision floating-point operations.

[0170] Real-time computation is costly: Every time the sun's position (elevation angle, azimuth angle) needs to be obtained, this series of complex calculations must be performed from scratch. For applications that require updates every second or even more frequently (such as high-precision solar trackers), low-computing-power embedded MCUs (microcontrollers) may not be able to meet real-time requirements, or they may consume a large amount of CPU resources, affecting the execution of other tasks.

[0171] High demand for floating-point operations: The PSA algorithm relies on high-precision floating-point operations. Many low-cost embedded MCUs (especially 8-bit, 16-bit, or low-end 32-bit MCUs) either lack a hardware floating-point unit or have a very weak FPU performance, resulting in extremely slow floating-point operations that consume a large number of clock cycles.

[0172] Breakdown of the principle of table lookup method:

[0173] Pre-calculation: On a platform with sufficient computing power (such as a PC or server), the PSA astronomical algorithm (or other high-precision astronomical algorithms) is used to calculate the solar position data of a specific location (latitude) in advance throughout the year (or even for many years).

[0174] Time resolution: Determined based on application requirements. For example, solar tracking typically requires higher resolution (e.g., one data point every 1 minute, 5 minutes, 10 minutes, or 15 minutes). The higher the accuracy requirement, the smaller the time interval.

[0175] Data content: Each time point corresponds to two core data points: solar altitude angle and solar azimuth angle. Sometimes, auxiliary information such as sunrise and sunset time markers may also be included.

[0176] Fixed dimensionality: The table is generated for a specific dimension. If the device needs to be deployed in different dimensions, then separate tables need to be generated for different dimensions, or the dimension needs to be included as an index dimension in the table.

[0177] Data storage: The pre-calculated elevation and azimuth angle data are stored in the non-volatile memory of the embedded device in chronological order (or in an easily indexable manner).

[0178] Runtime operation: Embedded devices require a precise real-time clock to obtain the current date and time.

[0179] Timestamp Conversion: Converts the current date and time into a "yearly timestamp". The simplest way is to calculate the number of minutes (or seconds, depending on the table resolution) that have elapsed since January 1, 00:00:00. That is: Yearly Minutes = (Cumulative Sum of Days in Months [month-1] + day-1) * 1440 + hour * 60 + minute.

[0180] Table lookup: Use the calculated number of minutes per year as an index to directly access the stored data table.

[0181] Output: Output the retrieved (or interpolated) elevation and azimuth values ​​to the application.

[0182] The solar position lookup table method in this embodiment completely avoids the complex astronomical formulas and numerous floating-point operations of the PSA algorithm, greatly reducing the computational load during runtime. The lookup and simple interpolation are extremely fast, completing in milliseconds or even microseconds even on low-end MCUs, easily meeting the demands of high-frequency updates and real-time requirements. Furthermore, it reduces CPU load, freeing up valuable CPU resources for other critical tasks (communication, control logic, sensor reading, etc.). In addition, by storing angle values ​​using integers or fixed-point numbers and using integer operations during interpolation, it is completely independent of the hardware FPU. Moreover, the time overhead of the lookup operation is deterministic, which is beneficial for real-time system design.

[0183] In an optional embodiment of the present invention, the temperature monitoring array module further includes a temperature compensation unit, which is used for:

[0184] Real-time humidity, real-time air pressure, measured temperature of each component inside the HUD, and measured temperature of the HUD's internal environment are collected. Based on the real-time humidity and real-time air pressure, a pre-calibrated temperature compensation model is used to calculate the compensation values ​​of the measured temperature of each component inside the HUD and the measured temperature of the HUD's internal environment, so as to obtain the true temperature of each component inside the HUD and the true temperature of the HUD's internal environment.

[0185] or,

[0186] Real-time humidity, real-time air pressure, measured temperature of each component inside the HUD, and measured temperature of the HUD's internal environment are collected. Based on the real-time humidity and real-time air pressure, the temperature compensation value corresponding to the real-time humidity and real-time air pressure is found from a preset temperature compensation table to obtain the true temperature of each component inside the HUD and the true temperature of the HUD's internal environment. The preset temperature compensation table is obtained in advance through calibration experiments simulating different combinations of humidity and air pressure.

[0187] In this embodiment, the anti-interference capability in extreme environments is improved by adding a pressure and humidity sensor to compensate for the temperature coefficient.

[0188] Temperature sensor readings (such as thermistors, RTDs, thermocouples, and semiconductor temperature sensors) are easily distorted by various environmental factors in extreme environments. These interferences do not represent the true temperature changes of the measured object and generally include the following influencing factors:

[0189] (1) Temperature coefficient drift of the temperature sensor itself:

[0190] Principle: All temperature sensors have inherent accuracy and stability specifications. At extreme temperatures (extremely high or low temperatures), the sensor's sensing element, packaging material, and internal circuit characteristics may undergo nonlinear changes.

[0191] Problem: The correlation between the sensor's output voltage / resistance / frequency and the actual temperature (i.e., its calibration curve) may shift or become distorted, causing the measured value to deviate from the actual value. This drift is usually non-linear and difficult to predict.

[0192] (2) The influence of environmental humidity:

[0193] principle:

[0194] Changes in thermal conductivity: High humidity air has a high water vapor content, which changes the thermal conductivity of the air. This affects the heat exchange rate between the sensor and the measured medium (usually air), causing the sensor to take longer to reach thermal equilibrium or resulting in deviations in the equilibrium temperature (especially in dynamic measurements or when temperature gradients are present).

[0195] Material moisture absorption and expansion: The sensor's encapsulation material (plastic, epoxy resin, etc.) may absorb moisture and expand in a high humidity environment, which will generate stress on the internal sensitive element, change its physical properties (such as resistance value, piezoelectric properties), and thus introduce additional measurement errors.

[0196] Condensation: Condensation or even frost may form on the sensor surface during rapid temperature changes or when humidity is extremely high. The presence of liquid water can drastically alter the local heat conduction path, severely distorting temperature measurements (for example, readings during condensation will approximate the dew point temperature rather than the true air temperature).

[0197] Electrochemical effects / leakage current: In high humidity environments, tiny conductive water films may form between sensor pins, circuit boards, or packages, leading to increased leakage current or parasitic electrochemical effects that interfere with the electrical signals output by the sensor.

[0198] (3) The influence of ambient air pressure:

[0199] principle:

[0200] Variation in air thermal conductivity: The thermal conductivity of a gas is pressure-dependent. Changes in air pressure (such as low air pressure at high altitudes or pressure fluctuations within a closed container) alter the thermal conductivity of the air surrounding the sensor, affecting the efficiency of heat exchange between the sensor and the environment, and thus impacting measurement accuracy (especially under free convection cooling conditions).

[0201] Changes in gas heat capacity: Changes in gas pressure also mean changes in the number of gas molecules per unit volume, thus altering the gas's heat capacity. This affects the state of thermal equilibrium between the sensor and the gas medium.

[0202] Pressure effects in sealed cavities (for some sensors): Some types of sensors (such as MEMS sensors) may contain tiny cavities. Changes in air pressure can cause deformation of the cavity or affect the thermodynamic properties of the internal gas, indirectly affecting the performance of temperature-sensitive elements.

[0203] Therefore, to combat the aforementioned interference and improve measurement accuracy and reliability under extreme temperature, humidity, and pressure environments, independent barometric pressure and humidity sensors are introduced, using their readings to compensate the temperature sensor output in real time. The core logic is as follows:

[0204] Data collection:

[0205] The system simultaneously reads raw data from three sensors: temperature sensor output (T_raw), humidity sensor output (RH), and barometric pressure sensor output (P).

[0206] Establish a compensation model:

[0207] Through rigorous calibration experiments and modeling, the influence of air pressure (P) and relative humidity (RH) on the error (ΔT) of temperature sensor readings (T_raw) was determined.

[0208] Model form (example):

[0209] Linear / polynomial model: ΔT=f(P,RH)=a0+a1*P+a2*RH+a3*P*RH+a4*P 2 +a5*RH 2 +...(coefficients a0, a1,... are obtained by fitting experimental data)

[0210] Lookup Table: Under laboratory conditions, with precise control of three variables—temperature, humidity, and air pressure—and extensive combination tests within extreme environmental ranges, the T_raw value corresponding to each (P,RH,T_true) point is recorded. A three-dimensional lookup table is constructed, taking (P,RH,T_raw) as input and outputting the compensated T_corrected. Alternatively, a two-dimensional table can be constructed, taking (P,RH) as input and outputting the compensation value ΔT under those conditions.

[0211] Physical Model: Based on the principles of thermodynamics and heat transfer, a physical equation for heat exchange between the sensor and the environment is established. Air pressure (affecting air density and thermal conductivity) and humidity (affecting thermal conductivity and latent heat) are substituted as parameters to derive a compensation formula. This method is typically more complex but has clear physical implications.

[0212] Real-time compensation calculation:

[0213] In embedded systems, a pre-calibrated compensation model (formula or lookup table) is applied based on the currently read P and RH values.

[0214] Calculate the compensation value (ΔT): Calculate the expected error ΔT of the temperature sensor reading under the current P and RH conditions based on the model.

[0215] To obtain the true temperature (T_true): T_true = T_raw - ΔT

[0216] Through temperature compensation in this embodiment, systematic errors caused by environmental interference are actively offset, making the temperature closer to the true temperature of the measured medium. This allows temperature sensors that might fail or experience a significant drop in accuracy under extreme conditions to continue providing valuable, calibrated data, reducing misjudgments or control errors caused by environmental interference, and greatly improving the reliability and stability of the temperature measurement system under extreme or rapidly changing temperature / humidity / pressure environments.

[0217] The following are two specific scenario examples of the present invention:

[0218] Example 1: Summer highway scene

[0219] (1) Input conditions:

[0220] Time: 08:30, July 15, 2025 (Beijing Time)

[0221] Location: 31.2°N, 121.5°E (towards Shanghai)

[0222] Vehicle orientation: Due east (90° azimuth)

[0223] Ambient temperature: 35℃

[0224] (2) System behavior:

[0225] Solar calculation module output: Solar altitude angle = 42.3°, azimuth angle = 94.7° → Predicted reflected light spot coverage of the PGU region center.

[0226] Temperature monitoring data: Obtain the internal temperature of the HUD, ΔT = 32℃

[0227] Risk Decision: Trigger Level 2 Response

[0228] Actuator: HUD backlight brightness reduced to 70% of full power.

[0229] Example 2: Low-angle sunlight scene in winter

[0230] (1) Input conditions:

[0231] Time: 16:00, December 22, 2025 (Harbin)

[0232] Location: 45.8°N, 126.5°E

[0233] Vehicle orientation: 230° southwest

[0234] Ambient temperature: -25℃

[0235] (2) System behavior:

[0236] Solar calculation output: Elevation angle = 12.8°, Azimuth angle = 221.5° → Predicted reflected light spot deviates from the PGU region.

[0237] Temperature monitoring data: Obtain the internal temperature of the HUD, ΔT = 35℃

[0238] Risk Decision: Maintain Normal Mode

[0239] To avoid false triggering (traditional pure temperature schemes are prone to false alarms due to the lack of solar backflow prediction).

[0240] The present invention has the following functions and effects:

[0241] 1. Improved protection efficiency, reducing the rate of solar backflow damage from 1% in traditional solutions to 0.01%;

[0242] 2. Cost optimization and improvement, expected to reduce costs by 15% compared to pure physical protection solutions (reducing overly designed light-shielding structures);

[0243] 3. Through a dual-channel verification mechanism, it can meet functional safety certification and ISO26262 ASIL-B level requirements;

[0244] 4. Combining astronomical solar position prediction with microscale temperature field analysis solves the problem of misinterpretation of a single signal;

[0245] 5. Establish a tiered response mechanism to avoid a decline in user experience caused by excessive protection;

[0246] Reference Figure 5 The flowchart illustrates the steps of the HUD dynamic protection method for backflow prevention based on multi-source sensing provided in an embodiment of the present invention, which may specifically include:

[0247] Step 501: Determine the angle of incidence of the sun relative to the windshield, and predict the position of the reflected light spot based on the angle of incidence of the sun relative to the windshield.

[0248] Step 502: Real-time acquisition of the temperature of each component inside the HUD and the ambient temperature inside the HUD;

[0249] Step 503: Determine the risk level of sunlight backflow into the internal components of the HUD based on the location of the reflected light spot, the temperature of each component inside the HUD, and the ambient temperature inside the HUD.

[0250] Step 504: Control the HUD brightness decay and mirror rotation based on the risk level of sunlight backflow into the internal components of the HUD.

[0251] In an optional embodiment of the present invention, determining the angle of incidence of the sun relative to the windshield and predicting the position of the reflected light spot based on the angle of incidence of the sun relative to the windshield includes:

[0252] Based on real-time vehicle pose and real-time time, astronomical algorithms are used to calculate the angle of incidence of the sun relative to the windshield, establish a local coordinate system for the windshield, and project the vector of the angle of incidence of the sun relative to the windshield onto the windshield plane to predict the position of the reflected light spot.

[0253] or,

[0254] Based on the real-time vehicle location and time, the incident angle of the sun relative to the windshield is queried from the pre-stored table of future sun position data. A local coordinate system of the windshield is established, and the vector of the incident angle of the sun relative to the windshield is projected onto the windshield plane to predict the position of the reflected light spot. The pre-stored table of future sun position data is obtained by pre-calculating and storing the incident angle of the vehicle relative to the windshield at different times and positions in the future.

[0255] In an optional embodiment of the present invention, the risk level of solar backflow into the internal components of the HUD is determined based on the location of the reflected light spot, the temperature of each component inside the HUD, and the ambient temperature inside the HUD, including:

[0256] Calculate the temperature difference between the temperature of each sensitive component inside the HUD and the ambient temperature inside the HUD;

[0257] Determine whether the reflected light spot falls on the sensitive component inside the HUD based on the position of the reflected light spot.

[0258] Based on the preset multi-level solar backflow risk response strategy, the temperature difference between the temperature of each sensitive component inside the HUD and the temperature of the HUD's internal environment, and whether the reflected light spot falls on the sensitive component inside the HUD, the solar backflow risk level of the sensitive component is determined.

[0259] In an optional embodiment of the present invention, real-time acquisition of the temperature of each component location inside the HUD and the ambient temperature inside the HUD includes:

[0260] Real-time humidity, real-time air pressure, measured temperature of each component inside the HUD, and measured temperature of the HUD's internal environment are collected. Based on the real-time humidity and real-time air pressure, a pre-calibrated temperature compensation model is used to calculate the compensation values ​​of the measured temperature of each component inside the HUD and the measured temperature of the HUD's internal environment, so as to obtain the true temperature of each component inside the HUD and the true temperature of the HUD's internal environment.

[0261] or,

[0262] Real-time humidity, real-time air pressure, measured temperature of each component inside the HUD, and measured temperature of the HUD's internal environment are collected. Based on the real-time humidity and real-time air pressure, the temperature compensation value corresponding to the real-time humidity and real-time air pressure is found from a preset temperature compensation table to obtain the true temperature of each component inside the HUD and the true temperature of the HUD's internal environment. The preset temperature compensation table is obtained in advance through calibration experiments simulating different combinations of humidity and air pressure.

[0263] In an optional embodiment of the present invention, the solar backflow risk level of a sensitive component is determined based on a preset multi-level solar backflow risk level response strategy, the temperature difference between the temperature of each sensitive component inside the HUD and the ambient temperature inside the HUD, and whether the reflected light spot falls on the sensitive component inside the HUD, including:

[0264] Determine whether the temperature difference between the temperature of each sensitive component inside the HUD and the ambient temperature inside the HUD is greater than the preset temperature rise threshold.

[0265] If not, then there is no risk of sunlight backflow.

[0266] If so, and the reflected light spot falls on the sensitive component inside the HUD, then there is a risk of solar backflow. Based on the first correspondence between temperature difference and solar backflow risk level in the preset multi-level solar backflow risk level response strategy, and the temperature difference between the temperature of each sensitive component inside the HUD and the temperature of the HUD's internal environment, the solar backflow risk level corresponding to the sensitive component is determined.

[0267] If so, and the reflected light spot does not fall on the sensitive components inside the HUD, then there is no risk of solar backflow. The system then enters the local temperature gradient detection phase. Based on the second correspondence between temperature difference and solar backflow risk level in the preset multi-level solar backflow risk level response strategy, and the temperature difference between the temperature of each sensitive component inside the HUD and the temperature of the HUD's internal environment, the solar backflow risk level corresponding to the sensitive component is determined. The temperature difference in the second correspondence is greater than the temperature difference in the first correspondence.

[0268] As the method embodiments are basically similar to the system embodiments, the description is relatively simple, and relevant parts can be found in the description of the system embodiments.

[0269] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0270] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. The same or similar parts between the various embodiments can be referred to each other.

[0271] Although embodiments of the invention have been shown and described, those skilled in the art will understand that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the claims and their equivalents.

[0272] Finally, it should be noted that specific examples have been used in this document to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. Furthermore, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of this application. Therefore, the content of this specification should not be construed as a limitation of this application. The above embodiments are merely preferred embodiments given to fully illustrate the present invention, and the protection scope of the present invention is not limited thereto. Equivalent substitutions or modifications made by those skilled in the art based on the present invention are all within the protection scope of the present invention.

Claims

1. A HUD sunlight backwash dynamic protection system based on multi-source perception, characterized in that, The application comprises the following: A sun position prediction module for determining the incident angle of the sun relative to the windshield and predicting the reflected light spot position according to the incident angle of the sun relative to the windshield; A temperature monitoring array module for obtaining the temperature of each component position inside the HUD and the temperature of the internal environment of the HUD in real time; A risk decision module for determining the sunlight backflow risk level of the internal components of the HUD according to the reflected light spot position, the temperature of each component position inside the HUD, and the temperature of the internal environment of the HUD; A dynamic actuator module for controlling the brightness attenuation and mirror movement of the HUD according to the sunlight backflow risk level of the internal components of the HUD.

2. The system of claim 1, wherein, The sun position prediction module is used to: Based on the real-time vehicle pose and real-time time, the incident angle of the sun relative to the windshield is calculated using astronomical algorithms, a local coordinate system of the windshield is established, the incident angle vector of the sun relative to the windshield is projected onto the windshield plane, and the reflected light spot position is predicted; Or, Based on the real-time vehicle position and real-time time, the incident angle of the sun relative to the windshield is queried from a future sun position data prestorage table, a local coordinate system of the windshield is established, the incident angle vector of the sun relative to the windshield is projected onto the windshield plane, and the reflected light spot position is predicted; the future sun position data prestorage table is obtained by pre-calculating and storing the incident angle of the sun relative to the windshield at different times and different positions in the future.

3. The system of claim 1, wherein, The risk decision module is used to: Calculate the temperature difference between each sensitive component inside the HUD and the internal environment temperature of the HUD; Determine whether the reflected light spot falls on the sensitive components inside the HUD according to the reflected light spot position; Determine the sunlight backflow risk level of the sensitive components according to the preset multi-level sunlight backflow risk level response strategy, the temperature difference between each sensitive component inside the HUD and the internal environment temperature of the HUD, and whether the reflected light spot falls on the sensitive components inside the HUD.

4. The system of claim 1, wherein, The temperature monitoring array module further comprises a temperature compensation unit, which is used to: Collect real-time humidity, real-time air pressure, measured temperature of each component position inside the HUD, and measured temperature of the internal environment of the HUD, and calculate the compensation value of the measured temperature of each component position inside the HUD and the measured temperature of the internal environment of the HUD based on the real-time humidity and real-time air pressure using a pre-calibrated temperature compensation model to obtain the true temperature of each component position inside the HUD and the true temperature of the internal environment of the HUD; Or, Collect real-time humidity, real-time air pressure, measured temperature of each component position inside the HUD, and measured temperature of the internal environment of the HUD, and find the temperature compensation value corresponding to the real-time humidity and real-time air pressure from a preset temperature compensation table based on the real-time humidity and real-time air pressure to obtain the true temperature of each component position inside the HUD and the true temperature of the internal environment of the HUD; the preset temperature compensation table is obtained by simulating different humidity and air pressure combinations in a pre-calibration experiment.

5. The system of claim 3, wherein, The risk decision module is further used to: Determine whether the temperature difference between each sensitive component inside the HUD and the internal environment temperature of the HUD is greater than a preset temperature rise determination threshold; If not, it is determined that there is no sunlight backflow risk. If yes, and the reflected light spot falls on the sensitive components inside the HUD, it is determined that there is a risk of sunlight backflow, and the temperature difference between the temperature of each sensitive component inside the HUD and the temperature of the environment inside the HUD is determined according to the first correspondence relationship between the temperature difference and the sunlight backflow risk level in the preset multi-level sunlight backflow risk level response strategy, so as to determine the sunlight backflow risk level corresponding to the sensitive component. If no, and the reflected light spot does not fall on the sensitive components inside the HUD, it is determined that there is no risk of sunlight backflow, and the temperature difference between the temperature of each sensitive component inside the HUD and the temperature of the environment inside the HUD is determined according to the second correspondence relationship between the temperature difference and the sunlight backflow risk level in the preset multi-level sunlight backflow risk level response strategy, so as to determine the sunlight backflow risk level corresponding to the sensitive component; the temperature difference in the second correspondence relationship is greater than the temperature difference in the first correspondence relationship.

6. The HUD sunlight backwash dynamic protection method based on multi-source perception, characterized in that, The method comprises: determining the incident angle of the sun relative to the windshield, and predicting the position of the reflected light spot according to the incident angle of the sun relative to the windshield; real-time acquisition of the temperature of each component position inside the HUD and the temperature of the environment inside the HUD; determination of the sunlight backflow risk level of the components inside the HUD according to the position of the reflected light spot, the temperature of each component position inside the HUD, and the temperature of the environment inside the HUD; control of the HUD brightness attenuation and mirror movement according to the sunlight backflow risk level of the components inside the HUD.

7. The method of claim 6, wherein, determining the incident angle of the sun relative to the windshield, and predicting the position of the reflected light spot according to the incident angle of the sun relative to the windshield, comprising: based on the real-time vehicle pose and real-time time, the incident angle of the sun relative to the windshield is calculated by using astronomical algorithm, a local coordinate system of the windshield is established, the incident angle vector of the sun relative to the windshield is projected onto the windshield plane, and the position of the reflected light spot is predicted; or, based on the real-time vehicle position and real-time time, the incident angle of the sun relative to the windshield is queried from a future sun position data prestorage table, a local coordinate system of the windshield is established, the incident angle vector of the sun relative to the windshield is projected onto the windshield plane, and the position of the reflected light spot is predicted; the future sun position data prestorage table is obtained by pre-computing and storing the incident angle of the sun relative to the windshield at different times and different positions in the future.

8. The method of claim 6, wherein, determination of the sunlight backflow risk level of the components inside the HUD according to the position of the reflected light spot, the temperature of each component position inside the HUD, and the temperature of the environment inside the HUD, comprising: calculating the temperature difference between each sensitive component inside the HUD and the temperature of the environment inside the HUD; determining whether the reflected light spot falls on the sensitive components inside the HUD according to the position of the reflected light spot; determination of the sunlight backflow risk level of the sensitive components according to the preset multi-level sunlight backflow risk level response strategy, the temperature difference between each sensitive component inside the HUD and the temperature of the environment inside the HUD, and whether the reflected light spot falls on the sensitive components inside the HUD.

9. The method of claim 6, wherein, real-time acquisition of the temperature of each component position inside the HUD and the temperature of the environment inside the HUD, comprising: Collect real-time humidity, real-time air pressure, measured temperature of each component position in the HUD and measured temperature of the internal environment of the HUD, and based on the real-time humidity and real-time air pressure, calculate the compensation value of the measured temperature of each component position in the HUD and the measured temperature of the internal environment of the HUD by using the pre-calibrated temperature compensation model, to obtain the real temperature of each component position in the HUD and the real temperature of the internal environment of the HUD. Or, Collect real-time humidity, real-time air pressure, measured temperature of each component position in the HUD and measured temperature of the internal environment of the HUD, and based on the real-time humidity and real-time air pressure, find the temperature compensation value corresponding to the real-time humidity and real-time air pressure from a pre-set temperature compensation table to obtain the real temperature of each component position in the HUD and the real temperature of the internal environment of the HUD; the pre-set temperature compensation table is obtained by simulating different humidity and air pressure combinations and calibrating experiments in advance.

10. The method of claim 8, wherein, According to the pre-set multi-level sunlight backflow risk level response strategy, the temperature difference between the temperature of each sensitive component in the HUD and the temperature of the internal environment of the HUD, and whether the reflected light spot falls on the sensitive component in the HUD, determine the sunlight backflow risk level of the sensitive component, including: Determine whether the temperature difference between the temperature of each sensitive component in the HUD and the temperature of the internal environment of the HUD is greater than a pre-set temperature rise determination threshold; If not, it is determined that there is no sunlight backflow risk; If yes, and the reflected light spot falls on the sensitive component in the HUD, it is determined that there is sunlight backflow risk, and the corresponding sunlight backflow risk level of the sensitive component is determined according to the first corresponding relationship between the temperature difference and the sunlight backflow risk level in the pre-set multi-level sunlight backflow risk level response strategy and the temperature difference between the temperature of each sensitive component in the HUD and the temperature of the internal environment of the HUD; If yes, and the reflected light spot does not fall on the sensitive component in the HUD, it is determined that there is no sunlight backflow risk, and the corresponding sunlight backflow risk level of the sensitive component is determined according to the second corresponding relationship between the temperature difference and the sunlight backflow risk level in the pre-set multi-level sunlight backflow risk level response strategy and the temperature difference between the temperature of each sensitive component in the HUD and the temperature of the internal environment of the HUD; the temperature difference in the second corresponding relationship is greater than the temperature difference in the first corresponding relationship.

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