Vehicle lamp intelligent control method, device and equipment and storage medium

Abstract

The invention relates to the technical field of vehicle lamp control, in particular to a vehicle lamp intelligent control method which comprises the following steps: acquiring road surface information through radar or camera equipment, and inputting the road surface information into a first model for controlling a vehicle lamp; the first model can identify and determine lane lines on two sides through training by means of a deep learning algorithm, and determines a lane center line and a lane center line vanishing point according to the lane lines; calculating an included angle between a straight line from a lane center line vanishing point to a vehicle lamp center and a current main optical axis of the vehicle lamp; and rotating the vehicle lamp by using the vehicle lamp intelligent control device to enable at least part of the light to irradiate the lane center line vanishing point. According to the technical scheme, after the vanishing point of the lane center line is recognized, the vehicle lamp intelligent control device is used for rotating the main optical axis of the vehicle lamp so that at least part of the light can irradiate the vanishing point of the center line, and therefore the length of the lane center line can be prolonged, and more road conditions can be observed when a vehicle runs on a large-angle curve; and driving is safer.

Description

Technical Field

[0001] The present disclosure relates to the technical field of vehicle light control, and in particular to a vehicle light intelligent control method, device, equipment and storage medium. Background Art

[0002] With the development of science and technology, the functions and performance of automobiles are constantly improving. Among them, as an important part of automobiles, the intelligent and automated control of headlights has become a hot topic of research. In the field of vehicle engineering, the control of traditional headlights is mainly achieved through hardware circuits and simple algorithms, which cannot adapt to complex driving environments. In the field of intelligent transportation systems, the control of headlights needs to interact with information such as road conditions, other vehicles and pedestrians to achieve more intelligent control. In the field of sensor technology, various sensors are used to collect status data of headlights, such as visual perception information, radar detection information, and cloud perception information. These data provide the basis for the intelligent and automated control of headlights.

[0003] Existing technologies mainly control the on or off state, brightness, flickering frequency and energy consumption of car lights through hardware circuits and simple algorithms. In addition, there are some technologies that collect the status data of car lights through sensors, and then send these data to the cloud for storage and analysis to achieve remote control of car lights. However, these technologies have some problems, such as being unable to adapt to complex driving environments, low control accuracy, and excessive energy consumption. First, these technologies cannot adapt to complex driving environments. For example, at night, rainy and foggy weather, parameters such as the brightness, flickering frequency and energy consumption of car lights need to be adjusted to adapt to different driving environments. Secondly, the control accuracy of existing technologies is not high. For example, in the adjustment of parameters such as the on or off state, brightness, flickering frequency and energy consumption of car lights, refined control cannot be achieved, thereby affecting the use effect of car lights. Existing technologies consume too much energy. For example, in the adjustment of parameters such as the brightness, flickering frequency and energy consumption of car lights, a large amount of electrical energy is required, thereby increasing the energy consumption of the car. Therefore, there is a large room for improvement in existing car light control technologies.

[0004] In addition, the biggest challenge of night driving comes from driving on mountain roads, that is, driving on sloping and curved roads, especially large bends, large uphill and downhill slopes, and large deviations between the vehicle's driving direction and the direction of the headlights. Or the headlights are not adjusted in time, making it impossible for the driver to understand the road conditions in the first place. For people with less driving experience, traffic accidents are likely to occur.

[0005] Chinese patent number "CN117841826A" and the name of the patent are "A vehicle headlight adjustment method, device, vehicle and storage medium" which discloses a method for adjusting the headlights, but it is overly dependent on navigation system information. If the positioning is inaccurate, the headlights cannot be accurately controlled, especially in remote mountainous areas. If the navigation data is delayed, it will directly lead to a delay in the control information, which will cause the headlight position to be untimely adjusted. Such poor stability will seriously affect the driving experience.

[0006] Chinese patent number "CN115805862B" and the name of the patent "An adaptive headlamp adjustment method and related equipment" discloses a method for automatically adjusting vehicle lights. However, if it relies on sensors, it may affect the camera image on bumpy roads. If these interference effects cannot be identified, erroneous calculation results will occur, especially for the judgment of the vanishing point of the road. The road conditions in different regions are completely different. For example, highways are relatively standardized and easier to judge, but the slope of the highway is not large and the bending radius is relatively large. Therefore, there is less demand for intelligent control of vehicle lights. However, mountain roads are relatively curved and the slope is relatively large. The environment on both sides of the road is also very different. The vanishing point is difficult to automatically judge after fitting and calculating the camera image. This technical solution cannot judge S-shaped curves. As the light illumination weakens, only an area can be determined. How to make accurate judgments and determine a point is also a major problem. Inaccurate point positions affect subsequent calculations, and ultimately the vehicle lights cannot be accurately controlled.

[0007] Chinese patent numbers "CN113221748B" and "CN102815299B" disclose methods for lane recognition, which determine the center line of the lane through lane recognition. However, they are unable to accurately recognize complex S-curves, and they have not been used in the field of vehicle light control technology. Summary of the invention

[0008] The present disclosure provides a method, device, equipment and storage medium for intelligent control of vehicle lights, so as to at least solve the technical problem in the prior art that vehicle lights cannot be controlled timely and accurately on complex mountain roads.

[0009] According to a first aspect of the present disclosure, there is provided a method for intelligently controlling a vehicle light, comprising the following steps: acquiring road surface information through a radar or a camera device, and inputting the road surface information into a first model for controlling the vehicle light; The first model relies on a deep learning algorithm that is trained to identify and determine the lane lines on both sides, and determines the center line of the lane and the vanishing point of the center line based on the lane lines; Calculate the angle between the straight line from the vanishing point of the lane centerline to the center of the headlight and the current main optical axis of the headlight; The headlights are rotated by using the headlight intelligent control device so that at least part of the light is irradiated on the vanishing point of the lane center line.

[0010] In one possible implementation mode, after the headlights are turned to a certain angle or while they are being turned using the first model, a navigation system is used for verification, and the steps are as follows: obtaining position information and navigation system information, confirming the vehicle's current direction of travel and the theoretical headlight angle under the direction of travel based on the position information and the navigation system information, comparing the theoretical headlight angle with the actual headlight angle, and if the angle deviation does not exceed 20%, no adjustment is made; if the angle deviation is between 20% and 50%, the headlights are turned to a larger angle using an intelligent headlight control device; if the angle deviation exceeds 50%, an error is reported, or a control mode is manually selected, or a custom control mode is defined, or the reliability of the navigation system or the first model is evaluated using sensor information.

[0011] In one possible implementation mode, utilizing sensor information to evaluate the reliability of a navigation system requires obtaining sensor information, the obtained sensor information including inclination information, steering angle information, acceleration information, speed information, and distance information. The navigation system information confirms theoretical data of the current position based on map data, and determines whether the theoretical data is within a reasonable error range with the feedback data of each sensor information, and determines whether there is any delay in the navigation system information based on the size of the error.

[0012] In one possible implementation, after confirming that there is no delay in the navigation system information, a distance sensor is used to measure the length of the main optical axis of the headlights, and based on the navigation system information, it is confirmed whether a longer headlight illumination distance can be obtained, and based on the speed information, it is confirmed whether the headlight illumination distance needs to be increased.

[0013] In one possible implementation, the road information is input into the second model for controlling the headlights. The second model is trained based on a deep learning algorithm to recognize vehicles and faces. The headlight intelligent control device turns the headlights to avoid vehicles or faces. Face recognition only needs to identify whether it is a face, and does not need to identify specific identity information.

[0014] According to a second aspect of the present disclosure, a vehicle light intelligent control device is provided, comprising an information collection module, a controller module and a vehicle light module. The information collection module is equipped with a camera device and a radar device. The camera device can obtain visible light image information, and the radar device can obtain three-dimensional data; The controller module can exchange data with the vehicle computer and obtain sensor information and navigation system information; The vehicle light module includes a main axis distance sensor, a lamp holder, a rotating structure and a driver. The lamp holder is provided with a plurality of lamp beads, which are installed on a rotating structure provided with a plurality of rotating shafts, so that the lamp holder as a whole can rotate around a plurality of axes. The plurality of lamp beads are provided with independent rotating shafts so that the lamp beads can rotate around the rotating shafts. The driver can drive the lamp beads to rotate. The main axis distance sensor is arranged at the main optical axis position of the lamp holder and can measure the length of the main optical axis.

[0015] In one possible implementation, the vehicle light module further includes a left axle distance sensor, a right axle distance sensor, an upper axle distance sensor, and a lower axle distance sensor.

[0016] In one possible implementation mode, a plurality of lamp beads are arranged vertically and fixed on a strip circuit board, an elastic heat sink and a supporting heat sink are provided at the rear of the strip circuit board, a shaft sleeve is provided at the rear of the supporting heat sink, and the shaft sleeve is installed on the rotating shaft, a plurality of strip circuit boards are arranged horizontally and a left control board and a right control board are respectively provided at the outer sides of the strip circuit boards on both sides, and adjacent strip circuit boards are limited by elastic heat sinks.

[0017] According to a third aspect of the present disclosure, there is provided an electronic device, including: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the vehicle light intelligent control method described in the present disclosure.

[0018] According to a fourth aspect of the present disclosure, a non-transitory computer-readable storage medium storing computer instructions is provided, wherein the computer instructions are used to enable the computer to execute the vehicle light intelligent control method described in the present disclosure.

[0019] Compared with the prior art, the intelligent control method of vehicle lights disclosed in the present invention has the following beneficial effects: The center line of the lane is usually obtained by further calculation after obtaining the lane line through edge detection on both sides of the lane. Therefore, the center line of the lane can usually be obtained when the edge contour on one side is relatively clear. Especially for complex mountain roads, the contour on one side may be very unclear, which makes it difficult to determine the range of the road surface and it is impossible to accurately control the headlights. The center line of the lane can be obtained according to the contour on one side and the width of the road surface. Moreover, the center line of the lane is generally the main driving route of the vehicle. Therefore, the center line of the lane needs to be better illuminated, especially on curved roads and mountain roads. If the headlights shine directly forward, they will inevitably slowly move away from the center line of the lane, and the vanishing point of the center line of the lane will also deviate from the main optical axis of the headlights, which makes it impossible to observe the road surface farther away. The technology of the present application After identifying the vanishing point of the center line of the lane, the technical solution uses the intelligent control device of the headlight to rotate the main optical axis of the headlight so that at least part of the light is irradiated on the vanishing point of the center line. In this way, the length of the center line of the lane can be extended. Of course, if the vehicle speed is slow, it may not be necessary to observe too far a distance, so it is also necessary to measure the irradiation distance of the headlight through a distance sensor. There will be different irradiation distances at different vehicle speeds, so part of the light can be irradiated on the vanishing point of the center line. Of course, if the vehicle speed is fast, as much light as possible is irradiated at a farther distance, especially on curves. The lighting effect of the entire curve should be considered, rather than the vanishing point of the curve. In this way, more road conditions can be observed when the vehicle is driving on a large-angle curve, making driving safer.

[0020] It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present disclosure, nor is it intended to limit the scope of the present disclosure. Other features of the present disclosure will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] The above and other objects, features and advantages of the exemplary embodiments of the present disclosure will become readily understood by reading the detailed description below with reference to the accompanying drawings. In the accompanying drawings, several embodiments of the present disclosure are shown in an exemplary and non-limiting manner, in which: In the drawings, the same or corresponding reference numerals represent the same or corresponding parts.

[0022] Figure 1 A schematic diagram of the implementation process of a vehicle light intelligent control method according to an embodiment of the present disclosure is shown; Figure 2 A schematic diagram of inputting a grayscale photo with a vehicle left turn label during training of a vehicle light intelligent control method according to an embodiment of the present disclosure is shown; Figure 3 A schematic diagram of inputting a grayscale photo with a vehicle right turn label during training of a vehicle light intelligent control method according to an embodiment of the present disclosure is shown; Figure 4A schematic diagram showing a grayscale photo with a vehicle right turn label but unclear outline of the right side of the road when inputting a vehicle light intelligent control method in training according to an embodiment of the present disclosure is shown; Figure 5 A schematic diagram showing a vehicle's forward photo after being processed by a road edge detection algorithm; Figure 6 A schematic diagram of the structure of a vehicle light module according to an embodiment of the present disclosure is shown; Figure 7 A schematic diagram of the interior of a headlight module according to an embodiment of the present disclosure is shown; Figure 8 A three-dimensional schematic diagram of the interior of a vehicle light module according to an embodiment of the present disclosure is shown; Fig. 9 Shows Figure 8 Enlarged view of point A in the middle; Fig.10 A schematic diagram of the structure of a vehicle light intelligent control device according to an embodiment of the present disclosure is shown; Fig.11 A schematic diagram of the structure of an electronic device according to an embodiment of the present disclosure is shown. DETAILED DESCRIPTION

[0023] In order to make the purpose, features, and advantages of the present disclosure more obvious and easy to understand, the technical solutions in the embodiments of the present disclosure will be clearly and completely described below in conjunction with the drawings in the embodiments of the present disclosure. Obviously, the described embodiments are only part of the embodiments of the present disclosure, not all of the embodiments. Based on the embodiments in the present disclosure, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present disclosure.

[0024] refer to Figure 1 The present invention discloses an intelligent control method for vehicle lights, comprising the following steps: obtaining road surface information through radar or camera equipment, and inputting the road surface information into a first model for controlling vehicle lights; the advantage of radar is that it is less affected by the environment and has good detection performance at night, while the camera equipment has a higher resolution and can identify more complex road conditions. For example, if there are no guardrails on both sides of the road, it is difficult to determine the lane line by radar, but by using the camera equipment, the lane information can be better identified under the premise that there are obvious dividing lines or other edge contours between the road surface and other areas.

[0025] The first model relies on deep learning algorithms to be trained to identify and determine the lane lines on both sides, and determine the lane center line and the lane center line vanishing point based on the lane lines; for example, OpenCV software is used for training. First, there are three situations in which vehicles drive straight, turn left, and turn right. Uphill and downhill only change the perspective, and the difference is small, so no further distinction is required. Figure 2 and Figure 3The vehicle's tilt angle is completely different when turning left or right, so the vehicle's driving condition can be determined based on the tilt direction of any lane line, such as Figure 4 As shown in the figure, the lane line on the right side of the photo is basically unclear, but the road boundary on the left side is relatively clear, so it can be determined as a right turn based on the edge line on the left side. There are also few cases where the left lane line and the right lane line are completely symmetrical and the straight line is driven. Figure 5 As shown in the figure, although there is a curve to the right, the radius of the curve is large, so it can be considered as a straight line. Figure 2-Figure 5 After the picture shown is labeled, it is input into the first model for model training. The edge detection method can quickly obtain multiple curves or lines. In order to reduce noise, the threshold of edge detection can be adjusted to achieve more accurate detection of lane lines. Figure 5 As shown in the figure, the photo has multiple lanes. The position of the lane line can be determined by fitting the position of the curve or line. Finally, the position of the center line of the lane can be calculated using the function. The illumination range of the headlight is large, so the position of the center line of the lane does not need to be calculated very accurately. The average value of the left and right lane curves can be directly taken. After obtaining the center line of the lane, the vanishing point of the center line of the lane can be confirmed. Figure 4 As shown, there is no obvious lane line in this driving environment, but according to the direction of the edge contour, it can still be determined as a right turn. The lane vanishing point is on the right side. The midpoint of the continuous area on the right side can be considered as the vanishing point of the lane centerline.

[0026] Calculate the angle between the straight line from the vanishing point of the center line of the lane to the center of the headlight and the current main optical axis of the headlight; the main optical axis of the headlight is the position of the lighting center of the headlight. For the convenience of calculation, the lighting direction of a certain lamp bead of the headlight can be considered as the main optical axis direction of the headlight. Some headlights have an overall structure of a single lamp bead, so there is generally a clear main optical axis. Therefore, different calculation methods are used for different headlights.

[0027] The main optical axis of the headlight is rotated by using the intelligent control device of the headlight so that at least part of the light is irradiated on the vanishing point of the center line. Generally speaking, the angle difference should be encountered when driving on a curve, so that the lighting at each position needs to be taken into account. Therefore, part of the light can be irradiated in the original direction, and part of the light can be rotated to a certain angle. In this way, the irradiation range can be expanded. Especially for the case of a large rotation angle, there will be a phenomenon of losing one thing while taking care of another. Rotating some of the lamp beads can achieve more uniform lighting at each position. Generally speaking, when the vehicle is driving at high speed, the vehicle generally has a small turning angle. In this way, the headlight can be rotated at a certain angle as a whole, so that the light is more concentrated and farther. However, when the vehicle is driving at a low speed, the vehicle can have a larger turning angle, so a larger irradiation distance is not required, but a larger irradiation range is required. At this time, part of the headlight structure can be rotated to adjust part of the light.

[0028] In one embodiment, after obtaining road surface information, the three situations of straight going, left turn and right turn are first judged. If it is a left turn or a right turn, a certain angle is pre-turned first, such as 5°. During the turning process, the vanishing point of the center line of the lane is judged simultaneously, and a second turn is performed after confirmation. This can speed up the reaction speed.

[0029] In one embodiment, after the headlight is turned to a certain angle or while it is being turned using the first model calculation, the navigation system is used for verification, and the steps are as follows: obtaining position information and navigation system information, confirming the current forward direction of the vehicle and the theoretical headlight angle under the forward direction according to the position information and navigation system information, comparing the theoretical headlight angle with the actual headlight angle, if the angle deviation range does not exceed 20%, no adjustment is made, if the angle deviation is between 20% and 50%, the headlight is turned to a large headlight angle using the headlight intelligent control device, if the angle deviation exceeds 50%, an error is reported, or a control mode is manually selected, or a custom control mode is defined, or the reliability of the navigation system or the first model is evaluated using sensor information. Verifying the rotation angle after or while the headlight is turned can reduce the time for the headlight to feedback, especially when the road conditions change rapidly when the vehicle turns. If the headlight cannot be fed back in time and turned, the vehicle's driving direction may be completely different from the headlight's irradiation direction, which results in a poor experience. The subsequent verification process helps the first model to learn and improve autonomously, so that the recognition accuracy is higher.

[0030] In one embodiment, the sensor information needs to be obtained to evaluate the reliability of the navigation system using sensor information. The obtained sensor information includes inclination information, steering angle information, acceleration information, speed information and distance information. The navigation system information confirms the theoretical data of the current position according to the map data, and determines whether the theoretical data is within a reasonable error range with the feedback data of each sensor information, and determines whether there is a delay in the navigation system information according to the size of the error. In general, the change in steering angle can be used to confirm whether there is a delay in the navigation system information. That is, when the turning angle of different places changes, it is compared with the turning angle change of the position located in the navigation map. If it is not synchronized, there is a delay. Whether there is a delay does not need to be continuously detected. Generally speaking, the accuracy of positioning information within a certain range is not much different, so it can be detected at intervals of a certain period of time. In addition, inclination information, acceleration information and speed information can be used as auxiliary reasons for judgment, especially when the inclination angle changes from uphill to downhill, which can be compared with the map information of the navigation system. Acceleration information and speed information can be judged with reference to personal driving habits. For example, people usually slow down before entering a curve and accelerate before exiting. These operating habits are recorded. Different curves have different speed ranges. Of course, compared with other information, the reliability of this information is poor and it is only used as a reference for further judgment of whether driving is delayed.

[0031] In one embodiment, after confirming that there is no delay in the navigation system information, a distance sensor is used to measure the length of the main optical axis of the headlight, and it is confirmed based on the navigation system information whether a longer headlight illumination distance can be obtained, and it is confirmed based on the speed information whether the headlight illumination distance needs to be increased.

[0032] In one embodiment, the road surface information is input into the second model for controlling the headlights. The second model is trained based on a deep learning algorithm to recognize vehicles and faces. The headlight intelligent control device turns the headlights to avoid vehicles or faces. Face recognition only needs to identify whether it is a face, and does not need to identify specific identity information.

[0033] In one embodiment, lane centerline detection can be lane line detection based on laser radar, that is, a method based on reflection intensity information. The method based on reflection intensity information is based on a grayscale image formed by laser radar reflection intensity information, or according to the combination of intensity information and elevation information, invalid information is filtered out and the lane line is fitted; the lane centerline can be obtained from the lane lines on both sides.

[0034] In one embodiment, lane centerline detection can be based on a hyperbolic model lane line detection algorithm; the Canny operator is used to detect the road edge; the Hough transform is used to extract road boundary points, and the extended Kalman filter is used for prediction to reduce the road scanning range; the left and right lane boundary parameters are matched with the hyperbolic model parameters, and the model parameters are solved by the least squares method to complete the lane boundary reconstruction; the lane centerline can be obtained from the lane lines on both sides.

[0035] In one embodiment, the S-curve can be identified, and the middle S-curve and the small S-curve can be distinguished. First, the starting line and the end point of the left turning point and the right turning point of the center line are found, and then the flag is extended. The extension flag is set for the last center line turning point that cannot be scanned, such as the last right turn into a straight road. Then the flag is cleared and the coordinates are reset to zero, and the least squares fitting slope of the center line is calculated. A short straight line before the turning point is fitted to obtain the slope, and the absolute value of the slope is taken. Then, the center line right turning point is found from bottom to top, and the slope and the interval where the turning point is located are used to assist in judgment, thereby reducing the probability of misjudgment. After finding the right turning point, record the coordinates of the right turning point, and mark the position, the centerline left turning point, and the extension mark position. When you are about to exit the S-curve, use the extension mark to extend the time to exit the S-curve. When exiting, you can only scan the right turning point but not the left turning point. Extend the S-curve mark to avoid the situation where the differential is too small to turn. Set the left turning point as the centerline coordinate of the first row of the scan line. If the left turning point and the right turning point are found, judge the curve attributes. If both turning points are found and the difference between the left turning point and the right turning point meets a certain range, then it is considered to meet the small S, otherwise it is a medium S.

[0036] In one embodiment, a method for intelligent control of vehicle lights based on multimodal perception includes the following specific steps: Step 1: multimodal data acquisition. By integrating visual perception information, radar detection information and cloud perception information, multimodal data of vehicle lights are obtained, including parameters such as the on or off state, brightness, flickering frequency and energy consumption of vehicle lights. Visual perception information includes ambient brightness around vehicle lights, obstacle information in front of the vehicle, etc., radar detection information includes vehicle distance and speed around vehicle lights, and cloud perception information includes weather information, traffic information, etc. Step 2: data preprocessing. The acquired state data is cleaned, classified, aggregated and analyzed to obtain processed state data. Data cleaning includes removing noise data and abnormal data, classification includes classifying data into different types, aggregation includes aggregating data according to dimensions such as time and location, and analysis includes statistical analysis and pattern recognition of data. Step 3: state recognition. According to the processed state data, the stability, energy consumption changes and abnormal conditions of the vehicle lights are identified through preset rules. The preset rules include the normal range of parameters such as the brightness, flashing frequency and energy consumption of the headlights. When the parameters exceed the normal range, they are identified as abnormal situations. Step 4: Reverse optimization control. According to the recognition results and the control parameters of the headlights, the control logic of the headlights is reversely optimized to obtain the reverse optimization results. The control parameters include the optimization of parameters such as the initial brightness setting and the flashing mode to improve the control accuracy. The reverse optimization adopts the reinforcement learning algorithm to find the optimal control parameters through continuous trial and error and adjustment. Step 5: Self-learning and adaptive control. According to the changes in the driving environment and the use of the headlights, the control parameters of the headlights are automatically adjusted to achieve intelligent and automated control. Self-learning and adaptive control adopts machine learning algorithms to improve the control accuracy and adaptability through continuous learning and adjustment. Step 6: Cloud computing and reinforcement learning. Using cloud computing and big data analysis technology, multi-angle joint learning is carried out to comprehensively enhance the common characteristics of data and improve data processing and analysis capabilities. Cloud computing and big data analysis technology can process a large amount of data and extract useful information from it, thereby improving data processing and analysis capabilities. Step 7: Optimized control parameters and instructions are generated. According to the reverse optimization results, the optimized light control parameters and instructions are generated to achieve refined control and improve the use effect of the light. The control parameters include the optimization of parameters such as initial brightness setting and flashing mode, and the instructions include turning the light on or off, brightness adjustment, flashing frequency adjustment, etc. The implementation of this embodiment requires the following equipment and conditions: visual perception equipment, radar equipment, cloud server, data processing equipment, machine learning equipment, reinforcement learning equipment, etc.Among them, visual perception devices include cameras, image processing chips, etc., radar equipment includes radar sensors, signal processing chips, etc., cloud servers include server hardware, cloud computing platforms, etc., data processing equipment includes data storage devices, data analysis equipment, etc., machine learning equipment includes machine learning algorithms, model training equipment, etc., and reinforcement learning equipment includes reinforcement learning algorithms, model training equipment, etc.

[0037] refer to Fig.10 , a vehicle light intelligent control device, comprising an information collection module, a controller module and a vehicle light module, The information collection module is equipped with a camera device and a radar device. The camera device can obtain visible light image information, and the radar device can obtain three-dimensional data; The controller module can exchange data with the vehicle computer and obtain sensor information and navigation system information; refer to Figure 6 , Figure 7 , Figure 8 and Fig. 9 The headlight module includes a main axis distance sensor 3, a lamp holder 1, a rotating structure 2 and a driver 6. The lamp holder 1 is provided with a plurality of lamp beads 11. The lamp holder 1 is mounted on a rotating structure 2 provided with a plurality of rotating shafts, so that the lamp holder 1 as a whole can rotate around a plurality of shafts. The plurality of lamp beads 11 are provided with independent rotating shafts 21 so that the lamp beads 11 can rotate around the rotating shafts 21. The driver 6 can drive the lamp beads 11 to rotate. The main axis distance sensor 3 is arranged at the main optical axis position of the lamp holder 1 and can measure the main optical axis length. The left and right sides of the lamp holder 1 are provided with a driving gear shaft 17 to drive the lamp holder 1 to rotate and adjust the headlight illumination height. The lower end of the lamp holder 1 is provided with a second driving shaft 18 to drive the lamp holder 1 to rotate left and right. A decorative panel 19 is provided at the front end of the lamp holder 1. To ensure the appearance, a lampshade and a lamp shell can be further provided outside the decorative panel 19. Decorative lamp beads or luminous lamp beads can be provided on the decorative panel 19.

[0038] In one embodiment, the headlight module further includes a left-axis distance sensor 31, a right-axis distance sensor 32, an upper-axis distance sensor 33, and a lower-axis distance sensor 34. The left-axis distance sensor 31, the right-axis distance sensor 32, the upper-axis distance sensor 33, and the lower-axis distance sensor 34 are respectively located in the four directions of the upper, lower, left, and right of the main-axis distance sensor 3. In this way, when the main-axis distance sensor 3 displays that the distance does not meet the speed standard, the angle of the main-axis distance sensor 3 can be adjusted according to the distances of the left-axis distance sensor 31, the right-axis distance sensor 32, the upper-axis distance sensor 33, and the lower-axis distance sensor 34. For example, if the distance detected by the upper-axis distance sensor 33 is greater than that of the main-axis distance sensor 3, if it is necessary to increase the illumination distance of the headlight, the headlight can be turned upward. Of course, this is only a reference factor. In practice, there may be a situation where two directions meet or even three directions meet. The specific rotation direction needs to be further referred to the results of other detection and judgment.

[0039] In one embodiment, a plurality of lamp beads 11 are arranged vertically and fixed on a strip circuit board 12, an elastic heat sink 14 and a supporting heat sink 15 are provided behind the strip circuit board 12, a sleeve 16 is provided behind the supporting heat sink 15, and the sleeve 16 is mounted on the rotating shaft 21, a plurality of strip circuit boards 12 are arranged horizontally, and a left control board 5 and a right control board 4 are respectively provided outside the strip circuit boards 12 on both sides, and adjacent strip circuit boards 12 are limited by the elastic heat sink 14. To ensure zoning control, a central control board 7 is provided at the center of the lamp head, and the lamp beads 11 can be installed at the end of the central control board 7, the central control board 7 rotates around the central control axis 71, the left control board 5 rotates around the left axis 51, and the right control board 4 rotates around the right axis 41, and the right control board 4, the left control board 5 and the central control board 7 are all provided with independent driving structures and can rotate independently.

[0040] refer to Figure 8 , at this time, the left control board 5 and the center control board 7 do not rotate, the right control board 4 rotates 10°, the elastic heat sink 14 has a certain elasticity, and the support heat sink 15 also rotates under the action of the elastic heat sink 14 when the right control board 4 rotates. Since the center control board 7 does not rotate, the rotation angles of the support heat sink 15 are different. The closer to the right control board 4, the closer the rotation angle is to 10°, and the farther away from the right control board 4, the smaller the rotation angle is, which can expand the illumination range of the car lights. The right control board 4, the left control board 5 and the center control board 7 can rotate the same angle at the same time, so that all the lamp beads 11 rotate the same angle. Generally, this method can be used when driving at high speed and the rotation angle is small. If the rotation angle is large and the illumination range needs to be expanded, the right control board 4, the left control board 5 and the center control board 7 need to rotate different angles, or even the right control board 4 or the left control board 5 does not rotate. The elastic heat sink 14 can make the light distribution more uniform, and there is no need to design multiple drive structures. Simplifying the drive method can reduce costs.

[0041] like Fig.11 As shown, the device 800 includes a computing unit 801, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 802 or a computer program loaded from a storage unit 808 to a random access memory (RAM) 803. In the RAM 803, various programs and data required for the operation of the device 800 can also be stored. The computing unit 801, the ROM 802, and the RAM 803 are connected to each other via a bus 804. An input / output (I / O) interface 805 is also connected to the bus 804.

[0042] Multiple components in the device 800 are connected to the I / O interface 805, including: an input unit 806, such as physical buttons in the car, touch screen buttons, etc.; an output unit 807, such as various types of displays, car light modules, etc.; a storage unit 808, such as a disk, an optical disk, etc.; and a communication unit 809, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 809 allows the device 800 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0043] The computing unit 801 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the computing unit 801 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, digital signal processors (DSPs), and any appropriate processors, controllers, microcontrollers, etc. The computing unit 801 performs the various methods and processes described above, such as the intelligent control method for vehicle lights. For example, in some embodiments, the intelligent control method for vehicle lights may be implemented as a computer software program, which is tangibly contained in a machine-readable medium, such as a storage unit 808. In some embodiments, part or all of the computer program may be loaded and / or installed on the device 800 via the ROM 802 and / or the communication unit 809. When the computer program is loaded into the RAM 803 and executed by the computing unit 801, one or more steps of the intelligent control method for vehicle lights described above may be performed. Alternatively, in other embodiments, the computing unit 801 may be configured to perform the intelligent control method for vehicle lights in any other appropriate manner (e.g., by means of a firmware vehicle light module). The device 800 can transmit information to the on-board computer via a data cable connection.

[0044] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0045] The program code for implementing the method of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that the program code, when executed by the processor or controller, enables the functions / operations specified in the flow chart and / or block diagram to be implemented. The program code may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0046] In the context of the present disclosure, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or device, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0047] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0048] The systems and techniques described herein may be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), and the Internet.

[0049] A computer system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The relationship of client and server is generated by computer programs running on respective computers and having a client-server relationship with each other. The server may be a cloud server, a server of a distributed system, or a server combined with a blockchain.

[0050] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps recorded in this disclosure can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solutions disclosed in this disclosure can be achieved, and this document does not limit this.

[0051] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of the features. In the description of the present disclosure, the meaning of "plurality" is two or more, unless otherwise clearly and specifically defined.

[0052] The above is only a specific embodiment of the present disclosure, but the protection scope of the present disclosure is not limited thereto. Any person skilled in the art who is familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present disclosure, which should be included in the protection scope of the present disclosure. Therefore, the protection scope of the present disclosure should be based on the protection scope of the claims.

Claims

1. A vehicle light intelligent control method, characterized in that: The method comprises the following steps: Acquire road surface information through radar or camera equipment, and input the road surface information into a first model for controlling the vehicle light; The first model relies on a deep learning algorithm to be trained to recognize and determine lane lines on both sides, and determines the lane center line and the lane center line vanishing point based on the lane lines; Calculating the angle between a straight line from the vanishing point of the lane centerline to the center of the headlight and the current main optical axis of the headlight; The vehicle light is rotated by using the vehicle light intelligent control device so that at least part of the light is irradiated on the vanishing point of the center line of the lane.

2. The vehicle light intelligent control method according to claim 1, characterized in that: After the headlight is rotated to a certain angle or is being rotated by using the first model, the navigation system is used for verification, and the steps are as follows: Obtain position information and navigation system information, confirm the vehicle's current direction of travel and the theoretical headlight angle under the direction of travel according to the position information and navigation system information, compare the theoretical headlight angle with the actual headlight angle, and if the angle deviation does not exceed 20%, do not make adjustments. If the angle deviation is between 20% and 50%, use the headlight intelligent control device to turn the headlight to a larger headlight angle. If the angle deviation exceeds 50%, report an error, or manually select a control mode, or customize the control mode, or use sensor information to evaluate the reliability of the navigation system or the first model.

3. The vehicle light intelligent control method according to claim 2, characterized in that: Using the sensor information to evaluate the reliability of the navigation system requires obtaining sensor information, which includes inclination information, steering angle information, acceleration information, speed information and distance information. The navigation system information confirms the theoretical data of the current position based on the map data, and determines whether the theoretical data is within a reasonable error range with the feedback data of each sensor information, and determines whether there is a delay in the navigation system information based on the size of the error.

4. The vehicle light intelligent control method according to claim 3, characterized in that: After confirming that there is no delay in the navigation system information, a distance sensor is used to measure the length of the main optical axis of the headlight. Based on the navigation system information, it is confirmed whether a longer headlight illumination distance can be obtained. Based on the speed information, it is confirmed whether the headlight illumination distance needs to be increased.

5. The vehicle light intelligent control method according to any one of claims 1 to 4, characterized in that: The road surface information is input into a second model for controlling the headlights. The second model is based on a deep learning algorithm and is trained to recognize vehicles and faces. The headlight intelligent control device turns the headlights to avoid vehicles or faces.

6. A vehicle light intelligent control device, characterized in that: Including information collection module, controller module and car light module, The information acquisition module is provided with a camera device and a radar device, the camera device can obtain visible light image information, and the radar device can obtain three-dimensional data; The controller module can exchange data with the vehicle computer and obtain sensor information and navigation system information; The vehicle light module includes a main axis distance sensor, a lamp holder, a rotating structure and a driver. The lamp holder is provided with a plurality of lamp beads, and the lamp holder is mounted on a rotating structure provided with a plurality of rotating shafts, so that the lamp holder as a whole can rotate around a plurality of axes. The plurality of lamp beads are provided with independent rotating shafts so that the lamp beads can rotate around the rotating shafts. The driver can drive the lamp beads to rotate. The main axis distance sensor is arranged at the main optical axis position of the lamp holder and can measure the length of the main optical axis.

7. The vehicle light intelligent control device according to claim 6, characterized in that: The vehicle light module further includes a left axle distance sensor, a right axle distance sensor, an upper axle distance sensor and a lower axle distance sensor.

8. The vehicle light intelligent control device according to claim 7, characterized in that: A plurality of the lamp beads are arranged in a vertical direction and fixed on the strip circuit board. An elastic heat sink and a supporting heat sink are provided at the rear of the strip circuit board. A shaft sleeve is provided at the rear of the supporting heat sink. The shaft sleeve is installed on the rotating shaft. A plurality of the strip circuit boards are arranged in a horizontal direction and a left control board and a right control board are respectively provided on the outer sides of the strip circuit boards on both sides. Adjacent strip circuit boards are limited by the elastic heat sink.

9. An electronic device, characterized in that: include: at least one processor; as well as a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the vehicle light intelligent control method according to any one of claims 1-5.

10. A non-transitory computer-readable storage medium storing computer instructions, characterized in that: The computer instructions are used to enable the computer to execute the vehicle light intelligent control method according to any one of claims 1-5.

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