Display control method and device, head-up display device and storage medium

Abstract

The invention provides a display control method and device, a head-up display device and a storage medium, and belongs to the technical field of intelligent display control. The display control device comprises a processor and a memory which are in communication connection; the processor is configured to adjust the current display brightness of the head-up display HUD based on the brightness calibration trigger instruction, and store the adjusted display brightness confirmed by the current user and the current ambient light intensity as current calibration data in the memory; and based on the multiple groups of calibration data in the memory, generating a personalized brightness control curve corresponding to the current user, so that the HUD controls the display brightness according to the personalized brightness control curve. According to the invention, the brightness control curve of the HUD, which is customized in a personalized manner, can be generated for different users.

Description

TECHNICAL FIELD

[0001] The present disclosure relates to the technical field of intelligent display control, and in particular to a display control method and device, a head-up display device, and a storage medium. BACKGROUND

[0002] A head-up display (HUD) projects driving information onto the windshield in front of the driver, so that the driver can obtain key information without lowering his head, thereby improving driving safety. The display brightness of the HUD directly affects the visual experience and driving safety of the driver. If the brightness is too high, it will cause glare and affect the driver's observation of the road conditions. If the brightness is too low, the information display will not be clear. Therefore, adaptive adjustment of the display brightness of the HUD is crucial.

[0003] However, the adjustment strategy of the display brightness of the existing HUD is fixed before leaving the factory, and all vehicles use the same default brightness control curve after leaving the factory. Since there are significant individual differences in the sensitivity of the human eyes of different drivers to light, for example, factors such as age, vision condition, and whether wearing sunglasses will affect the perception of brightness, and different drivers have different subjective definitions of comfortable brightness, the fixed universal brightness control curve at the time of leaving the factory often cannot meet the individualized needs of all users. SUMMARY

[0004] The present disclosure provides a display control method and device, a head-up display device, and a storage medium, which can generate a personalized brightness control curve of the HUD for different users.

[0005] The technical solution of the present disclosure is implemented as follows: In a first aspect, the present disclosure provides a display control device, which comprises a processor and a memory connected in communication; the processor is configured to adjust the current display brightness of the HUD based on a brightness calibration trigger instruction, and store the adjusted display brightness confirmed by the current user and the current ambient light intensity as current calibration data to the memory; and generate a personalized brightness control curve corresponding to the current user based on a plurality of sets of calibration data in the memory, so that the HUD controls the display brightness according to the personalized brightness control curve.

[0006] In a second aspect, the present disclosure provides a display control method, which comprises adjusting the current display brightness of the HUD based on a brightness calibration trigger instruction; storing the adjusted display brightness confirmed by the current user and the current ambient light intensity as current calibration data to the memory; and generating a personalized brightness control curve corresponding to the current user based on a plurality of sets of calibration data.

[0007] In a third aspect, the present disclosure provides a display control device, comprising: an adjusting part, a storing part and a generating part; the adjusting part is configured to adjust the current display brightness of the HUD based on the brightness calibration trigger instruction; the storing part is configured to store the adjusted display brightness confirmed by the current user and the current ambient light intensity as current calibration data to the memory; the generating part is configured to generate the personalized brightness control curve corresponding to the current user based on multiple sets of calibration data.

[0008] In a fourth aspect, the present disclosure provides a head-up display device, comprising a display control part and a display part; wherein the display control part is configured to control the display brightness of the display part based on the personalized brightness control curve generated by the display control device of the first aspect; the display part is configured to project the image on the windshield of the vehicle according to the display brightness determined by the display control part.

[0009] In a fifth aspect, the present disclosure provides a computer readable storage medium, which stores at least one instruction for being executed by a processor to implement the display control method of the second aspect.

[0010] In a sixth aspect, the present disclosure provides a vehicle comprising the head-up display device of the fourth aspect.

[0011] The present disclosure provides a display control device, which records each adjustment of the display brightness of the HUD of the user as calibration data by the processor 121, and generates the personalized brightness control curve based on the calibration data. Thus, the adjustment strategy of the display brightness of the HUD is not fixed at the factory, but dynamically evolves with the feedback of the user, so that the customers with different needs can adjust as needed, such as the users with weak vision or high contrast needs will obtain a brightness control curve with overall high brightness, and the light-sensitive users will obtain a soft brightness control curve, thereby realizing the personalized customization for each user and improving the driving experience of the user. BRIEF DESCRIPTION OF DRAWINGS

[0012] Figure 1 The architecture schematic diagram of the display control system provided by the present disclosure is shown.

[0013] Figure 2 The structure schematic diagram of the HUD provided by the present disclosure is shown.

[0014] Figure 3 The flowchart of the processor generating the personalized brightness control curve provided by the present disclosure is shown.

[0015] Figure 4 The comparison schematic diagram of the personalized brightness control curve and the default brightness control curve provided by the present disclosure is shown.

[0016] Figure 5 This is a schematic diagram of the personalized brightness control curves for different users provided in this disclosure.

[0017] Figure 6 This is a schematic diagram illustrating the process by which the processor, as provided in this disclosure, incorporates weather status information when generating a brightness control curve.

[0018] Figure 7 This is a comparative diagram of personalized brightness control curves under different weather labels provided in this disclosure.

[0019] Figure 8 This is a flowchart illustrating the process by which the processor, as provided in this disclosure, incorporates scene labels when generating brightness control curves.

[0020] Figure 9 A schematic diagram illustrating the process by which the processor, as provided in this disclosure, determines the adjusted display brightness of the HUD.

[0021] Figure 10 This is a schematic diagram of the composition of a display control device provided in this disclosure. Detailed Implementation

[0022] The technical solutions in this disclosure will now be clearly and completely described with reference to the accompanying drawings.

[0023] like Figure 1 The diagram shows an architecture of a display control system 10 provided in this disclosure. The display control system 10 includes a controller 12, vehicle sensors 13, a head-up display (HUD) 14, and a human-machine interface (HMI) 15. The example shown is of the display control system 10 mounted on an exemplary vehicle 11. Although the vehicle 11 shown is a bus, it should be understood that the vehicle 11 can be any type of vehicle without departing from the scope of this disclosure.

[0024] The controller 12 includes at least one processor 121 and a memory 122. The processor 121 may be a custom or commercially available processor, a central processing unit (CPU), a graphics processing unit (GPU), an auxiliary processor among several processors associated with the controller 12, a semiconductor-based microprocessor (in the form of a microchip or chipset), a macroprocessor, a combination thereof, or generally a device for executing instructions. The memory 122 may include random access memory (RAM) or read-only memory (ROM). Optionally, the memory 122 may include non-transitory computer-readable storage medium. The memory 122 may be used to store instructions, programs, code, code sets, or instruction sets. The controller 12 may also consist of multiple sub-controllers that are electrically communicating with each other.

[0025] The controller 12 communicates electrically with the vehicle sensors 13, HUD 14, and HMI 15. Electrical communication can be established using, for example, a CAN bus, Wi-Fi network, cellular data network, etc. It should be understood that various other wired and wireless technologies and communication protocols used for communicating with the controller 12 are within the scope of this disclosure.

[0026] Vehicle sensor 13 is used to acquire information about the environment surrounding vehicle 11. In an exemplary embodiment, vehicle sensor 13 includes camera 131, vehicle communication system 132, and light sensor 133. It should be understood that, without departing from the scope of this disclosure, vehicle sensor 13 may include additional sensors for determining characteristics of vehicle 11, such as vehicle speed, road curvature, and / or vehicle steering. As discussed above, vehicle sensor 13 is in electrical communication with controller 12.

[0027] Camera 131 is used to capture images and / or videos of the environment surrounding vehicle 11. In an exemplary embodiment, camera 131 is a photographic and / or video camera positioned to observe environmental information in front of vehicle 11. In one example, camera 131 is fixed inside vehicle 11 (e.g., in the roof lining of vehicle 11) and has a field of view through windshield 16. Alternatively, it may acquire image information inside vehicle 11, including the driver, for use in facial recognition or gesture recognition. In another example, camera 131 is fixed outside vehicle 11, for example, on the roof of vehicle 11, and has a view of the environment in front of vehicle 11. It should be understood that cameras with various sensor types are within the scope of this disclosure, such as charge-coupled device (CCD) sensors, complementary metal-oxide-semiconductor (CMOS) sensors, and / or high dynamic range sensors. Furthermore, cameras with various lens types, including, for example, wide-angle lenses and / or narrow-angle lenses, are also within the scope of this disclosure.

[0028] The light sensor 133 can be used to detect the ambient light level of the vehicle 11. Specifically, it can be a sensor that only detects ambient light level, or it can be a sensor that has both ambient light level detection and other functions (such as a rain sensor). In some examples, to detect the ambient light level inside the vehicle for controlling interior lighting, the light sensor 133 can be positioned on the roof console, near the reading lights. In some examples, to detect the ambient light level inside the vehicle for controlling the brightness of interior lighting and dashboard backlighting, the light sensor 133 can be positioned behind or inside the dashboard. In some examples, to detect ambient light entering the vehicle 11, the light sensor 133 can be positioned inside the windshield 16, near the rearview mirror. In some examples, to detect ambient light entering the vehicle 11 for determining the degree of impact on the driver, the light sensor 133 can be positioned inside or outside the steering wheel. In some examples, to detect the ambient light level outside the vehicle for automatically adjusting the anti-glare function of the rearview mirror, the light sensor 133 can be positioned behind or near the rearview mirror. To detect the illuminance of ambient light outside the vehicle for automatic headlight control, the light sensor 133 may be positioned near the front bumper or front grille. To detect the illuminance of ambient light outside the vehicle, avoid interference from direct or reflected light, and provide more accurate illuminance readings, the light sensor 133 may be positioned on the side or corner of the vehicle 11. In some advanced driver assistance systems, the light sensor 133 may be integrated with a forward-facing camera or other sensors to provide ambient light information.

[0029] The controller 12 uses the vehicle communication system 132 to communicate with other systems outside the vehicle 11. For example, the vehicle communication system 132 includes the ability to communicate with other vehicles, infrastructure, remote call centers, and / or personal devices. The vehicle communication system 132 may include one or more antennas and / or communication transceivers for receiving and / or transmitting signals. The vehicle communication system 132 is configured to wirelessly transmit information between the vehicle 11 and another vehicle. Furthermore, the vehicle communication system 132 is configured to wirelessly transmit information between the vehicle 11 and infrastructure or other vehicles.

[0030] refer to Figure 2 The diagram illustrates a structural schematic of HUD 14, with occupant 21 using HUD 14 as an example. Within the scope of this disclosure, in a non-limiting example, occupant 21 includes the driver, passengers, and / or any other person in vehicle 11. HUD 14 is used to display HUD image 25 (i.e., a notification symbol providing visual information to occupant 21) on the windshield 16 of vehicle 11.

[0031] HUD 14 is used to project HUD image 25 onto windshield 16 of vehicle 11. It should be understood that various devices designed for projection, including, for example, optical collimators, laser projectors, digital light projectors (DLP), etc., are within the scope of this disclosure.

[0032] HUD 14 includes a display control unit 141, which receives a display control strategy. The display control unit 141 is configured to determine (e.g., calculate) the type, size, shape, color, and brightness of the HUD image 25 to be displayed by the display unit 142 based on the display control strategy. The display control unit 141 controls the display unit 142 to display the HUD image 25. The display unit 142 projects the HUD image 25 onto the windshield 16 of the vehicle 11 based on the type, size, shape, color, and brightness of the HUD image 25 determined by the display control unit 141. When the HUD 14 is an AR-HUD, the HUD image 25 is projected by the display unit 142 onto the windshield 16 to display the HUD image 25 along the road surface 26.

[0033] The display control strategy includes adjusting the HUD 14's display brightness. As an optical display device, the visibility of the HUD 14 is highly dependent on the contrast between ambient lighting conditions and background brightness. Ideal display performance requires the virtual image brightness to dynamically and smoothly adjust with changes in external ambient light. For example, in bright sunlight, the HUD 14 needs to output sufficiently high display brightness to overcome background light interference and ensure clear visibility of information; while in low-light environments such as at night, in tunnels, or underground parking garages, the HUD 14's display brightness needs to be significantly reduced to avoid glare or eye fatigue for the driver, or even affecting the observation of road conditions in dark areas. Therefore, adaptive adjustment of display brightness has become a fundamental function of the HUD 14.

[0034] Although automatic brightness adjustment is now widespread, traditional HUD 14 brightness control typically uses a factory-preset curve. This means that based on ambient light intensity, the system consults a lookup table stored in the memory 122 and directly outputs the corresponding backlight drive value. This static control logic has significant technical flaws: First, it ignores the physiological differences in visual perception among individual drivers. For example, drivers of different ages have different sensitivities to light; under the same ambient light, a younger driver may find it clear, while an older driver may find it glaring or dim. Second, it cannot adapt to dynamic changes in the hardware environment, such as sensor aging and changes in windshield film transmittance. When the preset curve fails to meet the driver's needs, the driver must frequently adjust the brightness manually via physical buttons or the central control screen menu. However, in existing technology, this manual adjustment is usually one-time effective; the adjusted brightness value is only a temporary offset. Once the ambient light changes, the system reverts to the inaccurate preset curve, requiring repeated adjustments. This not only severely impacts the user experience but also increases the driver's workload and safety risks while driving.

[0035] Based on the above problems, this disclosure aims to provide a HUD display control device capable of adaptively and personally calibrating based on the driver's actual subjective feelings, utilizing the vehicle's own perception and interaction capabilities. This display control device can be the aforementioned controller 12, which includes at least one of the aforementioned processors 121 and memory 122. The processor 121 is the computing core of the display control device 100. In this embodiment, the processor 121 is configured to execute computer program instructions stored in the processor 122 to realize brightness data acquisition, processing, fitting, and control logic. The memory 122 is used to store various types of data and instructions, including but not limited to the operating system, applications, factory default brightness parameter tables, and the calibration data and personalized brightness control curves of this embodiment. In this embodiment, a dedicated area is allocated in the memory 122 for constructing a calibration database, which persistently stores the user's adjusted brightness preference data under different times and lighting conditions.

[0036] like Figure 3 As shown, processor 121 is configured to perform the following steps S301 to S303.

[0037] In step S301, the current display brightness of the HUD is adjusted based on the brightness calibration trigger command.

[0038] The brightness calibration trigger instruction refers to the logic control signal generated internally by the processor 121 to initiate the HUD brightness calibration process (including a series of actions such as entering calibration mode, recording environmental parameters, recording user preferences, and updating the control algorithm).

[0039] In some implementations, the brightness calibration trigger command is generated based on the driver's brightness calibration request operation, which is a passive trigger logic based on user request. The brightness calibration request operation can be voice, manual operation, gesture, etc. Specifically, the processor 121 listens to the driver's voice commands in real time through the vehicle microphone array, and listens to the operation status of physical buttons and touch screen through the vehicle CAN / LIN bus or interrupt signal.

[0040] The processor 121 analyzes the acquired signals and identifies request operations caused by unsuitable current brightness. For voice operation requests, it performs voice parsing and uses natural language processing to identify keyword slots. For example, if it recognizes phrases such as "HUD is too dark," "can't see clearly," "glaring," or "adjust brightness to 80%," it generates a brightness calibration trigger command. For manual operation requests, it generates a brightness calibration trigger command when it detects that the brightness adjustment menu has been accessed or the brightness slider position has changed. This brightness calibration trigger command may include a trigger source identifier and the user's desired adjustment direction or value (such as increasing PWM by 10%).

[0041] The processor 121 responds to the brightness calibration trigger command to adjust the display brightness of the HUD, and after the adjustment is stable (the user stops the operation or confirms), it locks the current ambient light intensity and the adjusted display brightness as the current calibration data.

[0042] In other possible implementations, the generation of the brightness calibration trigger command is based on active triggering logic according to the calibration data coverage. The processor 121 collects the current ambient light intensity through the light sensor 133 at a preset frequency, such as 10Hz. The processor 121 reads multiple preset illumination intervals from the calibration rule base in the memory 122. The preset illumination intervals refer to dividing the full range of illumination intensity that the ambient light sensor can recognize into several continuous but discrete numerical ranges based on the characteristics of human vision and typical weather scenarios. Each interval corresponds to a reference brightness parameter. For example, if the full range of illumination is 0 to 20000 cd / m², the resulting N preset illumination intervals are: [0, 10), extremely dark; [10, 50), tunnel lighting section; ... [1500, 2000), cloudy day ... [18000, 20000], strong direct light, etc. This preset illumination interval covers all illumination intensities that the light sensor 133 can recognize. The illumination range covered by existing calibration data refers to the range in which at least one (or several statistically significant) calibration data of ambient light intensity and corresponding display brightness, which has been confirmed by the current user, has been stored within the full-range illumination division.

[0043] The processor 121 receives the current ambient light intensity transmitted by the light sensor 133 in real time and compares the current ambient light intensity with the illumination interval corresponding to the existing calibration data stored in the memory 122. If there is no corresponding historical calibration data within the illumination interval where the current ambient light intensity is located, or if the number of calibration data within the illumination interval is insufficient (e.g., less than 3 sets), the processor 121 determines that the illumination interval does not cover the current ambient light intensity. To avoid frequently disturbing the user when the illumination changes frequently, such as under the shade of a tree, the processor 121 executes filtering logic. If the current illumination intensity remains stable within the corresponding illumination interval for more than a preset time, such as 5 seconds, it ensures that the current illumination intensity is in a stable scene. Furthermore, considering the vehicle status (vehicle speed, steering angle), if the vehicle is under high load conditions (such as sharp turns or high-speed lane changes), triggering is temporarily suppressed.

[0044] If the conditions of no coverage, stable light intensity, and vehicle not under high load are met, the processor 121 will actively initiate interaction through the AI ​​voice assistant to determine whether the display brightness of the HUD needs to be adjusted. If the user indicates that no adjustment is needed, the processor 121 will record the current default brightness as the calibration value of the light range (i.e., the user has accepted the current setting by default). If the user indicates that adjustment is needed, the processor will guide the user into the calibration process.

[0045] Human eye conditions are dynamic (e.g., fatigue level, whether sunglasses are worn). Even within a calibrated lighting range, a user's subjective perception can shift. This dual-trigger mechanism retains a trigger channel for user requests. Regardless of whether the current ambient light intensity is covered, as long as the user feels uncomfortable and initiates an action, the parameters of the existing lighting range are dynamically updated, ensuring that the HUD's display brightness always matches the user's current actual needs. Furthermore, automatic triggering ensures that all possible lighting scenarios are covered, improving the comprehensiveness and completeness of the calibration and laying a data foundation for generating accurate and reliable personalized brightness control curves.

[0046] Based on the brightness calibration trigger command, processor 121 generates a new brightness control signal and sends it to HUD 14. For example, if the brightness calibration trigger command is "brighten it a little," processor 121 may increase the duty cycle by a certain amount based on the current PWM value. At this time, the display brightness of HUD 14 physically changes, and the driver will see the effect of the brightness change and provide real-time feedback of the adjustment result to HUD 14. Processor 121 will continue to respond to the command until the user stops adjusting (e.g., releasing the button, stopping the voice command, or confirming satisfaction). At this point, processor 121 locks the final adjusted display brightness and records it as the target display brightness.

[0047] In step S302, the adjusted display brightness and current ambient light intensity confirmed by the current user are stored as current calibration data in the memory.

[0048] The above adjustments only change the current register value, and this preference will be lost once the vehicle restarts or the ambient light changes drastically. Therefore, after the adjustment is completed, the processor 121 uses the average ambient light intensity during the adjustment process as a set of calibration data, along with the target display brightness, and stores this calibration data in the memory 122. This transforms the user's adjustment behavior into a training sample with long-term reference value. To prevent data from accumulating indefinitely, the memory 122 may employ a first-in-first-out queue structure or a storage strategy based on the coverage of illumination zones. For example, it may retain the 5 most recent sets of data for strong light zones and the 5 most recent sets of data for weak light zones.

[0049] In step S303, a personalized brightness control curve corresponding to the current user is generated based on multiple sets of calibration data.

[0050] Multiple sets of calibration data refer to current calibration data and historical calibration data. As the user uses the device over time, multiple sets of calibration data will accumulate in the memory 122. When the calibration data accumulates to a certain extent, or after each new calibration data is stored, the processor 121 generates a personalized brightness control curve corresponding to the current user based on the multiple sets of calibration data in the memory.

[0051] In order to make the generated personalized brightness control curve more universal for the current user, in some possible ways, the processor 121 periodically checks the current calibration status or after each new calibration data is stored in the memory 122 to determine whether the conditions for generating a personalized brightness control curve are met.

[0052] Specifically, if at least one of the stored calibration data shows that the display brightness has reached the theoretical maximum display brightness of the HUD 14 hardware, it means that the user has adjusted the brightness to the maximum under certain lighting conditions (usually strong light). This indicates that the user's brightness demand has reached the physical boundary of the HUD 14. Even if there is a stronger environment than this lighting, the output brightness of the HUD 14 can only be maintained at the maximum value allowed by the hardware. Therefore, the highlight part of the curve has been determined, and this situation can be judged as meeting the conditions for generating a personalized brightness control curve.

[0053] Another scenario is that users may never encounter extreme lighting conditions (such as glare from snow) in their daily use, or their visual preference may be for darkness. While there may be a lot of calibration data available, the theoretically designed maximum brightness value has not yet been achieved. In this case, the ratio of the preset brightness intervals with existing calibration data to the total preset brightness intervals is calculated and recorded as the coverage ratio. When the coverage ratio is greater than or equal to the coverage threshold, it can be determined that the conditions for generating a personalized brightness control curve are met.

[0054] The personalized brightness control curve refers to a mathematical function relationship, where the independent variable is the ambient light intensity and the dependent variable is the display brightness of the HUD 14. Specifically, the processor 121 reads all valid calibration data from the memory 122, which is distributed across different ambient light intensity ranges. The processor 121 uses a preset algorithm (such as least squares, polynomial regression, or spline interpolation) to fit these discrete calibration data.

[0055] In some feasible implementations, multiple sets of calibration data are: D = {(x1, y1), (x1, y1), ..., (xm, ym)}, where xi represents the ambient light intensity at the i-th calibration, and yi represents the display brightness of the HUD 14 confirmed by the user at the i-th calibration. xi is normalized or logarithmically transformed to prevent numerical instability during fitting due to excessive differences in the magnitude of illumination values. The processor 121 selects a higher-order polynomial function of a preset order to describe the variation of the HUD 14's display brightness with illumination intensity. A higher-order polynomial function refers to an algebraic function of the ambient light intensity with a highest degree greater than or equal to 3, expressed as: where n is the order. a 0 to a nThe coefficients are to be determined. Higher-order polynomials are used because lower-order polynomials cannot describe the nonlinear sensitivity changes of the human eye in different lighting ranges (scotopic vision, photopic vision); while higher-order polynomials (such as 3rd or 4th order) have inflection points and can simulate the characteristics of an S-shaped curve, that is, it tends to flatten in the extremely dark and extremely bright areas and increases linearly in the middle area.

[0056] Processor 121 uses a nonlinear regression algorithm to find the optimal coefficients. a 0 to a n This minimizes the error between the fitted curve and the actual data points. The nonlinear regression algorithm refers to a statistical method used to determine the parameters of a nonlinear model; in this disclosure, it refers to the method for determining the coefficients of the aforementioned higher-order polynomial. The specific process is as follows: Define the sum of squared errors as the objective function. The processor 121 minimizes the objective function by solving the normal equation or using gradient descent. The root mean square error is calculated; if the root mean square error exceeds a threshold, it indicates that the calibration data is extremely discrete and may contain errors. Therefore, the point with the largest residual is removed, and the fit is re-applied. Since the higher-order polynomial may oscillate in the sparse data region, causing brightness to decrease with illumination, the processor 121 checks the derivative. f'(x) If the function is always greater than or equal to 0 within the valid interval, and not satisfied, a regularization term is introduced or the order is reduced and the function is recalculated. The final determined function... f(x) This refers to the personalized brightness control curve. The processor 121 discretizes it into a lookup table or directly stores the coefficients for real-time brightness control. The use of a high-order polynomial in this embodiment is merely an example; nonlinear functions such as trispline interpolation can also be used, but this disclosure does not limit the application to this method.

[0057] Compared to linear interpolation, polynomial functions can capture the overall trend of data (such as logarithmic growth). Even in blank areas without calibrated data, the curve can provide reasonable interpolation or extrapolation values ​​based on its overall shape. Piecewise interpolation, on the other hand, can only follow a straight line in sparse data areas (such as when the span between two points is large), often deviating from the actual needs of the human eye (human perception is non-linear). Furthermore, piecewise interpolation has discontinuous derivatives at data points, resulting in sharp angles. When the illumination fluctuates near a calibrated data point, the rate of change in brightness will suddenly change, while the polynomial curve is smooth throughout, conforming to the physiological characteristics of human eye adaptation to light. In addition, regardless of the number of calibrations, polynomials only need to store a few coefficients; while interpolation methods require storing a complete coordinate table, demanding more storage resources.

[0058] For example, such as Figure 4As shown, assuming the user calibrated the device under different light intensities throughout the day, obtaining four calibration data points: the calibration data corresponding to point A, point B, point C, and point D, as shown in the figure. The traditional factory default brightness control curve might be a straight line L1 that increases linearly with light intensity, shown as a solid line in the figure. However, the user's data points from A to D deviate significantly from L1. The processor 121 determines a new curve L2 based on the data points from A to D, shown as a dashed line in the figure, ensuring that L2 passes through or is as close as possible to the data points from A to D, while maintaining a smooth transition in the intermediate region where no data is available. During the fitting process, the processor 121 adheres to monotonicity constraints, ensuring that the generated curve shows a monotonically increasing display brightness of the HUD 14 as the ambient light increases.

[0059] After the personalized brightness control curve is fitted, the processor 121 obtains a new set of curve parameters (such as the coefficients of the polynomial, or generates a new lookup table), which represents the personalized brightness control curve.

[0060] After the personalized brightness control curve is generated, the processor 121 immediately (or upon the next vehicle start-up) replaces the original control logic with this new curve. Subsequently, when the ambient light sensor transmits a new ambient light intensity, the processor 121 no longer consults the factory default table, but instead substitutes it into the function corresponding to the new personalized brightness control curve, calculates the corresponding display brightness, and drives the HUD 14 to execute. Thus, even if the user does not perform any subsequent operations, when the vehicle travels to a lighting environment similar to the previously calibrated scene, the HUD 14 will automatically adjust to the brightness value that the user previously found comfortable; and when traveling to a lighting environment that the user has not adjusted, the HUD 14 will also provide a predicted value that matches the user's preferred trend based on the curve's fitting trend.

[0061] For example, such as Figure 5 As shown, the memory 122 maintains personalized brightness control curves for different users. Taking users A, B, and C as examples, after the vehicle starts, the user identification devices in the vehicle, such as face recognition (user image information collected by camera 131), fingerprint recognition, and voiceprint recognition, identify the current user and then retrieve the personalized brightness control curve corresponding to the current user into the HUD 14, so that the display brightness of the HUD 14 is adjusted based on the personalized brightness control curve.

[0062] In this embodiment, the processor 121 records each adjustment of the HUD 14's display brightness by the user as calibration data and refits and generates a personalized brightness control curve based on this calibration data. This means that the HUD 14's display brightness adjustment strategy is not fixed at the factory but dynamically evolves with each user feedback. This allows customers with different needs to adjust as required; for example, users with weaker eyesight who require high contrast will receive an overall higher brightness control curve, while light-sensitive users will receive a softer brightness control curve. The display control device provided in this application incorporates user feedback into the underlying control loop, enabling the HUD 14's display brightness control to shift from passive execution to active adaptation to the user, achieving personalized customization for each user and improving the user's driving experience.

[0063] The perceived brightness of a HUD 14 is influenced not only by ambient light intensity, but also by the fact that users with different color temperatures may require different display brightness levels even under the same ambient light intensity. Therefore, as... Figure 6 As shown, processor 121 is also configured to perform steps S601 to S604.

[0064] In step S601, the current weather status information is obtained.

[0065] In step S602, the adjusted display brightness confirmed by the current user and the current ambient light intensity are used as the current calibration data, and the current weather status information is associated with the current calibration data as a weather tag.

[0066] In step S603, the current calibration data is stored in the memory.

[0067] In step S604, a personalized brightness control curve corresponding to the current user is generated based on multiple sets of calibration data associated with the current weather status information.

[0068] Weather conditions refer to the comprehensive set of meteorological environments that affect the propagation characteristics of light in the atmosphere (scattering, refraction, transmittance) and the optical properties of windshield surfaces, such as sunny days and rainy days. Different weather conditions mean different background brightness and color temperature distributions, which can affect the contrast sensitivity of the human eye.

[0069] The processor 121 employs a sensor fusion and cloud-based verification strategy to determine the weather label. It reads data from the rain sensor to determine the presence and intensity of rainfall, such as light rain, moderate rain, or heavy rain. It reads the windshield wiper status via the CAN bus to confirm whether the wipers are working and their frequency, such as intermittent, low speed, or high speed, which can serve as secondary confirmation of rainfall. It also reads data from the outside temperature sensor, combining this information with rainfall data to determine whether it is rain or snow.

[0070] Visual image analysis can also be used to identify sky regions in external environment images using semantic segmentation algorithms, and calculate cloud cover. Dark channel priors are used to calculate the transmittance map of the external environment image to detect haze concentration. Foggy weather causes the background to appear washed out, reducing contrast and requiring special brightness compensation.

[0071] It can also obtain real-time weather service data of the current GPS location, such as visibility and weather phenomenon codes, to correct misjudgments by local sensors (e.g., misjudging water flow during a car wash as a rainstorm).

[0072] Processor 121 maps the determined results to weather labels, such as: Sunny day, strong direct sunlight, illumination greater than 50kLux, cloud cover less than 30%, no rain; in this scenario, the background brightness is extremely high, requiring high brightness to counteract it. Cloudy day, diffused light, moderate illumination, cloud cover greater than 70%, no rain; in this scenario, the background light is soft with less glare. Rainy day: Interference from the medium; when the wipers are on or rain is detected, the water film on the windshield scatters light, creating a halo and reducing the clarity of the HUD 14. Fog / haze: low contrast, visibility less than 500m, high background grayscale, prone to whitening, making it extremely difficult to discern information.

[0073] Under the same ambient light intensity, such as 2000 Lux, 2000 Lux on a sunny day typically appears at dusk or in shadow, with a darker background, while 2000 Lux on a rainy day may be accompanied by strong specular reflection from the road surface and scattering from water droplets. If only ambient light intensity is considered, the same brightness will be output. However, this embodiment distinguishes between sunny and rainy days, and can maintain an independent, personalized control curve with higher overall brightness for rainy days to compensate for the contrast loss caused by water film scattering, thereby solving the problem of poor visibility on rainy days.

[0074] For example, such as Figure 7 As shown, this is the personalized brightness control curve for different weather tags for user A. The solid line represents the change in display brightness with light intensity on sunny days; the dashed line represents the change in display brightness with light intensity on rainy days; and the dotted line represents the change in display brightness with light intensity on cloudy days.

[0075] During the fitting process of calibration data, all calibration data contribute equally to the curve. However, in real-world applications, the contributions of calibration data to the curve may differ depending on the specific scenario. Therefore, as... Figure 8 As shown, in some embodiments, the processor 121 is also configured to perform steps S801 to S805.

[0076] In step S801, current weather status information and environmental image data in front of the vehicle are acquired.

[0077] In step S802, the current background glare index or background complexity is determined based on the environmental image data.

[0078] In step S803, the adjusted display brightness confirmed by the current user and the current ambient light intensity are used as the current calibration data, the current weather status information is used as the weather label, and the background glare index and / or background complexity are used as the scene label and associated with the current calibration data.

[0079] In step S804, the current calibration data is stored in the memory.

[0080] In step S805, based on multiple sets of calibration data associated with the current weather status information, the multiple sets of calibration data are weighted according to the scene label to generate a personalized brightness control curve corresponding to the current user.

[0081] Background glare index is a physical indicator that quantifies the degree of uncomfortable or disabling glare caused to the human eye by the distribution of bright light sources in the driver's field of vision. In this disclosure, it refers to the light intensity that causes the human eye's pupil to be forced to contract and the retinal light adaptation level to drift, thereby reducing the interference light intensity on the HUD's ability to perceive virtual images.

[0082] Processor 121 performs brightness channel analysis on the environmental image data, sets a brightness threshold (e.g., 10,000 cd / m²), and identifies all connected regions in the environmental image data whose brightness exceeds this threshold, such as the sun, oncoming headlights, streetlights, and large-area reflections, denoted as the glare source set {S1, S2…Sn}. The human eye is most sensitive to glare at the center of the field of vision, and the HUD virtual image is usually located near the center of the field of vision. The Guth position index model can be used to calculate the deviation angle of each glare source Si relative to the driver's line of sight center or the center of the HUD display area; the smaller the deviation angle, the larger the weight P.

[0083] Background glare index can be calculated using the formula: Confirmed, among which G Background glare index, k For coefficients, L back The average brightness of the environmental image data. L i The brightness of the i-th glare source. w i The solid angle of the i-th glare source represents the size of the light source in the field of view. P i This refers to the weight of the i-th glare source. This formula is for illustrative purposes only and is not intended to limit the confirmation of the background glare index of this disclosure.

[0084] For example, when a vehicle is driving on a tree-lined avenue, the sunlight is blocked by the leaves, and there are no bright spots in the field of vision. This is a low-glare scenario, and the driver's pupils are large, making them sensitive to even the faint light from the HUD 14. When the vehicle is driving directly towards the setting sun, with the sun directly above the virtual image of the HUD, this is a high-glare scenario. The strong direct light makes it difficult for the driver to see clearly, and the brightness of the HUD 14 must be greatly increased to see it clearly.

[0085] Background complexity refers to the richness and disorder of visual elements such as texture, color, and edges in the HUD virtual image overlay area and its surrounding background. High-complexity backgrounds can produce a visual masking effect, that is, the texture of the background can interfere with the human eye's recognition of the edges of foreground characters, leading to an increase in the driver's cognitive load and a longer reaction time.

[0086] Processor 121 analyzes the region of interest (ROI) covered by the HUD virtual image in the environmental image data. It uses the Canny or Sobel operator to perform edge detection on the ROI, calculating the proportion of edge pixels to total pixels. The denser the edges, such as leaves or densely packed building windows, the stronger the interference. It can also calculate the Shannon entropy of the RGB histogram of the ROI; a higher entropy value indicates a more chaotic background color. Furthermore, it performs a Fast Fourier Transform on the ROI to analyze the energy proportion of high-frequency components; more high-frequency components indicate a coarser and more complex texture. The calculation results are weighted and summed to determine the background complexity of the final environmental image data.

[0087] For example, a highway at night without lights (completely black background), a smooth snow-covered road surface, and a cloudless blue sky. The background is clean and has low complexity, allowing for clear edges on the HUD characters and requiring low brightness. A bustling city night scene, filled with billboards, car lights, and pedestrians, presents an extremely cluttered background. If the HUD 14's brightness is insufficient, the characters will be submerged in background noise, necessitating a relatively higher brightness to enhance the signal-to-noise ratio.

[0088] The processor 121 packages the collected and calculated data into a tagged calibration record and stores it in the calibration database of the memory 122. For example, a set of calibration data might have the structure: {ambient light intensity, display brightness, weather label, scene label}. The memory 122 maintains multiple buckets or uses a circular buffer; for example, it maintains a separate queue for rainy days and another queue for sunny days, thus ensuring the purity of the data during subsequent fitting.

[0089] Traditional nonlinear regression algorithms assume that all calibration data are equally important. However, in HUD calibration, data from extreme scenarios is unreliable. For example, if a user sets the display brightness to its maximum when facing a strong sunset (extremely high glare), this calibration data actually reflects the anti-glare requirement, not the general requirement under that lighting condition. If it is directly used for fitting, the curve will show excessively high brightness in normal scenarios.

[0090] In this embodiment, the confidence weight of each sample point is calculated using the background glare index and / or background complexity. Higher confidence weights are assigned to calibration data with low glare and low complexity, while lower confidence weights are assigned to data with high glare and high complexity. This avoids the need to adjust display brightness due to glare or background complexity, and can be applied to general scenarios.

[0091] For example, the formula for determining the confidence weight based on the background glare index and background complexity is as follows: ,in, w k The confidence weight of the k-th group of calibration data. α and β It is the adjustment coefficient. G k The background glare index refers to the value of the k-th set of calibration data. C k This refers to the background complexity of the k-th set of calibration data.

[0092] Taking the least squares method as an example of a nonlinear regression algorithm, in this embodiment, the processor 121 uses the weighted least squares method to fit the calibration data under a specific weather label. The goal is to find the coefficients of the higher-order polynomial, such that the weighted sum of squared residuals is: The value of is the smallest, where w k The confidence weight of the k-th group of calibration data. P t,k The display brightness refers to the value in the k-th set of calibration data. P e,k This refers to the display brightness determined by a higher-order polynomial function. The fitting process follows monotonicity and boundary constraints. Monotonicity constraints ensure that within the effective illumination range, the derivative of the higher-order polynomial is greater than or equal to 0, meaning the display brightness should not decrease with increasing light intensity. Boundary constraints ensure that the calculated display brightness is between the minimum and maximum values ​​allowed by the hardware.

[0093] After entering the calibration process, the specific brightness adjustment process is as follows: Figure 9 As shown, processor 121 is configured to perform the following steps S901 to S905.

[0094] In step S901, the adjustment amplitude is obtained from the brightness calibration trigger command.

[0095] In step S902, the display brightness of the current HUD is adjusted according to the adjustment range.

[0096] The adjustment range refers to the magnitude of change in the display brightness (PWM duty cycle or brightness level) of the HUD 14 during a single brightness adjustment operation. It is not a fixed value, but a variable that is dynamically determined based on the semantic strength or operational characteristics of the user command.

[0097] The brightness calibration trigger command is a voice command generated based on a voice operation request. To achieve precise control, the processor 121 uses an NLP model to semantically classify the voice command, mapping the ambiguous natural language into quantified adjustment ranges. If a fine-tuning keyword is identified, the adjustment range is smaller, such as 10%. If a coarse-tuning keyword is identified, the adjustment range is larger, such as 60%.

[0098] The processor 121 outputs the adjusted display brightness based on the adjustment range. Before outputting, the processor 121 checks whether the brightness exceeds the hardware's allowed range from minimum to maximum. If it does, the adjusted display brightness is controlled to the boundary value, and voice feedback is provided indicating that the maximum or minimum brightness has been reached. Based on the adjusted display brightness and user interaction, the process proceeds to the next stage after user confirmation. Otherwise, the above adjustment process continues.

[0099] This variable step size adjustment mechanism allows for a larger adjustment range to be applied in one step when the brightness deviation is large, such as when it is too dark, reducing the number of interaction cycles; when the deviation is small, a smaller adjustment range can avoid over-adjustment.

[0100] In step S903, an adjustment coefficient is determined based on the ratio of the difference between the adjusted display brightness and the display brightness before adjustment.

[0101] The display brightness before adjustment is the brightness value output based on the original brightness control curve before the user initiates the calibration process. The display brightness after adjustment is the brightness value that the user finally confirms is satisfactory. The current ambient light intensity is the real-time illumination value collected by the sensor. To improve accuracy, the current ambient light intensity can be the average illumination value during the adjustment process.

[0102] The adjustment factor is a dimensionless scaling factor used to characterize the degree of deviation of a user's personalized brightness requirement from the original brightness control curve. The formula for calculating the adjustment factor is: 1 + (Adjusted display brightness - Original display brightness) / Original display brightness. For example, if the original display brightness was 50% and the user felt it was too dark, and the adjusted display brightness is 60%, then the difference is 10%. The difference ratio 10 / 50 is 0.2, so the adjustment factor is 1 + 0.2 = 1.2, indicating that under the current ambient light intensity, the user wants a 20% increase in display brightness.

[0103] If the processor 121 determines that this calibration is the user's first calibration, or the user has selected the global application mode, the processor 121 will execute the synchronization adjustment strategy in step S804. If the user already has a calibration history, the processor 121 will execute step S805, adjusting only the preset illumination range to which the current ambient light intensity belongs.

[0104] In step S904, the display brightness of multiple preset illumination zones is adjusted synchronously using adjustment coefficients.

[0105] Human visual preferences are generally consistent globally; for example, users with weaker vision require 20% more light than the standard value under any lighting conditions. During the initial calibration, the processor 121 applies the calculated adjustment coefficients to all preset brightness ranges. This method greatly improves calibration efficiency. Users no longer need to perform calibrations separately on cloudy, rainy, sunny, and nighttime days; they only need to complete one interaction in any typical scenario to automatically deduce personalized parameters for the entire scene, achieving single-point calibration with global effectiveness.

[0106] In step S905, the display brightness of the preset illumination range to which the current ambient light intensity belongs is adjusted using an adjustment coefficient.

[0107] After the initial calibration, subsequent recalibrations are performed because user preferences may change non-linearly with light intensity; for example, users may prefer brighter light during the day but darker light at night. Therefore, in this case, only the display brightness within the preset light range is adjusted.

[0108] Furthermore, users may calibrate the same preset brightness range multiple times. If a preset brightness range already has sufficient calibration data and a personalized brightness control curve has been generated, and the preset brightness range is calibrated again, an adjustment coefficient will be generated accordingly. Each subsequent calibration will generate an adjustment coefficient. If the adjustment coefficient is simply used directly, a single user error will seriously affect the user profile. Therefore, the adjustment coefficient can be a weighted sum of the previous adjustment coefficient and the current adjustment coefficient, i.e., the final adjustment coefficient = current adjustment coefficient × λ + previous adjustment coefficient × (1-λ), where λ is the weight. The larger λ is, the greater the contribution of the current adjustment coefficient to the final adjustment coefficient. However, it will also retain some of the influence of the user's historical preferences, thereby preventing a single error from ruining the entire user profile.

[0109] Based on the same inventive concept as the aforementioned technical solutions, this disclosure also provides a display control method. This display control method is used to implement the function of the processor 121 in any of the above-described display control device embodiments; to avoid repetition, it will not be described further here.

[0110] This disclosure also provides a display control device, such as... Figure 10As shown, the display control device includes: an adjustment section 1001, a storage section 1002, and a generation section 1003; the adjustment section 1001 is configured to adjust the current display brightness of the HUD based on a brightness calibration trigger command; the storage section 1002 is configured to store the adjusted display brightness confirmed by the current user and the current ambient light intensity as current calibration data in a memory; the generation section 1003 is configured to generate a personalized brightness control curve corresponding to the current user based on multiple sets of calibration data.

[0111] In some embodiments, the display control device further includes: an acquisition part and an association part; the acquisition part is configured to acquire current weather status information; the association part is configured to associate and store the current weather status information as a weather tag in the current calibration data; the generation part 1003 is configured to generate a personalized brightness control curve corresponding to the current user based on multiple sets of calibration data associated with the current weather status information.

[0112] In some embodiments, the display control device further includes a weighted processing section and a determination section; the determination section is configured to determine the current background glare index and / or background complexity based on environmental image data in front of the vehicle; the association section is configured to associate and store the background glare index and / or background complexity as scene labels in the current calibration data; the weighted processing section is configured to perform weighted processing on multiple sets of calibration data according to the scene labels when generating a personalized brightness control curve.

[0113] In some embodiments, the determining part is configured to determine an adjustment coefficient based on the difference ratio between the adjusted display brightness and the display brightness before adjustment before generating a personalized brightness control curve; the adjusting part 1001 is configured to use the adjustment coefficient to synchronously adjust the display brightness of multiple preset illumination ranges.

[0114] In some embodiments, the adjustment portion 1001 is configured to adjust the display brightness of the preset illumination range to which the current ambient light intensity belongs using an adjustment coefficient.

[0115] In some embodiments, the adjustment section 1001 is configured to parse the adjustment range from the brightness calibration trigger instruction and adjust the display brightness of the current HUD according to the adjustment range.

[0116] In some embodiments, the generation section 1003 is configured to generate a personalized brightness control curve for the current user based on multiple sets of calibration data when the proportion of multiple sets of calibration data covering a preset illumination range reaches a preset threshold, or when the display brightness corresponding to any calibration data is the maximum display brightness.

[0117] In some embodiments, the brightness calibration trigger instruction is generated based on the user's brightness calibration request operation, or it is generated when the current ambient light intensity does not belong to a preset illumination range covered by existing calibration data.

[0118] In some embodiments, the generation section 1003 is configured to fit multiple sets of calibration data using a nonlinear regression algorithm to determine the coefficients of a higher-order polynomial of a preset order, so that the error between the personalized brightness control curve corresponding to the higher-order polynomial and the multiple sets of calibration data is within the error range.

[0119] This disclosure also provides a head-up display device, which includes a display control unit and a display unit; wherein the display control unit is configured to control the display brightness of the display unit based on a personalized brightness control curve generated by the display control unit; and the display unit is configured to project an image onto the windshield of the vehicle according to the display brightness determined by the display control unit.

[0120] This disclosure also provides a computer-readable storage medium storing at least one instruction that is executed by a processor to perform the functions of the display control device as described in the above embodiments.

[0121] This disclosure also provides a computer program product including computer instructions stored in a computer-readable storage medium; a processor of a computing device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computing device to perform the functions of the display control device described in the above embodiments.

[0122] Those skilled in the art will recognize that the functions described in this disclosure in one or more of the examples above can be implemented using hardware, software, firmware, or any combination thereof. When implemented in software, these functions can be stored in a computer-readable medium or transmitted as one or more instructions or code on a computer-readable medium. Computer-readable media include computer storage media and communication media, wherein communication media include any medium that facilitates the transfer of a computer program from one place to another. Storage media can be any available medium accessible to a general-purpose or special-purpose computer.

[0123] It should be noted that the technical solutions described in this disclosure can be combined arbitrarily as long as they do not conflict.

[0124] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A display control device characterized by comprising: The display control device comprises a processor and a memory connected in communication; The processor is configured to adjust the current display brightness of the head-up display (HUD) based on a brightness calibration trigger instruction, and store the adjusted display brightness confirmed by the current user and the current ambient light intensity as current calibration data in the memory; Furthermore, based on a plurality of sets of calibration data in the memory, a personalized brightness control curve corresponding to the current user is generated, so that the HUD controls the display brightness of the HUD according to the personalized brightness control curve.

2. The display control device according to claim 1, characterized by The processor is further configured to obtain current weather state information; and The current weather state information is stored in association with the current calibration data as a weather tag; and Based on a plurality of sets of calibration data associated with the current weather state information, a personalized brightness control curve corresponding to the current user is generated.

3. The display control device according to claim 1 or 2, wherein The processor is further configured to determine a current background glare index and / or a background complexity based on environmental image data in front of the vehicle; and The background glare index and / or the background complexity are stored in association with the current calibration data as a scene tag; When generating the personalized brightness control curve, the plurality of sets of calibration data are processed according to the scene tag.

4. The display control device according to claim 1, wherein The processor is configured to determine an adjustment coefficient based on a difference ratio between the adjusted display brightness and the display brightness before adjustment before generating the personalized brightness control curve; The display brightness of a plurality of preset light intervals is adjusted synchronously using the adjustment coefficient. The processor is configured to adjust the display brightness of a preset light interval to which the current ambient light intensity belongs using the adjustment coefficient.

5. The display control device according to claim 4, characterized by The processor is configured to:

6. The display control device according to claim 1, wherein Parse the adjustment amplitude from the brightness calibration trigger instruction; Adjust the display brightness of the current HUD according to the adjustment amplitude.

7. The display control device according to claim 1, wherein The processor is configured to generate the personalized brightness control curve corresponding to the current user based on the plurality of sets of calibration data when the proportion of the plurality of sets of calibration data covering the preset light intervals reaches a preset threshold, or when the display brightness corresponding to any calibration data is the maximum display brightness. The brightness calibration trigger instruction is generated based on a brightness calibration request operation of the user, or is generated when the current ambient light intensity does not belong to a preset light interval covered by existing calibration data.

8. The display control device according to claim 1, wherein 9. The display control device according to claim 1, wherein The processor is configured to fit the plurality of sets of calibration data by a nonlinear regression algorithm, determine the coefficients of a preset order high-order polynomial, so that the error between the personalized brightness control curve corresponding to the high-order polynomial and the plurality of sets of calibration data is within an error range. The display control method comprises:

10. A display control method characterized by comprising: Adjusting the current display brightness of the HUD based on a brightness calibration trigger instruction; ​ store the adjusted display brightness confirmed by the current user and the current ambient light intensity as current calibration data to a memory; generate a personalized brightness control curve corresponding to the current user based on multiple sets of calibration data.

11. A display control device characterized by comprising: The display control device comprises an adjusting part, a storing part and a generating part; The adjusting part is configured to adjust the current display brightness of the HUD based on a brightness calibration trigger instruction; The storing part is configured to store the adjusted display brightness confirmed by the current user and the current ambient light intensity as current calibration data to a memory; The generating part is configured to generate a personalized brightness control curve corresponding to the current user based on multiple sets of calibration data.

12. A head-up display device, characterized by comprising: The head-up display device comprises a display control part and a display part; wherein, The display control part is configured to control the display brightness of the display part based on the personalized brightness control curve generated by the display control device according to any one of claims 1 to 9; The display part is configured to project an image on the windshield of the vehicle according to the display brightness determined by the display control part.

13. A computer-readable storage medium, characterized in that, The computer readable storage medium stores at least one instruction for being executed by a processor to implement the display control method according to claim 10.

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