Automobile lighting adaptive control method, system and medium based on machine learning

Abstract

The present invention discloses a method, system, and medium for adaptive vehicle lighting control based on machine learning. The method comprises: obtaining vehicle status data, external environment information, and GPS positioning information of a target vehicle, wherein the external environment information includes light intensity information, road surface condition information, and weather condition information; determining the light intensity change rate, road surface reflectance coefficient, and visibility index based on the external environment information and GPS positioning information; performing feature fusion on the light intensity change rate, road surface reflectance coefficient, and visibility index to obtain external environment feature data; inputting the external environment feature data and vehicle status data into a pre-trained vehicle exterior light brightness prediction model to obtain a target exterior light brightness level; and adjusting and controlling the exterior light brightness of the target vehicle based on the target exterior light brightness level. The present invention improves the user's driving experience and driving safety and can be widely applied in the field of vehicle control technology.

Description

Technical Field

[0001] The present invention relates to the field of vehicle control technology, and in particular to a method, system and medium for adaptive control of automobile lighting based on machine learning. Background Art

[0002] Cars, as a common means of transportation, have become indispensable in human life. Existing headlight control systems often use fixed illumination. When drivers turn on their headlights, the brightness remains uniform and cannot be adjusted to suit the real-time environment. This prevents personalized lighting, hindering the user's driving experience and posing safety risks in low-light conditions. Summary of the Invention

[0003] The purpose of the present invention is to solve one of the technical problems existing in the prior art to at least a certain extent.

[0004] To this end, an object of an embodiment of the present invention is to provide a method for adaptively controlling automobile lighting based on machine learning, which improves the user's driving experience and driving safety.

[0005] Another object of an embodiment of the present invention is to provide an automobile lighting adaptive control system based on machine learning.

[0006] In order to achieve the above technical objectives, the technical solutions adopted by the embodiments of the present invention include:

[0007] In a first aspect, an embodiment of the present invention provides a method for adaptively controlling automobile lighting based on machine learning, comprising the following steps:

[0008] Obtaining vehicle status data, external environment information, and GPS positioning information of the target vehicle, wherein the external environment information includes light intensity information, road condition information, and weather condition information;

[0009] Determining a light intensity change rate, a road surface reflection coefficient, and a visibility index based on the external environment information and the GPS positioning information, and performing feature fusion on the light intensity change rate, the road surface reflection coefficient, and the visibility index to obtain external environment feature data;

[0010] Inputting the external environment feature data and the vehicle status data into a pre-trained automobile exterior light brightness prediction model to obtain a target exterior light brightness level;

[0011] The brightness of the exterior lights of the target vehicle is adjusted and controlled according to the target exterior light brightness level.

[0012] Furthermore, in one embodiment of the present invention, the acquiring of vehicle status data, external environment information, and GPS positioning information of the target vehicle specifically includes:

[0013] Acquiring the vehicle state data through a vehicle body controller, wherein the vehicle state data includes the speed and steering angle of the target vehicle;

[0014] monitoring the light intensity around the target vehicle in real time through an ambient light sensor to obtain the light intensity information;

[0015] monitoring the road conditions around the target vehicle in real time by using a camera and / or radar to obtain the road condition information;

[0016] Monitor the weather conditions around the target vehicle in real time using a rain sensor and a humidity sensor to obtain the weather condition information;

[0017] The GPS positioning information is obtained through a positioning module.

[0018] Furthermore, in one embodiment of the present invention, determining the light intensity change rate, road surface reflectance, and visibility index based on the external environment information and the GPS positioning information specifically includes:

[0019] determining the light intensity change rate according to the light intensity information;

[0020] determining a road surface slipperiness and a water and snow accumulation state according to the road surface state information, and determining a road surface reflection coefficient according to the road surface slipperiness and the water and snow accumulation state;

[0021] Real-time weather information of the vehicle's geographical location is acquired according to the GPS positioning information, and the visibility index is determined according to the real-time weather information and the weather condition information.

[0022] Furthermore, in one embodiment of the present invention, the feature fusion of the light intensity change rate, the road surface reflectance coefficient, and the visibility index to obtain external environment feature data specifically includes:

[0023] performing data standardization and vectorization processing on the illumination intensity change rate, the road surface reflection coefficient, and the visibility index to obtain an illumination intensity feature vector, a road surface reflection feature vector, and a visibility feature vector;

[0024] Vector concatenation is performed on the light intensity feature vector, the road surface reflection feature vector, and the visibility feature vector to obtain the external environment feature data.

[0025] Furthermore, in one embodiment of the present invention, the vehicle exterior light brightness prediction model is trained by the following steps:

[0026] Obtain sample data of the test vehicle's external environment characteristics and vehicle status, and determine the corresponding exterior light brightness level labels through manual labeling;

[0027] Determining a training sample based on the external environment feature sample data and the vehicle state sample data;

[0028] Inputting the training samples into a pre-built deep learning neural network to obtain exterior light brightness prediction results;

[0029] determining a loss value according to the exterior light brightness prediction result and the exterior light brightness level label;

[0030] The parameters of the deep learning neural network are updated according to the loss value to obtain the trained vehicle exterior light brightness prediction model.

[0031] Furthermore, in one embodiment of the present invention, the step of adjusting and controlling the brightness of the exterior lights of the target vehicle according to the target exterior light brightness level specifically includes:

[0032] generating an RLS signal adjustment instruction according to the target exterior light brightness level and a preset safety and energy-saving strategy;

[0033] The RLS signal adjustment instruction is sent to a vehicle light control unit, so that the vehicle light control unit gradually adjusts the brightness of the exterior light LED array of the target vehicle to the target exterior light brightness level.

[0034] Furthermore, in one embodiment of the present invention, after adjusting and controlling the brightness of the exterior lights of the target vehicle according to the target exterior light brightness level, the method further includes the following steps:

[0035] In response to a manual adjustment operation of an exterior light by a driver of the target vehicle, obtaining a corresponding manually adjusted brightness level;

[0036] Feedback samples are generated according to the external environment characteristic data, the vehicle status data, and the manually adjusted brightness level, and the automobile exterior light brightness prediction model is optimized according to the feedback samples.

[0037] In a second aspect, an embodiment of the present invention provides an automotive lighting adaptive control system based on machine learning, comprising:

[0038] A data acquisition module is used to obtain vehicle status data, external environment information and GPS positioning information of the target vehicle, wherein the external environment information includes light intensity information, road condition information and weather condition information;

[0039] a feature fusion module, configured to determine a light intensity change rate, a road surface reflectance coefficient, and a visibility index based on the external environment information and the GPS positioning information, and perform feature fusion on the light intensity change rate, the road surface reflectance coefficient, and the visibility index to obtain external environment feature data;

[0040] an exterior light brightness prediction module, configured to input the external environment feature data and the vehicle status data into a pre-trained automobile exterior light brightness prediction model to obtain a target exterior light brightness level;

[0041] The exterior light brightness control module is used to adjust and control the exterior light brightness of the target vehicle according to the target exterior light brightness level.

[0042] In a third aspect, an embodiment of the present invention provides a vehicle lighting adaptive control device based on machine learning, comprising:

[0043] at least one processor;

[0044] at least one memory for storing at least one program;

[0045] When the at least one program is executed by the at least one processor, the at least one processor implements the above-mentioned method for adaptive control of automobile lights based on machine learning.

[0046] In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium storing a program executable by a processor, wherein the program executable by the processor is used to execute the above-mentioned method for adaptive control of automobile lighting based on machine learning when executed by the processor.

[0047] The advantages and benefits of the present invention will be described in part in the following description and will become apparent from the following description or learned through practice of the present invention:

[0048] An embodiment of the present invention obtains vehicle status data, external environment information and GPS positioning information of a target vehicle, the external environment information including light intensity information, road condition information and weather condition information, determines the light intensity change rate, road surface reflection coefficient and visibility index based on the external environment information and GPS positioning information, performs feature fusion on the light intensity change rate, road surface reflection coefficient and visibility index to obtain external environment feature data, inputs the external environment feature data and vehicle status data into a pre-trained automobile exterior light brightness prediction model to obtain a target exterior light brightness level, and adjusts and controls the exterior light brightness of the target vehicle based on the target exterior light brightness level. The embodiment of the present invention obtains the vehicle status data, external environment information and GPS positioning information of the vehicle in real time, determines the light intensity change rate, road surface reflection coefficient and visibility index according to the external environment information and GPS positioning information, obtains the external environment feature data through feature fusion, inputs the external environment feature data and vehicle status data into a pre-trained automobile exterior light brightness prediction model to obtain the target exterior light brightness level, so that the exterior light brightness of the target vehicle can be adjusted and controlled according to the target exterior light brightness level. It can not only enhance lighting and improve driving vision at night or in bad weather, but also moderately reduce brightness during the day or when there is sufficient light, thereby achieving energy saving and consumption reduction, and improving the user's driving experience and driving safety. In addition, through continuous learning and optimization, the embodiment of the present invention can gradually adapt to different driving habits and environments, providing users with a more personalized, safe and environmentally friendly driving experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following introduction is made to the drawings required for use in the embodiments of the present invention. It should be understood that the drawings introduced below are only for the convenience of clearly describing some embodiments of the technical solutions of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without any creative work.

[0050] Figure 1 A flowchart of the steps of a method for adaptively controlling automobile lighting based on machine learning provided by an embodiment of the present invention;

[0051] Figure 2 A structural block diagram of a vehicle lighting adaptive control system based on machine learning provided by an embodiment of the present invention;

[0052] Figure 3 This is a structural block diagram of a vehicle lighting adaptive control device based on machine learning provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0053] The embodiments of the present invention are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention and are not to be construed as limiting the present invention. The step numbers in the following embodiments are provided for ease of explanation only and do not limit the order of the steps. The order of execution of the steps in the embodiments can be adaptively adjusted according to the understanding of those skilled in the art.

[0054] In the description of the present invention, "a plurality" means two or more. The terms "first" and "second" are used solely to distinguish technical features and are not to be construed as indicating or implying relative importance, or as implicitly indicating the number of the indicated technical features, or as implicitly indicating the order of the indicated technical features. Furthermore, unless otherwise defined, all technical and scientific terms used herein have the same meanings as commonly understood by those skilled in the art.

[0055] Reference Figure 1 The embodiment of the present invention provides a method for adaptively controlling automobile lighting based on machine learning, which specifically includes the following steps:

[0056] S101, obtaining vehicle status data, external environment information, and GPS positioning information of a target vehicle, where the external environment information includes light intensity information, road condition information, and weather condition information;

[0057] S102, determining the illumination intensity change rate, the road surface reflectance coefficient, and the visibility index based on the external environment information and the GPS positioning information, and performing feature fusion on the illumination intensity change rate, the road surface reflectance coefficient, and the visibility index to obtain external environment feature data;

[0058] S103, inputting the external environment feature data and the vehicle status data into a pre-trained automobile exterior light brightness prediction model to obtain a target exterior light brightness level;

[0059] S104: Adjust and control the brightness of the exterior lights of the target vehicle according to the target exterior light brightness level.

[0060] This embodiment of the present invention uses a machine learning algorithm to intelligently control exterior light brightness and RLS (Road Lighting System) signal patterns. In practice, onboard sensors collect real-time driving environment data, such as light intensity, road conditions, and weather conditions. This data, combined with vehicle status data (speed, steering angle) and GPS positioning information, is fed into a machine learning model for comprehensive analysis. This model then generates RLS signal adjustment instructions based on pre-defined safety and energy-saving strategies, which then drive the lighting system to execute the adjustments.

[0061] It can be appreciated that the embodiment of the present invention acquires the vehicle status data, external environment information and GPS positioning information of the vehicle in real time, determines the light intensity change rate, road surface reflection coefficient and visibility index based on the external environment information and GPS positioning information, obtains external environment feature data through feature fusion, inputs the external environment feature data and vehicle status data into a pre-trained automobile exterior light brightness prediction model to obtain the target exterior light brightness level, so that the exterior light brightness of the target vehicle can be adjusted and controlled according to the target exterior light brightness level. This not only enhances lighting and improves driving vision at night or in bad weather, but also moderately reduces brightness during the day or when there is sufficient light, thereby achieving energy saving and consumption reduction, and improving the user's driving experience and driving safety. In addition, through continuous learning and optimization, the embodiment of the present invention can gradually adapt to different driving habits and environments, providing users with a more personalized, safe and environmentally friendly driving experience.

[0062] As an optional embodiment, obtaining vehicle status data, external environment information, and GPS positioning information of the target vehicle specifically includes:

[0063] S1011. Acquire vehicle status data through a vehicle body controller, where the vehicle status data includes a speed and a steering angle of the target vehicle;

[0064] S1012. Monitor the light intensity around the target vehicle in real time using an ambient light sensor to obtain light intensity information;

[0065] S1013. Real-time monitoring of road conditions around the target vehicle using a camera and / or radar to obtain road condition information;

[0066] S1014. Monitor the weather conditions around the target vehicle in real time using a rain sensor and a humidity sensor to obtain weather condition information.

[0067] S1015. Obtain GPS positioning information through the positioning module.

[0068] Specifically, the real-time speed and steering angle are obtained through the body controller to obtain vehicle status data; the light intensity around the vehicle, including natural light, street lights, tunnel lights, etc., is monitored in real time through photosensitive sensors (such as ambient light sensors) to obtain light intensity information; the wetness, water accumulation, snow accumulation and other conditions of the road are identified using cameras, millimeter-wave radars or laser radars (LiDAR), and the degree of vehicle bumps is judged in combination with acceleration sensors to obtain road condition information; rainfall, haze, sandstorms and other weather conditions are judged through rain sensors, humidity sensors and camera image analysis (such as raindrop detection algorithms) to obtain weather condition information; the vehicle's geographic location (such as urban roads, highways, tunnels) and local time (day and night, seasonal changes) are obtained in combination with GPS to supplement light forecast information.

[0069] In some optional embodiments, after acquiring multi-source sensor data, it is also necessary to align the sensor data's timestamps to eliminate the time difference between devices. In addition, the sensor data needs to be cleaned to remove outliers (such as camera reflection interference) and noise filtering (such as radar signal interference).

[0070] In some optional embodiments, Kalman filtering or deep learning models (such as Transformer) are used to fuse multi-source data, for example, combining camera images and radar point clouds to enhance the accuracy of road condition recognition.

[0071] As an optional implementation, the light intensity change rate, road surface reflectance, and visibility index are determined based on the external environment information and GPS positioning information, which specifically includes:

[0072] S1021. Determine the light intensity change rate according to the light intensity information;

[0073] S1022: Determine the road surface slipperiness and the state of accumulated water and snow based on the road surface condition information, and determine a road surface reflection coefficient based on the road surface slipperiness and the state of accumulated water and snow;

[0074] S1023. Acquire real-time weather information of the vehicle's geographical location based on the GPS positioning information, and determine a visibility index based on the real-time weather information and weather condition information.

[0075] Specifically, the rate of change of light intensity (such as the instantaneous change in darkness when entering a tunnel) is determined based on the light intensity at multiple consecutive moments; the road surface slipperiness and the state of water and snow accumulation are determined based on the road surface condition information, and the road surface reflection coefficient (the scattering effect of a wet and slippery road surface on light) is determined based on the road surface slipperiness and the state of water and snow accumulation; the GPS positioning is matched with the weather API data to obtain real-time meteorological information of the vehicle's geographic location, and the visibility index (corresponding to the light penetration requirements in rainy and foggy days) is comprehensively judged based on the real-time meteorological information and weather condition information.

[0076] As an optional implementation, feature fusion is performed on the light intensity change rate, road surface reflectance, and visibility index to obtain external environment feature data, which specifically includes:

[0077] S1024. Perform data standardization and vectorization processing on the illumination intensity change rate, the road surface reflectance coefficient, and the visibility index to obtain an illumination intensity feature vector, a road surface reflectance feature vector, and a visibility feature vector;

[0078] S1025. Perform vector concatenation on the light intensity feature vector, the road surface reflection feature vector, and the visibility feature vector to obtain external environment feature data.

[0079] Specifically, data of different dimensions (such as light units in lux and road humidity in percentage) are converted into a unified numerical range and then vectorized to obtain light intensity feature vectors, road surface reflection feature vectors, and visibility feature vectors; the light intensity feature vectors, road surface reflection feature vectors, and visibility feature vectors are vector-concatenated to obtain external environment feature data.

[0080] As an optional embodiment, the vehicle exterior light brightness prediction model is trained by the following steps:

[0081] S201, obtaining external environment feature sample data and vehicle status sample data of the test vehicle, and determining corresponding exterior light brightness level labels through manual labeling;

[0082] S202, determining a training sample based on the external environment feature sample data and the vehicle state sample data;

[0083] S203: Input the training sample into a pre-built deep learning neural network to obtain a prediction result of the exterior light brightness;

[0084] S204, determining a loss value based on the exterior light brightness prediction result and the exterior light brightness level label;

[0085] S205. Update the parameters of the deep learning neural network according to the loss value to obtain a trained vehicle exterior light brightness prediction model.

[0086] Specifically, the input data for the vehicle exterior light brightness prediction model includes vehicle status data and external environment feature data. The model architecture can adopt a deep learning neural network, and the model output is a discrete brightness level (e.g., 0-100%). Sample data of the test vehicle's external environment feature and vehicle status are obtained, and the most appropriate exterior light brightness level label is determined through manual labeling. The sample data of the test vehicle's external environment feature and vehicle status are then input into the deep learning neural network as training samples for training.

[0087] After inputting the training samples into the initialized deep learning neural network, the model outputs the recognition result, i.e., the exterior light brightness prediction result. The model's prediction accuracy can be evaluated based on the exterior light brightness prediction result and the aforementioned exterior light brightness level labels, thereby updating the model's parameters. For the automotive exterior light brightness prediction model, the accuracy of the model's prediction results can be measured using a loss function. The loss function is defined on a single training data point and measures the prediction error for that training data point. Specifically, the loss value for that training data point is determined by combining the label of the training data point with the model's prediction result for that training data point. In actual training, a training dataset contains many training data points, so a cost function is generally used to measure the overall error of the training dataset. The cost function is defined on the entire training dataset and calculates the average prediction error of all training data points, which can better measure the model's prediction performance. For general machine learning models, the aforementioned cost function, combined with a regularization term to measure model complexity, serves as the training objective function. Based on this objective function, the loss value for the entire training dataset can be calculated. There are many types of commonly used loss functions, such as 0-1 loss function, square loss function, absolute loss function, logarithmic loss function, cross entropy loss function, etc., which can all be used as loss functions for machine learning models. They will not be elaborated on here one by one. In the embodiment of the present invention, any one of the loss functions can be selected to determine the loss value of training. Based on the loss value of training, the back propagation algorithm is used to update the parameters of the model. After several rounds of iteration, a trained car exterior light brightness prediction model can be obtained. The specific number of iterations can be pre-set, or the training is considered complete when the test set meets the accuracy requirements.

[0088] By inputting the target vehicle's external environmental characteristic data and vehicle status data into the vehicle exterior light brightness prediction model, the optimal target exterior light brightness level inferred by the model can be obtained. For example, medium brightness is used to balance energy consumption and visibility in cloudy urban environments at night, while high brightness and high-frequency flashing are used in heavy rain scenes on highways to enhance penetration and warn following vehicles.

[0089] As a further optional embodiment, the brightness of the exterior lights of the target vehicle is adjusted and controlled according to the target exterior light brightness level, which specifically includes:

[0090] S1041. Generate an RLS signal adjustment instruction based on the target exterior light brightness level and the preset safety and energy-saving strategy;

[0091] S1042: Send an RLS signal adjustment instruction to the vehicle light control unit, so that the vehicle light control unit gradually adjusts the brightness of the exterior light LED array of the target vehicle to the target exterior light brightness level.

[0092] Specifically, the preset safety strategy (such as the ISO 22179 standard) and energy-saving strategy (such as low-power mode) are used as constraints, and the corresponding RLS signal adjustment instructions are determined according to the target exterior light brightness level. The instructions are sent to the headlight control unit (ECU) via the CAN bus to adjust the current of the LED array or the projection area of ​​the matrix headlights. Gradual adjustment is also supported (to avoid sudden changes in brightness that affect the driver's adaptation).

[0093] As a further optional embodiment, after adjusting and controlling the exterior light brightness of the target vehicle according to the target exterior light brightness level, the following steps are further included:

[0094] S105, in response to a manual adjustment operation of the exterior lights by the driver of the target vehicle, obtaining a corresponding manually adjusted brightness level;

[0095] S106: Generate feedback samples based on the external environment feature data, the vehicle status data, and the manually adjusted brightness level, and optimize the vehicle exterior light brightness prediction model based on the feedback samples.

[0096] Specifically, after adjusting and controlling the brightness of the target vehicle's exterior lights, the adjusted effect can also be monitored in real time, for example, by verifying through a camera whether the actual lighting range matches the expected one. At the same time, if the driver manually intervenes (such as the user turns up the brightness), feedback samples are generated based on the external environment feature data, vehicle status data, and manually adjusted brightness level. The vehicle exterior light brightness prediction model is optimized based on the feedback samples to further improve the accuracy of the model prediction.

[0097] The above describes the method steps of an embodiment of the present invention. It is understood that the embodiment of the present invention obtains vehicle status data, external environment information, and GPS positioning information of the vehicle in real time, determines the light intensity change rate, road surface reflectance coefficient, and visibility index based on the external environment information and GPS positioning information, obtains external environment feature data through feature fusion, inputs the external environment feature data and vehicle status data into a pre-trained vehicle exterior light brightness prediction model, and obtains a target exterior light brightness level. The exterior light brightness of the target vehicle can then be adjusted and controlled based on the target exterior light brightness level. This not only enhances lighting and improves driving vision at night or in inclement weather, but also moderately reduces brightness during the day or when there is sufficient light, achieving energy conservation and consumption reduction, and improving the user's driving experience and driving safety. In addition, through continuous learning and optimization, the embodiment of the present invention can gradually adapt to different driving habits and environments, providing users with a more personalized, safe, and environmentally friendly driving experience.

[0098] Reference Figure 2 , an embodiment of the present invention provides an automobile lighting adaptive control system based on machine learning, comprising:

[0099] A data acquisition module is used to obtain vehicle status data, external environment information and GPS positioning information of the target vehicle. The external environment information includes light intensity information, road condition information and weather condition information;

[0100] A feature fusion module is used to determine the light intensity change rate, road surface reflection coefficient, and visibility index based on external environment information and GPS positioning information, and to perform feature fusion on the light intensity change rate, road surface reflection coefficient, and visibility index to obtain external environment feature data;

[0101] The exterior light brightness prediction module is used to input the external environment feature data and vehicle status data into a pre-trained automobile exterior light brightness prediction model to obtain the target exterior light brightness level;

[0102] The exterior light brightness control module is used to adjust and control the exterior light brightness of the target vehicle according to the target exterior light brightness level.

[0103] The contents of the above method embodiments are all applicable to the present system embodiments. The functions specifically implemented by the present system embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0104] Reference Figure 3 , an embodiment of the present invention provides a vehicle lighting adaptive control device based on machine learning, comprising:

[0105] at least one processor;

[0106] at least one memory for storing at least one program;

[0107] When the at least one program is executed by the at least one processor, the at least one processor implements the aforementioned method for adaptive control of automobile lights based on machine learning.

[0108] The contents of the above method embodiments are all applicable to the present device embodiments. The functions specifically implemented by the present device embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0109] An embodiment of the present invention further provides a computer-readable storage medium storing a program executable by a processor. When the program is executed by the processor, it is used to execute the above-mentioned method for adaptive control of automobile lights based on machine learning.

[0110] A computer-readable storage medium according to an embodiment of the present invention can execute a method for adaptive control of automobile lighting based on machine learning provided by an embodiment of the method according to the present invention, can execute any combination of implementation steps of the embodiment of the method, and has the corresponding functions and beneficial effects of the method.

[0111] The embodiment of the present invention also discloses a computer program product or computer program, which includes computer instructions stored in a computer-readable storage medium. A processor of a computer device can read the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device performs Figure 1 The method shown.

[0112] In some optional embodiments, the function / operation mentioned in the block diagram may not occur in the order mentioned in the operation diagram. For example, depending on the function / operation involved, the two boxes shown in succession can actually be executed substantially simultaneously or the above-mentioned boxes can sometimes be executed in reverse order. In addition, the embodiment presented and described in the flow chart of the present invention is provided in an exemplary manner for the purpose of providing a more comprehensive understanding of the technology. The disclosed method is not limited to the operation and logic flow presented herein. Optional embodiments are contemplated in which the order of the various operations is changed and the sub-operations described as a part of a larger operation are performed independently.

[0113] In addition, although the present invention is described in the context of functional modules, it should be understood that, unless otherwise stated, one or more of the above-mentioned functions and / or features can be integrated into a single physical device and / or software module, or one or more functions and / or features can be implemented in separate physical devices or software modules. It is also understood that a detailed discussion of the actual implementation of each module is not necessary for understanding the present invention. More specifically, given the properties, functions, and internal relationships of the various functional modules in the devices disclosed herein, the actual implementation of the module will be understood within the routine skills of an engineer. Therefore, a person skilled in the art can implement the present invention set forth in the claims using ordinary skills without undue experimentation. It is also understood that the specific concepts disclosed are merely illustrative and are not intended to limit the scope of the present invention, which is determined by the full scope of the appended claims and their equivalents.

[0114] If the above functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the above methods of each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, and other media that can store program code.

[0115] The logic and / or steps represented in the flowcharts or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing the logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (e.g., a computer-based system, a system including a processor, or other system that can fetch and execute instructions from an instruction execution system, apparatus, or device). For purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0116] More specific examples (a non-exhaustive list) of computer-readable media include the following: an electrical connection with one or more wires (electronic devices), a portable computer disk cartridge (magnetic devices), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disc read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable media on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other media, followed by editing, deciphering, or processing in another suitable manner as necessary, and then stored in a computer memory.

[0117] It should be understood that various parts of the present invention can be implemented using hardware, software, firmware, or a combination thereof. In the above-described embodiments, multiple steps or methods can be implemented using software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented using hardware, as in another embodiment, any one of the following technologies known in the art or a combination thereof can be used: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application-specific integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0118] In the above description of this specification, reference to the terms "one embodiment / example," "another embodiment / example," or "certain embodiments / examples" means that the specific features, structures, materials, or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

[0119] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to the embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the claims and their equivalents.

[0120] The above is a specific description of the preferred implementation of the present invention, but the present invention is not limited to the above embodiments. Those skilled in the art can make various equivalent modifications or substitutions without violating the spirit of the present invention. These equivalent modifications or substitutions are all included in the scope defined by the claims of this application.

Claims

1. A method for adaptive control of automobile lighting based on machine learning, characterized in that: The following steps are involved: Obtaining vehicle status data, external environment information, and GPS positioning information of the target vehicle, wherein the external environment information includes light intensity information, road condition information, and weather condition information; Determining a light intensity change rate, a road surface reflection coefficient, and a visibility index based on the external environment information and the GPS positioning information, and performing feature fusion on the light intensity change rate, the road surface reflection coefficient, and the visibility index to obtain external environment feature data; Inputting the external environment feature data and the vehicle status data into a pre-trained automobile exterior light brightness prediction model to obtain a target exterior light brightness level; Adjusting and controlling the brightness of the exterior lights of the target vehicle according to the target exterior light brightness level; The acquisition of vehicle status data, external environment information, and GPS positioning information of the target vehicle specifically includes: Acquiring the vehicle state data through a vehicle body controller, wherein the vehicle state data includes the speed and steering angle of the target vehicle; monitoring the light intensity around the target vehicle in real time through an ambient light sensor to obtain the light intensity information; monitoring the road conditions around the target vehicle in real time by using a camera and / or radar to obtain the road condition information; Monitor the weather conditions around the target vehicle in real time using a rain sensor and a humidity sensor to obtain the weather condition information; Acquire the GPS positioning information through a positioning module; The determining of the light intensity change rate, the road surface reflection coefficient, and the visibility index according to the external environment information and the GPS positioning information specifically includes: determining the light intensity change rate according to the light intensity information; determining a road surface slipperiness and a state of accumulated water and snow according to the road surface condition information, and determining a road surface reflection coefficient according to the road surface slipperiness and the state of accumulated water and snow; Real-time weather information of the vehicle's geographical location is acquired according to the GPS positioning information, and the visibility index is determined according to the real-time weather information and the weather condition information.

2. The method for adaptive control of automobile lighting based on machine learning according to claim 1, characterized in that: The feature fusion of the light intensity change rate, the road surface reflectance coefficient, and the visibility index to obtain external environment feature data specifically includes: performing data standardization and vectorization processing on the illumination intensity change rate, the road surface reflection coefficient, and the visibility index to obtain an illumination intensity feature vector, a road surface reflection feature vector, and a visibility feature vector; Vector concatenation is performed on the light intensity feature vector, the road surface reflection feature vector, and the visibility feature vector to obtain the external environment feature data.

3. The method for adaptive control of automobile lighting based on machine learning according to claim 1, characterized in that: The vehicle exterior light brightness prediction model is trained by the following steps: Obtain sample data of the test vehicle's external environment characteristics and vehicle status, and determine the corresponding exterior light brightness level labels through manual labeling; Determining a training sample based on the external environment feature sample data and the vehicle state sample data; Inputting the training samples into a pre-built deep learning neural network to obtain exterior light brightness prediction results; determining a loss value according to the exterior light brightness prediction result and the exterior light brightness level label; The parameters of the deep learning neural network are updated according to the loss value to obtain the trained vehicle exterior light brightness prediction model.

4. The method for adaptively controlling automobile lighting based on machine learning according to claim 1, characterized in that: The step of adjusting and controlling the brightness of the exterior lights of the target vehicle according to the target exterior light brightness level specifically includes: generating an RLS signal adjustment instruction according to the target exterior light brightness level and a preset safety and energy-saving strategy; The RLS signal adjustment instruction is sent to a vehicle light control unit, so that the vehicle light control unit gradually adjusts the brightness of the exterior light LED array of the target vehicle to the target exterior light brightness level.

5. The method for adaptive control of automobile lighting based on machine learning according to any one of claims 1 to 4, characterized in that: After the exterior light brightness of the target vehicle is adjusted and controlled according to the target exterior light brightness level, the following steps are further included: In response to a manual adjustment operation of an exterior light by a driver of the target vehicle, obtaining a corresponding manually adjusted brightness level; Feedback samples are generated according to the external environment characteristic data, the vehicle status data, and the manually adjusted brightness level, and the automobile exterior light brightness prediction model is optimized according to the feedback samples.

6. A car lighting adaptive control system based on machine learning, characterized in that: include: A data acquisition module is used to obtain vehicle status data, external environment information and GPS positioning information of the target vehicle, wherein the external environment information includes light intensity information, road condition information and weather condition information; a feature fusion module, configured to determine a light intensity change rate, a road surface reflectance coefficient, and a visibility index based on the external environment information and the GPS positioning information, and perform feature fusion on the light intensity change rate, the road surface reflectance coefficient, and the visibility index to obtain external environment feature data; an exterior light brightness prediction module, configured to input the external environment feature data and the vehicle status data into a pre-trained automobile exterior light brightness prediction model to obtain a target exterior light brightness level; An exterior light brightness control module, configured to adjust and control the exterior light brightness of the target vehicle according to the target exterior light brightness level; The acquisition of vehicle status data, external environment information, and GPS positioning information of the target vehicle specifically includes: Acquiring the vehicle state data through a vehicle body controller, wherein the vehicle state data includes the speed and steering angle of the target vehicle; monitoring the light intensity around the target vehicle in real time through an ambient light sensor to obtain the light intensity information; monitoring the road conditions around the target vehicle in real time by using a camera and / or radar to obtain the road condition information; Monitor the weather conditions around the target vehicle in real time using a rain sensor and a humidity sensor to obtain the weather condition information; Acquire the GPS positioning information through a positioning module; The determining of the light intensity change rate, the road surface reflection coefficient, and the visibility index according to the external environment information and the GPS positioning information specifically includes: determining the light intensity change rate according to the light intensity information; determining a road surface slipperiness and a state of accumulated water and snow according to the road surface condition information, and determining a road surface reflection coefficient according to the road surface slipperiness and the state of accumulated water and snow; Real-time weather information of the vehicle's geographical location is acquired according to the GPS positioning information, and the visibility index is determined according to the real-time weather information and the weather condition information.

7. A vehicle lighting adaptive control device based on machine learning, characterized in that: include: at least one processor; at least one memory for storing at least one program; When the at least one program is executed by the at least one processor, the at least one processor implements the vehicle lighting adaptive control method based on machine learning as described in any one of claims 1 to 5.

8. A computer-readable storage medium storing a program executable by a processor, characterized in that: The processor-executable program is used to execute the vehicle lighting adaptive control method based on machine learning as described in any one of claims 1 to 5 when executed by the processor.

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