A dimming method and system for vehicle ambient light

Abstract

The application provides a dimming method and system for a vehicle atmosphere lamp, the method comprising: obtaining an initial spectral data set of a multi-channel spectrum of an atmosphere light source of the vehicle, wherein the initial spectral data set comprises all possible combinations of the multi-channel spectrum; obtaining spectral action characteristics of a target material of an illuminated surface; determining an adapted light color for the target material based on the initial spectral data set, the spectral action characteristics of the target material, and a preset light color evaluation index; and selecting a target spectral data set corresponding to the adapted light color from the initial spectral data set. In addition, the application also provides a vehicle supporting dimming of a vehicle atmosphere lamp.

Description

Technical Field

[0001] This invention relates to the field of intelligent vehicles, and more specifically, to a dimming method and system for vehicle ambient lighting based on metamerism. Background Technology

[0002] In early vehicle design, vehicle lighting primarily focused on illumination. However, in recent years, with the development of intelligent vehicles and consumers' increasing demands for driving experience, vehicle lighting has gradually shifted from simple illumination to creating an ambiance that combines aesthetics and comfort. Interior ambient lighting, as a lighting device that enhances the driving experience and the perceived quality of the vehicle, has gradually become a standard feature in modern cars. However, most current vehicle interior ambient lighting systems only offer simple dimming and color changing capabilities, failing to meet consumers' needs for personalization and intelligent features.

[0003] In addition, current vehicle interior ambient lighting cannot differentiate dimming based on different materials of the vehicle interior, or achieve a consistent light color effect for different color schemes of the interior, thus making the current ambient lighting dimming solution not intelligent enough.

[0004] Therefore, in order to obtain more personalized lighting solutions, it is desirable to provide a dimming solution for vehicle ambient lighting that can adjust the spectral distribution of different wavelengths through metamerism technology to achieve multiple different expressions of the same light color, and then implement differentiated dimming solutions for different materials to create the best lighting experience for the vehicle interior environment. Summary of the Invention

[0005] This summary is provided to introduce, in a simplified form, some concepts that will be further described in the following detailed description. This summary is not intended to identify key or essential features of the claimed subject matter, nor is it intended to help determine the scope of the claimed subject matter.

[0006] To address the above problems, according to a first aspect of the present invention, a dimming method for vehicle ambient lighting is provided, the method comprising: acquiring an initial spectral dataset of a multi-channel spectrum of an ambient light source for the vehicle, wherein the initial spectral dataset includes all possible ratios of the multi-channel spectrum; acquiring the spectral characteristics of a target material for the illuminated surface; determining an appropriate color for the target material based on the initial spectral dataset, the spectral characteristics of the target material, and a preset colorimetric evaluation index; and selecting a target spectral dataset corresponding to the appropriate color from the initial spectral dataset.

[0007] In the embodiments of the present invention, by obtaining the spectral characteristics of the vehicle interior materials and referring to preset evaluation indicators (efficiency and color difference before and after the interaction of the light source and the material), the appropriate light color for the corresponding interior material (e.g., materials of different color systems) can be selected to form a metamerism combination, thereby presenting differentiated dimming schemes for different materials and realizing a more personalized dimming strategy for vehicle interior ambient lighting.

[0008] According to one embodiment of the present invention, obtaining the spectral characteristics of a target material for an illuminated surface further includes: establishing a material spectral characteristics database for different materials, wherein the spectral characteristics are classified according to the color of the material in the database; and selecting a corresponding spectral characteristics from the database according to the color system of the target material, wherein the spectral characteristics include reflectance characteristics or transmittance characteristics.

[0009] According to a further embodiment of the present invention, classifying spectral characteristics based on the color of the material further includes: calculating the chromaticity parameters of each material in the color space; and classifying the materials according to different color systems based on the chromaticity parameters and spectral characteristics to obtain different spectral characteristics for materials of different color systems, wherein materials of the same color system exhibit similar spectral characteristics and have similar adaptability to metamerism light sources.

[0010] According to a further embodiment of the present invention, materials can be classified according to different color systems using the following methods: clustering based on hue angle, principal component analysis based on spectral characteristics, or linear discriminant analysis.

[0011] According to a further embodiment of the present invention, determining the appropriate light color for the target material based on the initial spectral dataset, the spectral characteristics of the target material, and a preset light color evaluation index further includes: multiplying the wavelength values ​​of each group of multi-channel spectra in the initial spectral dataset with the spectral reflectance or transmittance of the target material at the corresponding wavelength to obtain the material's reflection or transmission spectrum; calculating the actual light color evaluation index value regarding the efficiency or color difference before and after the interaction of each group of multi-channel spectra with the target material based on the obtained material reflection or transmission spectrum, wherein the actual light color evaluation index value includes one or more of spectral transmission radiation efficiency or color difference; and selecting the appropriate light color whose actual light color evaluation index value conforms to the preset light color evaluation index.

[0012] According to a further embodiment of the present invention, determining the appropriate light color for the target material further includes: if the target material is a multi-characteristic material, then determining the appropriate light color for the target material based on the spectral reflectance differences of different texture regions of the target material, so that the light source is the same color but the color contrast at the texture of the material is obvious.

[0013] According to a further embodiment of the present invention, determining the appropriate light color for the target material further includes: determining the appropriate light color for the target material based on the ambient light surrounding the target material, so that the reflected or transmitted color of the target material is similar to or converges with the ambient light color.

[0014] According to a further embodiment of the present invention, selecting a target spectral dataset corresponding to the adaptive light color from the initial spectral dataset further includes: selecting a multi-channel spectrum corresponding to the light source color and the adaptive light color from each group of multi-channel spectra in the initial spectral dataset according to the adaptive light color, so as to obtain a corresponding target spectral dataset.

[0015] According to a further embodiment of the present invention, the method alternatively includes: determining a desired light color effect for the target material; and selecting a target spectral dataset from the initial spectral dataset that corresponds to the desired light color effect.

[0016] According to a further embodiment of the present invention, the method further includes: calculating actual rhythm evaluation index values ​​associated with each group of multi-channel spectra in the target spectral dataset; and selecting an optimized target spectral dataset from the target spectral dataset whose actual rhythm evaluation index values ​​conform to a preset rhythm evaluation index, wherein the rhythm evaluation index is related to the user's driving needs.

[0017] In the embodiments of the present invention, after selecting the appropriate light color for the material and obtaining the corresponding metamerism combination, the metamerism light source under the corresponding EDI value can be further screened based on the preset rhythm evaluation index (such as melanopsin equivalent daylight illuminance (melEDI) for human rhythm effects) to meet the user's driving needs.

[0018] According to a further embodiment of the present invention, the rhythm evaluation index includes one or more of the following: melanopsine equivalent daylight illuminance (mel EDI), melanopsine equivalent lux (EML), and spectral blue light ratio, which are related to the human rhythm effect.

[0019] According to a further embodiment of the present invention, selecting an optimized spectral dataset from the target spectral dataset whose actual rhythm evaluation index value conforms to a preset rhythm evaluation index further includes: determining the driver state and vehicle state; determining the desired melanopsine equivalent sunlight illuminance value based on the determined driver state and vehicle state; and selecting an optimized target spectral dataset from the target spectral dataset whose actual melanopsine equivalent sunlight illuminance value conforms to the desired melanopsine equivalent sunlight illuminance value.

[0020] According to a second aspect of the present invention, a dimming system for vehicle ambient lighting is provided, the system comprising a data acquisition module configured to: acquire an initial spectral dataset of a multi-channel spectrum of an ambient light source for the vehicle, wherein the initial spectral dataset includes all possible ratios of the multi-channel spectrum; acquire spectral characteristics of a target material for the illuminated surface; a spectral processing module configured to determine an appropriate color for the target material based on the initial spectral dataset, the spectral characteristics of the target material, and a preset colorimetric evaluation index; and a spectral selection module configured to select a target spectral dataset corresponding to the appropriate color from the initial spectral dataset.

[0021] According to a further embodiment of the present invention, the spectral selection module is further configured to: calculate actual rhythm evaluation index values ​​associated with each group of multi-channel spectra in the target spectral dataset; and select optimized target spectral datasets from the target spectral datasets whose actual rhythm evaluation index values ​​conform to preset rhythm evaluation indexes, wherein the rhythm evaluation indexes include one or more of the following for human rhythm effects: melanopsine equivalent daylight illuminance (mel EDI), melanopsine equivalent lux (EML), and spectral blue light ratio.

[0022] According to a third aspect of the invention, a vehicle is provided that includes a dimming system as described in any of the preceding aspects.

[0023] These and other features and advantages will become apparent from the following detailed description and with reference to the accompanying drawings. It should be understood that the foregoing general description and the following detailed description are illustrative only and do not limit the scope of the claims. Attached Figure Description

[0024] To gain a more detailed understanding of the manner in which the features of the present invention are described above, reference can be made to various embodiments to provide a more specific description of the above-briefly summarized aspects, some of which are illustrated in the accompanying drawings. However, it should be noted that the drawings illustrate only certain typical aspects of the invention and should not be considered as limiting its scope, as this description may allow for other equivalent and effective aspects.

[0025] Figure 1 A schematic architecture diagram of a dimming system for vehicle ambient lighting according to an embodiment of the present invention is shown.

[0026] Figure 2 A schematic diagram of metamerism according to an embodiment of the present invention is shown.

[0027] Figure 3A diagram illustrating the material color system division and its spectral characteristic curves in the CIELAB color space according to an embodiment of the present invention is shown.

[0028] Figure 4A A schematic diagram is shown illustrating how light of the same color appears in different colors on the same material according to an embodiment of the present invention.

[0029] Figure 4B A schematic diagram illustrating different colors of light appearing as the same color on different materials is shown according to an embodiment of the present invention.

[0030] Figure 5 A schematic flowchart of a dimming method for vehicle ambient lighting according to an embodiment of the present invention is shown.

[0031] Figure 6 An exemplary vehicle supporting ambient lighting dimming according to an embodiment of the present invention is shown.

[0032] Figure 7 A schematic architecture diagram of a dimming system for vehicle ambient lighting according to an embodiment of the present invention is shown. Detailed Implementation

[0033] The present invention will now be described in detail with reference to the accompanying drawings, and its features will become further apparent from the following detailed description. Throughout this specification, the term "vehicle" refers to any type of automobile, including but not limited to cars, vans, trucks, buses, etc. For simplicity, the invention is described in relation to "automobiles." The terms "A or B" as used in this specification mean "A and B" and "A or B," and do not imply that A and B are exclusive unless otherwise stated.

[0034] Current intelligent vehicle lighting includes features such as interior ambient lighting, which can be integrated into the smart cockpit to create an immersive experience. Interior ambient lighting is generally used in areas such as the dashboard, doors, center console, dome lights, sunroof, headlight canopy, front and rear footwells, and rear seats, taking the form of light strips or dots. In terms of light emission methods, it can include, for example, point source direct-light ambient lighting (composed of a light distribution mask, LED circuit board, and housing), point source light strip ambient lighting (composed of a light-emitting module, light guide, and mounting bracket), and point source surface-light ambient lighting (composed of a light-emitting mask, surface light guide, LED circuit board, and housing), which can be used in different areas of the vehicle cabin.

[0035] However, most current vehicle interior ambient lighting can only perform simple dimming and color changing operations, without considering differentiated dimming for different interior materials, or some non-visual effects (rhythm effects), making it difficult to meet consumers' needs for personalization and intelligence.

[0036] In response, this application utilizes metamerism technology to adjust the spectral distribution of different wavelengths, thereby achieving multiple different expressions of the same light color. This allows for differentiated dimming schemes for different interior materials, creating the best lighting experience for the vehicle's interior environment.

[0037] Figure 1 A schematic architecture diagram of a dimming system 100 for vehicle ambient lighting according to an embodiment of the present disclosure is shown. Figure 1 As shown, the system 100 may include at least a data acquisition module 102, a spectrum processing module 104, and a spectrum selection module 106.

[0038] The data acquisition module 102 can acquire an initial spectral dataset of the multi-channel spectrum of the ambient light source for the vehicle, wherein the initial spectral dataset includes all possible ratios of the multi-channel spectrum.

[0039] In one implementation, a four-color light source (also known as a four-channel light source) can be used for dimming. The principle is to introduce additional white or amber light sources, and the proportions of these four light sources can be adjusted according to the desired different colors of light to form a new quadrilateral color gamut, thereby achieving effective spectral control for the same color of light. See also Figure 2 It shows a schematic diagram of the above metamerism technique. Figure 2 As can be seen, while maintaining the consistency of the light source color (e.g., blue in the left image and purple in the right image), effective control of the spectrum can be achieved (e.g., by adjusting the ratio of light sources before and after to change the position and height of the spectral peaks; see [reference]). Figure 2 (The curve in the graph) thus achieves diverse dimming effects.

[0040] In the case of the four-channel light source, the data acquisition module 102 can acquire the initial spectral data of the four channels of the vehicle interior ambient light source, assign different weights to each channel (for example, each channel outputs light according to the required output light intensity ratio) to generate all possible ratios of the four-channel spectrum.

[0041] Furthermore, the data acquisition module 102 can acquire the spectral characteristics of the target material (i.e., the target interior material) of the illuminated surface.

[0042] In one implementation, a database of spectral properties for different materials (e.g., materials of different color schemes) can be established, wherein spectral properties can be classified according to the color of the material in the database, and the spectral properties may include reflectance or transmission properties. Of course, it is understood that spectral properties can also be classified according to any other characteristics of the material (e.g., the material itself, such as genuine leather or fabric).

[0043] like Figure 3As shown, Figure 300 illustrates the material color system division and its spectral characteristic curves in the CIELAB color space according to an embodiment of the present invention. Figure 3 In this study, based on the established material spectral property database, in CIE1976L... * a * b * The CIELAB color space is divided into eight color families based on hue angle: red, orange, green, cyan, blue, magenta, and neutral colors. Each color family exhibits similar spectral characteristics in terms of reflectance / transmittance, and their adaptability to metameristic light sources is similar. Taking material spectral transmittance as an example, we can first calculate the chromaticity parameter 'a' of each material in the CIELAB color space. * b * Subsequently, K-means clustering was performed based on the hue angle H, and then the spectral transmittance was divided into several different color systems.

[0044] Of course, it is understandable that any other known method (such as principal component analysis based on spectral characteristics, linear discriminant analysis, etc.) can be used to classify materials according to different color systems.

[0045] The data acquisition module 102 can then select the appropriate spectral characteristics (e.g., reflectance or transmission characteristics) from the database based on the color system of the target material. Based on the spectral differences between color systems, the matching light color of each color system material can be explored, and then metamerism can be used to generate multiple sets of suitable spectra.

[0046] The spectral processing module 104 can then determine the appropriate light color for the target material based on the acquired initial spectral dataset, the spectral characteristics of the target material, and a preset light color evaluation index.

[0047] In one implementation, taking the spectral transmittance of red-toned materials as an example, the wavelength values ​​of each group of multi-channel spectra in the initial spectral dataset can be multiplied with the spectral transmittance of the target material at the corresponding wavelength to obtain the material's transmission spectrum. This is then combined with colorimetric evaluation indicators (e.g., spectral transmittance radiative efficiency (STLER), color difference, etc.). (etc.) can calculate the efficiency and color difference before and after the interaction of the light source and the material (i.e., the actual light color evaluation index value), and the calculation formula is as follows:

[0048]

[0049] Among them, K m =6831m / W; P(λ) is the spectral power distribution of the light source; ρ(λ) is the spectral characteristics of the material; V(λ) is the spectral luminous efficiency function of the human eye; Let L be the color matching function; X(λ), Y(λ), and Z(λ) are the tristimulus values ​​of the reflectance spectrum, respectively; L* Indicates psychometric brightness; red-green axis a * and yellow and blue axis b * These represent psychometric colorimetric values; X n Y n Z n The values ​​are the tristimulus values ​​of the standard illuminator.

[0050] Subsequently, you can select an appropriate light color that matches the preset light color evaluation index (e.g., high luminous efficiency, low color difference).

[0051] Of course, it is understandable that any other suitable light color evaluation index can be used to screen for the appropriate light color for different materials, thereby achieving a more personalized dimming strategy for vehicle interior ambient lighting.

[0052] In one implementation, for example, for multi-characteristic materials, a comprehensive evaluation can be conducted based on different characteristics to ensure light color matching while achieving clear texture and obvious contrast, thus meeting specific visual effect requirements. Specifically, based on the differences in spectral reflectance of different texture areas of the target material, a metamerism light source can be selected to achieve uniform light color but distinct color contrast at the material texture.

[0053] In another implementation, ambient light can be considered to select an appropriate light color that matches the surrounding environment, thus achieving color harmony. Specifically, the ambient light around the target material can be identified, and the light source color can be adjusted so that the material's reflected or transmitted color is similar to or converges with the ambient light color, avoiding or reducing strong contrast stimulation and achieving unified color harmony.

[0054] The spectral selection module 106 can then select a target spectral dataset corresponding to the aforementioned adapted light color from the initial spectral dataset.

[0055] Alternatively, the spectral selection module 106 can determine the desired light and color effect (and the color of the material under light source illumination) for the target interior material (which may be represented, for example, in chromaticity coordinates), and select a target spectral dataset from the initial spectral dataset that corresponds to the desired light and color effect.

[0056] Furthermore, the spectral selection module 106 can calculate the actual rhythm evaluation index values ​​associated with each group of multi-channel spectra in the target spectral dataset, and select from the target spectral dataset the optimized target spectral dataset whose actual rhythm evaluation index values ​​meet the preset rhythm evaluation index, wherein the rhythm evaluation index is related to the user's driving needs.

[0057] In one implementation, the rhythm evaluation index includes, but is not limited to, melanopoietic equivalent daylight illuminance (mel EDI), melanopoietic equivalent lux (EML), and the proportion of blue light in the spectrum, which are related to the effects of human rhythm.

[0058] Melanopic Equivalent Illuminance (mel EDI) is positively correlated with subjective alertness. Generally, in driving mode, a higher EDI value helps improve alertness and safety; in rest mode, a lower EDI value is beneficial for driver rest. For indoor daytime lighting, the recommended minimum EDI is 250 lux, the recommended maximum EDI for nighttime lighting is 10 lux, and the recommended maximum EDI for sleep environments is 1 lux. These values ​​are all measured at a vertical plane approximately 1.2 meters high (i.e., vertical illuminance at eye level when seated). The formula for calculating melanopic equivalent daylight illuminance (mel EDI) is as follows:

[0059]

[0060] Among them, E e，λ (λ) is taken in units of W / m 2 Measured values ​​of spectral power density distribution per unit area in the 380-780 nm wavelength range at / nm; S mel (λ) is the spectral photoluminescence efficiency function of melanopsin, with a peak sensitivity of 490 nm.

[0061] As described above, when the rhythm evaluation index includes mel EDI, the driver's state (e.g., fatigue, distraction, etc.) and vehicle state (e.g., whether in driving mode or rest mode) can be determined first when selecting the spectrum. Then, based on the determined driver and vehicle states, the desired EDI value can be determined, and an optimized target spectrum dataset with actual EDI values ​​matching the desired EDI value can be selected from the target spectrum dataset. This can help achieve different rhythmic effects with consistent light source color.

[0062] In one example, if driver fatigue is detected while the vehicle is in motion, a higher EDI value is desired to improve driver alertness. In this case, a multi-channel spectrum with a higher EDI value can be selected from the various multi-channel spectra.

[0063] In another example, if the vehicle is detected to be in rest mode (i.e., stationary), a lower EDI value is desired to allow the driver to get sufficient rest. In this case, a multi-channel spectrum with a lower EDI value can be selected from the various groups of multi-channel spectra.

[0064] Of course, it is understandable that any other suitable rhythm evaluation index can be used to further screen metamerism light sources that meet the user's driving needs.

[0065] Those skilled in the art will understand that the systems of the present invention can be implemented in hardware or software, and the systems can be combined or merged in any suitable manner.

[0066] Figures 4A-4B This further demonstrates the differentiated or consistent dimming effect achieved using metamerism technology. Figure 4A Scene 400 illustrates how the same color light produces different color effects on the same material. For example... Figure 4A As can be seen, since materials reflect light of different wavelengths to varying degrees, by adjusting the metamerism light source and illuminating the same material with a specific reflectivity, different light color effects can be presented on the illuminated interior surface, thereby achieving differentiated dimming for the same material.

[0067] Figure 4B Scene 402 illustrates how different colored lights produce the same color effect on different materials. For example... Figure 4B As can be seen, by using metamerism, materials with different reflectivities can be illuminated by different light source spectra to obtain a series of material reflection spectra and their corresponding combinations of light sources and materials. If the chromaticity difference of the reflection spectra is small enough, the materials can be considered to be the same color (i.e., the materials exhibit the same light color effect under the illumination of the light source).

[0068] Therefore, metamerism can be used to utilize the spectral characteristics of different materials to achieve differentiated / consistent dimming for the same / different materials, thereby enabling multiple different expressions of the same light color and creating the best lighting experience for the car interior environment.

[0069] Figure 5 A schematic flowchart of a dimming method 500 for vehicle ambient lighting according to an embodiment of the present invention is shown.

[0070] Method 500 begins at step 502, whereby data acquisition module 102 acquires an initial spectral dataset of the multi-channel spectrum of the ambient light source for the vehicle, wherein the initial spectral dataset may include all possible ratios of the multi-channel spectrum.

[0071] In step 504, the data acquisition module 102 can acquire the spectral characteristics of the target material for the illuminated surface (e.g., the interior surface of a vehicle).

[0072] Furthermore, the spectral processing module 104 can establish a material spectral characteristic database for different materials, wherein the spectral characteristics are classified according to the color of the material in the database; and select the corresponding spectral characteristics from the database according to the color system of the target material, wherein the spectral characteristics may include reflection characteristics or transmission characteristics.

[0073] In one implementation, in order to classify the spectral characteristics of materials based on their color, the chromaticity parameters of each material in the color space can be calculated, and the materials can be divided into different color systems based on the chromaticity parameters (e.g., using methods such as clustering based on hue angle, principal component analysis based on spectral characteristics, and linear discriminant analysis) to obtain different spectral characteristics for materials of different color systems. Materials of the same color system exhibit similar spectral characteristics and have similar adaptability to metamerism light sources.

[0074] In step 506, the spectral processing module 104 can determine the appropriate light color for the target material based on the initial spectral dataset, the spectral characteristics of the target material, and a preset light color evaluation index.

[0075] In one embodiment, the spectral processing module 104 can multiply the wavelength values ​​of each group of multi-channel spectra in the initial spectral dataset with the spectral reflectance or transmittance of the target material at the corresponding wavelength to obtain the material's reflection or transmission spectrum. Subsequently, based on the obtained material reflection or transmission spectrum, it can calculate the actual light color evaluation index value regarding the efficiency or color difference of each group of multi-channel spectra before and after interaction with the target material. The actual light color evaluation index value may include one or more of spectral transmission radiation efficiency or color difference, and it can select the appropriate light color that conforms to the preset light color evaluation index value (e.g., select an appropriate light color with high luminous efficiency and low color difference).

[0076] In another embodiment, if the target material is a multi-characteristic material, the appropriate light color for the target material can be determined based on the spectral reflectance differences of different texture regions of the target material, so that the light source is the same color but the color contrast at the texture of the material is obvious.

[0077] In addition, the appropriate light color for the target material can be determined based on the ambient light around the target material, so that the reflected or transmitted color of the target material is similar to or similar to the ambient light color, thereby achieving a color harmony effect.

[0078] In step 508, the spectral selection module 106 can select a target spectral dataset corresponding to the adaptive light color from the initial spectral dataset.

[0079] Specifically, the spectral selection module 106 can select the multi-channel spectrum corresponding to the light source color and the adaptive light color from each group of multi-channel spectra in the initial spectral dataset according to the adaptive light color, so as to obtain the corresponding target spectral dataset.

[0080] In other words, the spectrum selection module 106 can perform color screening on the multi-channel spectrum after the corresponding ratio of different channel spectra according to the suitable light color of each color material, so as to make the light source meet the color difference range of a sufficiently small range with the preset color, so as to obtain the metamerism light source combination suitable for each color material.

[0081] As an alternative to steps 506 and 508, the spectral selection module 106 can determine the desired light color effect for the target material and select the target spectral dataset corresponding to the desired light color effect from the initial spectral dataset.

[0082] Further optionally, in step 510, the spectral selection module 106 can calculate the actual rhythm evaluation index value associated with each group of multi-channel spectra in the target spectral dataset, and can subsequently select from the target spectral dataset an optimized target spectral dataset whose actual rhythm evaluation index value conforms to a preset rhythm evaluation index, wherein the rhythm evaluation index is related to the user's driving needs.

[0083] In one implementation, rhythm evaluation indicators may include, but are not limited to, melanopoietic equivalent daylight illuminance (mel EDI), melanopoietic equivalent lux (EML), and the proportion of blue light in the spectrum, which are related to the effects of human rhythm.

[0084] In a further embodiment, the spectral selection module 106 can determine the driver state and the vehicle state, determine the desired EDI value based on the determined driver state and the vehicle state, and select an optimized target spectral dataset from the target spectral dataset whose actual EDI value matches the desired EDI value.

[0085] Therefore, metamerism can be used to adjust the spectral distribution of different wavelengths, achieving multiple different expressions of the same light color. This allows for differentiated dimming solutions for different materials, creating the best lighting experience for the car's interior environment.

[0086] Figure 6 An exemplary vehicle 600 supporting ambient lighting dimming according to an embodiment of the present invention is shown. The vehicle 600 may include various software and hardware components connected via a bus 602.

[0087] For example, vehicle 600 may include at least sensor system 604, communication system 606, lighting system 608, driver monitoring system 610, and such as Figure 1 The dimming system 100 shown.

[0088] The sensor system 604 may include, for example, a wide variety of sensors mounted on the vehicle, including but not limited to any suitable number of accelerometers, gyroscopes and / or magnetometers, vision sensors (e.g., onboard cameras), millimeter-wave radar, lidar, etc.

[0089] Communication system 606 can be communicated via a short-range wireless communication protocol (e.g., Data messages and elements may be transmitted and received via (etc.) and / or via local area networks and / or wide area networks, and / or via cellular networks, and / or via any suitable wireless network. It should be understood that these are merely examples of networks that vehicle 400 may utilize on a wireless link, and the claimed subject matter is not limited in this respect. In one embodiment, communication system 606 may include various combinations of WAN, WLAN, and / or PAN transceivers. In one embodiment, communication system 606 may also include a Bluetooth transceiver, a ZigBee transceiver, or other PAN transceivers.

[0090] The lighting system 608 may include, but is not limited to, in-vehicle projection lights, in-vehicle LED light strips or other surface lighting or light-conducting materials, in-vehicle sunroof LED lights or other surface lighting or light-conducting materials, etc., and can be used, for example, to receive dimming schemes from the dimming system 100 and to perform personalized dimming operations for in-vehicle ambient lighting.

[0091] The driver monitoring system 610 can be used to collect driver status data to determine whether the driver is driving while fatigued, and to analyze other situations.

[0092] Figure 7 A schematic architecture diagram of a dimming system 700 for vehicle ambient lighting according to an embodiment of the present invention is shown. System 700 can be configured to perform various methods described herein (including, for example, regarding...). Figure 5 The methods described cover all aspects.

[0093] like Figure 7 As shown, system 700 may include one or more processors 702. The one or more processors 702 may include a central processing unit (CPU), which in some examples may be a multi-core CPU. Instructions executed at the CPU may be loaded, for example, from program memory associated with the CPU or from memory 604. The one or more processors 702 may also include additional processing components tailored for specific functions, such as a graphics processing unit (GPU), a digital signal processor (DSP), or a neural processing unit (NPU). In some examples, the one or more processors 602 may be based on the ARM or RISC-V instruction set.

[0094] System 700 also includes memory 704. Memory 704 may include RAM, ROM, or a combination thereof. Memory 704 may store computer-executable instructions that, when executed by at least one processor 702, cause the at least one processor to perform various functions described herein, including: acquiring an initial spectral dataset of a multi-channel spectrum of an ambient light source for the vehicle, wherein the initial spectral dataset includes all possible proportions of the multi-channel spectrum; acquiring spectral characteristics of a target material for the illuminated surface; determining an appropriate colorimetric color for the target material based on the initial spectral dataset, the spectral characteristics of the target material, and a preset colorimetric evaluation index; and selecting a target spectral dataset corresponding to the appropriate colorimetric color from the initial spectral dataset.

[0095] Understandable. Figure 7 This is merely one example of a system, and other systems with fewer, additional, or alternative aspects may also be consistent with this disclosure.

[0096] The various illustrative blocks and modules described herein can be implemented or executed using a general-purpose processor, DSP, ASIC, FPGA or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or any combination thereof designed to perform the functions described herein. The general-purpose processor may be a microprocessor, but in alternatives, the processor may be any conventional processor, controller, microcontroller, or state machine. The processor may also be implemented as a combination of computing devices (e.g., a combination of a DSP and a microprocessor, multiple microprocessors, one or more microprocessors working in conjunction with a DSP core, or any other such configuration).

[0097] The functions described herein can be implemented in hardware, software executed by a processor, firmware, or any combination thereof. If implemented in software executed by a processor, the functions can be stored or transmitted as one or more instructions or code on a computer-readable medium. Other examples and implementations fall within the scope of this disclosure and the appended claims. For example, due to the nature of software, the functions described herein can be implemented using software executed by a processor, hardware, firmware, hardwired, or any combination thereof. Features implementing the functions can also be physically located in various locations, including being distributed such that different parts of the function are implemented at different physical locations.

[0098] The foregoing description includes examples of various aspects of the claimed subject matter. It is certainly impossible to describe every conceivable combination of components or methods for the purpose of depicting the claimed subject matter, but those skilled in the art will recognize that many further combinations and arrangements of the claimed subject matter are possible. Thus, the disclosed subject matter is intended to cover all such changes, modifications, and variations that fall within the spirit and scope of the appended claims.

Claims

1. A dimming method for vehicle ambient lighting, the method comprising: Obtain an initial spectral dataset of the multi-channel spectrum of the ambient lighting source for the vehicle, wherein the initial spectral dataset includes all possible ratios of the multi-channel spectrum; Obtain the spectral characteristics of the target material on the illuminated surface; Based on the initial spectral dataset and the spectral characteristics of the target material, combined with the preset light color evaluation index, the appropriate light color for the target material is determined. as well as Select the target spectral dataset that corresponds to the adapted light color from the initial spectral dataset.

2. The method as described in claim 1, characterized in that, Obtaining the spectral characteristics of the target material on the illuminated surface further includes: Establish a material spectral characteristic database for different materials, wherein the spectral characteristics are classified according to the color of the material in the database; and The corresponding spectral characteristics are selected from the database according to the color system of the target material, wherein the spectral characteristics include reflection characteristics or transmission characteristics.

3. The method as described in claim 2, characterized in that, The classification of spectral properties based on the color of the material further includes: Calculate the chromaticity parameters of each material in the color space; and Based on the chromaticity parameters and spectral characteristics, the materials are classified into different color systems, resulting in different spectral characteristics for materials in different color systems. Among them, materials of the same color family exhibit similar spectral characteristics and have similar compatibility with metamerism light sources.

4. The method as described in claim 3, characterized in that, Materials can be classified according to different color systems using the following methods: clustering based on hue angle, principal component analysis based on spectral characteristics, or linear discriminant analysis.

5. The method as described in claim 1, characterized in that, Based on the initial spectral dataset, the spectral characteristics of the target material, and in conjunction with preset colorimetric evaluation indicators, determining the suitable colorimetric color for the target material further includes: The wavelength values ​​of each group of multichannel spectra in the initial spectral dataset are multiplied by the spectral reflectance or transmittance of the target material at the corresponding wavelength to obtain the material's reflectance or transmittance spectrum. Based on the obtained material reflection or transmission spectra, calculate the actual light and color evaluation index value for the efficiency or color difference before and after the interaction of each group of multi-channel spectra with the target material, wherein the actual light and color evaluation index value includes one or more of spectral transmission radiative efficiency or color difference; and Select the appropriate light color whose actual light color evaluation index value matches the preset light color evaluation index.

6. The method as described in claim 1, characterized in that, Determining the appropriate light color for the target material further includes: If the target material is a multi-characteristic material, then the appropriate light color is determined based on the difference in spectral reflectance of different texture regions of the target material, so that the light source is the same color but the color contrast at the texture of the material is obvious.

7. The method as described in claim 1, characterized in that, Determining the appropriate light color for the target material further includes: The appropriate light color for the target material is determined based on the ambient light surrounding the target material, so that the reflected or transmitted color of the target material is similar to or converges with the ambient light color.

8. The method as described in claim 1, characterized in that, Selecting a target spectral dataset corresponding to the adapted light color from the initial spectral dataset further includes: Based on the adapted light color, select the multi-channel spectrum corresponding to the light source color and the adapted light color from each group of multi-channel spectra in the initial spectral dataset to obtain the corresponding target spectral dataset.

9. The method as described in claim 1, characterized in that, The method may alternatively include: Determine the desired light and color effect for the target material; and Select a target spectral dataset that corresponds to the desired light color effect from the initial spectral dataset.

10. The method as described in claim 1, characterized in that, The method further includes: Calculate the actual rhythm evaluation index values ​​associated with each group of multichannel spectra in the target spectral dataset; and Select from the target spectral dataset an optimized target spectral dataset whose actual rhythm evaluation index values ​​conform to preset rhythm evaluation indexes, wherein the rhythm evaluation indexes are related to the user's driving needs.

11. The method as described in claim 10, characterized in that, The rhythm evaluation indicators include one or more of the following: melanopoietic equivalent daylight illuminance (mel EDI), melanopoietic equivalent lux (EML), and the proportion of blue light in the spectrum, which are related to the human rhythm effect.

12. The method as described in claim 11, characterized in that, Selecting an optimized spectral dataset from the target spectral dataset whose actual rhythm evaluation index values ​​conform to a preset rhythm evaluation index further includes: Determine the driver's status and the vehicle's status; The desired melanopsile equivalent daylight illuminance value is determined based on the determined driver and vehicle conditions; and Select from the target spectral dataset an optimized target spectral dataset in which the actual melanopyr equivalent daylight illuminance values ​​match the desired melanopyr equivalent daylight illuminance values.

13. A dimming system for vehicle ambient lighting, the system comprising: The data acquisition module is configured to: Obtain an initial spectral dataset of the multi-channel spectrum of the ambient lighting source for the vehicle, wherein the initial spectral dataset includes all possible ratios of the multi-channel spectrum; Obtain the spectral characteristics of the target material on the illuminated surface; A spectral processing module is configured to determine the appropriate light color for the target material based on the initial spectral dataset, the spectral characteristics of the target material, and a preset light color evaluation index. as well as A spectral selection module is configured to select a target spectral dataset corresponding to the adaptive light color from the initial spectral dataset.

14. The system as described in claim 13, characterized in that, The spectral selection module is further configured to: Calculate the actual rhythm evaluation index values ​​associated with each group of multichannel spectra in the target spectral dataset; and Select from the target spectral dataset an optimized target spectral dataset whose actual circadian rhythm evaluation index values ​​conform to preset circadian rhythm evaluation indexes, wherein the circadian rhythm evaluation indexes include one or more of the following for human circadian rhythm effects: melanoplasmic equivalent daylight illuminance (mel EDI), melanoplasmic equivalent lux (EML), and spectral blue light ratio.

15. A vehicle comprising a dimming system as described in any one of claims 13-14.

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