Low beam height adjusting method and device, electronic equipment and storage medium

Abstract

The invention discloses a dipped headlight height adjusting method and device, electronic equipment and a storage medium, and the method comprises the steps: collecting a face image of a driver through a camera in a cabin; a facial feature recognition algorithm based on deep learning is adopted, and three-dimensional coordinates of pupil centers are positioned in the facial image of the driver; determining the optimal height of the dipped headlight corresponding to the pupil center position; and adjusting the height parameter of the dipped headlight according to the determined optimal height of the dipped headlight. According to the dipped headlight height adjusting method and device, the electronic equipment and the storage medium, the problems that in the prior art, manual dipped headlight height adjusting operation is tedious, and real-time adjusting cannot be achieved in the driving process can be solved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of automobile headlamps, in particular to a low beam height adjustment method and device, an electronic device and a storage medium. BACKGROUND

[0002] Currently, there are two types of methods for adjusting the height of the low beam of a vehicle, namely manual adjustment and automatic adjustment. Manual adjustment requires the driver to stop the vehicle and manually adjust the knob, which is tedious and cannot be adapted in real time during driving. Existing automatic adjustment techniques are mostly based on vehicle body posture sensors (such as inclination sensors) and indirectly determine the adjustment requirements according to vehicle load, slope and other parameters.

[0003] The existing technology has obvious deficiencies: manual adjustment has poor timeliness and cannot adapt to changes in the driver's sitting posture during driving and different drivers driving; automatic adjustment based on the vehicle body posture does not take into account individual differences in drivers, and when the eyes are in different positions, the preset low beam height cannot match the actual line of sight requirements, which can easily cause problems such as dazzling of oncoming vehicles or insufficient field of view. The core reason for these problems is that the existing technology does not directly associate the driver's visual core reference point (eye position), resulting in insufficient adjustment accuracy and adaptability, and the inability to dynamically adjust in real time during driving. SUMMARY

[0004] The purpose of the present application is to provide a low beam height adjustment method, device, electronic device and storage medium, which can solve the problem of tedious operation of manually adjusting the height of the low beam and the inability to adjust in real time during driving.

[0005] The present application provides the following solutions:

[0006] According to one aspect of the present application, a low beam height adjustment method is provided, which comprises:

[0007] capturing a facial image of the driver through an in-cabin camera;

[0008] using a facial feature recognition algorithm based on deep learning to locate the three-dimensional coordinates of the pupil center in the facial image of the driver;

[0009] determining the optimal height of the low beam corresponding to the position of the pupil center;

[0010] adjusting the height parameter of the low beam according to the determined optimal height of the low beam.

[0011] Optionally, determining the optimal height of the low beam corresponding to the position of the pupil center comprises:

[0012] determining the optimal height of the low beam corresponding to the position of the pupil center by querying a preset standard mapping database.

[0013] Optionally, further comprising:

[0014] According to the correspondence between the collected pupil center position and the best height of the low beam, a standard mapping database is established.

[0015] Optionally, determining the best height of the low beam corresponding to the pupil center position comprises:

[0016] The best height of the low beam corresponding to the pupil center position is determined through the operation of the machine learning algorithm.

[0017] Optionally, the machine learning algorithm comprises a convolutional neural network algorithm.

[0018] Optionally, further comprising:

[0019] When the camera fails or image recognition fails for 5 consecutive frames, the system automatically switches to a body posture sensor assisted adjustment mode.

[0020] Optionally, further comprising:

[0021] When the change in the eye position coordinates collected for 3 consecutive frames is less than ±5mm, the adjustment action is triggered.

[0022] According to the two aspects of the present application, a low beam height adjustment device is provided, which comprises:

[0023] The acquisition module is configured to acquire a face image of the driver through an in-cabin camera;

[0024] The positioning module is configured to locate the three-dimensional coordinates of the pupil center in the face image of the driver by using a face feature recognition algorithm based on deep learning;

[0025] The determination module is configured to determine the best height of the low beam corresponding to the pupil center position;

[0026] The adjustment module is configured to adjust the height parameter of the low beam according to the determined best height of the low beam.

[0027] According to the three aspects of the present application, an electronic device is provided, which comprises:

[0028] a processor, a communication interface, a memory and a communication bus,

[0029] wherein the processor, the communication interface and the memory complete mutual communication through the communication bus;

[0030] The memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the low beam height adjustment method according to the foregoing description.

[0031] According to the four aspects of the present application, a computer readable storage medium is provided, and a computer program is stored on the computer readable storage medium, and the computer program is executed by a processor to implement the low beam height adjustment method as described above.

[0032] Through the above scheme, the following beneficial technical effects are obtained:

[0033] Without manual operation of the driver, the eye position is captured in real time through the camera, full-automatic dynamic adjustment of the low beam height is realized, and driving convenience is improved.

[0034] With the driver's eye position as the core reference, the adjustment accuracy is higher, and the situation of too far lighting dazzling or too close vision deficiency is avoided, and driving safety is improved.

[0035] Adapt to different drivers, different sitting postures of the same driver and various scenes, and the universality is higher, and driving fatigue caused by unsuitable lighting is reduced.

[0036] Without additional increase of complex heavy sensors, the function is expanded based on the existing camera (such as a driving monitoring camera) in the vehicle, the cost is controllable, and mass production adaptation is easy. BRIEF DESCRIPTION OF DRAWINGS

[0037] Figure 1 is a flowchart of the low beam height adjustment method provided by one or more embodiments of the present application;

[0038] Figure 2 is a flowchart of the low beam height adjustment method provided by one or more embodiments of the present application;

[0039] Figure 3 is a flowchart of the low beam height adjustment method provided by one or more embodiments of the present application;

[0040] Figure 4 is a flowchart of the low beam height adjustment method provided by one or more embodiments of the present application;

[0041] Figure 5 is a flowchart of the low beam height adjustment method provided by one or more embodiments of the present application;

[0042] Figure 6 is a structural diagram of the low beam height adjustment device provided by one or more embodiments of the present application;

[0043] Figure 7 is a structural diagram of the electronic device provided by one or more embodiments of the present application. DETAILED DESCRIPTION

[0044] The technical solutions of the present application will be described clearly and completely below in conjunction with the drawings. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

[0045] Figure 1 is a flowchart of the low-beam height adjustment method provided by one or more embodiments of the present application. Referring to Figure 1 , the low-beam height adjustment method comprises the following operation steps:

[0046] S11, acquiring a facial image of the driver through an in-cabin camera.

[0047] S12, positioning the three-dimensional coordinates of the pupil center in the facial image of the driver by using a facial feature recognition algorithm based on deep learning.

[0048] S13, determining the optimal height of the low beam corresponding to the position of the pupil center.

[0049] S14, adjusting the height parameter of the low beam according to the determined optimal height of the low beam.

[0050] In some scenarios, the illumination height of the low beam is of great significance to driving safety. The most typical example is the scenario of driving at night. In the case of driving at night, how to adjust the illumination height of the low beam appropriately can greatly help the driver to identify the obstacles in the surrounding environment, thereby improving the safety factor of driving. On the contrary, if the illumination height of the low beam is not adjusted appropriately, it cannot help the driver to identify the obstacles in front, but instead causes an obstacle to the driver's vision, which will reduce the safety of driving and even threaten the lives of the driver and other people in the vehicle.

[0051] Since the adjustment of the height of the low beam is so important, and the existing height adjustment method mostly relies on manual adjustment, the adjustment process is complicated and troublesome, which will inevitably cause unnecessary trouble to the safe driving of the vehicle.

[0052] In this embodiment, an automatic adjustment method of the height of the low beam is provided. In this automatic adjustment method, only one camera needs to be added in the cabin to complete the full-automatic adjustment of the height of the low beam.

[0053] The camera arranged in the cabin should be arranged at a position in front of the cabin. Specifically, the arrangement position should be such that the camera can clearly capture a complete image of the driver's face in the cabin. That is, the purpose of selecting the camera arrangement position is to collect a complete image of the driver's face. In any case, if the camera cannot capture an image of the driver's face or the captured image is incomplete, the selected arrangement position is not qualified.

[0054] According to the actual experience of selecting the arrangement position of the camera, the camera in the cabin is preferably arranged on the ceiling directly in front of the driver. By selecting such an arrangement position, the completeness of the driver's face image collection can be effectively ensured regardless of the height of the driver himself or the posture of the driver in the cabin.

[0055] After collecting the complete face image of the driver, the collected face image needs to be further processed, that is, the position of the driver's eye pupil, that is, the three-dimensional coordinates, is extracted from the complete face image.

[0056] The process of identifying the three-dimensional coordinates of the pupil from the face image adopts a face feature recognition algorithm based on deep learning. More specifically, a YOLO model is used to identify the driver's face image to obtain the position of the driver's pupil on the collected face image, and further determine the three-dimensional coordinates of the pupil in the cabin.

[0057] It should be noted that the collected driver's face image usually contains the driver's left eye and right eye. This means that in the process of identifying the three-dimensional coordinates of the pupil, both the three-dimensional coordinates of the pupil of the left eye and the three-dimensional coordinates of the pupil of the right eye need to be identified.

[0058] It should be understood that the two-dimensional coordinate position of the pupil directly identified in the collected image needs to be converted to the three-dimensional coordinate position of the pupil in the cabin.

[0059] The specific conversion method should be to first establish a three-dimensional coordinate space of the collected face image, and then establish a three-dimensional coordinate space of the whole cabin. From the three-dimensional coordinate space of the collected face image, the collected face image can be transformed to the three-dimensional coordinate space of the whole cabin through a series of translation and rotation operations. According to the translation distance, rotation angle and a series of parameters used in the above coordinate transformation process, the two-dimensional space coordinates of the pupil can be converted to the three-dimensional space coordinates of the whole cabin.

[0060] After determining the three-dimensional coordinates of the left and right eye pupils of the driver in the whole cabin, the best height parameter of the low beam can be further determined according to the three-dimensional coordinates.

[0061] Typically, the height parameter of the low beam can be determined by querying a pre-established standard mapping database.

[0062] In the standard mapping database, the correspondence between the three-dimensional coordinates of the pupils and the height parameter of the low beam is stored. For example, when the three-dimensional coordinates of the left eye pupil of the driver are [x1, y1, z1] and the three-dimensional coordinates of the right eye pupil are [u1, v1, w1], the optimal height of the low beam should be a1; when the three-dimensional coordinates of the left eye pupil of the driver are [x2, y2, z2] and the three-dimensional coordinates of the right eye pupil are [u2, v2, w2], the optimal height of the low beam is a2.

[0063] In addition, another way is to apply machine learning to determine the height parameter of the low beam. The simplest and most direct way is to apply a decision tree to determine the optimal height parameter of the low beam corresponding to the current driver's pupil position.

[0064] In addition, the machine learning algorithm described above can also be a support vector machine. The converted left eye coordinates and right eye coordinates of the driver are input into the support vector machine, and the coordinate data is divided by the hyperplane already established in the support vector machine, so as to determine the specific illumination height parameter of the low beam.

[0065] After the illumination height parameter of the low beam is determined, the actuator can be driven to adjust the specific illumination height of the low beam according to the height parameter that has been determined.

[0066] Figure 2 is a flowchart of the low beam height adjustment method provided by one or more embodiments of the present application. Referring to Figure 2 , the low beam height adjustment method includes the following operation steps:

[0067] S21, acquiring the face image of the driver through the in-cabin camera.

[0068] S22, using a deep learning-based face feature recognition algorithm to locate the three-dimensional coordinates of the pupil center in the face image of the driver.

[0069] S23, determining the optimal height of the low beam corresponding to the pupil center position by querying the pre-established standard mapping database.

[0070] S24, adjusting the height parameter of the low beam according to the determined optimal height of the low beam.

[0071] This embodiment specifically explains the establishment and use method of the standard mapping database.

[0072] The core task of the standard mapping database is to establish the correspondence between the pupil center position and the best height of the low beam. Only when the correspondence between the two is clear, the query of the standard mapping database will be meaningful.

[0073] In a typical embodiment, the standard mapping database establishes the correspondence between the pupil center position and the best height of the low beam in the form of a relational database. For example, in the data table of the relational database, write a data entry like this: when the center position of the left eye pupil is [x, y, z] and the center position of the right eye pupil is [u, v, w], the best height of the low beam should be a. Then, when the actually measured center position of the left eye pupil and the center position of the right eye pupil are exactly the above values, through the query of the standard mapping database, it can be known that in the current situation, the best height of the low beam should be adjusted to the value of a.

[0074] The way of building a database can be data full-amount database building, and data summary database building, which can be selected.

[0075] Data full-amount database building is to import all the raw collected data into the database without deletion, and complete the establishment of the standard mapping database.

[0076] Specific to the present embodiment, the center position data of the left eye pupil and the center position data of the right eye pupil obtained in the previous steps, as well as the corresponding low beam height data, are all recorded in each data record of the relational database.

[0077] The advantage of data full-amount database building is that the recorded data in the database is more detailed, and the support for decision-making is more comprehensive.

[0078] Data summary database building, simply speaking, is not to import all the raw data into the database, but to process the raw collected data first, and the data actually imported into the database is not the raw collected data itself, but the summarized data.

[0079] Taking the center position data of the left and right eye pupils in the present embodiment as an example, in three-dimensional position data, if the z-axis data is more important, the summary process can be to delete all the x-axis and y-axis data of the left eye pupil center and the right eye pupil center, and then average the left eye z-axis data and the right eye z-axis data. That is to say, the final uploaded left eye pupil center position and right eye pupil center position only contain a z-axis position data.

[0080] The advantage of data summary database building is that the established database is more lightweight, and the operation speed of the query operation is faster.

[0081] Figure 3 is a flowchart of the high beam height adjustment method provided by one or more embodiments of the present application. Referring to Figure 3 , the high beam height adjustment method comprises the following operation steps:

[0082] S31, acquiring a face image of the driver through an in-cabin camera.

[0083] S32, using a deep learning-based face feature recognition algorithm to locate the three-dimensional coordinates of the pupil center in the face image of the driver.

[0084] S33, determining the optimal height of the high beam corresponding to the position of the pupil center through the operation of the machine learning algorithm.

[0085] S34, adjusting the height parameter of the high beam according to the determined optimal height of the high beam.

[0086] In the foregoing embodiments of the present application, the preset database query method is used to determine the value of the optimal height of the high beam. In the present embodiment, the machine learning method is used to determine the optimal height of the high beam.

[0087] Machine learning algorithms can be divided into unsupervised machine learning algorithms, supervised machine learning algorithms, and semi-supervised machine learning algorithms.

[0088] In the unsupervised machine learning algorithm, the features in the sample have no characteristic and do not contain label information. Since the unsupervised learning does not contain any label information, it is not known what the final classification result of each set of feature data should be during learning.

[0089] The supervised machine learning algorithm is completely different. In supervised learning, the training sample contains both features and label information.

[0090] The semi-supervised machine learning algorithm, as the name implies, is a training data that contains both labeled information and unlabeled information.

[0091] It should be noted here that the machine learning algorithm used in the present embodiment should not be an unsupervised machine learning algorithm. The reason for this design is mainly due to the importance of the high beam height parameter to road safety. If the unsupervised method is used, the adjustment model will deviate, which will have a devastating impact on road safety.

[0092] Figure 4 is a flowchart of the high beam height adjustment method provided by one or more embodiments of the present application. Referring to Figure 4 , the high beam height adjustment method comprises the following operation steps:

[0093] S41, collect the facial image of the driver through the camera in the cockpit.

[0094] S42, locate the three-dimensional coordinates of the pupil center in the facial image of the driver by using a facial feature recognition algorithm based on deep learning.

[0095] S43, determine the optimal height of the low beam corresponding to the position of the pupil center.

[0096] S44, adjust the height parameter of the low beam according to the determined optimal height of the low beam.

[0097] S45, when the camera fails or the image recognition fails for 5 consecutive frames, the system automatically switches to a body posture sensor assisted adjustment mode.

[0098] The embodiment focuses on the abnormal processing part of the above low beam height adjustment mode.

[0099] It should be understood that the above adjustment mode of collecting images through the camera as the basis for low beam height adjustment has appeared abnormal, and if the height adjustment of the low beam is still carried out according to the established mode, not only the result of the height adjustment is no longer reliable, but also it will seriously threaten the life safety of the driver and other personnel in the vehicle.

[0100] Based on the above considerations, in the case that the height adjustment mode based on the camera collected images has already appeared abnormal, a corresponding exit mechanism needs to be taken.

[0101] The so-called exit is that the height adjustment of the low beam is no longer adjusted by the driver's facial image collected by the camera arranged in the cockpit. Instead, other mechanisms are used for height adjustment.

[0102] As for when to determine that the related mechanism has appeared abnormal, it is judged by the concept of image frame. Specifically, if the image processing of 5 consecutive frames in the camera collected images has appeared abnormal, it is considered that the adjustment mechanism of the above visual mode has appeared abnormal and needs to be exited and other judgment modes are used instead.

[0103] With the above abnormal processing mechanism, the above automatic height adjustment mode can be ensured to be more safe and reliable, and will not bring hidden dangers to the actual driving safety.

[0104] Figure 5 is a flowchart of the low beam height adjustment method provided by one or more embodiments of the present application. Referring to Figure 5 , the low beam height adjustment method includes the following operation steps:

[0105] S51, collect the facial image of the driver through the camera in the cockpit.

[0106] S52, a deep learning-based facial feature recognition algorithm is used to locate the three-dimensional coordinates of the pupil center in the driver's facial image.

[0107] S53, when the change in the eye position coordinates of three consecutive frames is less than ±5mm, the adjustment action is triggered.

[0108] S54, the best height of the low beam corresponding to the position of the pupil center is determined.

[0109] S55, according to the determined best height of the low beam, the height parameter of the low beam is adjusted.

[0110] The focus of this embodiment is on the anti-shake strategy.

[0111] It should be understood that a vehicle in normal driving will have some bumps. This means that the detected driver's pupil position will often have some jumps.

[0112] This poses a challenge for the height adjustment of the low beam. If the height adjustment of the low beam is performed every time the driver's pupil position changes is detected, a large number of adjustment operations will be invalid adjustment operations.

[0113] In this embodiment, in order to prevent the occurrence of a large number of invalid adjustment actions, the threshold for performing the low beam height adjustment operation is raised.

[0114] Specifically, only when the driver's pupil position changes in the detection results of at least three consecutive frames of driver's facial images, the height parameter adjustment action is triggered. If only one or two frames of facial images have changes in the pupil position, no action is taken.

[0115] Here, it should be noted that the change judgment standard mentioned here is based on the previous frame of facial image before the change occurs. That is, the three consecutive images are compared with one image to determine whether the pupil position has changed. It is not that three images are compared with their previous images.

[0116] In addition to increasing the number of compared image frames, a position change threshold is further set. Only when the position changes in the three consecutive facial images are more than ±5mm, the height parameter adjustment action is performed.

[0117] After the above judgment operation, a large number of invalid adjustment actions are filtered out, and only when the height adjustment is really needed, the above height adjustment action will be triggered.

[0118] Figure 6This is a structural diagram of a low beam headlight height adjustment device provided in one or more embodiments of the present invention. See also... Figure 6 The low beam headlight height adjustment device includes:

[0119] The acquisition module 61 is used to acquire facial images of the driver via an in-cabin camera.

[0120] The positioning module 62 is used to locate the three-dimensional coordinates of the pupil center in the driver's facial image using a deep learning-based facial feature recognition algorithm.

[0121] The determination module 63 is used to determine the optimal height of the low beam headlight corresponding to the center position of the pupil.

[0122] The adjustment module 64 is used to adjust the height parameters of the low beam headlights according to the determined optimal height of the low beam headlights.

[0123] It is worth noting that although only some basic functional modules are disclosed in the embodiments of this invention, it does not mean that the composition of this system is limited to the above-mentioned basic functional modules. On the contrary, what this embodiment intends to express is that, based on the above-mentioned basic functional modules, those skilled in the art can arbitrarily add one or more functional modules in combination with existing technology to form an infinite number of embodiments or technical solutions. That is to say, this system is open rather than closed. The fact that this embodiment only discloses a few basic functional modules should not be considered as the scope of protection of the claims of this invention being limited to the disclosed basic functional modules. At the same time, for the convenience of description, the above device is described separately according to its functions as various units and modules. Of course, in implementing this invention, the functions of each unit and module can be implemented in one or more software and / or hardware.

[0124] like Figure 7 As shown, the present invention also provides an electronic device, including: a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus; the memory stores a computer program, and when the computer program is executed by the processor, the processor performs the steps of the low beam headlight height adjustment method.

[0125] Figure 7 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. For example... Figure 7 The structure shown in this embodiment of the invention includes an electronic device comprising one or more processors 710 and a memory 720; the processors 710 in this electronic device may be one or more. Figure 7Taking a processor 710 as an example; a memory 720 is used to store one or more programs; the one or more programs are executed by the one or more processors 710, causing the one or more processors 710 to implement the low beam headlight height adjustment method as described in any one of the embodiments of the present invention.

[0126] The electronic device may also include an input device 730 and an output device 740.

[0127] The processor 710, memory 720, input device 730, and output device 740 in this electronic device can be connected via a bus or other means. Figure 7 Taking the example of a connection between China and Israel via a bus.

[0128] The memory 720 in this electronic device serves as a computer-readable storage medium, capable of storing one or more programs. These programs can be software programs, computer-executable programs, or modules, such as the program instructions / modules corresponding to the low beam headlight height adjustment method provided in this embodiment of the invention. The processor 710 executes various functional applications and data processing of the electronic device by running the software programs, instructions, and modules stored in the memory 720, thereby implementing the low beam headlight height adjustment method described in the above embodiment.

[0129] The memory 720 may include a program storage area and a data storage area. The program storage area may store the operating system and applications required for at least one function; the data storage area may store data created based on the use of the electronic device. Furthermore, the memory 720 may include high-speed random access memory and may also include non-volatile memory, such as at least one disk storage device, flash memory device, or other non-volatile solid-state storage device. In some instances, the memory 720 may further include memory remotely located relative to the processor 710, which can be connected to the device via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0130] Input device 730 can be used to receive input digital or character information, and to generate key signal inputs related to user settings and function control of the electronic device. Output device 740 may include display devices such as a display screen.

[0131] The present invention also provides a computer-readable storage medium, comprising: storing a computer program executable by a vehicle, wherein when the computer program is run on the vehicle, the vehicle performs the steps of the low beam headlight height adjustment method.

[0132] Specifically, the computer storage medium in this embodiment of the invention can be any combination of one or more computer-readable media. The computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. For example, a computer-readable storage medium can be—but is not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of computer-readable storage media (a non-exhaustive list) include: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this embodiment, the computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0133] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for adjusting the height of low beam headlights, characterized in that, The method for adjusting the low beam headlight height includes: The driver's facial image is captured using an in-cabin camera; A deep learning-based facial feature recognition algorithm is used to locate the three-dimensional coordinates of the pupil center in the driver's facial image; Determine the optimal height for the low beam headlights corresponding to the center position of the pupil; Adjust the height parameters of the low beam headlights according to the determined optimal low beam headlight height.

2. The method according to claim 1, characterized in that, Determine the optimal low beam headlight height corresponding to the center of the pupil, including: By querying a pre-defined standard mapping database, the optimal height of the low beam headlights corresponding to the center position of the pupil is determined.

3. The method according to claim 2, characterized in that, Also includes: A standard mapping database was established based on the correspondence between the collected pupil center position and the optimal height of the low beam headlight.

4. The method according to claim 1, characterized in that, Determine the optimal low beam headlight height corresponding to the center of the pupil, including: By using machine learning algorithms, the optimal height of the low beam headlights corresponding to the center position of the pupil is determined.

5. The method according to claim 4, characterized in that, Machine learning algorithms include: convolutional neural network algorithms.

6. The method according to claim 1, characterized in that, Also includes: When the camera malfunctions or image recognition fails for 5 consecutive frames, the system automatically switches to the vehicle posture sensor-assisted adjustment mode.

7. The method according to claim 1, characterized in that, Also includes: When the change in eye position coordinates is less than ±5mm in three consecutive frames, the adjustment action is triggered.

8. A low beam headlight height adjustment device, characterized in that, The low beam headlight height adjustment device includes: The acquisition module is used to acquire facial images of the driver via an in-cabin camera; The positioning module is used to locate the three-dimensional coordinates of the pupil center in the driver's facial image using a deep learning-based facial feature recognition algorithm. The determination module is used to determine the optimal height of the low beam headlights corresponding to the center position of the pupil; The adjustment module is used to adjust the height parameters of the low beam headlights according to the determined optimal low beam headlight height.

9. An electronic device, characterized in that, The electronic device includes: Processor, communication interface, memory, and communication bus. The processor, communication interface, and memory communicate with each other through a communication bus. The memory stores a computer program that, when executed by the processor, causes the processor to perform the steps of the low beam headlight height adjustment method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, A computer program is stored on the computer-readable storage medium, which, when executed by a processor, implements the low beam headlight height adjustment method according to any one of claims 1 to 7.

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