Vehicle lamp control method and device, electronic equipment, storage medium and vehicle

Abstract

The invention relates to the technical field of vehicles, and discloses a vehicle lamp control method and device, electronic equipment, a storage medium and a vehicle. The method comprises the steps that in response to turn-on of a high beam, driving data of an opposite vehicle driving in the opposite direction of a target vehicle at the current moment is obtained; acquiring historical driving data of the opposite vehicle at the previous moment; according to the historical driving data of the opposite vehicle at the previous moment, amplitude limiting and filtering are carried out on the driving data of the opposite vehicle at the current moment, so that the driving data are cleaned, and the driving data after amplitude limiting and filtering are obtained; performing Kalman filtering on the position of the opposite vehicle relative to the image acquisition point of the target vehicle according to the historical driving data and the driving data after amplitude limiting filtering to obtain an optimal estimation value of the position of the opposite vehicle relative to the image acquisition point of the target vehicle at the current moment; and adjusting the vehicle lamp of the target vehicle according to the optimal estimation value. Therefore, the position information of the target vehicle can be accurately obtained, and the vehicle lamp of the vehicle can be accurately adjusted according to the position information.

Description

Technical Field

[0001] The present invention relates to the field of vehicle technology, and in particular to a method for controlling a vehicle light, a device for controlling a vehicle light, an electronic device, a non-transitory computer-readable storage medium, and a vehicle. Background Art

[0002] At present, since the camera's collection capability is subject to certain limitations when the vehicle is driving at night, there are certain errors in the left boundary angle, right boundary angle, and distance between the vehicle and the target vehicle collected by the camera, making the left boundary angle and right boundary angle of the left and right headlight modules calculated based on this also likely to be inaccurate.

[0003] In addition, the left boundary angle and the right boundary angle at the moment of light driving are predicted and compensated according to the motion equation of the vehicle. However, the complexity of the relative motion of the two vehicles is not fully considered, so that the final left boundary angle and the right boundary angle relative to the left and right headlight modules cannot accurately represent the angle required at the moment of light driving, increasing the probability of inappropriate target vehicle area being closed, resulting in dazzling the driver of the other vehicle. Summary of the invention

[0004] The present invention aims to solve at least one of the technical problems in the related art to a certain extent. To this end, the first object of the present invention is to provide a method for controlling a headlight, which can accurately obtain the position information of the oncoming vehicle at the time of driving the headlight, and accurately turn off unnecessary headlights or adjust the illumination intensity of necessary headlights according to the position information of the oncoming vehicle, thereby increasing the field of view of the target vehicle, improving the anti-glare effect of the target vehicle on the oncoming vehicle, and improving the safety and reliability of driving.

[0005] A second objective of the present invention is to provide a control device for a vehicle light.

[0006] A third objective of the present invention is to provide an electronic device.

[0007] A fourth object of the present invention is to provide a non-transitory computer-readable storage medium.

[0008] A fifth object of the present invention is to provide a vehicle.

[0009] To achieve the above-mentioned purpose, a first embodiment of the present invention proposes a method for controlling a vehicle light, comprising: in response to turning on the high beam, obtaining driving data of an oncoming vehicle that is currently traveling in the opposite direction of a target vehicle; wherein the driving data includes the left boundary angle and left boundary angular velocity of the left boundary of the oncoming vehicle relative to the image acquisition point of the target vehicle, the right boundary angle and right boundary angular velocity of the right boundary of the oncoming vehicle relative to the image acquisition point of the target vehicle, and the relative distance between the oncoming vehicle and the target vehicle; obtaining the historical driving data of the oncoming vehicle at the previous moment; wherein the historical driving data includes the left boundary angle and right boundary angular velocity of the left boundary of the oncoming vehicle relative to the image acquisition point of the target vehicle at the previous moment. The left boundary angle and the left boundary angular velocity, the right boundary angle and the right boundary angular velocity of the right boundary of the oncoming vehicle relative to the image acquisition point of the target vehicle, and the relative distance between the oncoming vehicle and the target vehicle; performing limiting filtering on the driving data of the oncoming vehicle at the current moment according to the historical driving data of the oncoming vehicle at the previous moment to clean the driving data and obtain the driving data after limiting filtering; performing Kalman filtering on the position of the image acquisition point of the oncoming vehicle relative to the target vehicle according to the historical driving data and the driving data after limiting filtering to obtain the optimal estimated value of the position of the oncoming vehicle relative to the image acquisition point of the target vehicle at the current moment; and adjusting the lights of the target vehicle according to the optimal estimated value.

[0010] In addition, the vehicle lamp control method according to the above embodiment of the present invention may also have the following additional technical features:

[0011] According to some embodiments of the present invention, the driving data of the oncoming vehicle at the current moment is limited and filtered based on the historical driving data of the oncoming vehicle at the previous moment to clean the driving data and obtain the driving data after limiting and filtering, including: compressing the historical driving data according to a preset numerical compression ratio to obtain a preset filtering threshold; screening the driving data according to the historical driving data and the preset filtering threshold to obtain the driving data after limiting and filtering.

[0012] According to some embodiments of the present invention, Kalman filtering is performed on the position of the image acquisition point of the oncoming vehicle relative to the target vehicle based on historical driving data and driving data after limiting filtering to obtain the optimal estimate of the position of the image acquisition point of the oncoming vehicle relative to the target vehicle at the current moment, including: constructing an optimal estimate matrix for the position of the image acquisition point of the oncoming vehicle relative to the target vehicle; determining the Kalman filter equation for the position of the image acquisition point of the oncoming vehicle relative to the target vehicle based on the optimal estimate matrix; performing Kalman filtering on the position of the image acquisition point of the oncoming vehicle relative to the target vehicle at the previous moment based on the Kalman filter equation and historical driving data to obtain the historical optimal estimate position of the image acquisition point of the oncoming vehicle relative to the target vehicle at the previous moment; performing Kalman filtering on the position of the image acquisition point of the oncoming vehicle relative to the target vehicle at the current moment based on the Kalman filter equation, the historical optimal estimate position and driving data after limiting filtering to obtain the optimal estimate of the position of the image acquisition point of the oncoming vehicle relative to the target vehicle at the current moment.

[0013] According to some embodiments of the present invention, the optimal estimation matrix is ​​expressed as:

[0014]

[0015] Among them, P x is the lateral distance of the left or right boundary of the oncoming vehicle relative to the image acquisition point of the target vehicle, P y V is the relative distance between the left or right boundary of the oncoming vehicle and the image acquisition point of the target vehicle. x is the rate of change of the lateral distance, V y is the rate of change of relative distance.

[0016] According to some embodiments of the present invention, the optimal estimate value includes a first optimal estimate value of the left side boundary of the oncoming vehicle relative to the image acquisition point of the target vehicle and a second optimal estimate value of the right side boundary of the oncoming vehicle relative to the image acquisition point of the target vehicle at the current moment; adjusting the headlights of the target vehicle according to the optimal estimate value includes: converting the first optimal estimate value of the left side boundary of the oncoming vehicle relative to the image acquisition point of the target vehicle into a first converted optimal estimate value of the left side boundary of the oncoming vehicle relative to the headlights of the target vehicle according to the relative position of the headlights of the target vehicle and the image acquisition point; converting the second optimal estimate value of the right side boundary of the oncoming vehicle relative to the image acquisition point of the target vehicle into a second converted optimal estimate value of the right side boundary of the oncoming vehicle relative to the headlights of the target vehicle according to the relative position of the headlights of the target vehicle and the image acquisition point; determining the illumination range of the oncoming vehicle illuminated by the headlights according to the first converted optimal estimate value and the second converted optimal estimate value; and adjusting the headlights of the target vehicle according to the illumination range.

[0017] According to some embodiments of the present invention, the headlights of a target vehicle include several light sources; adjusting the headlights of the target vehicle according to an illumination range includes: determining a target light source from among the several light sources that illuminates an oncoming vehicle according to the illumination range and an illumination angle of each of the several light sources; determining an illumination intensity of the target light source according to a relative distance between the oncoming vehicle and the target vehicle at a current moment; and controlling the target light source according to the illumination intensity.

[0018] The method for controlling a vehicle light according to an embodiment of the present invention comprises: in response to turning on a high beam, obtaining driving data of an oncoming vehicle that is traveling in the opposite direction of a target vehicle at the current moment; wherein the driving data comprises a left boundary angle and a left boundary angular velocity of a left boundary of the oncoming vehicle relative to an image acquisition point of the target vehicle at the current moment, a right boundary angle and a right boundary angular velocity of a right boundary of the oncoming vehicle relative to an image acquisition point of the target vehicle at the current moment, and a relative distance between the oncoming vehicle and the target vehicle; obtaining historical driving data of the oncoming vehicle at the previous moment; wherein the historical driving data comprises a left boundary angle and a left boundary angular velocity of a left boundary of the oncoming vehicle relative to an image acquisition point of the target vehicle at the previous moment. and the left boundary angular velocity, the right boundary angle and right boundary angular velocity of the right boundary of the oncoming vehicle relative to the image acquisition point of the target vehicle, and the relative distance between the oncoming vehicle and the target vehicle; according to the historical driving data of the oncoming vehicle at the previous moment, the driving data of the oncoming vehicle at the current moment is limited and filtered to clean the driving data and obtain the driving data after the limited filtering; according to the historical driving data and the driving data after the limited filtering, the position of the image acquisition point of the oncoming vehicle relative to the target vehicle is Kalman filtered to obtain the optimal estimated value of the position of the image acquisition point of the oncoming vehicle relative to the target vehicle at the current moment; according to the optimal estimated value, the headlights of the target vehicle are adjusted. Thus, the method can accurately obtain the position information of the oncoming vehicle at the moment of light driving, and accurately turn off unnecessary lights or adjust the illumination intensity of necessary lights according to the position information of the oncoming vehicle, increase the field of view of the target vehicle, improve the anti-glare effect of the target vehicle on the oncoming vehicle, and improve the safety and reliability of driving.

[0019] The second purpose of the present invention is to propose a control device for vehicle lights, which can accurately obtain the position information of oncoming vehicles at the time of light driving, and accurately turn off unnecessary vehicle lights or adjust the illumination intensity of required vehicle lights according to the position information of oncoming vehicles, thereby increasing the field of view of target vehicles, improving the anti-glare effect of target vehicles on oncoming vehicles, and improving driving safety and reliability.

[0020] To achieve the above-mentioned purpose, a second embodiment of the present invention proposes a control device for a vehicle light, comprising: a first acquisition module, configured to acquire driving data of an oncoming vehicle that is currently driving in the opposite direction of a target vehicle in response to the high beam being turned on; wherein the driving data includes the left boundary angle and left boundary angular velocity of the left side boundary of the oncoming vehicle relative to the image acquisition point of the target vehicle at the current moment, the right boundary angle and right boundary angular velocity of the right side boundary of the oncoming vehicle relative to the image acquisition point of the target vehicle, and the relative distance between the oncoming vehicle and the target vehicle; a second acquisition module, configured to acquire the historical driving data of the oncoming vehicle at the previous moment; wherein the historical driving data includes the left boundary angle of the left side boundary of the oncoming vehicle relative to the image acquisition point of the target vehicle at the previous moment. the right boundary angle and left boundary angular velocity, the right boundary angle and right boundary angular velocity of the right boundary of the oncoming vehicle relative to the image acquisition point of the target vehicle, and the relative distance between the oncoming vehicle and the target vehicle; a data processing module, configured to perform limiting filtering on the driving data of the oncoming vehicle at the current moment according to the historical driving data of the oncoming vehicle at the previous moment, so as to clean the driving data and obtain the driving data after limiting filtering; a Kalman filtering module, configured to perform Kalman filtering on the position of the image acquisition point of the oncoming vehicle relative to the target vehicle according to the historical driving data and the driving data after limiting filtering, and obtain the optimal estimated value of the position of the image acquisition point of the oncoming vehicle relative to the target vehicle at the current moment; an adjustment module, configured to adjust the headlights of the target vehicle according to the optimal estimated value.

[0021] The control device of the vehicle light according to the embodiment of the present invention comprises: in response to turning on the high beam, a first acquisition module acquires the driving data of the oncoming vehicle that is driving in the opposite direction of the target vehicle at the current moment; wherein the driving data includes the left boundary angle and the left boundary angular velocity of the left boundary of the oncoming vehicle relative to the image acquisition point of the target vehicle, the right boundary angle and the right boundary angular velocity of the right boundary of the oncoming vehicle relative to the image acquisition point of the target vehicle, and the relative distance between the oncoming vehicle and the target vehicle; and a second acquisition module acquires the historical driving data of the oncoming vehicle at the previous moment; wherein the historical driving data includes the left boundary angle and the left boundary angular velocity of the left boundary of the oncoming vehicle relative to the image acquisition point of the target vehicle at the previous moment. The angular velocity of the side boundary, the right boundary angle of the right boundary of the oncoming vehicle relative to the image acquisition point of the target vehicle and the angular velocity of the right boundary, and the relative distance between the oncoming vehicle and the target vehicle; the data processing module performs a limiting filter on the driving data of the oncoming vehicle at the current moment according to the historical driving data of the oncoming vehicle at the previous moment, so as to clean the driving data and obtain the driving data after the limiting filter; the Kalman filter module performs a Kalman filter on the position of the image acquisition point of the oncoming vehicle relative to the target vehicle according to the historical driving data and the driving data after the limiting filter, and obtains the optimal estimated value of the position of the image acquisition point of the oncoming vehicle relative to the target vehicle at the current moment; the adjustment module adjusts the lights of the target vehicle according to the optimal estimated value. Thus, the device can accurately obtain the position information of the oncoming vehicle at the moment of light driving, and accurately turn off unnecessary lights or adjust the illumination intensity of necessary lights according to the position information of the oncoming vehicle, increase the field of view of the target vehicle, improve the anti-glare effect of the target vehicle on the oncoming vehicle, and improve the safety and reliability of driving.

[0022] To achieve the above objectives, a third aspect of the present invention provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the above method for controlling vehicle lights when executing the program.

[0023] The electronic device according to the embodiment of the present invention can accurately obtain the position information of the oncoming vehicle at the time of light driving by executing the above-mentioned method for controlling the vehicle lights, and accurately turn off unnecessary vehicle lights or adjust the illumination intensity of necessary vehicle lights according to the position information of the oncoming vehicle, thereby increasing the field of view of the target vehicle, improving the anti-glare effect of the target vehicle on the oncoming vehicle, and improving the safety and reliability of driving.

[0024] To achieve the above-mentioned purpose, a fourth aspect of the present invention proposes a non-transitory computer-readable storage medium, which stores computer instructions for enabling a computer to implement the above-mentioned vehicle light control method.

[0025] According to the non-transitory computer-readable storage medium of an embodiment of the present invention, by executing the above-mentioned method for controlling the vehicle lights, it is possible to accurately obtain the position information of the oncoming vehicle at the time of light driving, and accurately turn off unnecessary vehicle lights or adjust the illumination intensity of necessary vehicle lights according to the position information of the oncoming vehicle, thereby increasing the field of view of the target vehicle, improving the anti-glare effect of the target vehicle on the oncoming vehicle, and improving the safety and reliability of driving.

[0026] In order to achieve the above-mentioned object, a fifth aspect of the present invention provides a vehicle, including the above-mentioned control device for vehicle lights.

[0027] According to the vehicle of the embodiment of the present invention, the above-mentioned headlight control device can accurately obtain the position information of the oncoming vehicle at the time of light driving, and accurately turn off unnecessary headlights or adjust the illumination intensity of necessary headlights according to the position information of the oncoming vehicle, thereby increasing the field of view of the target vehicle, improving the anti-glare effect of the target vehicle on the oncoming vehicle, and improving the safety and reliability of driving.

[0028] Additional aspects and advantages of the present invention will be given in part in the following description and in part will be obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 is a flow chart of a method for controlling a vehicle light according to some embodiments of the present invention;

[0030] Figure 2 is a schematic diagram of a coordinate transformation principle according to some embodiments of the present invention;

[0031] Figure 3 A schematic diagram of the relationship between the left and right boundary angles of an oncoming vehicle and the illumination angles of a plurality of light sources in a single row according to some embodiments of the present invention;

[0032] Figure 4 is a block diagram of a control device for a vehicle light according to some embodiments of the present invention;

[0033] Figure 5 is a block diagram of an electronic device according to some embodiments of the present invention;

[0034] Figure 6 is a block diagram of a vehicle according to some embodiments of the present invention. DETAILED DESCRIPTION

[0035] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with specific embodiments and with reference to the accompanying drawings.

[0036] It should be noted that, unless otherwise defined, the technical terms or scientific terms used in the embodiments of the present invention should be understood by people with ordinary skills in the field to which the present invention belongs. The "first", "second" and similar words used in the embodiments of the present invention do not indicate any order, quantity or importance, but are only used to distinguish different components. "Include" or "comprise" and similar words mean that the elements or objects appearing before the word include the elements or objects listed after the word and their equivalents, without excluding other elements or objects. "Connect" or "connected" and similar words are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to indicate relative positional relationships. When the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0037] As mentioned in the background technology section, in the related technology, the control system generally includes a camera acquisition module, other signal acquisition modules, a CAN bus, a control unit and a vehicle low beam drive module.

[0038] According to the left boundary angle, right boundary angle, distance between the vehicle and the target vehicle collected by the camera acquisition module, and the speed, yaw rate, longitudinal acceleration, and lateral acceleration collected by other signal acquisition modules, the predicted lateral distance compensation and relative distance compensation are calculated, thereby calculating the left boundary angle, right boundary angle, lateral distance between the vehicle and the target vehicle, and longitudinal distance between the vehicle and the target vehicle of the target vehicle relative to the vehicle. Secondly, according to the relationship between the left and right headlight module coordinates and the previous coordinate origin, the left boundary angle and right boundary angle of the target vehicle relative to the vehicle are moved to the left boundary angle and right boundary angle relative to the left and right headlight modules. Finally, according to the relationship between the left boundary angle, right boundary angle relative to the left and right headlight modules and the illumination angle of each headlight in the left and right headlight modules, it is determined whether the headlights need to be turned off or the illumination intensity of the headlights needs to be reduced.

[0039] In the process of implementing the present invention, the applicant discovered that since the camera's collection capability is subject to certain limitations when the vehicle is driving at night, there are certain errors in the left boundary angle, right boundary angle, and distance between the vehicle and the target vehicle collected by the camera, making the left boundary angle and right boundary angle of the left and right headlight modules calculated based on this also likely to be inaccurate.

[0040] In addition, the left boundary angle and the right boundary angle at the moment of light driving are predicted and compensated according to the motion equation of the vehicle. However, the complexity of the relative motion of the two vehicles is not fully considered, so that the final left boundary angle and the right boundary angle relative to the left and right headlight modules cannot accurately represent the angle required at the moment of light driving, increasing the probability of inappropriate target vehicle area being closed, resulting in dazzling the driver of the other vehicle.

[0041] The following describes a method for controlling a vehicle light, a device for controlling a vehicle light, an electronic device, a storage medium, and a vehicle according to embodiments of the present invention with reference to the accompanying drawings.

[0042] refer to Figure 1 , which is a flow chart of a method for controlling a vehicle light according to some embodiments of the present invention.

[0043] like Figure 1 As shown, the vehicle light control method according to the embodiment of the present invention may include the following steps:

[0044] S101, in response to the high beam being turned on, obtaining driving data of an oncoming vehicle that is currently traveling in the opposite direction of the target vehicle; wherein the driving data includes a left boundary angle and a left boundary angular velocity of a left boundary of the oncoming vehicle relative to an image acquisition point of the target vehicle, a right boundary angle and a right boundary angular velocity of a right boundary of the oncoming vehicle relative to an image acquisition point of the target vehicle, and a relative distance between the oncoming vehicle and the target vehicle.

[0045] Specifically, it is possible to determine whether the high beam of the target vehicle is on based on the ambient illumination intensity and the vehicle speed. For example, the ambient illumination intensity detected by the rain light sensor is obtained, and it is determined whether the high beam of the target vehicle is on based on the vehicle speed. When the high beam of the target vehicle is on, the camera set on the vehicle can be used to collect the left boundary angle α of the left boundary of the oncoming vehicle traveling in the opposite direction of the target vehicle at the current moment relative to the image collection point (camera collection point) of the target vehicle. L and the left boundary angular velocity ω L , the right boundary angle of the oncoming vehicle relative to the right boundary angle of the target vehicle's image acquisition point α R and the right boundary angular velocity ω R , the relative distance P between the oncoming vehicle and the target vehicle in the longitudinal direction y .

[0046] In some embodiments, when the ambient illumination intensity is less than a preset illumination intensity threshold and the vehicle speed is higher than a preset vehicle speed threshold, the high beam can be automatically turned on, and the initial illumination intensity setting value of the high beam is positively correlated with the vehicle speed, that is, the higher the vehicle speed, the greater the illumination intensity. When the ambient illumination intensity is higher than the preset illumination intensity threshold, or the vehicle speed is lower than the preset vehicle speed threshold, the high beam of the headlights is automatically turned off, wherein the illumination intensity threshold and the vehicle speed threshold can be set according to actual conditions.

[0047] S102, obtaining the historical driving data of the oncoming vehicle at the previous moment; wherein the historical driving data includes the left boundary angle and left boundary angular velocity of the left boundary of the oncoming vehicle relative to the image acquisition point of the target vehicle at the previous moment, the right boundary angle and right boundary angular velocity of the right boundary of the oncoming vehicle relative to the image acquisition point of the target vehicle, and the relative distance between the oncoming vehicle and the target vehicle.

[0048] Specifically, the electronic control unit on the vehicle can obtain the left boundary angle α of the oncoming vehicle at the last moment captured by the camera relative to the left boundary angle of the target vehicle image acquisition point. L-1 and the left boundary angular velocity ω L-1 , the right boundary angle of the oncoming vehicle relative to the right boundary angle of the target vehicle's image acquisition point α R-1 and the right boundary angular velocity ω R-1 , the relative distance P between the oncoming vehicle and the target vehicle y-1 .

[0049] S103, performing limiting filtering on the driving data of the oncoming vehicle at the current moment according to the historical driving data of the oncoming vehicle at the previous moment, so as to clean the driving data and obtain driving data after limiting filtering.

[0050] Specifically, after obtaining the driving data of the oncoming vehicle traveling in the opposite direction of the target vehicle at the current moment and the historical driving data of the oncoming vehicle at the previous moment, the driving data of the oncoming vehicle at the current moment is limited and filtered according to the historical driving data of the oncoming vehicle at the previous moment, so as to clean the driving data and remove the abnormal left boundary angle of the oncoming vehicle at the current moment relative to the image acquisition point of the target vehicle and the left boundary angle α L and the left boundary angular velocity ω L , the right boundary angle of the oncoming vehicle relative to the right boundary angle of the target vehicle's image acquisition point α R and the right boundary angular velocity ω R , the relative distance P between the oncoming vehicle and the target vehicle in the longitudinal direction ySignal, where the filtering threshold means that during the amplitude limiting filtering process, the signal value is limited to a preset range, and the signal value exceeding this range will be truncated or limited. The filtering threshold can be a preset value (fixed value) or the amplitude of the input signal at the previous moment (a certain proportion of the previous value), and the left boundary angle α of the normal oncoming vehicle at the current moment is obtained. L and the left boundary angular velocity ω L , the right boundary angle of the oncoming vehicle relative to the right boundary angle of the target vehicle's image acquisition point α R and the right boundary angular velocity ω R , the relative distance P between the oncoming vehicle and the target vehicle in the longitudinal direction y Signal.

[0051] Among them, the limiting filter is also called the program judgment filter method. Its basic principle is to limit the input signal to a preset range. When the amplitude of the input signal exceeds this range, the signal will be truncated or appropriately attenuated to ensure that the output signal fluctuates within the allowed range. This method is particularly suitable for removing high-frequency noise and sudden interference signals while retaining the main characteristics and trends of the original signal. The advantage of the limiting filter method is that it is simple to implement and can respond quickly and filter out abnormal signals. It is suitable for eliminating random interference, such as noise when the power switch is turned on and off.

[0052] As a specific embodiment, the driving data of the oncoming vehicle at the current moment is median filtered according to the historical driving data of the oncoming vehicle at the previous moment to clean the driving data, that is, the absolute value of the difference between the signal value of the oncoming vehicle at the current moment and the median value of the signal samples in the window is calculated. If the absolute value is less than a certain threshold, the signal value of the oncoming vehicle at the current moment is used; if the absolute value is not less than a certain threshold, the median value of the signal samples in the window is used to replace the signal value of the oncoming vehicle at the current moment.

[0053] S104, performing Kalman filtering on the position of the image acquisition point of the oncoming vehicle relative to the target vehicle based on the historical driving data and the driving data after limiting filtering, to obtain the optimal estimate of the position of the image acquisition point of the oncoming vehicle relative to the target vehicle at the current moment.

[0054] Specifically, after obtaining the driving data after the limiting filter, the Kalman filter is performed on the position of the image acquisition point of the oncoming vehicle relative to the target vehicle according to the historical driving data of the oncoming vehicle at the previous moment and the driving data after the limiting filter. First, the predicted value of the position of the image acquisition point of the oncoming vehicle relative to the target vehicle at the previous moment is obtained. Then, the predicted value of the position of the image acquisition point of the oncoming vehicle relative to the target vehicle at the current moment (camera acquisition moment) can be obtained based on the predicted value of the position of the image acquisition point of the oncoming vehicle relative to the target vehicle at the previous moment. Finally, according to the predicted value of the position of the image acquisition point of the oncoming vehicle relative to the target vehicle at the current moment and the actual measured value of the position of the image acquisition point of the oncoming vehicle relative to the target vehicle at the current moment, the optimal estimated value of the position of the image acquisition point of the oncoming vehicle relative to the target vehicle at the current moment can be obtained, so that more accurate position information of the target vehicle at the camera acquisition moment can be obtained.

[0055] Among them, Kalman filter is an efficient recursive filter that can estimate the state of a dynamic system from a series of incomplete and noisy measurements. The key idea of ​​Kalman filter is data fusion and iterative update. By combining prior information and actual measurements, the system can better estimate the current state and gradually improve the accuracy of the estimate through iterative updates. The algorithm is divided into two steps, the prediction step and the update step. In the prediction step, Kalman filter will generate an estimate of the current state, including uncertainty; in the update step, the estimate will be updated through weighted averaging, and the more certain the measurement is, the higher the weighting ratio.

[0056] S105, adjusting the headlights of the target vehicle according to the optimal estimated value.

[0057] Specifically, after obtaining the optimal estimate of the position of the oncoming vehicle relative to the image acquisition point of the target vehicle at the current moment, the illumination intensity of the headlights of each serial number that illuminates the position of the oncoming vehicle can be obtained according to the optimal estimate, and the illumination intensity of the headlights of each serial number that does not illuminate the position of the oncoming vehicle is equal to the default illumination intensity when the automatic high beam is turned on. The optimal estimate is sent to the control unit through the CAN bus (or CAN-FD bus or CAN-XL bus or Ethernet, etc.), and the control unit adjusts the headlights of the target vehicle according to the optimal estimate. For example, when the optimal estimate is greater than the headlight intensity of the target vehicle, the headlight intensity of the target vehicle is increased to the optimal estimate; when the optimal estimate is less than the headlight intensity of the target vehicle, the headlight intensity of the target vehicle is reduced to the optimal estimate. When the high beam of the target vehicle is not turned on, the illumination intensity of the headlights of each serial number is determined according to other information such as the state of the high beam switch and the state of the high beam module. In this way, the position information of the oncoming vehicle at the moment the light is driven can be accurately obtained, and unnecessary lights can be accurately turned off or the illumination intensity of the necessary lights can be adjusted according to the position information of the oncoming vehicle, thereby increasing the field of view of the target vehicle, improving the anti-glare effect of the target vehicle on the oncoming vehicle, and improving the safety and reliability of driving.

[0058] Among them, the car lights include a car light array composed of multiple light sources. The light source can be a car light source, each of which can be independently turned on and off, or the multiple light sources can be divided into multiple groups, each of which can be independently turned on and off. In addition, the car lights are usually vehicle headlights.

[0059] In some embodiments of the present invention, the driving data of the oncoming vehicle at the current moment is subjected to limiting filtering according to the historical driving data of the oncoming vehicle at the previous moment, so as to clean the driving data and obtain the driving data after limiting filtering, including: compressing the historical driving data according to a preset numerical compression ratio to obtain a preset filtering threshold; filtering the driving data according to the historical driving data and the preset filtering threshold to obtain the driving data after limiting filtering. Among them, the preset numerical compression ratio and the preset filtering threshold can be calibrated according to actual conditions.

[0060] Specifically, the historical driving data of the oncoming vehicle at the last moment is compressed according to a preset numerical compression ratio to reduce the amount of the historical driving data of the oncoming vehicle at the last moment, while retaining the important historical driving data of the oncoming vehicle at the last moment, wherein the numerical compression ratio can be a numerical value, indicating how much proportion of the original data will be retained, for example, if the compression ratio is 5:1, then only 1 out of every 5 data points is retained. According to the distribution of the compressed historical driving data and application requirements, a preset filtering threshold is set, and then the compressed historical driving data is traversed, and the driving data of the oncoming vehicle at the current moment is filtered according to the historical driving data of the oncoming vehicle at the last moment and the preset filtering threshold, and the data points exceeding the preset filtering threshold are restricted or eliminated to remove the abnormal data points exceeding the preset filtering threshold, thereby obtaining smoother and more stable driving data of the oncoming vehicle at the current moment. Among them, for the data points exceeding the preset filtering threshold, they can be set as the threshold boundary, or replaced with the nearest data point close to the threshold.

[0061] In some embodiments of the present invention, Kalman filtering is performed on the position of the image acquisition point of the oncoming vehicle relative to the target vehicle based on historical driving data and driving data after limiting filtering to obtain the optimal estimate of the position of the image acquisition point of the oncoming vehicle relative to the target vehicle at the current moment, including: constructing an optimal estimate matrix of the position of the image acquisition point of the oncoming vehicle relative to the target vehicle; determining the Kalman filter equation of the position of the image acquisition point of the oncoming vehicle relative to the target vehicle based on the optimal estimate matrix; performing Kalman filtering on the position of the image acquisition point of the oncoming vehicle relative to the target vehicle at the previous moment based on the Kalman filter equation and historical driving data to obtain the historical optimal estimate position of the image acquisition point of the oncoming vehicle relative to the target vehicle at the previous moment; performing Kalman filtering on the position of the image acquisition point of the oncoming vehicle relative to the target vehicle at the current moment based on the Kalman filter equation, the historical optimal estimate position and driving data after limiting filtering to obtain the optimal estimate of the position of the image acquisition point of the oncoming vehicle relative to the target vehicle at the current moment.

[0062] Further, in some embodiments of the present invention, the optimal estimation matrix is ​​expressed as:

[0063]

[0064] Among them, P x is the lateral distance of the left or right boundary of the oncoming vehicle relative to the image acquisition point of the target vehicle, P y V is the relative distance between the left or right boundary of the oncoming vehicle and the image acquisition point of the target vehicle. x is the rate of change of the lateral distance, V y is the rate of change of relative distance.

[0065] Specifically, first, the image data of the oncoming vehicle and its surrounding environment is obtained through the camera, and the collected images are preprocessed by grayscale conversion, filtering and denoising, histogram equalization and other preprocessing operations to improve the accuracy and stability of subsequent processing. Secondly, the preprocessed images are used to obtain key feature information of the oncoming vehicle and the road environment through feature extraction algorithms, which may include edge detection, corner point detection, contour extraction, etc. Then, the oncoming vehicle in the image is detected by machine learning algorithms (such as support vector machines, convolutional neural networks, etc.), and the movement trajectory of the oncoming vehicle is continuously monitored by tracking algorithms. Combining the extracted image features and the oncoming vehicle detection results, mathematical models and algorithms (such as triangulation, vision-based time positioning and map construction algorithms, etc.) are used to accurately locate the oncoming vehicle. The key technology for estimating the position of the oncoming vehicle relative to the image acquisition point of the target vehicle is image registration / alignment. Find the transformation matrix between the two image coordinate systems, and use this transformation matrix to map one image to another image, so that the points corresponding to the same position in the two images correspond one to one. Finally, the detected feature points and pose estimation results are used to construct an optimal estimation matrix containing position and pose information. This matrix can describe the precise position and direction of the oncoming vehicle relative to the target vehicle.

[0066] By formula The optimal estimation matrix can be determined, in which the lateral distance of the left boundary of the oncoming vehicle relative to the image acquisition point of the target vehicle minus the lateral distance of the right boundary of the oncoming vehicle relative to the image acquisition point of the target vehicle can be calculated to obtain the width of the oncoming vehicle. The state equation and measurement equation of the Kalman filter algorithm for the position of the oncoming vehicle relative to the image acquisition point of the target vehicle are constructed according to the optimal estimation matrix. The following equation describes the prediction of the system state X, namely:

[0067] X(k|k-1)=A*X(k-1|k-1)+B*U(k)

[0068] Among them, X(k|k-1) is the prediction of the state at time k based on all known information at time k-1; A is the state transfer matrix, which describes how the system state changes over time; X(k-1|k-1) is the optimal state estimate at time k-1; B is the control input matrix, which describes how the control input U(k) affects the system state; U(k) is the control input at time k.

[0069] The prediction of the state covariance matrix P can be described according to the following equation, namely:

[0070] P(k|k-1)=A*P(k-1|k-1)*A T +Q

[0071] Where P(k|k-1) is the prediction of the state covariance matrix at time k based on all known information at time k-1; P(k-1|k-1) is the state covariance matrix at time k-1; A T is the transpose of the state transfer matrix A; Q is the process noise covariance matrix, which describes the statistical characteristics of the process noise.

[0072] The difference between the measured value y and the predicted state X(k|k-1) can be described by the following equation:

[0073] y(k)=Z(k)-h(X(k|k-1))

[0074] Among them, y(k) is the measurement residual at time k; Z(k) is the actual measurement value at time k; h is the function that maps the predicted state to the measurement space.

[0075] The measurement residual covariance matrix S can be described according to the following equation, namely:

[0076] S(k)=H(k)P(k|k-1)H(k) T +R

[0077] Among them, S(k) is the measurement residual covariance matrix at time k; H(k) is the observation matrix, which describes how the state is mapped to the measurement space; R is the measurement noise covariance matrix, which describes the statistical characteristics of the measurement noise.

[0078] The Kalman gain K can be described by the following equation, which is used to weigh the credibility between the predicted state and the measured value, namely:

[0079] K(k)=P(k|k-1)H(k) T S(k) -1

[0080] The update of the predicted state based on the measured value can be described by the following equation:

[0081] X(k|k)=X(k|k-1)+K(k)y(k)

[0082] P(k|k)=(IK(k)H(k))P(k|k-1)

[0083] Among them, P(k|k) is the optimal estimate at time k; P(k|k-1) is the estimate calculated based on the optimal estimate at time k-1 and the state equation.

[0084] State Matrix dt is the cycle time of the camera sending the target position signal, and the control matrix Control volume Measurements h(X(k|k-1)) is the value converted from the estimated value calculated by the state equation into the measured value Z. The measurement matrix H is a nonlinear function h(X(k|k-1)) of P x , P y 、V x 、V y The Jacobian matrix formed by the first-order derivative of is:

[0085]

[0086] I is the identity matrix of the same dimension as the state vector,

[0087] Optimize the state equation and set And assign initial values ​​to system variables, for example, the initial state matrix System state covariance matrix

[0088] The system parameters (e.g., system process noise Q, measurement noise R) are calibrated and debugged according to the test parameter error of the camera and the system status. The following are two typical values: system process noise Measurement noise

[0089] Substituting the above initialization parameters and calibration parameters into the formula for iterative calculation, the optimal estimated value X of the position of the oncoming vehicle relative to the image acquisition point of the target vehicle at any time can be obtained. The above formula only lists the optimal estimated value of the left position of the oncoming vehicle relative to the image acquisition point of the target vehicle. The same method can be used to obtain the optimal estimated value of the right position of the oncoming vehicle relative to the image acquisition point of the target vehicle.

[0090] The optimal estimated value X of the position of the oncoming vehicle relative to the image acquisition point of the target vehicle is calculated according to the state equation X(k|k-1)=A*X(k-1|k-1)+B*U(k), and the estimated position X(k+1|k) at the next cycle k+1 time after prediction can be obtained. The estimated position X(k+1|k) at the next cycle k+1 time and the optimal estimated position X(k|k) at the current cycle k time are calculated according to a certain proportional coefficient α (exponential smoothing method), and the left lateral distance P of the predicted position of the oncoming vehicle relative to the image acquisition point of the target vehicle can be obtained. xl _Predict=(1-α)*P x (k|k)+a*P x (k+1|k) and the predicted left relative (longitudinal) distance P of the oncoming vehicle relative to the position of the image acquisition point of the target vehicle yl _Predict=(1-α)*Py (k|k)+a*P y (k+1|k). In the same way, the right lateral distance of the predicted oncoming vehicle relative to the position of the image acquisition point of the target vehicle and the right relative (longitudinal) distance of the predicted oncoming vehicle relative to the position of the image acquisition point of the target vehicle can be obtained. The exponential smoothing method can reduce the problem of inconsistency between the position of the image acquisition point of the oncoming vehicle relative to the target vehicle at the time of light driving and the position of the image acquisition point of the oncoming vehicle relative to the target vehicle at the time of camera acquisition due to system delay.

[0091] Among them, there may be a case where the proportional coefficient a≥1 exists in the left lateral distance, left relative (longitudinal) distance, right lateral distance and right relative (longitudinal) distance of the predicted oncoming vehicle relative to the position of the image acquisition point of the target vehicle. In this case, the extrapolation method can be used to calculate the left lateral distance, left relative (longitudinal) distance, right lateral distance and right relative (longitudinal) distance of the predicted oncoming vehicle relative to the position of the image acquisition point of the target vehicle. Extrapolation is a basic type of interpolation method, which involves prediction or inference outside of known data points.

[0092] As a specific embodiment, the relative distance P between the oncoming vehicle and the target vehicle in the longitudinal direction is x It is changed to the relative distance d between the oncoming vehicle and the target vehicle. After the change, when the extended Kalman calculation is used later, the function h(X(k|k-1)) and the measurement matrix H that convert the measurement value Z and the estimated value calculated by the state equation into the measurement value Z style need to be changed accordingly, that is,

[0093]

[0094]

[0095] As a specific embodiment, the left boundary angle α of the image acquisition point of the target vehicle at the current moment is calculated by L and the left boundary angular velocity ω L Perform Kalman filtering and calculate the relative distance P between the oncoming vehicle and the target vehicle x The Kalman filter is used to calculate the optimal estimate of each.

[0096] As a specific embodiment, the target vehicle and the oncoming vehicle can also be realized by simplifying the calculation of the external control quantity of the Kalman filter when the target vehicle and the oncoming vehicle are driving in opposite directions, that is, by calculating the longitudinal acceleration α of the target vehicle. y , lateral acceleration α x To simplify the control Among them, fac_x1 and fac_y can be calibrated according to actual conditions.

[0097] In some embodiments of the present invention, the optimal estimate value includes a first optimal estimate value of the left side boundary of the oncoming vehicle relative to the image acquisition point of the target vehicle and a second optimal estimate value of the right side boundary of the oncoming vehicle relative to the image acquisition point of the target vehicle at the current moment; adjusting the headlights of the target vehicle according to the optimal estimate value includes: converting the first optimal estimate value of the left side boundary of the oncoming vehicle relative to the image acquisition point of the target vehicle into a first converted optimal estimate value of the left side boundary of the oncoming vehicle relative to the headlights of the target vehicle according to the relative position of the headlights of the target vehicle and the image acquisition point; converting the second optimal estimate value of the right side boundary of the oncoming vehicle relative to the image acquisition point of the target vehicle into a second converted optimal estimate value of the right side boundary of the oncoming vehicle relative to the headlights of the target vehicle according to the relative position of the headlights of the target vehicle and the image acquisition point; determining the illumination range of the oncoming vehicle illuminated by the headlights according to the first converted optimal estimate value and the second converted optimal estimate value; and adjusting the headlights of the target vehicle according to the illumination range.

[0098] Specifically, refer to Figure 2 , is a schematic diagram of the coordinate transformation principle according to some embodiments of the present invention, wherein h is the relative (longitudinal) distance between the image acquisition point (camera) of the target vehicle and the headlight of the target vehicle, d L is the lateral distance between the image acquisition point (camera) of the target vehicle and the headlight (left headlight) of the target vehicle, d R It is the lateral distance between the image acquisition point (camera) of the target vehicle and the headlight (right headlight) of the target vehicle.

[0099] Taking into account the structure of the oncoming vehicle, by increasing the left and right lateral compensation values ​​and the longitudinal compensation value d yl _offs,d yr _offs and d x _offs, convert the first optimal estimated value of the left boundary of the oncoming vehicle relative to the image acquisition point of the target vehicle into the first converted optimal estimated value of the left boundary of the oncoming vehicle relative to the headlight of the target vehicle (based on the left boundary angle θ of the center of the left headlight module Lbl ), and convert the second optimal estimated value of the right boundary of the oncoming vehicle relative to the image acquisition point of the target vehicle into the second converted optimal estimated value of the right boundary of the oncoming vehicle relative to the headlight of the target vehicle (based on the right boundary angle θ of the center of the left headlight module Lbr ), based on the left boundary angle θ of the center of the left headlight module Lbl , right boundary angle θ Lbr Calculated as follows:

[0100]

[0101] The same method can be used to calculate the left boundary angle based on the center of the right headlight module and the right boundary angle based on the center of the right headlight module, and the position of the coordinate system based on the image acquisition point (camera) of the target vehicle can be converted into the position based on the left and right headlight modules, so that the points corresponding to the same position in the space in the two coordinate systems correspond one to one.

[0102] After obtaining the first conversion optimal estimate and the second conversion optimal estimate, the position and motion state of the oncoming vehicle can be determined according to the first conversion optimal estimate and the second conversion optimal estimate. According to the position of the oncoming vehicle and the illumination angle of the headlights of the target vehicle, the illumination range of the headlights of the target vehicle can be calculated, which can be achieved through geometric calculation. Taking into account the illumination angle and distance of the headlights of the target vehicle, the boundary of the illumination range is obtained by determining the intersection of the headlights of the target vehicle and the road surface. According to the calculation result of the illumination range, adjust the headlights of the target vehicle to ensure that no glare is caused to the oncoming vehicle. Among them, adjusting the headlights of the target vehicle can include adjusting the height and angle of the headlights to avoid direct light to the eyes of the driver of the oncoming vehicle.

[0103] In some embodiments, the headlight adjustment can be achieved through manual adjustment or automatic adjustment. When manual adjustment is adopted, the reasonable position of the low beam headlight should be at the position of the person's hip when the person stands at a certain distance from the headlight. When automatic adjustment is adopted, it is necessary to rely on sensors and control systems to dynamically adjust the illumination angle of the headlight according to real-time data.

[0104] In some embodiments of the present invention, the headlights of the target vehicle include several light sources; adjusting the headlights of the target vehicle according to the illumination range includes: determining a target light source from several light sources that illuminates an oncoming vehicle according to the illumination range and the illumination angle of each of the several light sources; determining the illumination intensity of the target light source according to the relative distance between the oncoming vehicle and the target vehicle at the current moment; and controlling the target light source according to the illumination intensity.

[0105] Specifically, refer to Figure 3 , is a schematic diagram of the relationship between the left and right boundary angles of an oncoming vehicle and the illumination angles of a plurality of light sources in a single row according to some embodiments of the present invention, Figure 3 It can be seen that the headlight module of the target vehicle includes several light sources. Lbl <θ R (i)≤θ Lbr or θ Lbl ≤θ L (i)<θ LbrIn any of the cases where (i represents the light source number), it means that the i-th light source illuminates the oncoming vehicle, and the serial numbers of all light sources irradiating the oncoming vehicle are recorded, that is, light source number 3 and light source number 4 irradiating the oncoming vehicle are recorded, and light source number 3 and light source number 4 are used as the target light sources irradiating the oncoming vehicle.

[0106] Relationship between reference light intensity E and light source luminous intensity I The illumination intensity of the target light source irradiating the oncoming vehicle can be calculated, where E is the illumination intensity of the receiving surface. The illumination intensity threshold E under different motion modes and different distances between the two vehicles can be obtained by looking up the table according to the maximum illumination intensity limit that may cause glare in relevant regulations and the actual calibration method. Thd ; I is the maximum luminous intensity of each light source, that is, I max (i) can be obtained through optical simulation; β is the angle between the normal of the receiving surface and the line from its center to the light source, which can be simplified as (θ L (i)+θ R (i)) / 2; r is the distance between the target object and the headlight module,

[0107] According to the formula The illumination intensity of the i-th light source irradiating the oncoming vehicle is calculated, and then the target light source is controlled according to the illumination intensity of the target light source. The position information of the oncoming vehicle at the moment of light driving can be accurately obtained, and unnecessary lights can be accurately turned off or the illumination intensity of necessary lights can be adjusted according to the position information of the oncoming vehicle, thereby increasing the field of view of the target vehicle, improving the anti-glare effect of the target vehicle on the oncoming vehicle, and improving the safety and reliability of driving.

[0108] If the ith light source does not irradiate the oncoming vehicle, the irradiance of the ith light source Bri(i) = Bri default (i) refers to the default high beam intensity when the automatic high beam mode is turned on.

[0109] The same method can be used to calculate the illumination intensity of each serial number light source of the right headlight module.

[0110] In summary, the method for controlling a vehicle light according to an embodiment of the present invention includes: in response to turning on the high beam, obtaining driving data of an oncoming vehicle that is currently traveling in the opposite direction of a target vehicle; wherein the driving data includes the left boundary angle and left boundary angular velocity of the left boundary of the oncoming vehicle relative to the image acquisition point of the target vehicle, the right boundary angle and right boundary angular velocity of the right boundary of the oncoming vehicle relative to the image acquisition point of the target vehicle, and the relative distance between the oncoming vehicle and the target vehicle; obtaining the historical driving data of the oncoming vehicle at the previous moment; wherein the historical driving data includes the left boundary of the oncoming vehicle at the previous moment relative to the left boundary of the image acquisition point of the target vehicle. Angle and left boundary angular velocity, right boundary angle and right boundary angular velocity of the right boundary of the oncoming vehicle relative to the image acquisition point of the target vehicle, and the relative distance between the oncoming vehicle and the target vehicle; according to the historical driving data of the oncoming vehicle at the previous moment, the driving data of the oncoming vehicle at the current moment is limited and filtered to clean the driving data and obtain the driving data after limiting filtering; according to the historical driving data and the driving data after limiting filtering, the position of the image acquisition point of the oncoming vehicle relative to the target vehicle is Kalman filtered to obtain the optimal estimated value of the position of the image acquisition point of the oncoming vehicle relative to the target vehicle at the current moment; according to the optimal estimated value, the headlights of the target vehicle are adjusted. Therefore, the method can accurately obtain the position information of the oncoming vehicle at the moment of light driving, and accurately turn off unnecessary lights or adjust the illumination intensity of necessary lights according to the position information of the oncoming vehicle, increase the field of view of the target vehicle, improve the anti-glare effect of the target vehicle on the oncoming vehicle, and improve the safety and reliability of driving.

[0111] It should be noted that the method of the embodiment of the present invention can be performed by a single device, such as a computer or a server. The method of this embodiment can also be applied to a distributed scenario and completed by multiple devices cooperating with each other. In the case of such a distributed scenario, one of the multiple devices can only perform one or more steps in the method of the embodiment of the present invention, and the multiple devices will interact with each other to complete the above method.

[0112] It should be noted that some embodiments of the present invention are described above. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the above embodiments and still achieve the desired results. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0113] Corresponding to the above embodiment, the present invention further proposes a control device for a vehicle light.

[0114] like Figure 4 As shown, the control device for a vehicle light according to the embodiment of the present invention may include: a first acquisition module 410 , a second acquisition module 420 , a data processing module 430 , a Kalman filter module 440 and an adjustment module 450 .

[0115] Among them, the first acquisition module 410 is configured to obtain the driving data of the oncoming vehicle that is currently traveling in the opposite direction of the target vehicle in response to the high beam being turned on; wherein the driving data includes the left boundary angle and left boundary angular velocity of the left side boundary of the oncoming vehicle relative to the image acquisition point of the target vehicle, the right boundary angle and right boundary angular velocity of the right side boundary of the oncoming vehicle relative to the image acquisition point of the target vehicle, and the relative distance between the oncoming vehicle and the target vehicle; the second acquisition module 420 is configured to obtain the historical driving data of the oncoming vehicle at the previous moment; wherein the historical driving data includes the left boundary angle and left boundary angular velocity of the left side boundary of the oncoming vehicle relative to the image acquisition point of the target vehicle at the previous moment, the right boundary angle and right boundary angular velocity of the right side boundary of the oncoming vehicle The right side boundary angle and the right side boundary angular velocity of the side boundary relative to the image acquisition point of the target vehicle, and the relative distance between the oncoming vehicle and the target vehicle; the data processing module 430 is configured to perform limiting filtering on the driving data of the oncoming vehicle at the current moment according to the historical driving data of the oncoming vehicle at the previous moment, so as to clean the driving data and obtain the driving data after limiting filtering; the Kalman filter module 440 is configured to perform Kalman filtering on the position of the image acquisition point of the oncoming vehicle relative to the target vehicle according to the historical driving data and the driving data after limiting filtering, and obtain the optimal estimate of the position of the image acquisition point of the oncoming vehicle relative to the target vehicle at the current moment; the adjustment module 450 is configured to adjust the headlights of the target vehicle according to the optimal estimate.

[0116] In some embodiments of the present invention, the data processing module 430 performs limiting filtering on the driving data of the oncoming vehicle at the current moment based on the historical driving data of the oncoming vehicle at the previous moment to clean the driving data and obtain driving data after limiting filtering. It is specifically used for: compressing the historical driving data according to a preset numerical compression ratio to obtain a preset filtering threshold; screening the driving data according to the historical driving data and the preset filtering threshold to obtain driving data after limiting filtering.

[0117] In some embodiments of the present invention, the Kalman filter module 440 performs Kalman filtering on the position of the image acquisition point of the oncoming vehicle relative to the target vehicle based on historical driving data and driving data after limiting filtering to obtain the optimal estimate of the position of the image acquisition point of the oncoming vehicle relative to the target vehicle at the current moment, and is specifically used for: constructing an optimal estimate matrix for the position of the image acquisition point of the oncoming vehicle relative to the target vehicle; determining the Kalman filter equation for the position of the image acquisition point of the oncoming vehicle relative to the target vehicle based on the optimal estimate matrix; performing Kalman filtering on the position of the image acquisition point of the oncoming vehicle relative to the target vehicle at the previous moment based on the Kalman filter equation and historical driving data to obtain the historical optimal estimate position of the position of the image acquisition point of the oncoming vehicle relative to the target vehicle at the previous moment; performing Kalman filtering on the position of the image acquisition point of the oncoming vehicle relative to the target vehicle at the current moment based on the Kalman filter equation, the historical optimal estimate position and driving data after limiting filtering to obtain the optimal estimate of the position of the image acquisition point of the oncoming vehicle relative to the target vehicle at the current moment.

[0118] In some embodiments of the present invention, the optimal estimation matrix is ​​expressed as:

[0119]

[0120] Among them, P x is the lateral distance of the left or right boundary of the oncoming vehicle relative to the image acquisition point of the target vehicle, P y V is the relative distance between the left or right boundary of the oncoming vehicle and the image acquisition point of the target vehicle. x is the rate of change of the lateral distance, V y is the rate of change of relative distance.

[0121] In some embodiments of the present invention, the optimal estimate value includes a first optimal estimate value of the left side boundary of the oncoming vehicle relative to the image acquisition point of the target vehicle at the current moment and a second optimal estimate value of the right side boundary of the oncoming vehicle relative to the image acquisition point of the target vehicle; the adjustment module 450 adjusts the headlights of the target vehicle according to the optimal estimate value, specifically for: converting the first optimal estimate value of the left side boundary of the oncoming vehicle relative to the image acquisition point of the target vehicle into a first converted optimal estimate value of the left side boundary of the oncoming vehicle relative to the headlights of the target vehicle according to the relative position of the headlights of the target vehicle and the image acquisition point; converting the second optimal estimate value of the right side boundary of the oncoming vehicle relative to the image acquisition point of the target vehicle into a second converted optimal estimate value of the right side boundary of the oncoming vehicle relative to the headlights of the target vehicle according to the relative position of the headlights of the target vehicle and the image acquisition point; determining the illumination range of the oncoming vehicle illuminated by the headlights according to the first converted optimal estimate value and the second converted optimal estimate value; and adjusting the headlights of the target vehicle according to the illumination range.

[0122] In some embodiments of the present invention, the headlights of the target vehicle include several light sources; the adjustment module 450 adjusts the headlights of the target vehicle according to the illumination range, and is specifically used to: determine the target light source that illuminates the oncoming vehicle from the several light sources according to the illumination range and the illumination angle of each of the several light sources; determine the illumination intensity of the target light source according to the relative distance between the oncoming vehicle and the target vehicle at the current moment; and control the target light source according to the illumination intensity.

[0123] It should be noted that, for details not disclosed in the control device for vehicle lights in the embodiment of the present invention, please refer to the details disclosed in the control method for vehicle lights in the embodiment of the present invention, and no further details will be given.

[0124] In summary, the control device of the vehicle light according to the embodiment of the present invention includes: in response to the high beam being turned on, the first acquisition module acquires the driving data of the oncoming vehicle that is driving in the opposite direction of the target vehicle at the current moment; wherein the driving data includes the left boundary angle and left boundary angular velocity of the left boundary of the oncoming vehicle relative to the image acquisition point of the target vehicle at the current moment, the right boundary angle and right boundary angular velocity of the right boundary of the oncoming vehicle relative to the image acquisition point of the target vehicle, and the relative distance between the oncoming vehicle and the target vehicle; the second acquisition module acquires the historical driving data of the oncoming vehicle at the previous moment; wherein the historical driving data includes the left boundary angle and left boundary angular velocity of the left boundary of the oncoming vehicle relative to the image acquisition point of the target vehicle at the previous moment and the left boundary angular velocity, the right boundary angle of the right boundary of the oncoming vehicle relative to the image acquisition point of the target vehicle and the right boundary angular velocity, and the relative distance between the oncoming vehicle and the target vehicle; the data processing module performs limiting filtering on the driving data of the oncoming vehicle at the current moment according to the historical driving data of the oncoming vehicle at the previous moment, so as to clean the driving data and obtain the driving data after limiting filtering; the Kalman filtering module performs Kalman filtering on the position of the image acquisition point of the oncoming vehicle relative to the target vehicle according to the historical driving data and the driving data after limiting filtering, and obtains the optimal estimated value of the position of the image acquisition point of the oncoming vehicle relative to the target vehicle at the current moment; the adjustment module adjusts the lights of the target vehicle according to the optimal estimated value. Thus, the device can accurately obtain the position information of the oncoming vehicle at the moment of light driving, and accurately turn off unnecessary lights or adjust the illumination intensity of necessary lights according to the position information of the oncoming vehicle, increase the field of view of the target vehicle, improve the anti-glare effect of the target vehicle on the oncoming vehicle, and improve the safety and reliability of driving.

[0125] For the convenience of description, the above system is described as being divided into various modules according to their functions. Of course, when implementing the present invention, the functions of each module can be implemented in the same or multiple software and / or hardware.

[0126] The system of the above embodiment is used to implement the corresponding method in any of the above embodiments, and has the beneficial effects of the corresponding method embodiment, which will not be repeated here.

[0127] Corresponding to the above embodiment, the present invention further provides an electronic device.

[0128] refer to Figure 5 , is a block diagram of an electronic device according to some embodiments of the present invention, showing a more specific hardware structure diagram of an electronic device provided by this embodiment, the device may include: a processor 510, a memory 520, an input / output interface 530, a communication interface 540 and a bus 550. The processor 510, the memory 520, the input / output interface 530 and the communication interface 540 are connected to each other in communication within the device through the bus 550.

[0129] The processor 510 can be implemented by a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this specification.

[0130] The memory 520 can be implemented in the form of ROM (Read Only Memory), RAM (Random Access Memory), static storage device, dynamic storage device, etc. The memory 520 can store an operating system and other application programs. When the technical solutions provided in the embodiments of this specification are implemented by software or firmware, the relevant program codes are stored in the memory 520 and are called and executed by the processor 510.

[0131] The input / output interface 530 is used to connect the input / output module to realize information input and output. The input / output module can be configured in the device as a component (not shown in the figure), or it can be externally connected to the device to provide corresponding functions. The input device may include a keyboard, a mouse, a touch screen, a microphone, various sensors, etc., and the output device may include a display, a speaker, a vibrator, an indicator light, etc.

[0132] The communication interface 540 is used to connect a communication module (not shown) to realize communication interaction between the device and other devices. The communication module can realize communication through a wired mode (such as USB, network cable, etc.) or a wireless mode (such as mobile network, WIFI, Bluetooth, etc.).

[0133] The bus 550 comprises a pathway that transmits information between the various components of the device (eg, the processor 510 , the memory 520 , the input / output interface 530 , and the communication interface 540 ).

[0134] It should be noted that, although the above device only shows the processor 510, the memory 520, the input / output interface 530, the communication interface 540 and the bus 550, in the specific implementation process, the device may also include other components necessary for normal operation. In addition, it can be understood by those skilled in the art that the above device may also only include the components necessary for implementing the embodiments of the present specification, and does not necessarily include all the components shown in the figure.

[0135] The electronic device of the above embodiment is used to implement the corresponding method in any of the above embodiments, and has the beneficial effects of the corresponding method embodiment, which will not be described in detail here.

[0136] Based on the same inventive concept, corresponding to the method of any of the above embodiments, the present invention also provides a non-transitory computer-readable storage medium, which stores computer instructions, and the computer instructions are used to enable a computer to execute the method of any of the above embodiments.

[0137] The above-mentioned non-transitory computer-readable storage medium can be any available medium or data storage device that can be accessed by a computer, including but not limited to magnetic storage (such as floppy disks, hard disks, magnetic tapes, magneto-optical disks (MO), etc.), optical storage (such as CD, DVD, BD, HVD, etc.), and semiconductor storage (such as ROM, EPROM, EEPROM, non-volatile memory (NAND FLASH), solid-state drive (SSD)), etc.

[0138] The computer instructions stored in the storage medium of the above embodiment are used to enable a computer to execute the method of any embodiment in the above exemplary method part, and have the beneficial effects of the corresponding method embodiment, which will not be repeated here.

[0139] Based on the same inventive concept, corresponding to the method of any of the above embodiments, the present invention also provides a vehicle.

[0140] refer to Figure 6 , is a block diagram of a vehicle according to some embodiments of the present invention, wherein the vehicle 600 includes the above-mentioned vehicle light control device 400.

[0141] According to the vehicle of the embodiment of the present invention, the above-mentioned headlight control device can accurately obtain the position information of the oncoming vehicle at the time of light driving, and accurately turn off unnecessary headlights or adjust the illumination intensity of necessary headlights according to the position information of the oncoming vehicle, thereby increasing the field of view of the target vehicle, improving the anti-glare effect of the target vehicle on the oncoming vehicle, and improving the safety and reliability of driving.

[0142] In addition, although the operations of the method of the present invention are described in a specific order in the accompanying drawings, this does not require or imply that the operations must be performed in this specific order, or that all the operations shown must be performed to achieve the desired results. On the contrary, the steps depicted in the flow chart can be performed in a different order. Additionally or alternatively, some steps can be omitted, multiple steps can be combined into one step, and / or one step can be decomposed into multiple steps.

[0143] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0144] It should be noted that, unless otherwise defined, the technical terms or scientific terms used in the embodiments of the present invention should be understood by people with ordinary skills in the field to which the present invention belongs. The "first", "second" and similar words used in the embodiments of the present invention do not indicate any order, quantity or importance, but are only used to distinguish different components. "Include" or "comprise" and similar words mean that the elements or objects appearing before the word include the elements or objects listed after the word and their equivalents, without excluding other elements or objects. "Connect" or "connected" and similar words are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to indicate relative positional relationships. When the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0145] Although the spirit and principle of the present invention have been described with reference to several specific embodiments, it should be understood that the present invention is not limited to the disclosed specific embodiments, and the division of various aspects does not mean that the features in these aspects cannot be combined to benefit, and this division is only for the convenience of expression. The present invention is intended to cover various modifications and equivalent arrangements included in the spirit and scope of the attached claims. The scope of the attached claims conforms to the broadest interpretation, thereby including all such modifications and equivalent structures and functions.

Claims

1. A method for controlling a vehicle lamp, characterized in that: include: In response to the high beam being turned on, the driving data of the oncoming vehicle that is currently traveling in the opposite direction of the target vehicle is obtained; wherein the driving data includes the left boundary angle and left boundary angular velocity of the left boundary of the oncoming vehicle relative to the image acquisition point of the target vehicle, the right boundary angle and right boundary angular velocity of the right boundary of the oncoming vehicle relative to the image acquisition point of the target vehicle, and the relative distance between the oncoming vehicle and the target vehicle; Acquire the historical driving data of the oncoming vehicle at the last moment; wherein the historical driving data includes the left boundary angle and left boundary angular velocity of the left boundary of the oncoming vehicle relative to the image acquisition point of the target vehicle, the right boundary angle and right boundary angular velocity of the right boundary of the oncoming vehicle relative to the image acquisition point of the target vehicle, and the relative distance between the oncoming vehicle and the target vehicle; Performing limiting filtering on the driving data of the oncoming vehicle at the current moment according to the historical driving data of the oncoming vehicle at the previous moment, so as to clean the driving data and obtain driving data after limiting filtering; Performing Kalman filtering on the position of the image acquisition point of the oncoming vehicle relative to the target vehicle according to the historical driving data and the driving data after the amplitude limiting filter, to obtain an optimal estimate of the position of the image acquisition point of the oncoming vehicle relative to the target vehicle at the current moment; The lights of the target vehicle are adjusted according to the optimal estimate.

2. The vehicle light control method according to claim 1, characterized in that: The step of performing limiting filtering on the driving data of the oncoming vehicle at the current moment according to the historical driving data of the oncoming vehicle at the previous moment, so as to clean the driving data and obtain the driving data after limiting filtering, comprises: Compressing the historical driving data according to a preset numerical compression ratio to obtain a preset filtering threshold; The driving data is filtered according to the historical driving data and the preset filtering threshold to obtain driving data after limiting filtering.

3. The vehicle light control method according to claim 2, characterized in that: The step of performing Kalman filtering on the position of the image acquisition point of the oncoming vehicle relative to the target vehicle according to the historical driving data and the driving data after the amplitude limiting filtering to obtain the optimal estimated value of the position of the image acquisition point of the oncoming vehicle relative to the target vehicle at the current moment comprises: Constructing an optimal estimation matrix of the position of the oncoming vehicle relative to the image acquisition point of the target vehicle; Determine a Kalman filter equation for the position of the image acquisition point of the oncoming vehicle relative to the target vehicle according to the optimal estimation matrix; Performing Kalman filtering on the position of the image acquisition point of the oncoming vehicle relative to the target vehicle at the last moment according to the Kalman filter equation and the historical driving data to obtain the historical optimal estimated position of the position of the image acquisition point of the oncoming vehicle relative to the target vehicle at the last moment; Kalman filtering is performed on the position of the image acquisition point of the oncoming vehicle relative to the target vehicle at the current moment according to the Kalman filter equation, the historical optimal estimated position and the driving data after the limiting filter to obtain the optimal estimated value of the position of the image acquisition point of the oncoming vehicle relative to the target vehicle at the current moment.

4. The method for controlling a vehicle lamp according to claim 3, characterized in that: The optimal estimation matrix is ​​expressed as: Among them, P x is the lateral distance between the left boundary or the right boundary of the oncoming vehicle and the image acquisition point of the target vehicle, P y V is the relative distance between the left boundary or the right boundary of the oncoming vehicle and the image acquisition point of the target vehicle. x is the rate of change of the lateral distance, V y is the rate of change of the relative distance.

5. The vehicle light control method according to claim 4, characterized in that: The optimal estimation value includes a first optimal estimation value of the left boundary of the oncoming vehicle relative to the image acquisition point of the target vehicle and a second optimal estimation value of the right boundary of the oncoming vehicle relative to the image acquisition point of the target vehicle at the current moment; The step of adjusting the headlights of the target vehicle according to the optimal estimated value comprises: Converting a first optimal estimation value of the left side boundary of the oncoming vehicle relative to the image acquisition point of the target vehicle into a first converted optimal estimation value of the left side boundary of the oncoming vehicle relative to the headlight of the target vehicle according to the relative position of the headlight of the target vehicle and the image acquisition point; Converting a second optimal estimated value of the right side boundary of the oncoming vehicle relative to the image acquisition point of the target vehicle into a second converted optimal estimated value of the right side boundary of the oncoming vehicle relative to the headlight of the target vehicle according to the relative position of the headlight of the target vehicle and the image acquisition point; determining an illumination range of the oncoming vehicle illuminated by the vehicle light according to the first conversion optimal estimation value and the second conversion optimal estimation value; The headlights of the target vehicle are adjusted according to the illumination range.

6. The vehicle light control method according to claim 5, characterized in that: The headlights of the target vehicle include a plurality of light sources; The step of adjusting the headlights of the target vehicle according to the illumination range includes: Determining a target light source from among the plurality of light sources that irradiates the oncoming vehicle according to the irradiation range and an irradiation angle of each of the plurality of light sources; Determining the illumination intensity of the target light source according to the relative distance between the oncoming vehicle and the target vehicle at the current moment; The target light source is controlled according to the illumination intensity.

7. A control device for a vehicle lamp, characterized in that: include: A first acquisition module is configured to acquire driving data of an oncoming vehicle that is traveling in the opposite direction of the target vehicle at the current moment in response to the high beam being turned on; wherein the driving data includes a left boundary angle and a left boundary angular velocity of a left boundary of the oncoming vehicle relative to an image acquisition point of the target vehicle at the current moment, a right boundary angle and a right boundary angular velocity of a right boundary of the oncoming vehicle relative to an image acquisition point of the target vehicle at the current moment, and a relative distance between the oncoming vehicle and the target vehicle; A second acquisition module is configured to acquire the historical driving data of the oncoming vehicle at the last moment; wherein the historical driving data includes the left boundary angle and left boundary angular velocity of the left boundary of the oncoming vehicle relative to the image acquisition point of the target vehicle, the right boundary angle and right boundary angular velocity of the right boundary of the oncoming vehicle relative to the image acquisition point of the target vehicle, and the relative distance between the oncoming vehicle and the target vehicle; A data processing module is configured to perform limiting filtering on the driving data of the oncoming vehicle at a current moment according to the historical driving data of the oncoming vehicle at a previous moment, so as to clean the driving data and obtain driving data after limiting filtering; A Kalman filter module is configured to perform Kalman filtering on the position of the image acquisition point of the oncoming vehicle relative to the target vehicle based on the historical driving data and the driving data after the amplitude limiting filter, so as to obtain an optimal estimate of the position of the image acquisition point of the oncoming vehicle relative to the target vehicle at a current moment; The adjustment module is configured to adjust the headlights of the target vehicle according to the optimal estimated value.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the method for controlling a vehicle light according to any one of claims 1 to 6 is implemented.

9. A non-transitory computer-readable storage medium storing computer instructions, characterized in that: The computer instructions are used to enable the computer to implement the vehicle light control method according to any one of claims 1 to 6.

10. A vehicle, characterized in that: Comprising the control device of the vehicle light as claimed in claim 7.

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