Automatic anti-dazzling high beam illumination method and illumination device

Abstract

The invention provides an automatic anti-dazzling distance light illumination method and an illumination device.The method comprises the steps that when an automobile is in a distance light illumination mode, point cloud data, collected by a laser radar, in front of the automobile are analyzed, and a target person and the movement trend of the target person are obtained; determining relative positions of a target face in the target person and the automobile at different time nodes according to the movement trend and the driving state of the automobile; and according to the relative positions at different time nodes, the execution module is controlled to adjust the illumination brightness of the headlamp to the target face at the corresponding time nodes. According to the anti-dazzling control method and device, the computing power and configuration requirements for the controller are reduced, the method and device are suitable for complex and changeable road environments and driving states and the motion state of the target person, and quick response and accurate anti-dazzling control are achieved.

Description

Technical Field

[0001] This application relates to the technical field of automotive lighting. Specifically, this application relates to an automatic anti-glare high beam lighting method and lighting device. Background Art

[0002] In modern automotive driving scenarios, night driving safety has always been a matter of great concern. When a driver uses high beams at night, although a better road lighting effect can be obtained, it is also easy to cause glare to oncoming vehicle drivers or pedestrians, seriously affecting traffic safety.

[0003] Traditional anti-glare technologies mostly rely on manually switching between high and low beams or using fixed anti-glare areas. These methods cannot be dynamically adjusted according to the actual road conditions and have many limitations. For example, manually switching between high and low beams is not only inconvenient to operate, but also drivers often cannot accurately judge the switching timing in a timely manner, resulting in the risk of glare still existing; while the setting of fixed anti-glare areas cannot adapt to complex and changing road environments and the positions and movement states of different target persons, and cannot achieve precise anti-glare control.

[0004] For example, in the technical solution with the publication number CN107128242A, by collecting the road condition image in front of the vehicle, determining the predetermined target in front of the vehicle and the position between the predetermined target and the vehicle from the road condition image, and controlling a plurality of reflectors corresponding to the predetermined target according to the position relationship between the predetermined target and the vehicle to adjust the illumination brightness towards the predetermined target.

[0005] In the technical solution with the publication number CN117246226A, by real-time obtaining the image in front of the vehicle during the vehicle driving process, using image recognition technology to identify oncoming vehicles and pedestrians in the image in front of the vehicle, and calculating and obtaining the distance information between the vehicle and oncoming vehicles and pedestrians as well as the height information of oncoming vehicles and pedestrians; calculating the angle information of the high beam light emitted by the headlamp covering oncoming vehicles and pedestrians, determining the corresponding area on the glass cover according to the angle information, adaptively changing the voltage or current of the area, adjusting the color of the area on the glass cover to adjust the light transmittance, and then controlling the reduction of the high beam light intensity corresponding to the area.

[0006] However, the above technical solutions all require rapid analysis and processing of real-time road condition images, which have high requirements for the computing power and configuration of the controller. When the vehicle speed is relatively fast, it cannot respond in a timely manner; in addition, under complex lighting conditions (such as rainy and foggy weather), the method of image analysis is prone to poor robustness in target recognition. Summary of the Invention

[0007] The main objective of this application is to provide an automatic anti-glare high-beam lighting method and lighting device, so as to reduce the computing power and configuration requirements for the controller, adapt to complex and changeable road environments, driving states, and the movement states of target persons, and achieve fast response and precise anti-glare control.

[0008] To achieve the above-mentioned invention objective, this application provides an automatic anti-glare high-beam lighting method, which is applied to the controller of a lighting device. The lighting device further includes a lidar, an execution module, and a headlight. The controller is electrically connected to the lidar and the execution module respectively, and the execution module is connected to the headlight. The method includes:

[0009] When the vehicle is in the high-beam lighting mode, analyze the point cloud data in front of the vehicle collected by the lidar to obtain the target person and the movement trend of the target person;

[0010] Determine the relative positions of the target face in the target person and the vehicle at different time nodes according to the movement trend and the driving state of the vehicle;

[0011] According to the relative positions at different time nodes, control the execution module to adjust the lighting brightness of the headlight for the target face at the corresponding time node.

[0012] Preferably, the headlight includes an LED light source, a lens, and a reflection structure. The lens is arranged at the opening of the semi-closed cavity surrounded by the reflection structure. The LED light source is located in the semi-closed cavity and is arranged opposite to the lens. The reflection structure includes a plurality of reflectors arranged in an array. The execution module is connected to each reflector respectively. When the execution module adjusts the deflection angle of at least one reflector in the reflection structure, the light emitted by the LED light source is reflected by the reflector with the adjusted deflection angle and then enters the lens, and after passing through the lens, at least one lighting area is formed in front of the vehicle.

[0013] Preferably, the step of controlling the execution module to adjust the lighting brightness of the headlight for the target face at the corresponding time node according to the relative positions at different time nodes includes:

[0014] Determine the target lighting area and the minimum lighting brightness where the target face is located at different time nodes according to the relative positions at different time nodes;

[0015] Determine the reflector mode corresponding to the target lighting area and the minimum lighting brightness according to a preset look-up table, and obtain the reflector modes at different time nodes. The reflector mode includes at least one target reflector that needs to be correspondingly adjusted when the lighting brightness in the target lighting area is the minimum lighting brightness and the target deflection angle of each target reflector;

[0016] According to the mirror modes at different time nodes, at corresponding time nodes, control the execution module to adjust the deflection angle of each corresponding target mirror in the headlamp to a corresponding target deflection angle, where the target deflection angle includes the angle at which the target mirror faces away from the LED light source.

[0017] Further, before determining the mirror mode corresponding to the target illumination area and the minimum illumination brightness according to the preset look-up table, it further includes:

[0018] Create a simulation model of the headlamp;

[0019] Divide the illumination area in front of the vehicle into multiple target illumination areas;

[0020] Configure an initial deflection angle for each mirror of the reflection structure in the simulation model;

[0021] Simulate the simulation model to simulate the mirror mode of each target illumination area at the minimum illumination brightness, and construct a look-up table according to the simulation data.

[0022] Further, the automatic anti-glare high-beam illumination method further includes:

[0023] Classify all the data obtained and analyzed by the controller according to the priority to obtain multiple data packets;

[0024] Calculate the upload rate between the controller and the cloud platform;

[0025] When the upload rate is lower than the preset upload rate, upload the multiple data packets to the cloud platform in the order from high to low priority. The cloud platform includes a distributed storage system for storing the multiple data packets to different storage nodes in the order from high to low priority.

[0026] Preferably, calculating the upload rate between the controller and the cloud platform includes:

[0027] The controller sends test data packets of a fixed size to the cloud platform at preset time intervals;

[0028] Record the sending time and receiving time of the test data packets for each preset time interval;

[0029] According to the size, sending time, and receiving time of the test data packets for each preset time interval, calculate the upload rate for each preset time interval;

[0030] Calculate the average value of the upload rates for all preset time intervals to obtain the upload rate between the controller and the cloud platform.

[0031] Preferably, the lidar is installed at the front of the vehicle, and the movement trend includes the position information of the target person at different time nodes.

[0032] Preferably, determining the relative position between the target face of the target person and the vehicle at different time nodes according to the movement trend and the driving state of the vehicle includes:

[0033] Determine the face position of the target face in the target person, predict the local positions of the target face in the target person at different time nodes according to the movement trend and the face position, and convert the local positions at different time nodes into the first global positions in the vehicle coordinate system respectively;

[0034] Predict the second global positions of the vehicle at different time nodes according to the driving state of the vehicle;

[0035] Calculate the relative positions between the target face of the target person and the vehicle at different time nodes according to the first global positions of the target face in the target person at different time nodes and the second global positions of the vehicle at the corresponding time nodes.

[0036] Preferably, analyzing the point cloud data in front of the vehicle collected by the lidar to obtain the target person and the movement trend of the target person includes:

[0037] Use the clustering algorithm to cluster each point cloud data in front of the vehicle continuously collected by the lidar to obtain the clustering results of each point cloud data;

[0038] Identify the target person in the clustering results of each point cloud data, and extract the geometric features of the target person from the clustering results of each point cloud data respectively;

[0039] Calculate the central position coordinates of the target person in each point cloud data according to the geometric features of each point cloud data;

[0040] Use the central position coordinates of each point cloud data to update the state vector and covariance matrix of the preset Kalman filter to obtain the covariance matrices of multiple predicted states;

[0041] Generate the movement trend of the target person according to the covariance matrices of the multiple predicted states.

[0042] This application also provides an illumination device, including a controller, a lidar, an execution module and a headlight. The controller is electrically connected to the lidar and the execution module respectively. The execution module is connected to the headlight. The controller includes a processor and a memory. Among them, the memory stores a computer program, and when the processor executes the computer program, it implements the automatic anti-glare high-beam illumination method as described in any one of the above.

[0043] An automatic anti-glare high beam lighting method and lighting device provided by the present application collect and analyze point cloud data using lidar. Even under complex lighting conditions, it can accurately identify the target person in front of the vehicle and analyze their movement trend to predict the movement trajectory of the target person in advance, effectively avoiding the glare risk caused by sudden situations. Secondly, according to the predicted relative position change between the target person and the vehicle, the lighting brightness of the headlamp is precisely controlled at different time nodes. When the target person is in a position where they may be dazzled, the lighting brightness in the face area is reduced in a timely manner. This dynamic adjustment and warning mechanism does not require the controller to process each piece of real-time image data, reducing the computing power and configuration requirements for the controller. At the same time, it can also respond quickly to minimize the impact of glare on other road users and reduce the probability of traffic accidents. In addition, it can also accurately adjust the lighting brightness and range according to the relative position of the target person at different time nodes, concentrating the lighting resources in the areas where they are needed and avoiding unnecessary energy waste. Description of the Drawings

[0044] Figure 1 It is a schematic flow chart of an automatic anti-glare high beam lighting method according to an embodiment of the present application;

[0045] Figure 2 It is a schematic block diagram of the structure of a headlamp according to an embodiment of the present application;

[0046] Figure 3 It is a schematic diagram of the distribution of reflectors in a reflection structure according to an embodiment of the present application;

[0047] Figure 4 It is a schematic diagram of automatic anti-glare high beam lighting according to an embodiment of the present application;

[0048] Figure 5 It is a schematic block diagram of the structure of an automatic anti-glare high beam lighting device according to an embodiment of the present application;

[0049] Figure 6 It is a schematic block diagram of the structure of a controller in a lighting device according to an embodiment of the present application.

[0050] The realization, functional characteristics and advantages of the purpose of the present application will be further described with reference to the embodiments and the accompanying drawings. Detailed Embodiments

[0051] In order to make the purpose, technical solutions and advantages of the present application more clear and understandable, the present application will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0052] Reference Figure 1, in one embodiment, the present application provides an automatic anti-glare high-beam lighting method, which is applied to a controller of a lighting device. The lighting device further includes a lidar, an execution module, and a headlight. The controller is electrically connected to the lidar and the execution module respectively, and the execution module is connected to the headlight. The method includes:

[0053] S11. When the vehicle is in the high-beam lighting mode, analyze the point cloud data in front of the vehicle collected by the lidar to obtain the target person and the movement trend of the target person;

[0054] S12. Determine the relative positions of the target face in the target person and the vehicle at different time nodes according to the movement trend and the driving state of the vehicle;

[0055] S13. According to the relative positions at different time nodes, control the execution module to adjust the lighting brightness of the headlight on the target face at the corresponding time node.

[0056] The point cloud data collected by the lidar contains the three-dimensional space information of the environment in front of the vehicle. First, a filtering method based on a distance threshold can be used to remove the points in the point cloud data that are significantly beyond the normal object reflection distance range to remove the noise points in the point cloud data. Then, a density-based clustering algorithm is used to divide the point cloud data into different clusters, and each cluster may correspond to a target, including people, vehicles, road signs, etc.

[0057] Preferably, the lidar is installed at the front of the vehicle, and the movement trend includes the position information, movement direction, speed, and acceleration of the target person at different time nodes.

[0058] For identifying target persons, such as pedestrians or drivers and passengers of oncoming vehicles, a human model matching method can be used. The human model can be constructed based on the size and shape characteristics of the human body, and the shape characteristics include the proportional relationship of the head, torso, and limbs. By matching the clustered point cloud clusters with the human model and calculating the matching degree, when the matching degree exceeds a certain threshold, the cluster of point clouds is determined to be a target person.

[0059] For the analysis of the movement trend of the target person, the future movement direction and speed can be predicted according to the position changes of the target person in the continuous frame point cloud data. For example, by establishing a motion state equation of the target person, including state variables such as position and speed, and using the position data at different times collected by the lidar to update the state estimation, the movement trend of the target person can be obtained, such as moving towards the vehicle, moving away from the vehicle, or moving laterally, etc.

[0060] The speed, steering angle, and acceleration of the vehicle are collected in real time by sensors installed on the vehicle to form the driving state of the vehicle. The sensors include a vehicle speed sensor, a steering angle sensor, and an accelerometer. According to the movement trend of the target person and the driving state of the vehicle, such as vehicle speed, driving direction, etc., the coordinate transformation method is used to determine the relative position between the target face and the vehicle at different time nodes.

[0061] Among them, the vehicle can establish its own coordinate system, convert the position information of the target person collected by the lidar into the vehicle coordinate system, and combine the driving speed and direction of the vehicle to predict the position of the target face in the vehicle coordinate system at different future time nodes, that is, the relative position. For example, every 0.1 second is used as a time node.

[0062] According to the relative position between the target face and the vehicle at different time nodes, an illumination brightness attenuation model based on distance and angle can be used to control the illumination brightness of the headlight. This model can use the position information of the target face as an input variable and output a corresponding illumination brightness value. Among them, the position information includes the distance between the target face and the vehicle and the angle relative to the vehicle headlight. For example, when the target face is relatively close to the vehicle and at the direct front angle of the headlight irradiation, the illumination brightness is reduced from the normal brightness of 100% to 60%; when the target face is far away or at the edge angle of the headlight irradiation, the illumination brightness is reduced from the normal brightness of 100% to 90% to ensure the illumination effect while reducing the glare effect on the target face.

[0063] To facilitate the understanding of this application, the following uses a specific example to illustrate this application:

[0064] For example, if a vehicle is driving at a speed of 60 kilometers per hour at night and the high beam illumination mode is turned on. At this time, the lidar starts to collect the point cloud data in front of the vehicle. The point cloud data collected by the lidar contains the road ahead, road signs, and a pedestrian who is crossing the road. By analyzing and processing the point cloud data, using the clustering and human body model matching methods, the pedestrian is successfully identified as the target person, and the movement trend of the pedestrian is analyzed to be walking towards the right side of the vehicle at a speed of 1.5 meters per second.

[0065] According to the movement trend of the pedestrian and the driving speed of the vehicle, the relative position between the pedestrian and the vehicle at the next few time nodes is calculated. For example, after 0.1 second, the position of the pedestrian relative to the vehicle may be 3 meters to the right front of the vehicle, forming a 30-degree angle with the driving direction of the vehicle; after 0.2 second, the position may become 2.8 meters to the right front of the vehicle, and the angle becomes 35 degrees, etc.

[0066] According to the above relative position, the execution module of the headlight starts to adjust the illumination brightness. For the relative position after 0.1 second, since the pedestrian is relatively close to the vehicle and within the illumination range of the headlight, the brightness of the headlight will be reduced to an appropriate level, such as from the normal high beam brightness of 100% to 60%. For the relative position after 0.2 seconds, it will be reduced from the normal high beam brightness of 100% to 65% to avoid causing glare to the pedestrian.

[0067] An automatic anti-glare high beam illumination method provided by the present application uses lidar to collect point cloud data and analyze it. Even under complex lighting conditions, it can accurately identify the target person in front of the vehicle and analyze its movement trend to predict the movement trajectory of the target person in advance, effectively avoiding the glare risk caused by sudden situations. Secondly, according to the predicted relative position change between the target person and the vehicle, the illumination brightness of the headlight is precisely controlled at different time nodes. When the target person is in a position where glare may occur, the illumination brightness of the face area is timely reduced. This dynamic adjustment and warning mechanism does not require the controller to process each piece of real-time image data, reducing the computing power and configuration requirements for the controller. At the same time, it can also respond quickly to minimize the impact of glare on other road users and reduce the probability of traffic accidents. In addition, it can also accurately adjust the illumination brightness and range according to the relative position of the target person at different time nodes, concentrating the illumination resources in the required area and avoiding unnecessary energy waste.

[0068] Reference Figure 2 As shown, in one embodiment, the headlight includes an LED light source 1, a lens 4, and a reflection structure 3. The lens 4 is disposed at the opening of the semi-closed cavity surrounded by the reflection structure 3. The LED light source 1 is located within the semi-closed cavity and is disposed opposite to the lens 4. The reflection structure 3 includes a plurality of reflectors 31 arranged in an array. The execution module 2 is respectively connected to each reflector 31. When the execution module 2 adjusts the deflection angle of at least one reflector 31 in the reflection structure 3, the light emitted by the LED light source 1 is reflected by the reflector 31 with the adjusted deflection angle and then enters the lens 4, and after passing through the lens 4, at least one illumination area is formed in front of the vehicle.

[0069] Specifically, the headlight is composed of an LED light source 1, a lens 4, a reflection structure 3, a driving unit 6, and a lamp cover 5. The lens 4 and the lamp cover 5 are installed at the opening position of the semi-closed cavity formed by the reflection structure 3. The lamp cover 5 is used to protect the internal structure of the headlight, and the LED light source 1 is disposed within the cavity and opposite to the lens 4. The driving unit 6 is electrically connected to the LED light source 1 and is used to drive the LED light source 1 to emit light. Among them, reference Figure 3As shown, the reflection structure 3 includes a plurality of mirrors 31 arranged in an array, and each mirror 31 is connected to the execution module 2.

[0070] When the LED light source 1 emits light, the light first projects onto the mirrors 31 of the reflection structure 3. By adjusting the deflection angle of at least one mirror 31 through the execution module 2, the reflection direction of the light is changed. The adjusted light is reflected from the mirror 31 and enters the lens 4. The lens 4 converges and shapes the light, enabling the light to be transmitted with a specific distribution and intensity, forming one or more illumination areas in front of the vehicle.

[0071] Among them, the execution module 2 receives instructions from the controller and precisely adjusts the deflection angle of each mirror 31 based on the required illumination area and brightness requirements calculated by the controller according to information such as the relative position of the target person. For example, if it is necessary to weaken the light intensity in a certain specific direction to avoid glare on the target person's face, the execution module 2 will adjust the deflection angle of the corresponding mirror 31 to change the path of the light that was originally partially reflected in that direction, reducing the light in that area; conversely, if it is necessary to increase the illumination brightness in a certain area, the deflection angle of the mirror 31 will be adjusted to an angle that reflects more light to that area, and a suitable illumination area will be formed through the lens 4.

[0072] In this embodiment, through the cooperation of the mirror array and the execution module, the illumination area and brightness of the headlamp can be finely adjusted dynamically. To meet the needs of the target person in different positions and motion states, the distribution of light can be precisely controlled to achieve the anti-glare effect, reducing the risk of traffic accidents caused by glare and improving the safety of night driving. At the same time, for the driver himself, on the premise of ensuring a good lighting vision, it reduces visual fatigue caused by frequently switching between high beams and low beams or glare reflection, etc., improving driving comfort. In addition, by precisely reflecting and controlling the light using the mirror, the light energy emitted by the LED light source can be utilized more efficiently, guiding the light to the areas that need illumination, reducing the waste of light energy, and improving the overall illumination efficiency of the headlamp, which helps to save energy and reduce emissions.

[0073] In one embodiment, controlling the execution module to adjust the illumination brightness of the headlamp on the target person's face at corresponding time nodes according to the relative position at different time nodes includes:

[0074] Determine the target illumination area and the minimum illumination brightness where the target person's face is located at different time nodes according to the relative position at different time nodes;

[0075] Determine the mirror mode corresponding to the target lighting area and the minimum lighting brightness according to a preset look-up table, and obtain the mirror modes at different time nodes. The mirror mode includes at least one target mirror that needs to be correspondingly adjusted when the target lighting area is at the minimum lighting brightness and the target deflection angle of each target mirror.

[0076] According to the mirror modes at different time nodes, control the execution module to adjust the deflection angle of each corresponding target mirror in the headlamp to the corresponding target deflection angle at the corresponding time node. The target deflection angle includes the angle at which the target mirror faces away from the LED light source.

[0077] Based on the relative position of the target face at different time nodes, divide the target lighting area where it is located. This can be achieved by dividing the lighting range in front of the vehicle into multiple preset area units, and each area unit corresponds to a certain spatial range. At the same time, according to factors such as the sensitivity of the target face to glare at this position, determine the minimum lighting brightness corresponding to each target lighting area. For example, when the target face is relatively close to the vehicle and at a directly front angle irradiated by the headlamp, to avoid glare, the minimum lighting brightness of this area is set to a relatively low value, for example, 10% of the normal lighting brightness.

[0078] Establish a look-up table in advance, which stores the mapping relationship between different target lighting areas, the corresponding minimum lighting brightness and the mirror mode. The mirror mode includes at least one target mirror that needs to be adjusted and its corresponding target deflection angle when achieving the minimum lighting brightness of the target lighting area. Among them, this look-up table can be obtained by simulating and testing the optical characteristics of the headlamp, and can quickly and accurately convert the lighting requirements into specific action parameters of the mirror.

[0079] At different time nodes, according to the mirror mode obtained from the look-up table, control the execution module to adjust the deflection angle of each corresponding target mirror in the headlamp to the target deflection angle. The target deflection angle includes the angle at which the target mirror faces away from the LED light source to change the reflection path of the light, so as to control the lighting brightness of the target lighting area to reach the predetermined minimum lighting brightness and achieve anti-glare lighting for the target face, for example Figure 4 as shown.

[0080] For example, when the vehicle is driving at night and a pedestrian appears in front, and the relative position between the pedestrian and the vehicle is constantly changing:

[0081] At time point t1, based on the position of the pedestrian, it is determined that the pedestrian is in the target lighting area A, and the lowest lighting brightness corresponding to this area is L1. By querying the look-up table, the mirror mode corresponding to area A and brightness L1 is obtained. This mode requires adjusting the deflection angles of the mirrors M1 and M2 in the mirror group. The target deflection angle of M1 is θ1, that is, the angle facing away from the LED light source, and the target deflection angle of M2 is θ2.

[0082] After receiving the instruction, the execution module adjusts the deflection angles of the mirrors M1 and M2 to θ1 and θ2 respectively at time point t1. At this time, the light emitted by the LED light source is reflected by the mirrors M1 and M2, changing the light distribution, so that the lighting brightness in the target lighting area A is reduced to L1, avoiding glare to the pedestrian.

[0083] As the relative position of the pedestrian and the vehicle changes, at time point t2, the pedestrian moves to the target lighting area B, and the corresponding lowest lighting brightness is L2. Query the look-up table again to obtain the corresponding mirror mode. It may be necessary to adjust the deflection angles of the mirrors M3 and M4 to new target angles, and the execution module then makes the adjustment to control the lighting brightness in area B to L2, continuously ensuring the anti-glare effect.

[0084] In this embodiment, through precise target lighting area division and lowest lighting brightness setting, combined with the look-up table to quickly determine the mirror mode, it is possible to achieve precise control of the headlamp lighting. While ensuring the basic lighting requirements of the road in front of the vehicle, it effectively avoids the light directly shining on the target face, thereby minimizing glare interference to the greatest extent and improving the safety of night driving. In addition, it can also dynamically adjust the deflection angle of the mirror according to the predicted changes in the relative position of the target face at different time nodes in advance, without the controller processing each piece of real-time image data, reducing the computing power and configuration requirements for the controller, and realizing real-time control of the lighting brightness, making the headlamp have good dynamic adaptability, able to handle various complex night driving scenarios, such as the movement of pedestrians and vehicles, always providing the best lighting environment for the driver, and at the same time protecting the visual safety of other road users.

[0085] In one embodiment, before determining the mirror mode corresponding to the target lighting area and the lowest lighting brightness according to the preset look-up table, it further includes:

[0086] Create a simulation model of the headlamp;

[0087] Divide the lighting area in front of the vehicle into multiple target lighting areas;

[0088] Configure an initial deflection angle for each mirror of the reflection structure in the simulation model;

[0089] Simulate the simulation model to simulate the mirror mode of each target lighting area at the lowest lighting brightness, and construct a comparison table based on the simulation data.

[0090] Create a simulation model of the headlight in optical simulation software according to the actual structural parameters of the headlight, including the characteristics of the LED light source, the optical characteristics of the lens, the geometry of the reflection structure, and the arrangement of the mirrors, etc. This simulation model can simulate the lighting effects of the headlight under different working conditions and provide a basic platform for subsequent lighting area division and mirror mode simulation.

[0091] Divide the lighting area within a certain range in front of the vehicle into multiple target lighting areas. The division method can be determined according to the actual lighting requirements and road scenarios. For example, it can be divided according to distance and angle, dividing the front area into several fan-shaped areas or rectangular areas, etc. Each area has different spatial positions and lighting requirements.

[0092] In the simulation model, configure an initial deflection angle for each mirror of the reflection structure. The setting of the initial deflection angle can be based on the basic lighting requirements of the mirror in the normal working state or can be initially set according to empirical values. This initial deflection angle will be used as the starting point for simulation and for subsequent mirror mode optimization.

[0093] Run the simulation model to simulate the mirror mode of each target lighting area at the lowest lighting brightness. By adjusting the deflection angle of the mirror, observe and record the deflection angle of each mirror and the corresponding lighting effect data when the lowest lighting brightness requirement of each target lighting area is reached. Construct a comparison table based on the simulation data. The comparison table stores the correspondence between the target lighting area, the lowest lighting brightness, and the mirror mode. The mirror mode includes the target mirror to be adjusted and its corresponding target deflection angle.

[0094] In this embodiment, by creating a simulation model and conducting simulations, it is possible to accurately determine the mirror modes required under different target illumination areas and minimum illumination brightness, providing an accurate control basis for the headlamp. This enables the mirror to be quickly and accurately adjusted according to the position of the target face during actual driving, achieving precise lighting control, effectively avoiding glare, and ensuring that the lighting requirements of the road are met. At the same time, before the actual production and manufacturing of the headlamp, using the simulation model for simulation and look-up table construction allows for the rapid optimization of the lighting control strategy in a virtual environment, avoiding repeated trials and modifications in actual hardware debugging, greatly saving time and costs, and improving the efficiency and quality of development. In addition, the look-up table obtained through simulation has undergone extensive virtual verification, ensuring the rationality and effectiveness of the mirror modes under various target illumination areas and brightness requirements. In actual applications, the mirror can be adjusted quickly and stably according to the look-up table, reducing lighting anomalies caused by unreasonable control strategies and enhancing the reliability and stability of the headlamp. Finally, this simulation- and look-up table-based method enables personalized lighting design according to different vehicle models, user requirements, and road scenarios. For example, for vehicle models with different headlamp shapes and optical requirements, by simply re-creating the simulation model and conducting corresponding simulations, a look-up table suitable for that vehicle model can be constructed to achieve a customized lighting control solution.

[0095] In one embodiment, the automatic anti-glare high-beam lighting method further includes:

[0096] Classify all the data obtained and analyzed by the controller according to priority to obtain multiple data packets;

[0097] Calculate the upload rate between the controller and the cloud platform;

[0098] When the upload rate is lower than the preset upload rate, upload the multiple data packets to the cloud platform in order from highest to lowest priority. The cloud platform includes a distributed storage system for storing the multiple data packets to different storage nodes in order from highest to lowest priority.

[0099] After the controller obtains and analyzes all the data, it classifies the data according to the importance and urgency of the data, dividing it into multiple data packets. For example, target person recognition data and motion trend data directly related to the safe driving of the vehicle can be classified into high-priority data packets; record data of the lighting brightness adjustment of the vehicle headlamp can be classified into medium-priority data packets; some auxiliary data such as environmental parameter records can be classified into low-priority data packets.

[0100] The controller calculates the upload rate to the cloud platform in real time or at regular intervals. It can detect the size of the uploaded data and the corresponding time within a certain period (e.g., every 1 second), and use the formula "upload rate = uploaded data size / upload time" to calculate. At the same time, it establishes a communication connection with the cloud platform to obtain the communication status information of the current network link, which further assists in accurately calculating the upload rate.

[0101] When the calculated upload rate is lower than the preset upload rate, multiple data packets are uploaded to the cloud platform in order of priority from high to low. Among them, the preset upload rate is a threshold set according to actual business requirements and network conditions, which is used to ensure the timeliness and reliability of data upload.

[0102] Preferably, the cloud platform can adopt a distributed storage system, which includes multiple storage nodes. Each storage node is distributed in different geographical locations or data center computer rooms and is connected together through a high-speed network. Each storage node is equipped with a large-capacity storage device, such as a hard disk array (RA ID) or a solid-state drive (SSD), to ensure the storage capacity and read / write speed of data. Each storage node is responsible for storing data within a certain priority range. In accordance with the priority order, high-priority data packets are stored in the core storage node with high reliability and fast access capabilities, medium-priority data packets are stored in the intermediate storage node with slightly lower performance but still able to ensure data security, and low-priority data packets are stored in the edge storage node with lower cost and larger capacity.

[0103] For example, when data packets are uploaded from the controller to the cloud platform, the distributed storage system disperses and stores the data in different storage nodes according to the priority of the data packets. High-priority data packets will be preferentially stored in the core storage node with higher performance and stronger reliability. The core storage node usually adopts a redundant backup strategy, such as RAID1 (mirroring) or RAID5 (striping with distributed parity), to ensure the security and recoverability of data.

[0104] Medium-priority data packets are stored in the intermediate storage node, which may adopt a storage strategy such as RAID6 with higher storage efficiency and a certain degree of redundancy. Low-priority data packets are stored in the edge storage node, which may adopt a strategy such as RAID0 (striping) that pays more attention to storage capacity and read / write speed, but may also be appropriately configured with redundancy according to actual needs.

[0105] In this embodiment, data is classified and prioritized so that, under a limited upload rate, the timely upload of critical data can be ensured first. The cloud platform stores data according to priorities through a distributed storage system, which facilitates subsequent rapid query, analysis, and processing based on the importance of different data, improving data management efficiency and the overall utilization efficiency of data. At the same time, a reasonable upload strategy and the design of the distributed storage system avoid excessive occupation of network and system resources by a large amount of data being uploaded simultaneously, preventing the loss or upload delay of important data caused by data congestion. It ensures that the system can still operate stably under various network conditions, especially when the upload rate is limited, and reliably uploads critical data to the cloud platform, enhancing the overall performance and reliability of the system. In addition, by uploading the critical data during the driving process of the vehicle to the cloud platform in a timely manner, centralized analysis and processing of the data of multiple vehicles can be achieved with the help of the cloud platform, and more valuable information can be mined, such as traffic flow analysis, driving behavior pattern recognition, etc., promoting the continuous progress of intelligent driving assistance systems and autonomous driving technologies.

[0106] In one embodiment, calculating the upload rate between the controller and the cloud platform includes:

[0107] The controller sends test data packets of a fixed size to the cloud platform at preset time intervals.

[0108] Record the sending time and receiving time of the test data packets for each preset time interval.

[0109] Based on the size, sending time, and receiving time of the test data packets for each preset time interval, calculate the upload rate for each preset time interval.

[0110] Calculate the average value of the upload rates for all preset time intervals to obtain the upload rate between the controller and the cloud platform.

[0111] The controller sends a test data packet of a fixed size to the cloud platform at preset time intervals (for example, once every 10 seconds). This preset time interval can be set according to actual network monitoring requirements and system resource conditions. The test data packet of a fixed size is to ensure a unified benchmark when calculating the upload rate.

[0112] For each preset time interval, record the sending time of the test data packet (i.e., the time point when data starts to be sent from the controller side) and the receiving time (i.e., the time point when the cloud platform successfully receives the test data packet). These two timestamp information are the key data for calculating the upload rate.

[0113] According to the formula: upload rate = test data packet size / (receiving time - sending time), calculate the upload rate corresponding to each preset time interval.

[0114] Average the upload rates calculated for all preset time periods to obtain the average upload rate between the controller and the cloud platform. This average value can more stably reflect the data upload capacity under the current network connection, avoiding misjudgment caused by fluctuations in a single transmission. Thus, by regularly sending test data packets of a fixed size and combining with time recording, the upload rate for each time period can be accurately calculated. By calculating the average upload rate, the instantaneous fluctuations of the network can be smoothed out to obtain a more reliable upload rate indicator. In addition, the change in the upload rate between the controller and the cloud platform can be monitored in real time. In an actual driving environment, the network conditions may change continuously due to factors such as vehicle position and base station signal. Through regular testing, it is possible to dynamically adapt to this change and timely adjust the data upload method and strategy to ensure the efficiency and stability of data transmission.

[0115] In one embodiment, determining the relative positions of the target face of the target person and the vehicle at different time nodes according to the motion trend and the driving state of the vehicle includes:

[0116] Determine the face position of the target face in the target person, predict the local positions of the target face in the target person at different time nodes according to the motion trend and the face position, and convert the local positions at different time nodes into the first global positions in the vehicle coordinate system respectively;

[0117] Predict the second global positions of the vehicle at different time nodes according to the driving state of the vehicle;

[0118] Calculate the relative positions of the target face of the target person and the vehicle at different time nodes according to the first global positions of the target face of the target person at different time nodes and the second global positions of the vehicle at the corresponding time nodes.

[0119] In this embodiment, face recognition technology can be used to determine the position of the target face in the target person at the current moment, which is the position in the local coordinate system (for example, the local coordinate system where the lidar or camera is located). Then, according to the motion trend of the target person, the local positions of the target face at different time nodes are predicted. For example, if the target face is currently at a certain position in the local coordinate system and moves at a certain speed and direction, its position at subsequent time points can be predicted through kinematic formulas.

[0120] Convert the predicted local positions at different time nodes into the first global position in the vehicle coordinate system. This is because during the driving process of the vehicle itself, there is a relative motion and positional relationship between its coordinate system and the local coordinate system. Through coordinate transformation algorithms such as rotation and translation transformations, the local positions can be accurately converted into the vehicle coordinate system, ensuring that the position information of the target face and the position information of the vehicle are in the same coordinate system, facilitating the calculation of relative positions.

[0121] Predict the second global position of the vehicle at different time nodes according to the driving state of the vehicle. For example, if the vehicle is currently driving at a certain speed and maintaining that speed and direction unchanged, its position at subsequent time points can be predicted through a simple kinematic model, thus considering the vehicle's own motion and providing the vehicle's position information for calculating the relative position between the target face and the vehicle.

[0122] Finally, according to the first global position of the target face in the target person at different time nodes and the second global position of the vehicle at the corresponding time nodes, calculate the relative positions between the target face in the target person and the vehicle at different time nodes through methods such as vector operations or coordinate difference calculations. The relative position can be represented as the position vector of the target face relative to the vehicle, including information such as distance and direction.

[0123] In summary, through the accurate determination of the target face position and prediction based on the motion trend, combined with the consideration of the vehicle's own motion state, the relative positions between the target face and the vehicle at different time nodes can be accurately calculated.

[0124] In one embodiment, the analysis of the point cloud data in front of the vehicle collected by the lidar to obtain the target person and the motion trend of the target person includes:

[0125] Use a clustering algorithm to cluster each point cloud data in front of the vehicle continuously collected by the lidar to obtain the clustering result of each point cloud data;

[0126] Identify the target person in the clustering result of each point cloud data, and extract the geometric features of the target person from the clustering result of each point cloud data respectively;

[0127] Calculate the central position coordinates of the target person in each point cloud data according to the geometric features of each point cloud data;

[0128] Use the central position coordinates of each point cloud data to update the state vector and covariance matrix of a preset Kalman filter to obtain the covariance matrices of multiple predicted states;

[0129] Generate the motion trend of the target person according to the covariance matrices of the multiple predicted states.

[0130] The lidar continuously collects the point cloud data in front of the vehicle, and each point cloud data contains the position information of a large number of points. The clustering algorithm is used to cluster each point cloud data. The purpose of clustering is to divide the points belonging to the same object in the point cloud data into one cluster, and the points of different objects into different clusters. For example, the points belonging to the target person (such as a pedestrian) are divided into one cluster, and the points belonging to the background environment (such as roads, buildings) are divided into other clusters.

[0131] Identify the cluster where the target person is located from the clustering results of each point cloud data. This can be achieved by setting certain rules or model matching. For example, according to the size range of the human body, the clusters that may contain the target person are screened out. Then, geometric features are extracted from each target person cluster. The geometric features can include the size of the target person (such as length, width, height), shape (such as the aspect ratio calculated from the distribution of the point cloud), and other information.

[0132] According to the geometric features of the target person in each point cloud data, calculate the central position coordinates of the target person in this point cloud data. The calculation method can be to take the average value of the coordinates of all points in the target person cluster, or determine a central point position according to the shape and size of the geometric features. For example, for a target person cluster that is roughly rectangular parallelepiped-shaped, the geometric center of its rectangular parallelepiped can be taken as the central position coordinates.

[0133] This embodiment can predict the state at the next moment according to the central position coordinates, and by continuously updating the state vector (including information such as the position and speed of the target person) and the covariance matrix (reflecting the uncertainty of the state estimation), the covariance matrices of multiple predicted states can be obtained. According to the covariance matrix of the predicted state, the motion trend of the target person is generated, including information such as the future motion direction and speed of the target person.

[0134] This embodiment can update the motion state prediction of the target person in real time according to the new measurement data, can respond to the motion trend of the target person in a short time, so that the controller can timely adjust the headlight lighting strategy to adapt to the dynamic changes of the target person, and improves the real-time performance and dynamic performance of the controller. In addition, the update of the covariance matrix can reflect the reliability of the state estimation, so that the controller can still stably predict the motion trend of the target person in the face of a complex traffic environment and a certain degree of sensor noise, enhancing the robustness of the controller.

[0135] Reference Figure 5 As shown, an automatic anti-glare high beam lighting device is further provided in the embodiment of the present application. The device includes:

[0136] An analysis module 11, configured to analyze the point cloud data in front of the vehicle collected by the lidar when the vehicle is in the high beam lighting mode, so as to obtain the target person and the movement trend of the target person;

[0137] A determination module 12, configured to determine the relative positions of the target face in the target person and the vehicle at different time nodes according to the movement trend and the driving state of the vehicle;

[0138] A control module 13, configured to control the execution module to adjust the illumination brightness of the headlamp on the target face at the corresponding time node according to the relative positions at different time nodes.

[0139] As described above, it can be understood that each component of the automatic anti-glare high beam lighting device proposed in this application can implement the functions of any one of the above-mentioned automatic anti-glare high beam lighting methods, and the specific structure will not be elaborated.

[0140] An embodiment of this application also provides a lighting device, including a controller, a lidar, an execution module, and a headlamp. The controller is electrically connected to the lidar and the execution module respectively. The execution module is connected to the headlamp. The controller includes a processor and a memory. Among them, the memory stores a computer program, and when the processor executes the computer program, it implements the automatic anti-glare high beam lighting method described in any one of the above.

[0141] In one of its embodiments, the internal structure of the controller can be as Figure 6 shown. The controller includes a processor, a memory, a network interface, and a database connected through a system bus. Among them, the designed processor of the controller is used to provide computing and control capabilities. The memory of the controller includes a storage medium and an internal memory. The storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the storage medium. The database of the controller is used to store the relevant data of the automatic anti-glare high beam lighting method. The network interface of the controller is used to communicate with external devices through a network connection. When the computer program is executed by the processor, it implements an automatic anti-glare high beam lighting method.

[0142] An embodiment of this application also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, it implements an automatic anti-glare high beam lighting method.

[0143] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, storage, database, or other medium provided in this application and used in the embodiments can include non-volatile and / or volatile memories. Non-volatile memories can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memories can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (SSRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and Rambus dynamic RAM (RDRAM), etc.

[0144] It should be noted that in this article, the term "including", "comprising", or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, device, article, or method including a series of elements not only includes those elements but also includes other elements not explicitly listed, or further includes elements inherent to such a process, device, article, or method. Without further limitation, an element defined by the statement "including one..." does not exclude the existence of additional identical elements in the process, device, article, or method including that element.

[0145] The above are only the preferred embodiments of this application, and do not limit the patent scope of this application. Any equivalent structure or equivalent process transformation made by using the specification and drawings of this application, or directly or indirectly applied in other related technical fields, is equally included in the patent protection scope of this application.

Claims

1. An automatic anti-glare high beam lighting method, characterized in that, A controller applied to a lighting device, the lighting device further comprising a lidar, an execution module and a headlight. The controller is electrically connected to the lidar and the execution module respectively. The execution module is connected to the headlight. The method includes: When the vehicle is in the high beam lighting mode, analyze the point cloud data in front of the vehicle collected by the lidar to obtain the target person and the movement trend of the target person; Determine the relative positions of the target face in the target person and the vehicle at different time nodes according to the movement trend and the driving state of the vehicle; According to the relative positions at different time nodes, control the execution module to adjust the illumination brightness of the headlight on the target face at the corresponding time node.

2. The method according to claim 1, characterized in that The headlight includes an LED light source, a lens and a reflection structure. The lens is disposed at the opening of the semi-closed cavity surrounded by the reflection structure. The LED light source is located in the semi-closed cavity and is disposed opposite to the lens. The reflection structure includes a plurality of reflectors arranged in an array. The execution module is connected to each reflector respectively. When the execution module adjusts the deflection angle of at least one reflector in the reflection structure, the light emitted by the LED light source is reflected by the reflector with the adjusted deflection angle and then enters the lens, and after being transmitted by the lens, at least one illumination area is formed in front of the vehicle.

3. The method according to claim 2, wherein The controlling the execution module to adjust the illumination brightness of the headlight on the target face at the corresponding time node according to the relative positions at different time nodes includes: Determine the target illumination area and the minimum illumination brightness where the target face is located at different time nodes according to the relative positions at different time nodes; Determine the reflector mode corresponding to the target illumination area and the minimum illumination brightness according to a preset look-up table to obtain the reflector modes at different time nodes. The reflector mode includes at least one target reflector that needs to be correspondingly adjusted and the target deflection angle of each target reflector when the illumination brightness in the target illumination area is the minimum illumination brightness; According to the reflector modes at different time nodes, control the execution module to adjust the deflection angle of each corresponding target reflector in the headlight to the corresponding target deflection angle at the corresponding time node. The target deflection angle includes the angle at which the target reflector faces away from the LED light source.

4. The method according to claim 3, characterized in that, Before determining the reflector mode corresponding to the target illumination area and the minimum illumination brightness according to the preset look-up table, it further includes: Create a simulation model of the headlight; Divide the illumination area in front of the vehicle into multiple target illumination areas; Configure an initial deflection angle for each reflector of the reflection structure in the simulation model; Simulate the simulation model to simulate the reflector mode of each target illumination area at the minimum illumination brightness, and construct a look-up table according to the simulation data.

5. The method according to claim 1, characterized in that It further includes: Classify all the data obtained and analyzed by the controller according to the priority to obtain multiple data packets; Calculate the upload rate between the controller and the cloud platform; When the upload rate is lower than the preset upload rate, multiple said data packets are uploaded to the cloud platform in the order of decreasing priority. The cloud platform includes a distributed storage system for storing multiple said data packets to different storage nodes in the order of decreasing priority.

6. The method according to claim 5, wherein Calculating the upload rate between the controller and the cloud platform includes: The controller sends test data packets of a fixed size to the cloud platform at preset time intervals. Record the sending time and receiving time of the test data packets for each preset time interval. Based on the size, sending time, and receiving time of the test data packets for each preset time interval, calculate the upload rate for each preset time interval. Calculate the average value of the upload rates for all preset time intervals to obtain the upload rate between the controller and the cloud platform.

7. The method according to claim 1, characterized in that, The lidar is installed at the front of the vehicle, and the motion trend includes the position information of the target person at different time nodes.

8. The method according to claim 1, characterized in that, Determining the relative position between the target face of the target person and the vehicle at different time nodes according to the motion trend and the driving state of the vehicle includes: Determine the face position of the target face in the target person, predict the local positions of the target face in the target person at different time nodes according to the motion trend and the face position, and convert the local positions at different time nodes into the first global positions in the vehicle coordinate system respectively. Predict the second global positions of the vehicle at different time nodes according to the driving state of the vehicle. Based on the first global positions of the target face in the target person at different time nodes and the second global positions of the vehicle at the corresponding time nodes, calculate the relative position between the target face of the target person and the vehicle at different time nodes.

9. The method according to claim 1, wherein Analyzing the point cloud data in front of the vehicle collected by the lidar to obtain the target person and the motion trend of the target person includes: Use a clustering algorithm to cluster each point cloud data in front of the vehicle continuously collected by the lidar to obtain the clustering result of each point cloud data. Identify the target person in the clustering result of each point cloud data, and extract the geometric features of the target person from the clustering result of each point cloud data respectively. Calculate the central position coordinates of the target person in each point cloud data according to the geometric features of each point cloud data. Use the central position coordinates of each point cloud data to update the state vector and covariance matrix of a preset Kalman filter to obtain multiple covariance matrices of predicted states. Generate the motion trend of the target person according to the multiple covariance matrices of predicted states.

10. A lighting device, characterized in that, It includes a controller, a lidar, an execution module, and a headlight. The controller is electrically connected to the lidar and the execution module respectively. The execution module is connected to the headlight. The controller includes a processor and a memory. Among them, the memory stores a computer program, and when the processor executes the computer program, it implements the automatic anti-glare high-beam lighting method according to any one of claims 1 to 9.

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