Motorcycle lighting intelligent control method and system, medium and program product

Abstract

The invention discloses a motorcycle lighting intelligent control method and system, a medium and a program product, and relates to the technical field of novel lighting, and the method comprises the steps: collecting environment light intensity, environment humidity, vehicle speed and three-axis acceleration data; calculating vibration frequency and dominant vibration strength based on the three-axis acceleration data; judging a current weather mode based on the environment humidity and the environment light intensity; determining a basic color temperature according to the current weather mode; determining a target beam spread angle and basic brightness according to the vehicle speed; performing dynamic compensation based on the vibration frequency and the basic color temperature to obtain a final target color temperature; performing dynamic compensation on the basic brightness based on the dominant vibration intensity to obtain final target brightness; and all the control parameters are coordinated through the dynamic priority management module, and a final control instruction is output so as to drive a motorcycle headlamp and an auxiliary lighting unit to execute self-adaptive lighting adjustment. The lighting safety and the visual comfort of the motorcycle are improved.

Description

Technical Field

[0001] This application relates to the field of new lighting technology, and in particular to a method, system, medium and program product for intelligent control of motorcycle lighting. Background Technology

[0002] As a highly mobile two-wheeled vehicle, the active lighting system of motorcycles plays a decisive role in driving safety at night and in low visibility conditions. However, compared with four-wheeled motor vehicles, motorcycles face more stringent lighting challenges in complex road environments: First, in high humidity and low illumination weather conditions such as rain and fog, traditional white headlights suffer from significant Rayleigh scattering, resulting in a sharp decrease in light penetration and a shortened effective visibility distance; Second, in driving scenarios with a wide range, such as slow maneuvering in the city and high-speed cruising on highways, fixed beam patterns cannot meet the needs of both wide near-field illumination and far-field focused illumination, easily causing blind spots or oncoming glare; Third, the severe vibration of the headlights when motorcycles are driven on bumpy roads not only causes the light spot to flicker and cause visual fatigue, but may also interfere with the driver's judgment of road conditions due to instantaneous fluctuations in light intensity.

[0003] Currently, most mainstream motorcycle lighting solutions employ static or single-parameter trigger-based control strategies. For example, some models automatically turn headlights on / off based solely on ambient light intensity, such as light-sensor switches, failing to distinguish between rain, fog, and nighttime scenarios. While some high-end products incorporate speed-adjustable light patterns, they rely on expensive matrix LEDs or mechanical dimming mechanisms and do not consider the impact of road surface stimuli on human visual perception. More advanced automotive adaptive headlight systems (AFS), while possessing multi-degree-of-freedom adjustment capabilities, typically rely on cameras, radar, or multi-sensor fusion platforms, resulting in high computational complexity, high power consumption, and high cost, making them difficult to adapt to the resource-constrained embedded electronic architecture of motorcycles.

[0004] Therefore, there is an urgent need for a low-cost, low-complexity intelligent lighting control method suitable for motorcycle platforms, which can improve the lighting safety and visual comfort of motorcycles in typical dangerous scenarios such as rain, fog, gear shifting, and bumps, with limited hardware resources. Summary of the Invention

[0005] To address the shortcomings of existing technologies and provide a low-cost, low-complexity intelligent lighting control method suitable for motorcycle platforms, thereby improving the lighting safety and visual comfort of motorcycles, this application provides a motorcycle lighting intelligent control method, system, medium, and program product.

[0006] Firstly, the objective of this invention is achieved through the following technical solution: A method for intelligent control of motorcycle lighting, comprising: Collect ambient light intensity, ambient humidity, vehicle speed, and triaxial acceleration data; calculate vibration frequency and dominant vibration intensity based on the triaxial acceleration data; The current weather pattern is determined based on the ambient humidity and the ambient light intensity. The base color temperature is determined based on the current weather pattern; the target beam diffusion angle and base brightness are determined based on the vehicle speed. Dynamic compensation is performed based on the vibration frequency and the base color temperature to obtain the final target color temperature; dynamic compensation is performed based on the dominant vibration intensity to obtain the final target brightness. The dynamic priority management module coordinates various control parameters and outputs the final control command to drive the motorcycle headlight and auxiliary lighting unit to perform adaptive lighting adjustment; wherein, the control parameters include the target beam diffusion angle, the final target color temperature and the final target brightness.

[0007] By adopting the above technical solution, the control parameters coordinated by the dynamic priority management module include the target beam diffusion angle, the final target color temperature, and the final target brightness. Without relying on image recognition, GNSS, or high-cost actuators, this invention achieves closed-loop adaptive control of the color temperature, beam pattern, and brightness of motorcycle headlights and auxiliary lights using only low-cost sensors (such as photosensors, humidity sensors, vehicle speed pulse sensors, and IMUs) and embedded software algorithms, significantly improving active safety in complex riding environments. Specifically, to improve the light penetration and visual recognition of motorcycle lights in rain and fog environments, this invention constructs a dual-condition decision logic based on ambient humidity and ambient light intensity to accurately identify different weather modes, enabling proactive switching to low color temperature yellow light illumination in rain and fog conditions. Secondly, based on vehicle speed, the target beam diffusion angle and basic brightness are dynamically mapped, ensuring a high degree of matching between the lighting distribution and the driving state. For example, at low speeds, it provides wide near-field coverage to enhance surrounding environment perception, while at high speeds, it narrows the beam and increases the center brightness of the motorcycle lights to ensure long-distance visibility. This achieves the technical objective of speed-adaptive beam pattern and brightness optimization without increasing hardware complexity. Furthermore, to suppress lighting instability caused by bumpy road surfaces, this invention extracts vibration frequency and intensity from triaxial acceleration data, which are then used for dynamic compensation of color temperature and brightness, respectively. For example, high-frequency vibrations are identified as road surface roughness indicators, triggering appropriate yellow light enhancement to alleviate visual flicker. Finally, a dynamic priority management module is introduced to make unified decisions on multiple modes such as rain / fog, nighttime, emergency, and energy saving, outputting final control commands to ensure control robustness and personnel safety under multiple conflicting scenarios.

[0008] In a preferred example, this application uses a dual-condition threshold decision logic or fuzzy logic decision-making to determine the weather pattern. When using a dual-condition threshold decision logic, if the ambient humidity is greater than a preset humidity threshold and the ambient light intensity is less than or equal to a preset light intensity threshold, the current weather mode is determined to be a rain / fog weather mode; otherwise, it is determined to be a normal weather mode; the preset light intensity threshold adopts a dual-threshold for day and night. When using fuzzy logic for decision-making, the ambient light intensity and ambient humidity are fuzzified into membership functions, input into a preset fuzzy rule base for inference, and after defuzzification using the centroid method, continuous target color temperature values ​​are output. As the base color temperature; The formula for calculating the center of gravity using the centroid method is as follows: , where n is the total number of discrete sampling points on the output universe of discourse; This represents the i-th discrete sampling point on the output universe of discourse; For the i-th sampling point After aggregation, the membership degree of the fuzzy set is output, with a value of 0 to 1.

[0009] By adopting the above technical solution, two parallel implementation paths are provided in the weather pattern discrimination stage: dual-condition threshold decision logic and fuzzy logic decision. When using dual-condition threshold decision logic, the weather pattern is only determined to be rainy or foggy when the ambient humidity is higher than the preset humidity threshold and the ambient light intensity is lower than the preset light intensity threshold. This effectively avoids the problem of a single light intensity threshold falsely triggering the yellow light mode in non-rainy / foggy dark environments such as tunnels, under bridges, and tree-lined roads, thus improving the accuracy and reliability of rain and fog recognition. When using fuzzy logic decision, the ambient light intensity and ambient humidity are fuzzified into linguistic variables with clear membership functions, input into a preset fuzzy rule base for reasoning, and the centroid method is used to defuzzify and output continuous target color temperature values ​​as the base color temperature. This upgrades the weather state discrimination from discrete binary to continuous and refined, and can more realistically reflect the gradual changes in the light-humidity coupled environment.

[0010] In a preferred embodiment of this application: the target beam diffusion angle and the basic brightness are obtained by lookup interpolation from a pre-stored nonlinear mapping table based on the vehicle speed; the nonlinear mapping table defines a negative correlation between vehicle speed and beam diffusion angle and a positive correlation between vehicle speed and basic brightness, and divides the vehicle speed range into multiple vehicle speed ranges corresponding to different combinations of beam diffusion angle and basic brightness; The target parameter is calculated using the nonlinear mapping table and the linear interpolation algorithm. The calculation formula for the linear interpolation algorithm is as follows: Target beam spread angle Target base brightness percentage Where V is the actual speed of the motorcycle currently being collected; The preset speed range to which the current vehicle speed V belongs; These are the endpoints of the vehicle speed range. , The corresponding beam spread angle; These are the endpoints of the vehicle speed range. , The corresponding base brightness value.

[0011] By employing the above technical solution, a mapping table is pre-stored that non-linearly correlates vehicle speed ranges with beam spread angle and base brightness. Combined with a linear interpolation algorithm, accurate and efficient conversion from vehicle speed to optical parameters is achieved. With the support of the linear interpolation algorithm, even at any vehicle speed value within the speed range, continuous and smooth target beam spread angle and base brightness percentage can be calculated.

[0012] In a preferred embodiment of this application, the step of dynamically compensating based on the vibration frequency and the base color temperature to obtain the final target color temperature includes: Color temperature compensation is calculated using a piecewise function based on the vibration frequency F to determine the vibration adjustment coefficient ΔK. in, =5Hz, =30Hz, =0.3, final target color temperature , To normalize the base color temperature tendency; Dynamic compensation is performed on the base brightness based on the dominant vibration intensity to obtain the final target brightness, including: Brightness compensation is calculated by determining the brightness compensation amount ΔB based on the dominant vibration intensity A, using the following formula: , These are adjustable weighting coefficients. The rate of change of vibration, Final target brightness , The target base brightness percentage.

[0013] By employing the above technical solution, vibration frequency and dominant vibration intensity are used for dynamic compensation of color temperature and brightness, respectively. The vibration adjustment coefficient ΔK accurately captures the mid-frequency vibrations (5–30Hz) excited by typical road bumps. Appropriately increasing the proportion of yellow light within this frequency band effectively alleviates visual flicker and discomfort caused by high-frequency light spot jitter. Simultaneously, the final target brightness is obtained by adding ΔB to the base brightness percentage. This dual compensation mechanism significantly improves lighting stability and visual continuity on bumpy road surfaces such as gravel roads, speed bumps, and potholes.

[0014] In a preferred embodiment of this application, the formula for calculating the dominant vibration intensity A is: ,in These represent the linear acceleration components measured by the inertial measurement unit in the X-axis direction, the Y-axis direction, and the Z-axis direction of the motorcycle body coordinate system, respectively; the final target brightness Limiting processing and first-order low-pass smoothing filter are applied to 70% to 130% of the rated brightness.

[0015] By adopting the above technical solution, the calculation formula for the dominant vibration intensity A fully reflects the amplitude of the combined acceleration experienced by the motorcycle in three-dimensional space, comprehensively covering the combined vibration energy of longitudinal acceleration, lateral cornering, and vertical bumps. Based on this, strict engineering constraints are imposed on the final target brightness: firstly, it is limited to a hard-limited range of 70% to 130% of the rated brightness to prevent LED overdrive or insufficient brightness due to overcompensation; secondly, a first-order low-pass smoothing filter is applied to effectively suppress overshoot, oscillation, and step changes during the dynamic response process.

[0016] In a preferred embodiment of this application: the dynamic priority management module is configured with preset multi-level working modes and their priority sequences. The multi-level working modes include energy-saving mode, emergency safety mode, rain and fog weather mode, night mode, and normal integration mode. The priority sequence from high to low is: emergency safety mode, rain and fog weather mode, night mode, normal integration mode, and energy-saving mode. The emergency safety mode is triggered by a system self-check fault signal or a collision risk warning signal, which forces the output of a preset set of safety lighting parameters and activates the optical warning function. The rain and fog weather mode is triggered by the judgment result of the dual-condition threshold decision logic or fuzzy logic decision, which forces the base color temperature to be set to the low color temperature yellow light band and increases the target brightness within the safety limit. The night mode is triggered when the ambient light intensity is lower than the preset night light intensity threshold, which activates the lighting system and executes an anti-glare light pattern adjustment strategy. The normal fusion mode is the system's default operating state, which executes a complete lighting decision-making process based on the fusion of multi-source sensor data. The energy-saving mode is triggered when the vehicle power supply voltage or remaining power is lower than a preset threshold, limiting the maximum output brightness and disabling non-critical lighting subsystems. The dynamic priority management module is also configured with conflict arbitration rules: when the triggering conditions of multiple working modes are met at the same time, only the core control parameters defined by the working mode with the highest priority are executed, and the parameter adjustments of other low-priority modes are suppressed, or only auxiliary fine-tuning is performed without violating the safety constraints of the high-priority mode.

[0017] By adopting the above technical solution, the triggering conditions and control behaviors are clearly defined for each working mode: Emergency safety mode is triggered by system failure or collision warning, forcibly outputting safety lighting parameters and activating the warning function; Rain and fog weather mode is triggered by weather judgment results, forcibly enabling low color temperature yellow light and moderately brightening it; Night mode is triggered by low light intensity, automatically turning on the lighting and optimizing anti-glare; Normal blending mode is the default state, executing the complete blending decision process; Energy saving mode is triggered by low battery, at which time brightness is limited and non-critical lighting subsystems are disabled. Conflict arbitration rules effectively prevent control logic chaos or security degradation caused by multiple concurrent conditions.

[0018] In a preferred embodiment of this application: the final control command includes a color temperature control command, a light pattern control command, and a brightness control command; and before generating the final control command, the following preprocessing operation is performed on the raw sensor data: Moving average filtering is applied to the ambient light intensity, ambient humidity, and vehicle speed signals respectively; The triaxial acceleration data is processed through a 2-10Hz bandpass digital filter to extract the dominant vibration intensity signal, and the vibration frequency F is extracted by zero-crossing rate detection. The color temperature control command calculates the duty cycle of the two pulse width modulation channels corresponding to the warm LED and the cool LED in the dual color temperature auxiliary lighting unit based on the final target color temperature. The light pattern control command is generated based on the target beam diffusion angle: in a matrix LED architecture, the independent control word of each lighting zone is output by querying the pre-stored LED zone lighting mode mapping table; in a mechanical dimming architecture, the stepping pulse sequence or analog PWM positioning signal is output based on the pre-calibrated mapping relationship between the beam diffusion angle and the stepping position of the actuator motor.

[0019] By adopting the above technical solution, the original sensor data is systematically preprocessed before the control command is generated: the ambient light intensity, ambient humidity and vehicle speed signals are subjected to moving average filtering to effectively suppress pulse noise caused by electromagnetic interference or poor contact; the triaxial acceleration data is first calculated to obtain the composite amplitude, and then the pure vibration intensity signal A is extracted by a 2-10Hz bandpass digital filter, and the vibration frequency F is extracted by the zero-crossing rate detection method, thereby effectively filtering out static gravity components and high-frequency electronic noise, and retaining the mid-frequency vibration characteristics directly related to road excitation.

[0020] Secondly, the objective of this invention is achieved through the following technical solution: A motorcycle lighting intelligent control system, the system comprising: The sensor module is used to collect data on ambient light intensity, ambient humidity, vehicle speed, and triaxial acceleration. A vibration feature extraction module is used to calculate the vibration frequency and dominant vibration intensity based on the triaxial acceleration data; A weather pattern determination module is used to determine the current weather pattern based on the ambient humidity and the ambient light intensity. The basic parameter setting module is used to determine the basic color temperature based on the current weather mode, and to determine the target beam diffusion angle and basic brightness based on the vehicle speed; The dynamic compensation module is coupled to the vibration feature extraction module and the basic parameter setting module, respectively. It is used to dynamically compensate the basic color temperature based on the vibration frequency to obtain the final target color temperature, and to dynamically compensate the basic brightness based on the dominant vibration intensity to obtain the final target brightness. The control and coordination module is used to coordinate the final target color temperature, the target beam diffusion angle, and the final target brightness through a dynamic priority management mechanism, and generate the final control command. The execution drive module is used to drive the motorcycle headlight and auxiliary lighting unit to perform adaptive lighting adjustment according to the final control command.

[0021] Thirdly, the objective of this invention is achieved through the following technical solution: A computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the above-described intelligent control method for motorcycle lighting.

[0022] Fourthly, the objective of this invention is achieved through the following technical solution: A computer program product includes a computer program / instructions that, when executed by a processor, implement the steps of a motorcycle lighting intelligent control method as described above.

[0023] In summary, this application includes at least one of the following beneficial technical effects: 1. By simultaneously collecting ambient light intensity, ambient humidity, vehicle speed, and triaxial acceleration data, and calculating the vibration frequency and dominant vibration intensity reflecting the road excitation characteristics, a multi-source perception fusion network for complex motorcycle riding scenarios was constructed. The network jointly determines the current weather pattern based on ambient humidity and ambient light intensity, and sets the base color temperature accordingly. At the same time, the target beam diffusion angle and base brightness are dynamically determined based on vehicle speed. Based on this, the vibration frequency is introduced to dynamically compensate the base color temperature to obtain the final target color temperature, and the dominant vibration intensity is introduced to dynamically compensate the base brightness to obtain the final target brightness. 2. This invention fundamentally solves the core safety problems of existing motorcycle lighting systems, such as insufficient penetration in rainy and foggy weather, mismatch between field of vision in high and low speed scenarios, and unstable lighting on bumpy roads; it achieves highly robust and highly adaptable intelligent lighting control on a low-cost hardware platform, significantly improving active driving safety at night and in adverse weather conditions. Attached Figure Description

[0024] Figure 1 This is a flowchart of a motorcycle lighting intelligent control method according to an embodiment of this application; Figure 2 This is a logic control flowchart of a motorcycle lighting intelligent control method in one embodiment of this application. Detailed Implementation

[0025] The present application will be further described in detail below with reference to the accompanying drawings.

[0026] In one embodiment, such as Figure 1 and Figure 2 As shown, this application discloses a smart control method for motorcycle lighting, which specifically includes the following steps: S1: Collect ambient light intensity, ambient humidity, vehicle speed, and triaxial acceleration data; calculate vibration frequency and dominant vibration intensity based on triaxial acceleration data.

[0027] In this embodiment, an embedded software control method running on a motorcycle electronic control unit (ECU) is provided, with a preferred single control cycle of 10 milliseconds. Ambient light intensity refers to the ambient illuminance measured by a photoresistor or photodiode sensor, measured in lux (lx); ambient humidity refers to the relative humidity measured by a capacitive humidity sensor, measured in %RH; vehicle speed refers to the real-time speed calculated from the pulse signal output from the motorcycle dashboard via an internal counter in the ECU, measured in km / h; triaxial acceleration data refers to the linear acceleration components along the three orthogonal axes of the vehicle coordinate system (X (forward direction), Y (lateral direction), and Z (vertical direction)) output by a low-cost MEMS inertial measurement unit (IMU) mounted near the main beam of the motorcycle frame, denoted as... The unit is m / s².

[0028] Specifically, the ECU first reads the raw values ​​from the four types of sensors mentioned above during each control cycle. Then, it performs feature extraction on the triaxial acceleration data: first, it calculates the raw resultant acceleration amplitude. This value includes the combined effects of static gravity and dynamic motion; next, Input a digital bandpass filter with a passband range of 2Hz to 10Hz to filter out low-frequency vehicle body attitude changes (such as cornering tilt) and high-frequency electronic noise, while retaining the mid-frequency components that reflect road bump excitation. The filtered signal is the "dominant vibration intensity" A.

[0029] Meanwhile, time-frequency analysis is performed on the filtered acceleration time-domain signal: In this embodiment, the zero-crossing rate detection method is preferably used to estimate the main frequency of the acceleration time-domain signal by counting the number of times the signal crosses zero point per unit time.

[0030] Furthermore, the frequency corresponding to the peak value in the spectrum can be extracted using Fast Fourier Transform (FFT). This dominant frequency is the "vibration frequency" F, measured in Hz, and is used to characterize the dynamic characteristics of road surface roughness.

[0031] S2: Determine the current weather pattern based on ambient humidity and ambient light intensity.

[0032] In this embodiment, the current weather mode is a logical state variable used to distinguish whether the environment is rainy or foggy and requires high-penetration lighting.

[0033] Specifically, the weather pattern determination is implemented using a dual-condition threshold decision logic or fuzzy logic decision-making: S201: When using dual-condition threshold decision logic, if the ambient humidity is greater than the preset humidity threshold and the ambient light intensity is less than or equal to the preset light intensity threshold, the current weather mode is determined to be a rain / fog weather mode; otherwise, it is determined to be a normal weather mode; the preset light intensity threshold adopts a dual threshold for day and night.

[0034] In this embodiment, the preset humidity threshold is 80%RH. The preset light intensity threshold is dynamically switched according to the day and night time period, that is, the preset light intensity threshold is 500lx in daytime mode and 50lx in nighttime mode.

[0035] S202: When using fuzzy logic decision-making, the ambient light intensity and ambient humidity are fuzzified into membership functions, input into the preset fuzzy rule base for reasoning, and after defuzzification using the centroid method, the continuous target color temperature value is output as the base color temperature.

[0036] In this embodiment, first, the environmental light intensity L is fuzzified: the domain of the environmental light intensity L is defined as [0, 1000] lx, which is divided into three fuzzy subsets: "dark", "medium", and "bright", and a triangular membership function is assigned to each. For example, the membership function of "dark" is 1 when L ≤ 100 lx and linearly decreases to 0 when 100 < L < 300 lx; "medium" is 1 when 100 < L < 500 lx and decreases outward; "bright" is 1 when L ≥ 500 lx. Similarly, the domain of the environmental humidity H is [0, 100] %RH, which is divided into three subsets: "low humidity", "medium humidity", and "high humidity", where the membership of "high humidity" is 1 when H ≥ 80%; the membership of "medium humidity" is 1 when 30% < L < 80%. Subsequently, the membership values of L and H are input into a preset fuzzy rule base for reasoning, which is implemented by a fuzzy logic controller.

[0037] Furthermore, for the rainy and foggy weather mode: the target base color temperature is set to the low color temperature yellow light band, with a typical value of 3000K, and the adjustable range: 2800K ~ 3200K. For the normal weather mode: is set to the high color temperature white light band, with a typical value of 6000K, and the adjustable range: 5500K ~ 6500K.

[0038] The fuzzy rule base contains the following example rules: rule IF(condition) 1 The light intensity is "dim" and the humidity is "high". 2 The light intensity is "medium" and the humidity is "high". 3 The light intensity is "bright" and the humidity is "high". 4 The light intensity is "dim" and the humidity is "medium". 5 The light intensity is "medium" and the humidity is "medium humidity". 6 The light intensity is "bright" and the humidity is "low". After defuzzification using the centroid method, a continuous target color temperature value is output as the base color temperature; the calculation formula of the centroid method is: , where n is the total number of discrete sampling points on the output domain, and the output domain is, for example, 2800K ~ 6500K; is the i-th discrete sampling point on the output domain; is the membership degree in the aggregated output fuzzy set at the i-th sampling point, with a value range of 0 to 1; for example, if the output fuzzy set has a high membership degree at 3000K and a low membership degree at 4000K, then will be close to 3000K.

[0039] Furthermore, in this embodiment, the dual-condition threshold decision logic and the fuzzy logic decision are not mutually exclusive, but rather form a "hierarchical decision-making" architecture. The system can select and enable one of them according to the hardware resources and functional requirements. In low-end models with limited resources, the dual-condition threshold decision logic is preferred because of its simple calculation and fast response; while in high-end models, a fuzzy logic controller can be loaded.

[0040] S3: Determine the base color temperature according to the current weather mode; determine the target beam divergence angle and the base brightness according to the vehicle speed.

[0041] In this embodiment, the base color temperature refers to the reference value of the color temperature determined by the weather mode before considering road vibration compensation, which is used to drive the dual color temperature LED light source; the target beam diffusion angle refers to the divergence angle of the main headlight beam on the horizontal plane, which directly affects the near field of view width; the base brightness refers to the reference percentage of the luminous flux output by the lamp determined by the vehicle speed before vibration compensation, relative to the rated maximum brightness.

[0042] Specifically, if the current weather mode is rainy or foggy, then the base color temperature will be adjusted. Set to the low color temperature yellow light band, typical value 3000K, adjustable range 2800K to 3200K; if in regular weather mode, then The system is set to a high color temperature white light band, with a typical value of 6000K and an adjustable range of 5500K to 6500K. Simultaneously, the system queries a pre-stored "vehicle speed-light pattern-brightness mapping table" in the ECU's non-volatile memory based on the current vehicle speed V. An example of the contents of the vehicle speed-light pattern-brightness mapping table is shown below: Speed ​​range (km / h) Beam spread angle (°) Base brightness (% rated) Mode Description 0-15 70 70% Very low speed / car maneuvering mode 15-30 60 75% City Low Speed ​​Mode 30-45 50 85% Medium-speed transition mode 45-60 40 95% Suburban expressway mode 60-80 30 105% High-speed driving mode >80 25 115% Ultra-high speed mode In this embodiment, the target beam spread angle and base brightness are obtained by lookup interpolation from a pre-stored nonlinear mapping table based on vehicle speed. The nonlinear mapping table defines a negative correlation between vehicle speed and beam spread angle, and a positive correlation between vehicle speed and base brightness, and divides the data into multiple vehicle speed intervals corresponding to different combinations of beam spread angle and base brightness. The nonlinear mapping table is a discretized nonlinear mapping table pre-stored in the non-volatile memory of the electronic control unit (ECU). It is a two-dimensional data structure consisting of multiple rows of records, each row corresponding to a preset vehicle speed interval and explicitly associating the recommended combination of beam spread angle and base brightness within that interval. The beam spread angle refers to the divergence angle of the main headlight beam on the horizontal plane, measured in degrees (°), and directly affects the near-field field of view width. The base brightness percentage refers to the percentage value of the luminous flux output by the luminaire relative to the rated maximum brightness of the luminaire, used to characterize the basic lighting intensity.

[0043] Specifically, the nonlinear mapping table is stored in the Flash memory before the ECU leaves the factory, and its typical contents are as follows: Speed ​​range (km / h) Beam spread angle (°) Base brightness (% rated) Mode Description 0-15 70 70% Very low speed / car maneuvering mode 15-30 60 75% City Low Speed ​​Mode 30-45 50 85% Medium-speed transition mode 45-60 40 95% Suburban expressway mode 60-80 30 105% High-speed driving mode >80 25 115% Ultra-high speed mode Specifically, within each control cycle, the ECU first reads the current vehicle speed V, in km / h. Then, it performs a lookup operation: traversing the vehicle speed range columns of the mapping table to find the range that satisfies the speed requirement. ≤V< The unique interval [ , ), and obtain the beam spread angle corresponding to the endpoint of the interval ( ) and base brightness value ( If V is exactly equal to the upper limit of a certain interval (e.g., V=30), then it is assigned to the next interval (i.e., [30, 45)). Next, linear interpolation calculations are performed on the beam spread angle and the base brightness, respectively: If the current vehicle speed V falls within a certain range, then a linear interpolation algorithm is used to calculate the continuous target beam spread angle. and base brightness percentage That is, the target parameters are calculated using a nonlinear mapping table and a linear interpolation algorithm. The formula for the linear interpolation algorithm is: Target beam spread angle Target base brightness percentage Where V is the actual speed of the motorcycle currently being collected; The preset speed range to which the current vehicle speed V belongs; These are the endpoints of the vehicle speed range. , The corresponding beam spread angle; These are the endpoints of the vehicle speed range. , The corresponding base brightness value. Calculation results. and As a continuous and smooth basic parameter, it is fed into the subsequent dynamic compensation module.

[0044] S4: Dynamic compensation is performed based on vibration frequency and base color temperature to obtain the final target color temperature; dynamic compensation is performed based on dominant vibration intensity to obtain the final target brightness.

[0045] In this embodiment, dynamic compensation refers to the real-time fine-tuning of the basic parameters of the base color temperature and base brightness using vibration characteristics (i.e., vibration frequency F and intensity A) extracted from the IMU, in order to cope with the impact of road bumps on lighting stability. The final target color temperature and final target brightness are the actual control targets after vibration feedback correction.

[0046] Specifically, regarding color temperature compensation, the base color temperature is first adjusted. Normalized to a color temperature tendency coefficient For example, 3000K corresponds to =0.8, 6000K corresponds =0.2. Normalized to a color temperature tendency coefficient. It linearly maps discrete base color temperature values ​​(such as 3000K or 6000K) to continuous variables in the interval [0, 1], where 0 represents a tendency towards pure white light and 1 represents a tendency towards pure yellow light. Yellow light is only moderately enhanced when mid-to-high frequency vibrations (5-30Hz) are present.

[0047] S401: Color temperature compensation calculates the vibration adjustment coefficient ΔK based on the vibration frequency F, using a piecewise function. in, =5Hz is the upper limit of low-frequency vehicle body posture changes, such as leaning when cornering. =30Hz is the high-frequency boundary for typical road surface bumps. For maximum compensation intensity, the value ranges from 0.1 to 0.5; in this embodiment, a value of 0.3 is used, resulting in the final target color temperature. , This is a normalized base color temperature tendency.

[0048] S402: Dynamically compensate for the base brightness based on the dominant vibration intensity to obtain the final target brightness, including: Brightness compensation is calculated by determining the brightness compensation amount ΔB based on the dominant vibration intensity A, using the following formula: , This is an adjustable weighting coefficient, with a value range of [value range missing]. ∈[1, 10]、 ∈[1, 10]、 ∈[0.05, 0.2], for example, The values ​​are 5, 20, and 0.1 respectively; The vibration rate of change is given by t, which is a control period of 10 ms; the final target brightness is given by t. , The target base brightness percentage.

[0049] Furthermore, the formula for calculating the dominant vibration intensity A is: ,in These represent the linear acceleration components measured by the inertial measurement unit (IMU) along the X-axis, Y-axis, and Z-axis of the motorcycle's coordinate system, respectively. The dominant vibration intensity A refers to the resultant acceleration amplitude obtained by vector synthesis of the three orthogonal axial acceleration components output by the IMU, used to comprehensively reflect the total energy of vibration excitation experienced by the motorcycle in three-dimensional space. The X-axis direction of the motorcycle's coordinate system is defined as the vehicle's forward direction, i.e., longitudinal. Final target brightness. Limiting processing and first-order low-pass smoothing filter are applied to 70% to 130% of the rated brightness.

[0050] The value A indicates that the greater the vibration intensity, the more bumpy the road surface, leading to drastic changes in the headlight beam angle and decreased lighting stability. To compensate for the impact of this shaking on the driver's vision, appropriately increasing the brightness can enhance the clarity of local vision, helping the driver to spot obstacles earlier on bumpy roads.

[0051] The vibration rate dA / dt reflects the dramatic changes in road surface bumps. When vibration suddenly intensifies (such as when driving on a gravel road), the lighting needs to respond quickly and compensate in advance to avoid safety hazards caused by lighting lag.

[0052] This indicates that the higher the vehicle speed V, the greater the impact of road surface changes on visibility per unit time, and the greater the need for increased brightness compensation to ensure lighting stability and safety at high speeds. The formula for calculating the brightness compensation ΔB is based on a feedforward + feedback compensation model commonly used in engineering experience and control theory, with weighting coefficients... The value can be determined through actual vehicle calibration tests, and the specific value can be adapted according to different vehicle models, lamp types and usage scenarios.

[0053] For example, weighting coefficients are set based on real-vehicle calibration experience. : A value of 3.0 indicates that for every 1 m / s² increase in dynamic intensity, the brightness compensation increases by 3%. A value of 1.5 indicates that for every 1 m / s³ increase in the vibration rate of change, the brightness compensation increases by 1.5%. A value of 0.1 means that the brightness compensation increases by 1% for every 10 km / h increase in vehicle speed.

[0054] In practical applications, the calibration road condition test data is shown in the table below: Lighting Test Results: Scene 1: Brightness is basically stable with no significant compensation, indicating no additional compensation is needed on flat roads, prioritizing energy saving. Scene 2: Slight brightness increase, clear visibility when passing speed bumps, compensating for changes in illumination angle caused by vehicle vibration. Scene 3: Significant brightness enhancement, clearer details on gravel roads, strong local contrast helps identify small stones. Scene 4: High brightness output, stable visibility on continuously bumpy roads, compensating for severe shaking and suppressing lighting flicker. Scene 5: Moderate brightness increase, longer visibility at high speeds, compensating for the impact of road seams at high speeds. Scene 6: Extremely high brightness compensation, illuminating potholes during off-road driving, ensuring safe visibility distance in extreme scenarios. Scene 7: Adjusted brightness increase while avoiding glare, indicating cautious compensation in high-speed scenarios, balancing safety and comfort.

[0055] Furthermore, to verify the effectiveness of the brightness compensation formula in this invention, based on typical calibration coefficients ( Tests were conducted in various driving scenarios. These tests covered typical scenarios including flat roads, urban speed bumps, gravel roads, continuous bumpy sections, highway joints, and off-road surfaces. The results are shown in the table below. The results show that the formula for calculating the brightness compensation amount can dynamically adjust the brightness according to road conditions and vehicle speed, effectively improving the driver's visual safety in complex road conditions, and the brightness change is smooth with no risk of glare.

[0056] Specifically, in the calculation Then, the system performs a limiting operation: This brightness limit is determined based on a comprehensive calibration of the electrical characteristics of LED devices, human visual comfort experiments, and road safety regulations: 70% is the minimum effective brightness required to ensure basic road visibility, and 130% is the upper limit allowing for short-term super-brightness to cope with emergency scenarios. Subsequently, the limited brightness will be... The signal is then fed into a first-order low-pass filter for smoothing. The first-order low-pass filter is implemented in discrete-time, and its recursive formula is as follows: Where k is the current control cycle number, This is the smoothed brightness value output for this task. α is the filtering coefficient, where 0 < α < 1, and a typical value is 0.1–0.3. The smaller α is, the stronger the filtering, and the slower but smoother the response; the larger α is, the faster the response, but fluctuations may remain.

[0057] S5: The dynamic priority management module coordinates various control parameters and outputs the final control command to drive the motorcycle headlight and auxiliary lighting unit to perform adaptive lighting adjustment; among which, the control parameters include the target beam diffusion angle, the final target color temperature and the final target brightness.

[0058] In this embodiment, the dynamic priority management module is a conflict arbitration engine built into the ECU software, used to handle decision conflicts when multiple operating modes are triggered simultaneously. The final control command is a set of electrical signals that can directly drive the hardware, including the PWM duty cycle for color temperature adjustment, the control word or motor command for light pattern adjustment, and the current / PWM setpoint for brightness adjustment.

[0059] Specifically, the dynamic priority management module is configured with preset multi-level working modes and their priority sequences, which are in the following order from high to low: emergency safety mode, rain and fog weather mode, night mode, normal fusion mode and energy saving mode.

[0060] Emergency safety mode is triggered by system self-check fault signals or collision risk warning signals, forcibly outputting a preset set of safety lighting parameters and activating the optical warning function. Emergency safety mode is the highest priority state in the entire lighting control system. When the vehicle experiences a serious malfunction or faces an imminent collision risk, it forcibly switches the lighting system to a preset, safety-verified fixed set of parameters. System self-check fault signals include failure signals of critical components detected by the ECU during power-on self-test or periodic diagnostics, such as main control chip abnormalities, communication bus interruptions, and open circuits in LED drivers. Collision risk warning signals may originate from simple collision sensors integrated into the motorcycle, such as acceleration threshold trigger switches.

[0061] For example, once the ECU receives any of the above trigger signals, the dynamic priority management module immediately interrupts the decision-making process of all other modes and forces the execution of the emergency safety mode. In emergency safety mode, the system outputs a fixed set of "safety lighting parameters": for example, the main headlights and all auxiliary lights are illuminated at 100% rated brightness, the color temperature is forcibly set to a high-visibility 6000K white light, and the beam diffusion angle is fixed at 60° to balance near and far vision; at the same time, the "optical warning function" is activated—that is, the hazard turn signals are controlled to flash synchronously at a frequency of 1Hz, or the daytime running lights are made to flash with specific coded pulses (such as SOS Morse code) to issue a clear danger warning to surrounding road users. Once this mode is triggered, it will continue to run until the system is reset or the fault is cleared, and it is not affected by any low-priority conditions.

[0062] The rain and fog weather mode is triggered by a decision based on a dual-condition threshold logic or fuzzy logic. It forcibly sets the base color temperature to a low color temperature yellow light band and increases the target brightness within safe limits. The dual-condition threshold logic means that the ambient humidity H ≥ 80%RH and the ambient light intensity L ≤ a preset light intensity threshold. The preset light intensity threshold is 500 lx in daytime mode and 50 lx in nighttime mode. Specifically, after triggering, the system forcibly sets the base color temperature to a low color temperature yellow light band (typically 3000K, range 2800K–3200K) to utilize the weak Rayleigh scattering of yellow light in water vapor to enhance penetration. Simultaneously, it moderately increases the target brightness within safe limits—for example, increasing it by 10%–15% from the original base brightness, but not exceeding 130% of the rated brightness limit—to avoid glare caused by excessive brightness on wet and slippery surfaces. Furthermore, the light pattern strategy can be fine-tuned, such as slightly widening the lower edge of the beam to illuminate reflective lines on wet surfaces.

[0063] Night mode is triggered when the ambient light intensity is lower than the preset night light intensity threshold, automatically activating the lighting system and implementing an anti-glare light pattern adjustment strategy. Night mode is the basic lighting mode for typical low-light environments; the preset night light intensity threshold is 50 lx. Specifically, when the ambient light intensity L ≤ 50 lx and is not covered by a higher priority mode, the system automatically activates night mode. At this time, the lighting system is activated from off or standby mode, and the base color temperature is set to 6000K white light to provide high color rendering. The light pattern control module implements an anti-glare strategy: for example, for matrix LED headlights with zone control capabilities, the system turns off the top 1-2 rows of LED units; for traditional reflector-type lamps, the light cutoff line is strictly controlled within 0.5°-1.0° below the horizontal line by finely adjusting the angle of the light distribution lens or the position of the shielding plate. The brightness is dynamically adjusted within the range of 75%-115% according to the vehicle speed.

[0064] Normal fusion mode is the system's default operating state, executing a complete lighting decision-making process based on multi-source sensor data fusion. Normal fusion mode is the system's default operating state when no special events trigger it. The ECU enters normal fusion mode when it detects that there are no faults, no rain or fog, it is not nighttime, and the battery is fully charged. In this mode, the system fully utilizes four-dimensional data including ambient light intensity, ambient humidity, vehicle speed, and three-axis acceleration to execute a complete intelligent lighting algorithm: real-time calculation of vibration frequency F and dominant vibration intensity A, dynamic compensation of color temperature and brightness, and output of lighting parameters highly matched to the current driving conditions (speed, road surface, and lighting).

[0065] The energy-saving mode is triggered when the vehicle power supply voltage or remaining battery power falls below a preset threshold, limiting maximum output brightness and disabling non-critical lighting subsystems. Energy-saving mode is a low-power operating state for power-constrained scenarios. The trigger conditions for energy-saving mode are "vehicle power supply voltage below 11.5V" or "Battery Management System (BMS) reporting remaining battery power below 20%". Non-critical lighting subsystems refer to auxiliary light sources that can be turned off while ensuring basic driving safety, such as daytime running lights, ambient lighting, and some auxiliary turn signals. Specifically, once energy-saving mode is triggered, the system immediately limits the maximum output brightness of the main headlights to no more than 80% of the rated value and completely disables all non-critical lighting subsystems. Color temperature and beam pattern can still be adjusted based on vehicle speed and weather (if not covered by higher priority), but the gain coefficients of all compensation algorithms are reduced to decrease computational load and power consumption. For example, the weighting coefficient in the brightness compensation amount ΔB... It was halved.

[0066] In this embodiment, the dynamic priority management module is further configured with conflict arbitration rules: when the triggering conditions of multiple working modes are met simultaneously, only the core control parameters defined by the highest priority working mode are executed, and parameter adjustments for other lower priority modes are suppressed, or only auxiliary fine-tuning is performed without violating the safety constraints of the higher priority modes. Auxiliary fine-tuning refers to allowing lower priority modes to optimize non-critical parameters without violating the safety boundaries of the core parameters.

[0067] Specifically, the system performs mode arbitration at the end of each control cycle: first, it scans the trigger flags of all modes, then searches for the first valid mode from high to low according to a preset priority sequence and sets it as the current active mode. For example, in the "rain / fog + low battery" scenario, the rain / fog weather mode (high priority) takes effect, forcing the use of yellow light; while the energy-saving mode (low priority) is triggered, but its "brightness limit" command can only reduce brightness within the range allowed by the rain / fog mode (e.g., if the rain / fog mode wants to be brightened to 110%, the energy-saving mode can suppress it to 90%, but it must not be lower than the minimum effective brightness required by rain / fog, which is 70%). As another example, in the "night + highway" scenario, the night mode activates the anti-glare cutoff line, while the normal fusion mode can dynamically adjust the beam width and center brightness according to vehicle speed under this constraint. All mode switching incorporates anti-shake delay (3 seconds for entry, 10 seconds for exit) and gradual transition of parameter exponents.

[0068] In one embodiment, the raw sensor data is preprocessed as follows before generating the final control command: S101: Perform moving average filtering on ambient light intensity, ambient humidity and vehicle speed signals respectively.

[0069] In this embodiment, the ECU first reads four types of raw signals within each 10ms control cycle. For ambient light intensity L, ambient humidity H, and vehicle speed V, the system maintains a FIFO queue of length N=5. After each new sampled value is enqueued, the arithmetic mean of all elements in the queue is calculated as the filtered output. For example, if five consecutive vehicle speed readings are 48.1, 48.3, 47.9, 48.5, and 48.2 km / h, then the filtered V = 48.2 km / h.

[0070] S102: Extract the dominant vibration intensity signal from the triaxial acceleration data using a 2-10Hz bandpass digital filter, and extract the vibration frequency F by zero-crossing rate detection.

[0071] In this embodiment, the zero-crossing rate (ZCR) refers to the number of times a discrete-time signal crosses the zero level per unit time. Its reciprocal is approximately proportional to the main period of the signal, and therefore can be used to estimate the fundamental frequency. In the motorcycle lighting control scenario, the vibration intensity signal A(t) after 2-10Hz bandpass filtering mainly contains periodic or quasi-periodic vibration components caused by road surface excitation, with its frequency range concentrated in the 5-30Hz range. This makes it very suitable for real-time frequency estimation using lightweight zero-crossing rate detection.

[0072] Specifically, the vibration frequency F is extracted using the zero-crossing rate detection method: First, the vibration intensity signal A after being filtered by a 2-10Hz bandpass is dynamically zero-point calibrated to eliminate minor DC offsets; then, within a 200ms sliding window, the number of effective zero-crossings Z that satisfy opposite signs and amplitudes greater than the threshold ε is counted; the vibration frequency is estimated according to the formula F=Z / (2×0.2); if the result is within the effective range of 2-50Hz and stable for two consecutive cycles, the vibration frequency F is updated; otherwise, the original value is maintained or smoothed.

[0073] S103: The color temperature control command calculates the duty cycle of the two pulse width modulation channels corresponding to the warm LED and the cool LED in the dual color temperature auxiliary lighting unit based on the final target color temperature.

[0074] In this embodiment, based on calculations, the warm-colored LED (typical color temperature 3000K) and the cool-colored LED (…) in the dual-color temperature auxiliary lighting unit… The pulse width modulation (PWM) duty cycle (typical color temperature 6000K). Assume the total brightness is determined by... If determined, the pulse width modulation duty cycle of the warm-colored LED is: Duty_warm = The pulse width modulation duty cycle of a cool-color LED is: Duty_cool= .

[0075] S104: Light pattern control command is generated based on the target beam diffusion angle: In a matrix LED architecture, the independent control word of each lighting zone is output by querying the pre-stored LED zone lighting mode mapping table; in a mechanical dimming architecture, the stepping pulse sequence or analog PWM positioning signal is output based on the pre-calibrated mapping relationship between the beam diffusion angle and the stepping position of the actuator motor.

[0076] In this embodiment, the system first determines the current lighting fixture type. If it is a matrix LED architecture, it queries a pre-stored LED partition lighting mode mapping table in Flash memory and sets the appropriate settings. It is divided into several settings, such as 25°–70°, with each setting corresponding to a 32-bit control word. Each bit controls the on / off state of one LED zone. For example, =45° may correspond to control word 0x00FF0000, indicating that only the middle 8 zones are illuminated. If it is a mechanical dimming architecture, then query " - The motor target position calibration curve (this calibration curve is usually a nonlinear polynomial or lookup table) is used to calculate the target number of steps. Then, generate the corresponding step pulse sequence or simulated PWM positioning signal (duty cycle corresponds to the position).

[0077] Brightness control command: (Limited and smoothed) Directly mapped to the PWM duty cycle setting value of the LED driver for the main headlight and auxiliary lighting units, or converted into the target constant current drive current value (by looking up a table or formula I=I_max× / 100%, where I_max is the maximum allowable drive current of the lighting unit (such as an LED headlight) under rated operating conditions. The brightness control command acts synchronously on all activated lighting channels.

[0078] In one embodiment, a smart control method for motorcycle lighting further includes: constructing a full-scene lighting risk map and dynamically adjusting the lighting strategy based on a light environment optimization model that predicts the risk. S100: Collects historical driving data, including time, geographical location, weather API information, vehicle speed sequence, acceleration spectrum, and corresponding manual intervention records or accident report data.

[0079] In this embodiment, the full-scene lighting risk map is a structured geographic-temporal-risk correlation database stored on a cloud server or local SD card. The area is divided into 100m × 100m grids, and the historical risk score for each grid is recorded at different time periods. The lighting risk feature matrix is ​​the input data table used to train the prediction model; each row corresponds to a historical travel segment and contains risk-related features across multiple dimensions.

[0080] S200: Based on the historical driving data, construct a lighting risk feature matrix, which includes road curvature coefficient, nighttime visibility index, probability of rain and fog occurrence, road surface bump level, and oncoming traffic density parameters.

[0081] In this embodiment, the road curvature coefficient is determined by the vehicle speed V and the triaxial acceleration. (Lateral) Calculation of instantaneous centripetal acceleration Then, combine V to estimate curvature For a continuous 1km section The root mean square (RMS) is taken as the curvature coefficient for this segment. Nighttime visibility index is obtained by nonlinear mapping of ambient light intensity L; for example, nighttime visibility index = 0.9 for L < 50 lx; ​​0.6 for 50 ≤ L < 200; and 0.2 for L ≥ 200. The probability of rain and fog occurrence is estimated by local weather API (obtainable via Bluetooth mobile hotspot) or historical humidity H statistical frequency. Road surface bumpiness level is determined by the average of the 95th quantile of the dominant vibration intensity A over a 1 km range. Oncoming traffic density is obtained by statistically analyzing the frequency of headlight current fluctuations or the number of high beam flicker counts detected by auxiliary photosensors.

[0082] S300: The XGBoost algorithm is used to train the lighting risk feature matrix to obtain the light environment risk prediction model, and the top TOP-M key risk features are selected based on SHAP value analysis, where M≥10.

[0083] In this embodiment, during the offline phase, the system periodically uploads anonymized historical travel data to the cloud. XGBoost is then used to train the model on the binary label of "whether manual intervention (such as manually turning on fog lights) or reporting a hazard," resulting in a light environment risk prediction model. After training, TOP-M key features are retained through SHAP value analysis, and the model size is compressed to adapt for vehicle deployment; where M≥10.

[0084] Specifically, the configuration of the light environment risk prediction model is as follows: the number of trees is 100-200, the maximum depth is 6-8 layers, the learning rate is 0.05-0.1, and an early stop mechanism (patience=10) is adopted to prevent overfitting. The labeled data include: (1) events in which the driver manually turns on the fog lights in rainless and foggy weather; (2) emergency braking events (deceleration > 0.5g and duration > 0.5s); (3) feedback of 'dangerous road sections' confirmed by the user through the vehicle HMI.

[0085] S400: During real-time operation, the contextual features of the current journey are input into the light environment risk prediction model to obtain the lighting risk probability of the road ahead; when the risk probability exceeds the dynamic threshold, the enhanced lighting mode is activated in advance, including pre-increasing brightness, expanding the near-field beam width, or activating auxiliary fog lights.

[0086] Specifically, during real-time operation, the ECU or mobile app continuously collects contextual features of the current journey, such as the current time being 22:30, L=30lx, V=45km / h, Factors such as drastic fluctuations are concatenated into a feature vector and input into a lightweight XGBoost model, which outputs the lighting risk probability P_risk for the road segment ahead 1–2 km, with a value ranging from 0 to 1. If P_risk > a preset risk threshold T_dyn, the road segment is identified as high-risk, and an enhanced lighting mode is immediately triggered. The preset risk threshold T_dyn is a dynamic threshold, adjusted using a formula such as T_dyn = 0.65 + 0.1 × (1 - SOC%), where SOC is the remaining battery percentage. The enhanced lighting mode is as follows: the main headlight brightness is increased by 15%, the beam spread angle is expanded from 40° to 50° to enhance near-field coverage, and the yellow light channel of the dual-color temperature auxiliary lights is automatically activated. Forced +0.2. This predictive adjustment is completed 5-10 seconds before entering a dangerous section of road.

[0087] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0088] In one embodiment, a motorcycle lighting intelligent control system is provided, which corresponds to the motorcycle lighting intelligent control method described in the above embodiment.

[0089] A motorcycle lighting intelligent control system includes a sensor module, a vibration feature extraction module, a weather mode determination module, a basic parameter setting module, a dynamic compensation module, a control coordination module, and an execution drive module. Detailed descriptions of each functional module are as follows: The sensor module is used to collect data on ambient light intensity, ambient humidity, vehicle speed, and triaxial acceleration. The vibration feature extraction module is used to calculate the vibration frequency and dominant vibration intensity based on triaxial acceleration data; The weather pattern determination module is used to determine the current weather pattern based on ambient humidity and ambient light intensity. The basic parameter setting module is used to determine the basic color temperature based on the current weather mode, and to determine the target beam diffusion angle and basic brightness based on the vehicle speed; The dynamic compensation module is coupled to the vibration feature extraction module and the basic parameter setting module, respectively. It is used to dynamically compensate the basic color temperature based on the vibration frequency to obtain the final target color temperature, and to dynamically compensate the basic brightness based on the dominant vibration intensity to obtain the final target brightness. The control and coordination module is used to coordinate the final target color temperature, target beam spread angle and final target brightness through a dynamic priority management mechanism, and generate the final control command. The execution drive module is used to drive the motorcycle headlights and auxiliary lighting units to perform adaptive lighting adjustment according to the final control command.

[0090] For specific limitations regarding a motorcycle lighting intelligent control system, please refer to the limitations of a motorcycle lighting intelligent control method mentioned above, which will not be repeated here. Each module in the above-mentioned motorcycle lighting intelligent control system can be implemented entirely or partially through software, hardware, or a combination thereof. Each module can be embedded in or independent of the processor in a computer device in hardware form, or it can be stored in the memory of a computer device in software form, so that the processor can call and execute the corresponding operations of each module.

[0091] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, the computer program performing the following steps when executed by a processor: S1: Collect ambient light intensity, ambient humidity, vehicle speed, and triaxial acceleration data; calculate vibration frequency and dominant vibration intensity based on triaxial acceleration data; S2: Determines the current weather pattern based on ambient humidity and ambient light intensity; S3: Determine the base color temperature based on the current weather pattern; determine the target beam spread angle and base brightness based on vehicle speed; S4: Dynamic compensation is performed based on vibration frequency and base color temperature to obtain the final target color temperature; dynamic compensation is performed based on dominant vibration intensity to obtain the final target brightness. S5: The dynamic priority management module coordinates various control parameters and outputs the final control command to drive the motorcycle headlight and auxiliary lighting unit to perform adaptive lighting adjustment.

[0092] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0093] In one embodiment, particularly according to embodiments of the present invention, the processes described above with reference to the flowchart can be implemented as a computer software program. For example, embodiments of the present invention include a computer program product comprising a computer program / instructions that, when executed by a processor, implement the steps of the intelligent control method for motorcycle lighting as described. In such embodiments, the computer program can be downloaded and installed from a network via a communication module, and / or installed from a removable medium. When the computer program is executed by a central processing unit (CPU), it performs the various functions defined in the present invention.

[0094] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is used as an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above.

[0095] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. A method for intelligent control of motorcycle lighting, characterized in that, include: Collect ambient light intensity, ambient humidity, vehicle speed, and triaxial acceleration data; The vibration frequency and dominant vibration intensity are calculated based on the triaxial acceleration data. The current weather pattern is determined based on the ambient humidity and the ambient light intensity. The base color temperature is determined based on the current weather pattern; the target beam diffusion angle and base brightness are determined based on the vehicle speed. Dynamic compensation is performed based on the vibration frequency and the base color temperature to obtain the final target color temperature; The base brightness is dynamically compensated based on the dominant vibration intensity to obtain the final target brightness. The dynamic priority management module coordinates various control parameters and outputs the final control command to drive the motorcycle headlight and auxiliary lighting unit to perform adaptive lighting adjustment; wherein, the control parameters include the target beam diffusion angle, the final target color temperature and the final target brightness.

2. The intelligent control method for motorcycle lighting according to claim 1, characterized in that, Weather pattern determination is implemented using a dual-condition threshold decision logic or fuzzy logic decision-making: When using a dual-condition threshold decision logic, if the ambient humidity is greater than a preset humidity threshold and the ambient light intensity is less than or equal to a preset light intensity threshold, the current weather mode is determined to be a rain / fog weather mode; otherwise, it is determined to be a normal weather mode; the preset light intensity threshold adopts a dual-threshold for day and night. When using fuzzy logic for decision-making, the ambient light intensity and ambient humidity are fuzzified into membership functions, input into a preset fuzzy rule base for inference, and after defuzzification using the centroid method, continuous target color temperature values ​​are output. As the base color temperature; The formula for calculating the center of gravity using the centroid method is as follows: , where n is the total number of discrete sampling points on the output universe of discourse; This represents the i-th discrete sampling point on the output universe of discourse; For the i-th sampling point After aggregation, the membership degree of the fuzzy set is output, with a value of 0 to 1.

3. The intelligent control method for motorcycle lighting according to claim 1, characterized in that, The target beam spread angle and the base brightness are obtained by looking up and interpolating from a pre-stored nonlinear mapping table based on the vehicle speed. The nonlinear mapping table defines a negative correlation between vehicle speed and beam spread angle and a positive correlation between vehicle speed and base brightness, and divides the vehicle speed range into multiple vehicle speed ranges corresponding to different combinations of beam spread angle and base brightness. The target parameter is calculated using the nonlinear mapping table and the linear interpolation algorithm. The calculation formula for the linear interpolation algorithm is as follows: Target beam spread angle Target base brightness percentage Where V is the actual speed of the motorcycle currently being collected; The preset speed range to which the current vehicle speed V belongs; These are the endpoints of the vehicle speed range. , The corresponding beam spread angle; These are the endpoints of the vehicle speed range. , The corresponding base brightness value.

4. The intelligent control method for motorcycle lighting according to claim 1, characterized in that, The process of dynamically compensating based on the vibration frequency and the base color temperature to obtain the final target color temperature includes: Color temperature compensation is calculated using a piecewise function based on the vibration frequency F to determine the vibration adjustment coefficient ΔK. in, =5Hz, =30Hz, =0.3, final target color temperature , To normalize the base color temperature tendency; Dynamic compensation is performed on the base brightness based on the dominant vibration intensity to obtain the final target brightness, including: Brightness compensation is calculated by determining the brightness compensation amount ΔB based on the dominant vibration intensity A, using the following formula: , These are adjustable weighting coefficients. The rate of change of vibration, Final target brightness , The target base brightness percentage.

5. The intelligent control method for motorcycle lighting according to claim 4, characterized in that, The formula for calculating the dominant vibration intensity A is: ,in These represent the linear acceleration components measured by the inertial measurement unit in the X-axis direction, the Y-axis direction, and the Z-axis direction of the motorcycle body coordinate system, respectively. The final target brightness Limiting processing and first-order low-pass smoothing filter are applied to 70% to 130% of the rated brightness.

6. The intelligent control method for motorcycle lighting according to claim 2, characterized in that, The dynamic priority management module is configured with preset multi-level working modes and their priority sequences. The multi-level working modes include energy-saving mode, emergency safety mode, rain and fog weather mode, night mode and normal integration mode. The priority sequence from high to low is: emergency safety mode, rain and fog weather mode, night mode, normal integration mode and energy-saving mode. The emergency safety mode is triggered by a system self-check fault signal or a collision risk warning signal, which forces the output of a preset set of safety lighting parameters and activates the optical warning function. The rain and fog weather mode is triggered by the judgment result of the dual-condition threshold decision logic or fuzzy logic decision, which forces the base color temperature to be set to the low color temperature yellow light band and increases the target brightness within the safety limit. The night mode is triggered when the ambient light intensity is lower than the preset night light intensity threshold, which activates the lighting system and executes an anti-glare light pattern adjustment strategy. The normal fusion mode is the system's default operating state, which executes a complete lighting decision-making process based on the fusion of multi-source sensor data. The energy-saving mode is triggered when the vehicle power supply voltage or remaining power is lower than a preset threshold, limiting the maximum output brightness and disabling non-critical lighting subsystems. The dynamic priority management module is also configured with conflict arbitration rules: when the triggering conditions of multiple working modes are met at the same time, only the core control parameters defined by the working mode with the highest priority are executed, and the parameter adjustments of other low-priority modes are suppressed, or only auxiliary fine-tuning is performed without violating the safety constraints of the high-priority mode.

7. The intelligent control method for motorcycle lighting according to claim 6, characterized in that, The final control command includes a color temperature control command, a light pattern control command, and a brightness control command. Before generating the final control command, the following preprocessing operations are performed on the raw sensor data: Moving average filtering is applied to the ambient light intensity, ambient humidity, and vehicle speed signals respectively; The triaxial acceleration data is processed through a 2-10Hz bandpass digital filter to extract the dominant vibration intensity signal, and the vibration frequency F is extracted by zero-crossing rate detection. The color temperature control command calculates the duty cycle of the two pulse width modulation channels corresponding to the warm LED and the cool LED in the dual color temperature auxiliary lighting unit based on the final target color temperature. The light pattern control command is generated based on the target beam diffusion angle: in a matrix LED architecture, the independent control word of each lighting zone is output by querying the pre-stored LED zone lighting mode mapping table; in a mechanical dimming architecture, the stepping pulse sequence or analog PWM positioning signal is output based on the pre-calibrated mapping relationship between the beam diffusion angle and the stepping position of the actuator motor.

8. A motorcycle lighting intelligent control system, characterized in that, The system includes: The sensor module is used to collect data on ambient light intensity, ambient humidity, vehicle speed, and triaxial acceleration. A vibration feature extraction module is used to calculate the vibration frequency and dominant vibration intensity based on the triaxial acceleration data; A weather pattern determination module is used to determine the current weather pattern based on the ambient humidity and the ambient light intensity. The basic parameter setting module is used to determine the basic color temperature based on the current weather mode, and to determine the target beam diffusion angle and basic brightness based on the vehicle speed; The dynamic compensation module is coupled to the vibration feature extraction module and the basic parameter setting module, respectively. It is used to dynamically compensate the basic color temperature based on the vibration frequency to obtain the final target color temperature, and to dynamically compensate the basic brightness based on the dominant vibration intensity to obtain the final target brightness. The control and coordination module is used to coordinate the final target color temperature, the target beam diffusion angle, and the final target brightness through a dynamic priority management mechanism, and generate the final control command. The execution drive module is used to drive the motorcycle headlight and auxiliary lighting unit to perform adaptive lighting adjustment according to the final control command.

9. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the intelligent control method for motorcycle lighting as described in any one of claims 1 to 7.

10. A computer program product comprising a computer program / instructions, characterized in that, When the computer program / instructions are executed by the processor, they implement the steps of the intelligent control method for motorcycle lighting as described in any one of claims 1 to 7.

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