Optimization method and system for light pattern of vehicle lamp based on space-time filtering and energy constraint

Abstract

The application discloses a kind of based on space-time filtering and energy constraint's car light type optimization method and system, method includes the following steps: step S1, obtain vehicle current operating state information and target light type data;Step S2, target light type data is filtered in time domain, and first optimization light type data is obtained;Step S3, first optimization light type data is partitioned by clustering analysis, and smoothing processing is carried out, and second optimization light type data is obtained;Step S4, under the whole lamp power constraint condition, based on the importance of different regions, power allocation is carried out to second optimization light type data, and third optimization light type data is obtained;Step S5, according to third optimization light type data generation PWM control signal.The application provides a kind of based on space-time filtering and energy constraint's car light type optimization method and system, to realize the smooth transition and reasonable distribution of light type, to improve lighting effect and system stability.

Description

Technical Field

[0001] This invention relates to a method and system for optimizing vehicle headlight patterns based on spatiotemporal filtering and energy constraints, belonging to the field of automotive electronics and intelligent lighting control technology. Background Technology

[0002] Currently, with the development of automotive intelligence, matrix LED headlights are increasingly widely used in adaptive high beam (ADB) systems. These systems typically dynamically adjust the brightness distribution of each light-emitting unit based on the road environment ahead and target detection results to achieve precise control of the high beam illumination area. However, existing technologies still have the following problems in practical applications: 1. The target light pattern is usually output in a discrete form by an external system, and there are large abrupt changes between adjacent moments, which causes obvious flickering during the output of the vehicle lights; 2. Because matrix LEDs are discrete light-emitting units, the light pattern boundary exhibits obvious stepped changes, affecting visual comfort; 3. Under the condition of limited overall lamp power, the lack of an effective energy distribution mechanism can easily lead to insufficient lighting in key areas or low overall energy utilization efficiency. Summary of the Invention

[0003] The technical problem to be solved by the present invention is to overcome the shortcomings of the prior art and provide a method and system for optimizing vehicle headlight patterns based on spatiotemporal filtering and energy constraints, so as to achieve smooth transition and reasonable allocation of light patterns, thereby improving the lighting effect and system stability.

[0004] To solve the above-mentioned technical problems, the technical solution of the present invention is as follows: This invention provides a method for optimizing vehicle headlight patterns based on spatiotemporal filtering and energy constraints, comprising the following steps: Step S1: Obtain the vehicle's current operating status information and target light pattern data; Step S2: Perform time-domain filtering on the target light pattern data to obtain the first optimized light pattern data; Step S3: The first optimized light pattern data is partitioned by cluster analysis and smoothed to obtain the second optimized light pattern data; Step S4: Under the constraint of total lamp power, the power of the second optimized light pattern data is allocated based on the importance of different regions to obtain the third optimized light pattern data; Step S5: Generate a PWM control signal based on the third optimized light pattern data to drive the matrix headlights for lighting output.

[0005] Furthermore, the vehicle's current operating status information includes vehicle speed, steering wheel angle, and ambient light intensity.

[0006] Furthermore, the target light pattern data is the target brightness value of each light-emitting unit of the matrix headlight at the current moment.

[0007] Furthermore, the expression for the first optimized light pattern data is as follows:

[0008] in, The first optimized light pattern data; α is the filter coefficient; For target light pattern data.

[0009] Furthermore, in step S3, the first optimized light pattern data is partitioned through cluster analysis, specifically including the following steps: Based on the brightness value of each light-emitting unit, the light-emitting units are clustered to obtain multiple partitions. The calculation formula for the clustering is as follows:

[0010] in, This represents the k-th partition.

[0011] Furthermore, the expression for the second optimized light pattern data is:

[0012] in, For the second optimized light pattern data; Let represent the neighborhood set of the i-th emitting unit; These are the neighborhood weight coefficients; The first optimized light pattern data.

[0013] Furthermore, in step S4, under the constraint of overall lamp power, the power of the second optimized light pattern data is allocated based on the importance of different regions to obtain the third optimized light pattern data, specifically including the following steps: Under the condition of satisfying the overall lamp power constraint, when the total power exceeds the limit, a weighted scaling strategy is adopted to allocate power to the light pattern, resulting in third optimized light pattern data. The expression of the third optimized light pattern data is as follows:

[0014] in, For the third optimization of optical pattern data; This is the maximum permissible power for the entire lamp; For the second optimized light pattern data; This represents the importance weighting coefficient for the light-emitting unit.

[0015] Furthermore, the overall lamp power constraint condition is as follows:

[0016] in, Let be the power of the i-th light-emitting unit. This is the maximum permissible power for the entire lamp.

[0017] Furthermore, the expression for the PWM control signal is as follows:

[0018] in, This is a PWM control signal. For maximum duty cycle, This is for the third optimization of light pattern data.

[0019] Another aspect of the present invention provides a system for optimizing vehicle headlight patterns using a method based on spatiotemporal filtering and energy constraints, comprising: The light pattern acquisition module is used to obtain the vehicle's current operating status information and target light pattern data; A time filtering module is used to filter the target light pattern to obtain first optimized light pattern data; A spatial smoothing module is used to perform clustering, partitioning, and smoothing processing on the first optimized light pattern data to obtain the second optimized light pattern data. An energy constraint module is used to allocate power to the second optimized optical pattern data to obtain the third optimized optical pattern data. The control output module is used to generate a control signal to drive the matrix headlights to output illumination after receiving the third optimized light pattern data.

[0020] By employing the above technical solution, this invention optimizes the input light pattern multiple times to reduce abrupt changes in the light pattern between adjacent moments, achieve more continuous light pattern boundaries, and provide more rational power distribution, thereby improving the safety and comfort of nighttime driving. It effectively reduces flickering during dynamic changes in the light pattern, improves the continuity of light pattern boundaries, and enhances visual comfort; it achieves rational energy distribution under power-constrained conditions, improving lighting efficiency; and it possesses good real-time performance and engineering feasibility, making it suitable for automotive-grade control systems. Attached Figure Description

[0021] Figure 1 The flowchart shows the vehicle headlight pattern optimization method based on spatiotemporal filtering and energy constraints of the present invention. Figure 2This is a schematic diagram of the system that applies the vehicle light pattern optimization method based on spatiotemporal filtering and energy constraints according to the present invention. Detailed Implementation

[0022] To make the content of this invention easier to understand, the invention will be further described in detail below with reference to specific embodiments and accompanying drawings.

[0023] Example 1 like Figure 1 As shown, this embodiment provides a method for optimizing vehicle headlight patterns based on spatiotemporal filtering and energy constraints, including the following steps: Step S1: Obtain the vehicle's current operating status information and target light pattern data.

[0024] The vehicle's current operating status information includes vehicle speed, steering wheel angle, and ambient light intensity.

[0025] The matrix headlights in this embodiment include multiple independently controlled light-emitting units, each corresponding to a brightness control channel. The target light pattern data is the target brightness value of each light-emitting unit of the matrix headlights at the current moment.

[0026] Step S2: Perform time-domain filtering on the target light pattern data to reduce the degree of abrupt changes in the light pattern between consecutive frames, and obtain the first optimized light pattern data.

[0027] The expression for the first optimized optical pattern data in this embodiment is as follows:

[0028] in, The first optimized light pattern data.

[0029] α is the filter coefficient, 0 < α < 1. α is a function of vehicle speed v, steering wheel angle Δ, and ambient light intensity E, and can be expressed as: =f(v,Δ,E), increasing one of the parameters increases the filter coefficient, which can improve the light pattern response speed. The filter coefficient α is closely related to the three parameters. For example, when the vehicle is at low speed or traveling straight, decreasing the filter coefficient can enhance the smoothness of the light pattern changes.

[0030] For target light pattern data.

[0031] Step S3: The first optimized light pattern data is partitioned through cluster analysis to achieve differentiated light pattern optimization; further smoothing is performed to make the light pattern boundaries smoother and improve the continuity of the light pattern boundaries, resulting in the second optimized light pattern data. Specifically: The first optimized light pattern data was partitioned using cluster analysis: Based on the brightness value of each light-emitting unit, the light-emitting units are clustered to obtain multiple partitions. The calculation formula for clustering is as follows:

[0032] in, This represents the k-th partition.

[0033] The zones include highlight areas, transition areas, and low-brightness areas.

[0034] After partitioning, the brightness of adjacent light-emitting units within each partition is weighted to smooth the light pattern boundaries, resulting in the second optimized light pattern data. The expression for the second optimized light pattern data is as follows:

[0035] in, For the second optimized light pattern data; Let represent the neighborhood set of the i-th emitting unit; These are neighborhood weighting coefficients, used to describe the degree of influence between light-emitting units; The first optimized light pattern data.

[0036] Step S4: Under the constraint of total lamp power, power allocation is performed on the second optimized light pattern data based on the importance of different areas to achieve priority lighting in key areas, thus obtaining the third optimized light pattern data. Specifically: Under the condition of satisfying the total lamp power constraint, when the total power exceeds the limit, a weighted scaling strategy is adopted to allocate power to the light pattern, resulting in the third optimized light pattern data. The expression for the third optimized light pattern data is as follows:

[0037] in, This is for the third optimization of light pattern data.

[0038] This is the maximum permissible power for the entire lamp.

[0039] This is for the second optimized light pattern data.

[0040] The importance weighting coefficient of the light-emitting units is set by assigning weighting coefficients to different light-emitting units to improve the lighting priority of key areas. For example, the weighting coefficient of the light-emitting units in the middle area of ​​a matrix headlight can be 1, and the weighting coefficient of the light-emitting units in the edge area can be 0.5.

[0041] The power constraint for the entire lamp is as follows:

[0042] in, Let be the power of the i-th light-emitting unit. This is the maximum permissible power for the entire lamp; Step S5: Generate a PWM control signal based on the third optimized light pattern data to drive the matrix headlights for lighting output.

[0043] The expression for the PWM control signal is as follows:

[0044] in, For maximum duty cycle, This is for the third optimization of light pattern data.

[0045] The PWM control signal is output to the headlight drive circuit to control each light-emitting unit and drive the matrix headlights to provide illumination.

[0046] Example 2 like Figure 2 As shown, this embodiment provides a system that applies the vehicle headlight pattern optimization method based on spatiotemporal filtering and energy constraints of Embodiment 1, including: The acquisition module, specifically the light pattern acquisition module, is used to obtain information on the vehicle's current operating status and target light pattern data.

[0047] The time filtering module is used to filter the target light pattern to obtain the first optimized light pattern data.

[0048] The spatial smoothing module is used to perform clustering, partitioning, and smoothing on the first optimized light pattern data to obtain the second optimized light pattern data.

[0049] The energy constraint module is used to allocate power to the second optimized optical pattern data to obtain the third optimized optical pattern data.

[0050] The control output module is used to generate control signals to drive the matrix headlights to output illumination after receiving the third optimized light pattern data.

[0051] This system first optimizes the continuity of the target light pattern based on a time-domain filtering model to suppress abrupt changes in light intensity between adjacent time points. Second, it partitions the light-emitting units using a clustering algorithm to achieve differentiated control based on the light pattern characteristics of different regions. On this basis, it couples the brightness of adjacent light-emitting units using a spatial domain smoothing model to achieve a gradual transition of the light pattern boundary. Furthermore, under the constraint of the overall lamp power, it constructs a weighted energy allocation mechanism to optimize the allocation of the output of each light-emitting unit, thereby improving energy utilization and demonstrating good engineering application value.

[0052] The specific embodiments described above further illustrate the technical problems, technical solutions, and beneficial effects of the present invention. It should be understood that the above descriptions are merely specific embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for optimizing vehicle headlight patterns based on spatiotemporal filtering and energy constraints, characterized in that, Includes the following steps: Step S1: Obtain the vehicle's current operating status information and target light pattern data; Step S2: Perform time-domain filtering on the target light pattern data to obtain the first optimized light pattern data; Step S3: The first optimized light pattern data is partitioned by cluster analysis and smoothed to obtain the second optimized light pattern data; Step S4: Under the constraint of total lamp power, the power of the second optimized light pattern data is allocated based on the importance of different regions to obtain the third optimized light pattern data; Step S5: Generate a PWM control signal based on the third optimized light pattern data to drive the matrix headlights for lighting output.

2. The method for optimizing vehicle headlight patterns based on spatiotemporal filtering and energy constraints according to claim 1, characterized in that, The vehicle's current operating status information includes vehicle speed, steering wheel angle, and ambient light intensity.

3. The method for optimizing vehicle headlight patterns based on spatiotemporal filtering and energy constraints according to claim 1, characterized in that, The target light pattern data refers to the target brightness value of each light-emitting unit of the matrix headlight at the current moment.

4. The method for optimizing vehicle headlight patterns based on spatiotemporal filtering and energy constraints according to claim 1, characterized in that, The expression for the first optimized light pattern data is as follows: ； in, The first optimized light pattern data; α is the filter coefficient; For target light pattern data.

5. The method for optimizing vehicle headlight patterns based on spatiotemporal filtering and energy constraints according to claim 1, characterized in that, In step S3, the first optimized light pattern data is partitioned through cluster analysis, specifically including the following steps: Based on the brightness value of each light-emitting unit, the light-emitting units are clustered to obtain multiple partitions. The calculation formula for the clustering is as follows: ； in, This represents the k-th partition.

6. The method for optimizing vehicle headlight patterns based on spatiotemporal filtering and energy constraints according to claim 1, characterized in that, The expression for the second optimized optical pattern data is: ； in, For the second optimized light pattern data; Let represent the neighborhood set of the i-th emitting unit; These are the neighborhood weight coefficients; The first optimized light pattern data.

7. The method for optimizing vehicle headlight patterns based on spatiotemporal filtering and energy constraints according to claim 1, characterized in that, In step S4, under the constraint of total lamp power, the power of the second optimized light pattern data is allocated based on the importance of different regions to obtain the third optimized light pattern data. This specifically includes the following steps: Under the condition of satisfying the overall lamp power constraint, when the total power exceeds the limit, a weighted scaling strategy is adopted to allocate power to the light pattern, resulting in third optimized light pattern data. The expression of the third optimized light pattern data is as follows: ； in, For the third optimization of optical pattern data; This is the maximum permissible power for the entire lamp; For the second optimized light pattern data; This represents the importance weighting coefficient for the light-emitting unit.

8. The method for optimizing vehicle headlight patterns based on spatiotemporal filtering and energy constraints according to claim 7, characterized in that, The overall lamp power constraint condition is as follows: ； in, Let be the power of the i-th light-emitting unit. This is the maximum permissible power for the entire lamp.

9. The method for optimizing vehicle headlight patterns based on spatiotemporal filtering and energy constraints according to claim 1, characterized in that, The expression for the PWM control signal is as follows: ； in, This is a PWM control signal. For maximum duty cycle, This is for the third optimization of light pattern data.

10. A system applying the vehicle headlight pattern optimization method based on spatiotemporal filtering and energy constraints as described in any one of claims 1 to 9, characterized in that, include: The light pattern acquisition module is used to obtain the vehicle's current operating status information and target light pattern data; A time filtering module is used to filter the target light pattern to obtain first optimized light pattern data; A spatial smoothing module is used to perform clustering, partitioning, and smoothing processing on the first optimized light pattern data to obtain the second optimized light pattern data. An energy constraint module is used to allocate power to the second optimized optical pattern data to obtain the third optimized optical pattern data. The control output module is used to generate a control signal to drive the matrix headlights to output illumination after receiving the third optimized light pattern data.

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