Multi-condition filtering and vehicle state linkage projection lamp adaptive control method

Abstract

The invention relates to the technical field of intelligent vehicle lamps, in particular to a multi-condition filtering and vehicle state linkage projection lamp self-adaptive control method, which comprises the following steps of extracting authority and abnormal identification, checking by combining wheel speed and acceleration, reading a slow flash code generation instruction, and executing quick flash to generate a self-matching scheme when monitoring that a ToF change rate is lower than a threshold value. According to the method, non-interactive targets are screened based on dynamic changes of the object contour three-dimensional curvature and the contact area, the identification specificity is improved, dynamic anomalies are verified in a two-way mode in combination with the wheel speed pulse interval and the acceleration change rate, the static interaction judgment reliability is enhanced, and the flash trigger logic is compared with the current lamp state code. According to the method, the consistency of light feedback and interaction intention is guaranteed, the stability of the projection lamp is improved by inhibiting lamp state switching caused by perturbation based on ground distance change freezing judgment, and the control accuracy and interaction consistency are improved in a multi-dimensional manner through state verification, feature screening, dynamic verification and freezing management.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent vehicle lights, and particularly to an adaptive control method for a projection lamp with multi-condition filtering and vehicle status linkage. Background Art

[0002] The technical field of intelligent vehicle lights includes intelligent control and management methods for vehicle lighting systems. The core content of this technical field lies in combining vehicle operating status, environmental changes, and driver needs, and through perception, decision-making, and execution mechanisms, realizing the adaptive adjustment of the functions of vehicle lights. Intelligent vehicle light technology covers systematic contents such as light source control, light distribution adjustment, energy consumption optimization, external environment perception, and interaction response. Generally speaking, intelligent vehicle light technology not only involves the intelligent change of lighting effects, but also includes the linkage with other vehicle subsystems such as the autonomous driving assistance system, night vision system, and vehicle-mounted sensing system, so as to realize more efficient, safe, and energy-saving lighting control and management.

[0003] Among them, the adaptive control method for a projection lamp with multi-condition filtering and vehicle status linkage refers to a method that dynamically controls the working mode of a vehicle projection lamp based on vehicle driving status information and external environmental conditions, and determines the adaptive adjustment strategy of the projection lamp pattern, brightness, and projection area through logical discrimination and combination matching by setting a variety of specific screening conditions for judging specific factors such as vehicle speed, driving direction, environmental brightness, meteorological conditions, and road type, and filtering and screening according to the set condition priorities. This method usually realizes the refined control of the working state of the projection lamp by collecting the output data of vehicle sensors in real time, combining the pre-set condition library and decision rules.

[0004] The prior art lacks cross-logic verification of the physical locking state of the tailgate when judging the vehicle standby state, and there is a problem of abnormal omission or misjudgment due to relying on a single signal for judgment, which is likely to cause confusion in the control logic. In the recognition of interaction objects, traditional methods are mostly based on environmental brightness or single-plane image information, lacking the comprehensive discrimination ability of three-dimensional contours and dynamic area changes, resulting in non-interaction objects such as falling objects and snow being misrecognized as valid interaction signals, triggering system misresponses. In existing interaction detections, a single wheel speed or acceleration signal is easily interfered by environmental vibrations or road condition fluctuations, and cannot effectively cancel external noise, affecting the accuracy of interaction judgment. The triggering of projection lamp actions mostly depends on simple state switching, lacking two-way comparison of triggering conditions and the current state, resulting in state switching lags or misflashes. In terms of ground change perception, traditional modes generally do not perform freezing judgment on small dynamic fluctuations of the ground, resulting in frequent fast flashing and switching of projection lamps when there is water accumulation fluctuation or ground jitter on rainy days, increasing visual interference and reducing driving safety and the stability of the user experience. Summary of the Invention

[0005] The object of the present invention is to solve the disadvantages existing in the prior art, and a projection lamp adaptive control method with multi-condition filtering and vehicle state linkage is proposed.

[0006] To achieve the above object, the present invention adopts the following technical solutions: A projection lamp adaptive control method with multi-condition filtering and vehicle state linkage, comprising the following steps: S1: Call the ACC activation status signal and the physical lock status signal of the tailgate, detect the output of the tailgate lock sensor when ACC is not activated, and generate a headlight control authority identifier when the tailgate is not locked and ACC is not activated; S2: Collect the three-dimensional coordinates of the object boundary contour in the projection area, extract the curvature distribution of the contour nodes, calculate the change in the contact area, and generate an object feature anomaly identifier when the curvature distribution deviates from the foot feature and the difference in the contact area exceeds the threshold; S3: Based on the headlight control authority identifier and the object feature anomaly identifier, call the wheel speed pulse signal and the three-axis acceleration values of the acceleration sensor, calculate the wheel speed pulse interval and the acceleration amplitude change rate, and generate a dynamic behavior correlation verification identifier; S4: According to the dynamic behavior correlation verification identifier, read the projection lamp extinguishing, slow flash, and fast flash state codes, compare the slow flash codes during interaction, and generate a projection lamp state maintenance instruction; S5: Based on the projection lamp state maintenance instruction, monitor the ground distance sequence output by the laser ToF, calculate the standard deviation and the mean change rate, and execute the fast flash action and generate a projection lamp self-matching control scheme when the fast flash is triggered and the change rate is lower than the freeze determination threshold.

[0007] As a further solution of the present invention, the headlight control authority identifier includes the ACC not activated state, the tailgate not locked state, and the power supply circuit conflict determination. The object feature anomaly identifier includes the curvature distribution deviation, the contact area change difference, and the foot feature template difference. The dynamic behavior correlation verification identifier includes the wheel speed pulse interval time, the acceleration amplitude change rate, and the stationary interval determination. The projection lamp state maintenance instruction includes the fast flash action instruction, the original state maintenance instruction, and the slow flash state comparison result. The projection lamp self-matching control scheme includes the distance sequence standard deviation, the mean change rate, and the freeze determination threshold.

[0008] As a further solution of the present invention, the specific steps of S1 are as follows: S101: Obtain the vehicle ACC activation status signal and the tailgate lock status signal, extract the tailgate lock output value when ACC is not activated based on the acquisition timestamp, and generate a tailgate lock status signal acquisition value; S102: Based on the acquired value of the tailgate locking state signal, call the ACC power supply circuit state signal, cross - judge the tailgate locking state and the ACC power supply circuit signal, set the state where the tailgate is unlocked and the ACC is not activated as the abnormal reference state, screen the signal combinations that meet the conditions of the abnormal reference state, and obtain the conflict quantity between the tailgate and the power supply circuit state; S103: According to the conflict quantity between the tailgate and the power supply circuit state, call the headlight control determination rule, perform permission identification on the abnormal reference state signal combination, and generate a headlight control permission identification.

[0009] As a further solution of the present invention, the specific steps of S2 are as follows: S201: Collect the three - dimensional coordinates of the object boundary contour in the projection area, extract the curvature values between the contour nodes, establish the corresponding relationship between the node numbers and the curvature values, and generate the node curvature distribution value; S202: Based on the node curvature distribution value, extract the node contact area data within adjacent time slices, calculate the change difference of the contact area between the nodes, set the reference values respectively according to the node curvature deviation and the area change difference, perform double screening, and screen out the node combinations that simultaneously meet the conditions of the deviation reference and the contact difference threshold to obtain the contact feature deviation quantity; S203: According to the contact feature deviation quantity, extract the object feature attribute information, and based on the distribution law of the deviation node range, call the standard features of the object category to generate an object feature abnormality identification.

[0010] As a further solution of the present invention, the specific formula for calculating the change difference of the contact area between the nodes is as follows: ; Wherein, represents the change difference of the contact area between the nodes, represents the measured value of the node contact area within the current time slice, represents the measured value of the node contact area within the adjacent time slice, represents the fixed constant of the time slice interval, represents the measured value of the node curvature distribution within the current time slice, represents the measured value of the node curvature distribution within the adjacent time slice.

[0011] As a further solution of the present invention, the specific steps of S3 are as follows: S301: Based on the headlight control permission identification and the object feature abnormality identification, extract the wheel speed pulse signal and the three - axis acceleration value, and generate a pulse time interval sequence; S302: Based on the pulse time interval sequence, extract the difference between adjacent pulse times, filter the data nodes within the stationary interval, extract the three-axis acceleration amplitudes of the corresponding nodes, calculate the acceleration amplitude change rate, and filter the data nodes with a change rate lower than the reference value according to the dynamic threshold to obtain the static contact change amount; S303: According to the static contact change amount, extract the dynamic feature information of the nodes, classify and identify them according to the node attribute determination rules, and generate a dynamic behavior association verification identifier.

[0012] As a further solution of the present invention, the specific formula for calculating the acceleration amplitude change rate is: ; Wherein, represents the acceleration amplitude change rate, represents the acceleration value of the axis of the current node, the corresponding reference axis, represents the unified pulse time difference between adjacent nodes, is the dynamic weight factor of acceleration and time difference, represents the sum of the absolute value terms of the product of the square terms of the three-axis acceleration components and the weighted time difference.

[0013] As a further solution of the present invention, the specific steps of S4 are as follows: S401: According to the dynamic behavior association verification identifier, read the status codes of the projection lamp extinguished, slow flash, and fast flash, and generate a projection lamp status code value; S402: Based on the projection lamp status code value, for the verification identifier in the interactive state, extract the current status code, compare it with the slow flash status code, set a reference according to the comparison consistency, filter the nodes that are consistent and inconsistent with the slow flash, and distinguish and generate a fast flash action instruction or a maintain original state instruction to obtain an action instruction determination value; S403: According to the action instruction determination value, extract the instruction type, classify and output the control instruction content, and generate a projection lamp status maintenance instruction.

[0014] As a further solution of the present invention, the specific steps of S5 are as follows: S501: Based on the projection lamp status maintenance instruction, monitor the ground distance sequence output by the laser ToF sensor, extract the continuous sampling points, and generate a ground distance sampling sequence; S502: Based on the ground distance sampling sequence, identify the distance standard deviation of the continuous sampling points, extract the ground distance mean of adjacent time slices, calculate the mean change rate, call the freeze determination threshold, filter out the time slices with a distance change rate lower than the freeze determination threshold, and establish a time slice index that meets the freeze conditions to obtain the ground freeze change amount; S503: Based on the ground freezing change amount, combined with the flash trigger instruction, filter the nodes that meet the flash trigger condition and the freezing condition, output the corresponding control action content, and generate the projection lamp self-matching control scheme amount.

[0015] As a further solution of the present invention, the specific formula for the mean change rate is: ; Wherein, represents the mean change rate at the moment, represents the mean ground distance of the time slice, represents the mean ground distance of the time slice, represents the standard deviation of the ground distances of the continuous sampling points in the time slice, represents the standard deviation of the ground distances of the continuous sampling points in the time slice, represents the absolute value of the difference in the mean ground distances from the time slice to the time slice.

[0016] Compared with the prior art, the advantages and positive effects of the present invention are as follows: In the present invention, by screening non-interaction targets based on the dynamic changes of the three-dimensional curvature and contact area of the object contour, the recognition specificity is improved. Combining the wheel speed pulse interval and the acceleration change rate for two-way verification of dynamic anomalies enhances the reliability of static interaction determination. Comparing the flash trigger logic with the current lamp state coding ensures that the light feedback is consistent with the interaction intention. Based on the ground distance change freezing judgment, the lamp state switching caused by micro-perturbations is suppressed, and the stability of the projection lamp is improved. Overall, through state verification, feature screening, dynamic verification, and freezing management, the control accuracy and interaction consistency are improved multi-dimensionally. Figure 1 For the steps and flow chart of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0018] Figure 1 It is the step flow schematic diagram of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0019] The following will describe the technical solutions in the present invention with reference to the drawings.

[0020] In the embodiments of the present invention, words such as "exemplarily" and "for example" are used to give examples, illustrations or explanations. Any embodiment or design solution described as an "example" in the present invention should not be construed as being more preferred or having more advantages than other embodiments or design solutions. Rather, the use of the word "example" is intended to present concepts in a specific manner. In addition, in the embodiments of the present invention, the meaning expressed by "and / or" can be both, or either one of the two.

[0021] In the embodiments of the present invention, "image" and "picture" can sometimes be used interchangeably. It should be noted that when the difference is not emphasized, the meaning they express is the same. "(of)", "corresponding", and "corresponding" can sometimes be used interchangeably. It should be noted that when the difference is not emphasized, the meaning they express is the same.

[0022] In the embodiments of the present invention, sometimes subscripts such as W1 may be written in a non-subscript form such as W1. When the difference is not emphasized, the meaning they express is the same.

[0023] To make the technical problems, technical solutions and advantages to be solved by the present invention clearer, the following will be described in detail with reference to the drawings and specific embodiments.

[0024] Please refer to Figure 1 , a projection lamp adaptive control method for multi-condition filtering and vehicle status linkage, comprising the following steps: S1: Call the vehicle ACC activation status signal and the tailgate physical locking status signal, detect the output signal of the tailgate lock sensor in the ACC deactivated state, and compare whether there is a conflict between the ACC power supply circuit and the tailgate locking mechanism state. When the tailgate is unlocked and the ACC is deactivated, generate a headlight control permission identifier; S2: Collect the three-dimensional coordinate set of the object boundary contour in the projection area, extract the curvature distribution law between the contour nodes, calculate the change difference of the contact area within adjacent time slices, and compare the difference between the curvature distribution law and the foot standard curvature template. When the curvature distribution deviates from the foot feature and the contact area difference exceeds the set difference threshold, generate an object feature anomaly identifier; S3: Based on the headlight control permission identifier and the object feature anomaly identifier, call the pulse signal output by the wheel speed sensor and the three-axis acceleration value output by the acceleration sensor, calculate the wheel speed pulse interval time and the acceleration amplitude change rate, and determine whether the wheel speed pulse interval time is in the stationary interval and the acceleration amplitude change rate is lower than the dynamic threshold, and generate a dynamic behavior association verification identifier; S4: According to the dynamic behavior association verification identifier, read the current off, slow flash, and fast flash status codes of the projection lamp. When the verification identifier is for interaction, compare whether the current status code is consistent with the slow flash status code, trigger the fast flash action instruction and the original status maintenance instruction, and generate the projection lamp status maintenance instruction; S5: Based on the projection lamp status maintenance instruction, monitor the ground distance sequence output by the laser ToF sensor, calculate the standard deviation of the distance sequence and the mean change rate. When the fast flash trigger instruction takes effect and the distance change rate is lower than the freeze determination threshold, execute the fast flash action and update the projection lamp status, and generate the projection lamp self-matching control scheme.

[0025] The vehicle headlight control permission identifier includes the ACC inactive state, the tailgate unlocked state, and the power supply loop conflict determination. The object feature anomaly identifier includes the deviation of the curvature distribution, the difference in the contact area change, and the difference in the foot feature template. The dynamic behavior association verification identifier includes the wheel speed pulse interval time, the acceleration amplitude change rate, and the stationary interval determination. The projection lamp status maintenance instruction includes the fast flash action instruction, the original status maintenance instruction, and the slow flash status comparison result. The projection lamp self-matching control scheme includes the standard deviation of the distance sequence, the mean change rate, and the freeze determination threshold.

[0026] The specific steps of S1 are as follows: S101: Obtain the vehicle ACC activation status signal and the tailgate lock status signal, extract the tailgate lock output value in the ACC inactive state based on the acquisition timestamp, and generate the tailgate lock status signal acquisition value; Obtain the vehicle ACC activation status signal and the tailgate lock status signal. First, read the current ACC ignition status of the vehicle through the CAN bus. The signal acquisition frequency of the ACC ignition status is set to 10 times per second. The tailgate lock status signal is read from the body control module, and the sampling frequency is also set to 10 times per second synchronously. In the ACC signal, 0 represents not activated, and 1 represents activated. In the tailgate signal, 0 represents not locked, and 1 represents locked. The data acquisition system records the system timestamp for each signal acquisition. The timestamp is recorded in milliseconds (ms). For example, at 1728391000 ms, ACC = 0 and tailgate = 1 are collected. At the same time, at 1728391010 ms, ACC = 0 and tailgate = 0 are collected again. For all the collected data, perform screening item by item. The screening logic is: determine whether the ACC signal value is 0. If so, retain the current sampling data; otherwise, eliminate it. Achieve preliminary screening by traversing the entire data set. After screening, further process the data with similar timestamps. If there are multiple records within the same second, take the record with the largest timestamp as the valid record. The reason is that the data with a larger timestamp is closer to the actual state change. For example, if there are records of ACC = 0 at 1728391000 ms and 1728391020 ms, give priority to the record at 1728391020 ms. The collected tailgate lock status signals are arranged in ascending order of time and unified into a new data table, including three columns: timestamp, ACC status, and tailgate lock status. Example data is like (1728391829 ms, ACC = 0, tailgate lock = 1), (1728391859 ms, ACC = 0, tailgate lock = 0). In this way, the true state output value set of the tailgate lock when ACC is not activated is completely collected to ensure the accuracy of subsequent data judgment.

[0027] S102: Based on the collected value of the tailgate lock status signal, call the ACC power supply circuit status signal, make a cross-judgment between the tailgate lock status and the ACC power supply circuit signal, set the state where the tailgate is not locked and ACC is not activated as the abnormal reference state, screen the signal combinations that meet the abnormal reference state conditions, and obtain the conflict quantity between the tailgate and the power supply circuit status; Based on the acquisition value of the tailgate lock state, the ACC power supply circuit status signal corresponding to the time stamp is called for each record. The power supply circuit status signal comes from the power management module. In the signal meaning, 0 represents no power supply and 1 represents power supply. When calling, the maximum time error tolerance is set to 50 ms. This tolerance value is obtained based on the analysis of the actual communication delay. In the vehicle stationary state, the sampling error usually does not exceed 30 ms. To prevent missed detection, the tolerance is expanded to 50 ms to improve the integrity of anomaly detection. For example, if the sampling time of the tailgate lock signal is 1728391850 ms, then the ACC power supply circuit signal is searched within the range of 1728391800 ms to 1728391900 ms. If the closest ACC power supply circuit data is found, the tailgate lock state and the power supply circuit state are cross-judged. The cross-judgment criterion is set as follows: If the tailgate lock state is 0 and the ACC power supply circuit state is 0, it is recorded as the abnormal reference state; otherwise, it is the normal state. The screening process is to match and judge item by item. For example, a set of data is (1728391859 ms, tailgate lock = 0, power supply circuit = 0), then it is recorded as the abnormal reference state. If a set of data is (1728391865 ms, tailgate lock = 1, power supply circuit = 0) or (tailgate lock = 0, power supply circuit = 1), it is the normal state. The screened abnormal combination records form the conflict volume between the tailgate and the power supply circuit state. The conflict volume data set is sorted in ascending order of the time stamp, and at the same time, the number of abnormal occurrences and the time are recorded for subsequent statistical analysis or control processing.

[0028] S103: According to the conflict volume between the tailgate and the power supply circuit state, call the headlight control determination rule to perform permission identification on the abnormal reference state signal combination and generate the headlight control permission identification; According to the conflict volume between the tailgate and the power supply circuit state, the headlight control determination rules are called in sequence for permission identification processing. The specific settings of the determination rules are as follows: First, read the current headlight control permission state. The permission value is defined in four levels: 0, 1, 2, and 3. 0 means completely prohibited, 1 means partial restriction, only the position lights can be turned on, 2 means normal permission, the low beam and high beam can be normally turned on, 3 means priority permission, and the warning lights can be manually turned on beyond the normal logic. When an abnormal reference state (the tailgate is not locked and there is no power supply for ACC) is detected, the headlight permission at the corresponding time node is directly set to 0, and the permission change log information is recorded at the same time. The permission change record item includes: the original permission value, the adjusted permission value, and the reason for adjustment (triggered by abnormal state). For example, if the original headlight permission value is 2, the permission value is directly changed to 0 after detecting the abnormality, and "timestamp 1728391859ms, permission reduced from 2 to 0, due to the tailgate not being locked and no power supply for ACC" is recorded. In terms of threshold setting, to prevent frequent permission changes caused by short-term misjudgment, the abnormal duration threshold is set to 2000ms, that is, if the abnormal state is detected continuously for 2 seconds, the permission change is executed. This threshold is set according to the normal switch action time of the vehicle. Usually, the tailgate locking action time of the vehicle is between about 1000ms and 1500ms. Considering communication delay and detection stability, it is more reasonable to set it to 2000ms. Threshold setting example: If abnormalities are detected at both 1728391859ms and 1728393860ms, it is determined that the abnormal duration is greater than 2000ms, and the permission is adjusted; otherwise, it is not adjusted. Finally, a permission adjustment result table is formed for subsequent system calls or abnormal statistical analysis.

[0029] The specific steps of S2 are as follows: S201: Collect the three-dimensional coordinates of the object boundary contour in the projection area, extract the curvature values between the contour nodes, establish the corresponding relationship between the node numbers and the curvature values, and generate the node curvature distribution values; Collect the three-dimensional coordinates of the object boundary contour within the projection area, and use a lidar or structured light sensor for real-time scanning. The scanning frequency is set to 20 Hz, that is, the three-dimensional point cloud data is updated 20 times per second. During the collection process, extract the outer contour points of the object. The contour points are recorded in the form of three-dimensional coordinates (x, y, z). The number of each contour node increases sequentially from 1 according to the collection order. For example, the number of the first sampling point is 1, the number of the second sampling point is 2, and so on. At the same time, calculate the curvature value between the contour nodes. The curvature value is estimated by taking the ratio of the chord length formed by three consecutive nodes to the arc length. The chord length is the straight-line distance directly connecting the two endpoints, and the arc length is the actual measured path length passing through the middle node. For example, if the coordinates of nodes 1, 2, and 3 are (10, 10, 0), (12, 11, 0), and (14, 13, 0) respectively, the chord length from node 1 to 3 is √[(14 - 10)²+(13 - 10)²]=(√25)=5 m, and the arc length of the middle path is the distance from node 1 to 2 √5 plus the distance from node 2 to 3 √8, with a total of about 5.7 m. Then the curvature value is 5 / 5.7 = 0.877. After calculating the curvature for all nodes, establish a relationship table in which the node number corresponds one-to-one with the curvature value. For example, node 1 corresponds to the curvature value 0.877, node 2 corresponds to the curvature value 0.894, and node 3 corresponds to the curvature value 0.881, generating a complete set of node curvature distribution values. This set is used for subsequent contact characteristic analysis.

[0030] S202: Based on the node curvature distribution values, extract the node contact area data within adjacent time slices, calculate the difference in the node contact area change, set reference values according to the node curvature deviation and the area change difference respectively, and perform double screening to select the node combinations that simultaneously meet the conditions of deviating from the reference and the contact difference threshold, obtaining the contact characteristic deviation; The calculation formula for the difference in the node contact area change is specifically as follows: ; Among them, represents the difference in the node contact area change, represents the measured value of the node contact area within the current time slice, represents the measured value of the node contact area within the adjacent time slice, represents the fixed constant of the time slice interval, represents the measured value of the node curvature distribution within the current time slice, represents the measured value of the node curvature distribution within the adjacent time slice; Parameter definition and data source: and : The measured value of the node contact area, with the unit of square millimeter (mm²), is obtained through real-time monitoring by an optical sensor. The experimental data range is ; mm², set mm² (current time slice), mm² (adjacent time slice); : The time slice interval is a fixed constant, in seconds (s), and the system default is s; and : The measured value of the node curvature distribution, in millimeters⁻¹ (mm⁻¹), is calculated by collecting surface geometric data through a three-dimensional laser scanner, and the data range is ; mm⁻¹, set mm⁻¹ (current time slice), mm⁻¹ (adjacent time slice); Calculation process: Calculate the contact area change term: ; Calculate the curvature geometric mean term: ; Synthesize the dynamic change factor: ; Take the absolute value and calculate the curvature mean: ; Final result: ; Basis for parameter setting and quantization description: and : The contact area is measured by a high-precision optical sensor, and the measurement error is ±0.5 mm²; : The time slice interval is controlled by the system synchronization clock, and the error is ±0.001 s; and : The curvature is calculated after fitting the surface equation with the three-dimensional scanned point cloud, and the formula is , and the calculation error is ±0.02 mm⁻¹; Result interpretation: This result shows that the dynamic difference parameter of the node contact area mm²·mm⁻¹, reflecting the combined effect of the contact area change and the curvature change within adjacent time slices. Compared with the preset threshold (e.g., 5.0 mm²·mm⁻¹), if , then this node combination is screened as a candidate object for the contact feature deviation amount and enters the subsequent double determination process.

[0031] S203: Extract the object feature attribute information according to the contact feature deviation amount. Based on the deviation node range distribution law, call the standard features of the object category to generate an object feature anomaly identifier. Extract the object feature attribute information from the overall data according to the contact feature deviation amount. First, count the number, distribution range, and concentrated area position of the deviation nodes. The distribution range is identified by the continuous section of the node numbers. For example, the continuously deviated nodes 5, 6, 7, and 8 indicate local concentrated distribution. If the distribution interval is less than 20 nodes, it is defined as a local feature. If it is greater than 50 nodes, it is defined as an overall feature. When counting the distribution, calculate the average curvature change amount and average area change amount of each deviation node at the same time. For example, the average curvature change amount of a certain object's deviation node is 0.08, and the average area change amount is 2.5 cm². Based on these statistical features, call the preset standard feature database of the object category. Different standard deviation characteristic intervals of different category objects are defined in this database. For example, for flexible objects such as sponges, the curvature change amount is usually greater than 0.07, and the area change amount is greater than 2.0 cm². For rigid objects such as metal blocks, the curvature change amount is usually less than 0.03, and the area change amount is less than 1.0 cm². By comparing the node statistical characteristics with the standard features, perform category matching judgment. If the object features are consistent with the standard flexible object characteristics, generate an anomaly identifier. The identifier content includes the number of deviation nodes, distribution interval, average curvature change amount, average area change amount, and the corresponding matching object category. For example, the identifier record: "Anomaly identifier, 52 deviation nodes, concentrated at nodes 5 - 56, average curvature change 0.08, average area change 2.5 cm², matching object category: flexible material". Finally, complete the generation of the object feature anomaly identifier.

[0032] The specific steps of S3 are as follows: S301: Based on the vehicle lamp control permission identifier and the object feature anomaly identifier, extract the wheel speed pulse signal and the three-axis acceleration value to generate a pulse time interval sequence. Based on the headlight control permission identifier and the abnormal object feature identifier, first read the latest generated headlight control permission identifier list and the abnormal object feature identifier list in the vehicle status monitoring module, and extract the corresponding timestamp information of the identifiers as the reference time for data synchronization. Subsequently, read the wheel speed pulse signal in the wheel speed sensor module. The pulse signal is represented by a certain number of pulses generated per revolution. Usually, a car wheel speed sensor generates 48 pulses per revolution. At the same time, read the three-axis acceleration values in the vehicle inertial measurement unit. The three axes respectively record the accelerations in the x, y, and z directions, with the unit of m / s² and a sampling frequency of 100 Hz, that is, 100 acceleration values are collected per second. The wheel speed pulse signal extracts the pulse time series by recording the timestamp of the pulse rising edge. For example, if the timestamps of three consecutive pulses are detected as 1728391000 ms, 1728391020 ms, and 1728391040 ms respectively, the pulse intervals are 20 ms and 20 ms respectively, generating a pulse time interval sequence. The three-axis acceleration values and the pulse time interval sequence are bound and recorded under the same time reference. The interpolation method is used to synchronize and align the acceleration data to the pulse interval time points, forming a pulse time interval sequence and a three-axis acceleration sequence set based on the pulse time points.

[0033] S302: Based on the pulse time interval sequence, extract the difference between adjacent pulse times, screen the data nodes within the stationary interval, extract the three-axis acceleration amplitudes corresponding to the nodes, calculate the acceleration amplitude change rate, and screen the data nodes with a change rate lower than the reference value according to the dynamic threshold to obtain the static contact change amount; The specific formula for calculating the acceleration amplitude change rate is: ; Among them, represents the acceleration amplitude change rate, represents the acceleration value of the axis of the current node, the corresponding reference axis, represents the unified pulse time difference between adjacent nodes, is the dynamic weight factor of acceleration and time difference, represents the sum of the absolute value terms of the square term of the three-axis acceleration component and the weighted time difference product; Parameter setting basis: Three-axis acceleration value: Obtained by real-time monitoring of the acceleration sensor. Taking a certain node as an example, the measured x-axis acceleration , y-axis , z-axis , and the value comes from the mean value filtered within the sensor sampling period.

[0034] Unified pulse time difference: Calculated through the pulse time interval sequence, the time difference between adjacent nodes , the numerical value is recorded by the high-precision timing module.

[0035] Dynamic weight factor: Based on historical data statistics and the analysis of the noise sensitivity of the product term of acceleration and time difference, it is set , and its value decreases as the sensor sampling frequency increases. When the sampling frequency is 50Hz, The empirical value range is .

[0036] Calculation process of the example: Calculate the square terms of the three-axis acceleration: ; ; .

[0037] Calculate the weighted time difference product term: ; ; .

[0038] Substitute into Calculate the weighted term: ; ; .

[0039] Sum the three-axis components: .

[0040] After taking the square root and dividing by : ; .

[0041] Significance of the numerical result: This result indicates that the acceleration amplitude change rate of the current node is , and after comparing its value with the dynamic threshold reference value, if it is lower than the reference value, it is determined as a candidate node for the static contact change amount.

[0042] S303: Extract the dynamic characteristic information of the node according to the static contact change amount, classify and identify it according to the node attribute determination rule, and generate a dynamic behavior association verification identifier; Extract the dynamic feature information of nodes according to the static contact change amount. First, record the time, acceleration amplitude, and change rate for each node in the static contact change amount set. Subsequently, perform classification and identification based on the node attribute determination rules. The node attribute determination rules are set as follows: If the acceleration amplitude of a node is between 9.7 m / s² and 9.9 m / s² and the change rate is lower than 0.0005, it is marked as a completely static node. If the acceleration amplitude is between 9.5 m / s² and 9.7 m / s² or between 9.9 m / s² and 10.1 m / s² and the change rate is between 0.0005 and 0.001, it is marked as a micro-motion node. If the acceleration amplitude exceeds the above range or the change rate exceeds 0.001, no static identification is performed. When setting the threshold range, the standard of the earth's gravitational acceleration 9.8 m / s² is referred to, and the allowable floating range of ±0.2 m / s² is determined in combination with the drift data of the inertial sensor. When classifying, the attributes of each node are judged item by item. For example, if the acceleration amplitude of a node is 9.75 m / s² and the change rate is 0.0003, it is determined as a completely static node. If the amplitude is 9.92 m / s² and the change rate is 0.0007, it is determined as a micro-motion node. After classification, generate a dynamic behavior association verification identifier according to the classification results of each group of nodes. The content of this identifier includes the node time range, the statistical quantity of classification results, and the abnormal mark information. The identifier example is: "Time period from 1728391000 ms to 1728395000 ms, number of completely static nodes: 20, number of micro-motion nodes: 5, no abnormal nodes". Finally, form the dynamic behavior association verification identifier.

[0043] The specific steps of S4 are as follows: S401: According to the dynamic behavior association verification identifier, read the state codes of the projection lamp being off, slow flashing, and fast flashing, and generate the projection lamp state code value; According to the dynamic behavior association verification identifier, first read the list of the most recently generated dynamic behavior association verification identifiers in the vehicle control system, extract the timestamps and classification results corresponding to each verification identifier, and then read the working status information of the projection lamp in the headlight control module. The specific content to be read is the extinguished state code, slow flash state code, and fast flash state code of the projection lamp. Specific values are set for the three state codes. For example, the extinguished state code is 0x00, the slow flash state code is 0x01, and the fast flash state code is 0x02. Extract the corresponding time period records of the state codes through the state manager, arrange them in ascending order of time, and at the same time associate the timestamps of the dynamic behavior verification identifiers to match the time accuracy with an error range of 50 ms. After the time matching is completed, read the projection lamp status at the corresponding moment and encode it into a projection lamp status code value in a unified format. The unified format is represented by eight-digit hexadecimal, and each projection lamp corresponds to an independent code value. For example, if the verification identifier is the interaction state and the projection lamp status is the slow flash state at the timestamp 1728391000 ms, then extract the slow flash state code 0x01 as the projection lamp status code value at this time node. Finally, generate a set of projection lamp status code values, and each record contains a timestamp and the corresponding code value for subsequent action instruction processing.

[0044] S402: Based on the projection lamp status code value, for the verification identifier with the interaction state, extract the current state code, compare it with the slow flash state code, set a benchmark according to the comparison consistency, screen the nodes that are consistent and inconsistent with the slow flash, and distinguish and generate a fast flash action instruction or a maintain original state instruction to obtain an action instruction determination value; Based on the projection lamp status coding value, process the records with the verification identifier in the interaction state. First, filter out all time nodes with the verification identifier in the interaction state, extract the current projection lamp status coding value corresponding to the time node, read the slow flash status coding value set to 0x01, and compare whether the current coding value is consistent with the slow flash status coding value. If they are consistent, record it as a slow flash consistent node; if not, record it as a slow flash inconsistent node. The comparison process is to compare item by item. For example, if the current node coding value is 0x01 and is equal to the slow flash status coding value 0x01, record it as consistent; otherwise, record it as inconsistent. Subsequently, filter according to the comparison result. The filtering criteria are as follows: for consistent nodes, directly set the action instruction to maintain the original state; for inconsistent nodes, set the action instruction to execute the fast flash action, and the corresponding coding value of the fast flash action is 0x02. The threshold setting standard is based on the actual visual response requirements. The slow flash frequency is set to 1Hz, the fast flash frequency is set to 3Hz, and the frequency difference greater than 2Hz is used as the judgment reference value. Based on this reference value, filter out the node set that needs to execute the fast flash instruction. For example, if the node timestamp is 1728392000ms, the current coding value is 0x00, and the slow flash coding value is 0x01, which is inconsistent, then generate a fast flash action instruction. If the node timestamp is 1728392050ms, the current coding value is 0x01, which is consistent with the slow flash coding value, then generate a maintain original state instruction. Finally, obtain the action instruction determination value set, and each record in the set includes the timestamp, the current state coding, the comparison result, and the generated action instruction.

[0045] S403: According to the action instruction determination value, extract the instruction type, classify and output the control instruction content, and generate a projection lamp status maintenance instruction; According to the action instruction determination value, extract the action instruction type information in each record. The action instruction types are divided into two categories: flash action instruction and maintain original state instruction. First, classify the set of action instruction determination values. The classification criterion is the action instruction code value. If the action instruction code is 0x02, it is classified as a flash action instruction. If the action instruction code is the maintain original state instruction code (taking 0x01 as the standard), it is classified as a maintain original state instruction. During the classification process, check the action instruction type one by one, and output the specific control instruction content according to the classification result. The format of the control instruction content is uniformly set as instruction type + target lamp number + execution action code. For example, if a certain record is timestamp 1728392100ms, the target lamp number is 01, and the action instruction type is a flash action, the control instruction output is "Instruction type: Flash, Number: 01, Action: 0x02". If the action instruction type is to maintain the original state, the control instruction output is "Instruction type: Maintain, Number: 01, Action: 0x01". On this basis, generate a projection lamp state maintenance instruction list for all classified control instructions. Each instruction in the list corresponds to a unique timestamp and lamp number, which is convenient for subsequent execution of control logic or recording historical action trajectories, and finally complete the generation of the projection lamp state maintenance instruction.

[0046] The specific steps of S5 are as follows: S501: Based on the projection lamp state maintenance instruction, monitor the ground distance sequence output by the laser ToF sensor, extract continuous sampling points, and generate a ground distance sampling sequence; Based on the projection lamp state maintenance instruction, first read the current valid projection lamp state maintenance instruction list in the vehicle control system, extract the instruction timestamp as the ground state monitoring reference time, and then call the laser ToF sensor to collect ground distance data at the set frequency. The sampling frequency of the ToF sensor is set to 100Hz, that is, 100 ground distance values are collected per second. Each sampling records the timestamp and the ground distance value, and the distance unit is millimeters (mm). During the sampling process, extract the continuous sampling point data. Define the continuous sampling point as the adjacent sampling data with a time interval not exceeding 10ms. If the timestamp of the current sampling point is 1000ms and the timestamp of the next point is within 1010ms, it is considered a continuous point. Otherwise, if the time interval is greater than 10ms, it is processed in segments, and the data set of each segment of continuous sampling is recorded to generate a ground distance sampling sequence. The ground distance sampling sequence is arranged in chronological order, and each element contains the timestamp and the corresponding ground distance value. For example, the sequence elements are recorded as (1000ms, 2500mm), (1010ms, 2502mm), (1020ms, 2503mm), and so on, forming a complete ground distance sampling sequence for subsequent processing.

[0047] S502: Based on the ground distance sampling sequence, identify the distance standard deviation of consecutive sampling points, extract the mean ground distance of adjacent time slices, calculate the mean change rate, call the freezing judgment threshold, filter out the time slices with a distance change rate lower than the freezing judgment threshold, establish the time slice index that meets the freezing conditions, and obtain the ground freezing change amount; The specific formula for the mean change rate is: ; where represents the mean change rate at the moment, represents the mean ground distance of the time slice, represents the mean ground distance of the time slice, represents the standard deviation of the ground distances of consecutive sampling points in the time slice, represents the standard deviation of the ground distances of consecutive sampling points in the time slice, represents the absolute value of the difference in the mean ground distances from the time slice to the time slice; In the formula, to calculate the mean change rate eigenvalue at the moment, the following steps are taken: Data collection and mean calculation: and are the means of the ground distances in the and time slices respectively. The specific values are obtained by taking the arithmetic mean of the distances of all sampling points within the time slice.

[0048] Suppose the sampling data within a certain time slice is as follows: , . It is calculated that m, m.

[0049] Standard deviation calculation: and represent the standard deviation of the ground distances of consecutive sampling points. According to the formula for the sample standard deviation, the aforementioned and data are used for calculation.

[0050] The calculation formula is , where is the number of samples.

[0051] The calculation of m, calculated in the same way to get m.

[0052] Absolute value of the mean difference: , calculate the time slice and the absolute value of the difference between the mean ground distances of the time slices.

[0053] Calculated to get m.

[0054] Substitute into the formula for calculation: Substitute the above calculation results into the main formula: ; This result shows that the eigenvalue of the mean ground distance change rate at the moment is 0.087, which is a quantitative evaluation of the change amplitude of the ground distance in the time slice. A low value reflects that the ground state changes slightly within this time slice, and is then used in the subsequent freezing determination threshold screening step to determine whether the ground is in a frozen state.

[0055] S503: According to the ground freezing change amount, combined with the fast flash trigger instruction, screen the nodes that meet the fast flash trigger condition and the freezing condition, output the corresponding control action content, and generate the projection lamp self-matching control scheme quantity; According to the ground freezing change amount, combined with the previously generated fast flash trigger instruction list, compare the timestamp correspondence item by item, screen the nodes that meet both the fast flash trigger condition and the freezing condition. The screening logic is that if the timestamp of a certain node is within the fast flash trigger instruction time range and the corresponding time slice index exists in the freezing change amount set, then it is determined that the node meets the dual conditions, record the screening result. During the screening process, if a node only meets one of the conditions, it is excluded. After the screening is completed, output the corresponding control action content for the qualified nodes. The action content includes the lamp number, action type, and execution time. The action type is defined as fast flash execution or fast flash delayed start. The delay start sets a delay value of 200ms. The delay value is determined according to the vehicle ambient light change response time. Usually, the human eye perceives a flicker delay between 100ms and 250ms. Therefore, the intermediate value of 200ms is taken as the start delay. For example, if the node timestamp is 1728393100ms and the lamp number is 02 and it meets the conditions, then output the control action content "Number 02, fast flash execution, delay 200ms". Finally, generate the projection lamp self-matching control scheme quantity according to the action content. Each record in the scheme quantity set details the lamp number, action instruction, delay setting, and execution timestamp for direct invocation by the subsequent vehicle projection system.

[0056] As described above, it is only the specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention can easily think of changes or substitutions, which should all be covered within the protection scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the said claims.

Claims

1. A projection lamp adaptive control method based on multi-condition filtering and vehicle status linkage, characterized in that: The following steps are involved: S1: Call the ACC activation status signal and the tailgate physical lock status signal to detect the tailgate lock sensor output when ACC is not activated. When the tailgate is not locked and ACC is not activated, generate a headlight control authority mark; S2: Collect the three-dimensional coordinates of the boundary contour of the object in the projection area, extract the curvature distribution of the contour nodes, calculate the change in contact area, and generate an abnormal object feature mark when the curvature distribution deviates from the foot feature and the contact area difference exceeds the threshold; S3: Based on the vehicle light control authority identifier and the object feature abnormality identifier, the wheel speed pulse signal and the three-axis acceleration value of the acceleration sensor are called to calculate the wheel speed pulse interval and the acceleration amplitude change rate, and generate a dynamic behavior association verification identifier; S4: reading the projector light off, slow flashing, and fast flashing state codes according to the dynamic behavior association verification identifier, comparing the slow flashing codes during interaction, and generating a projector light state maintenance instruction; S5: Based on the projection lamp state maintenance instruction, monitor the ground distance sequence output by the laser ToF, calculate the standard deviation and the mean change rate, and when the flash is triggered and the change rate is lower than the freezing judgment threshold, execute the flash action to generate a projection lamp self-matching control solution.

2. The projection lamp adaptive control method of multi-condition filtering and vehicle status linkage according to claim 1 is characterized in that: The vehicle light control authority identifier includes the ACC inactivated state, the tailgate unlocked state, and the power supply circuit conflict judgment; the object feature abnormality identifier includes the curvature distribution deviation, the contact area change difference, and the foot feature template difference; the dynamic behavior association verification identifier includes the wheel speed pulse interval time, the acceleration amplitude change rate, and the static interval judgment; the projection lamp state maintenance instruction includes the fast flash action instruction, the original state maintenance instruction, and the slow flash state comparison result; the projection lamp self-matching control scheme includes the distance sequence standard deviation, the mean change rate, and the freezing judgment threshold.

3. The projection lamp adaptive control method of multi-condition filtering and vehicle status linkage according to claim 1 is characterized in that: The specific steps of S1 are: S101: Acquire the vehicle ACC activation state signal and the tailgate lock state signal, extract the tailgate lock output value in the ACC inactive state based on the acquisition timestamp, and generate the tailgate lock state signal acquisition value; S102: based on the tailgate lock state signal acquisition value, calling the ACC power supply circuit state signal, cross-judging the tailgate lock state and the ACC power supply circuit signal, setting the tailgate unlocked and the ACC not activated as the abnormal reference state, screening the signal combination that meets the abnormal reference state condition, and obtaining the tailgate and power supply circuit state conflict amount; S103: According to the conflict amount between the tailgate and the power supply circuit state, a vehicle light control determination rule is called, an authority mark is performed on the abnormal reference state signal combination, and a vehicle light control authority mark is generated.

4. The projection lamp adaptive control method of multi-condition filtering and vehicle status linkage according to claim 1 is characterized in that: The specific steps of S2 are: S201: collecting the three-dimensional coordinates of the boundary contour of the object in the projection area, extracting the curvature values ​​between the contour nodes, establishing the corresponding relationship between the node number and the curvature value, and generating the node curvature distribution value; S202: based on the node curvature distribution value, extracting the contact area data of the nodes in adjacent time slices, calculating the contact area change difference between the nodes, setting the reference value according to the node curvature deviation and the area change difference, performing double screening, screening out the node combination that satisfies both the deviation reference and the contact difference threshold conditions, and obtaining the contact feature deviation; S203: Extracting object feature attribute information according to the contact feature deviation, calling object category standard features based on the deviation node range distribution law, and generating object feature abnormality identification.

5. The projection lamp adaptive control method of multi-condition filtering and vehicle status linkage according to claim 1 is characterized in that: The calculation formula for the difference in contact area change between nodes is specifically: ； in, Represents the difference in contact area between nodes, Represents the measured value of the node contact area in the current time slice, represents the contact area measurement of nodes in adjacent time slices, A fixed constant representing the time slice interval, Represents the measured value of the node curvature distribution in the current time slice, Represents the measured value of the node curvature distribution in adjacent time slices.

6. The projection lamp adaptive control method of multi-condition filtering and vehicle status linkage according to claim 1 is characterized in that: The specific steps of S3 are: S301: extracting wheel speed pulse signals and three-axis acceleration values ​​based on the vehicle light control authority identifier and the object feature abnormality identifier, and generating a pulse time interval sequence; S302: based on the pulse time interval sequence, extract the time difference between adjacent pulses, select the data nodes in the static interval, extract the three-axis acceleration amplitudes of the corresponding nodes, calculate the acceleration amplitude change rate, select the data nodes whose change rate is lower than the reference value according to the dynamic threshold, and obtain the static contact change amount; S303: Extracting node dynamic feature information according to the static contact variation, classifying identifiers according to node attribute determination rules, and generating dynamic behavior association verification identifiers.

7. The projection lamp adaptive control method of multi-condition filtering and vehicle status linkage according to claim 1 is characterized in that: The acceleration amplitude change rate calculation formula is specifically: ； in, represents the rate of change of acceleration amplitude, Represents the current node Axis acceleration value, The corresponding reference axis, represents the uniform pulse time difference between adjacent nodes, is the dynamic weight factor of acceleration and time difference, It represents the sum of the square terms of the three-axis acceleration components and the absolute value of the product of the weighted time difference.

8. The projection lamp adaptive control method of multi-condition filtering and vehicle status linkage according to claim 1 is characterized in that: The specific steps of S4 are: S401: Read the projection lamp off, slow flashing, and fast flashing state codes according to the dynamic behavior association verification identifier, and generate a projection lamp state code value; S402: Based on the state code value of the projection lamp, for the verification mark of the interactive state, extract the current state code, compare it with the slow flash state code, set a benchmark according to the comparison consistency, filter out nodes that meet the slow flash consistency and inconsistent nodes, distinguish between generating a fast flash action instruction or maintaining the original state instruction, and obtain an action instruction determination value; S403: extracting the instruction type according to the action instruction determination value, classifying and outputting the control instruction content, and generating a projection lamp state maintaining instruction.

9. The projection lamp adaptive control method of multi-condition filtering and vehicle status linkage according to claim 1, characterized in that: The specific steps of S5 are: S501: Based on the projection lamp state maintenance instruction, monitor the ground distance sequence output by the laser ToF sensor, extract continuous sampling points, and generate a ground distance sampling sequence; S502: Based on the ground distance sampling sequence, identify the distance standard deviation of continuous sampling points, extract the ground distance mean of adjacent time slices, calculate the mean change rate, call the freezing judgment threshold, filter out the time slices whose distance change rate is lower than the freezing judgment threshold, establish the time slice index that meets the freezing condition, and obtain the ground freezing change amount; S503: According to the ground freezing change amount, combined with the flash trigger instruction, the nodes that meet the flash trigger condition and the freezing condition are screened, the corresponding control action content is output, and the projection lamp self-matching control solution amount is generated.

10. The projection lamp adaptive control method of multi-condition filtering and vehicle status linkage according to claim 1, characterized in that: The mean change rate calculation formula is specifically: ； in, Representative The time-mean rate of change, Representative The mean ground distance of the time slice, Representative The mean ground distance of the time slice, Representative Standard deviation of ground distance of consecutive sampling points in a time slice, Representative Standard deviation of ground distance of consecutive sampling points in a time slice, Representative Time slice to The absolute value of the mean difference in ground distance between time slices.

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