Vehicle headlight brightness adaptive adjustment method, device, equipment and program product

Abstract

The application discloses a vehicle light language brightness self-adaptive adjustment method, device, equipment and program product, comprising: acquiring the current driving scene, the current environmental light intensity and the road participant state outside the target vehicle; determining the current light language type of the target vehicle, determining the corresponding light language brightness value interval according to the current light language type; constructing a multi-objective decision model with the optimization target of light language recognition probability maximization and visual discomfort probability minimization, determining the optimal light language brightness value in the light language brightness value interval based on the multi-objective decision model; and adjusting the current light language brightness of the target vehicle according to the optimal light language brightness value. The application realizes the accurate adaptation of the light language brightness and the vehicle surrounding environment, takes into account the recognizability of the light language and the visual feeling of other road participants, improves the accuracy of the vehicle light language brightness adjustment, guarantees the road traffic safety, and can be applied to the technical field of vehicle control.

Description

Technical Field

[0001] This invention relates to the field of vehicle control technology, and in particular to a method, device, equipment, and program product for adaptive adjustment of vehicle headlight brightness. Background Technology

[0002] Current vehicle light signal brightness adjustment technologies are mainly divided into two categories: The first category is a fixed brightness scheme, such as traditional car turn signals and warning lights, where the brightness of the light signal remains constant. In strong light environments, it is easily submerged, while in low light environments, it is too dazzling, affecting road traffic safety. The second category is a two-level switching scheme, such as the intelligent light signal system of some new energy vehicles, which only sets two brightness levels: "strong light" and "low light". This cannot adapt to complex lighting environments, and the brightness changes abruptly when switching between levels, which can easily cause visual stimulation to drivers, pedestrians, and other road users, also affecting road traffic safety.

[0003] The above problems urgently need to be addressed. Summary of the Invention

[0004] The purpose of this invention is to at least partially solve one of the technical problems existing in the prior art.

[0005] Therefore, one objective of this invention is to provide a method for adaptive adjustment of vehicle light signal brightness. This method determines the corresponding light signal brightness value range based on the current light signal type, and optimizes the light signal brightness value within this range using a multi-objective decision model to maximize the light signal recognition probability and minimize the probability of visual discomfort. This achieves precise adaptation of light signal brightness to the vehicle's surrounding environment, takes into account the recognizability of the light signal and the visual experience of other road users, improves the accuracy of vehicle light signal brightness adjustment, and ensures road traffic safety.

[0006] Another objective of this invention is to provide a vehicle headlight brightness adaptive adjustment device.

[0007] To achieve the above-mentioned technical objectives, the technical solutions adopted in the embodiments of the present invention include: On one hand, embodiments of the present invention provide a method for adaptive adjustment of vehicle headlight brightness, comprising the following steps: The current driving scene of the target vehicle, the current ambient light intensity, and the status of road participants outside the target vehicle are obtained. Determine the current light signal type of the target vehicle, and determine the corresponding light signal brightness value range based on the current light signal type; A multi-objective decision model is constructed with the optimization objectives of maximizing the probability of light signal recognition and minimizing the probability of visual discomfort. Based on the multi-objective decision model, the optimal light signal brightness value is determined within the light signal brightness value range. The current light signal brightness of the target vehicle is adjusted according to the optimal light signal brightness value; The multi-objective decision-making model includes a light signal recognition probability prediction sub-model, a visual discomfort probability prediction sub-model, and a multi-objective optimization function. The light signal recognition probability prediction sub-model is used to predict the light signal recognition probability corresponding to different light signal brightness values ​​by combining the current ambient light intensity and the state of the road participants. The visual discomfort probability prediction sub-model is used to predict the visual discomfort probability corresponding to different light signal brightness values ​​by combining the current ambient light intensity and the state of the road participants. The multi-objective optimization function is used to perform a weighted summation of the light signal recognition probability and the visual discomfort probability according to a dynamic weight coefficient to obtain a priority score corresponding to different light signal brightness values. The dynamic weight coefficient is determined according to the current driving scenario.

[0008] Furthermore, in one embodiment of the present invention, obtaining the current driving scene of the target vehicle, the current ambient light intensity, and the status of road participants outside the target vehicle specifically includes: The vehicle acquires road image information around the target vehicle through an onboard camera device, acquires radar detection information around the target vehicle through an onboard radar device, and acquires the current ambient light intensity through a light sensor. The current driving scenario is identified based on the road image information; Based on the road image information, identify several road participants outside the target vehicle, and determine the position, speed, and attitude of each road participant based on the radar detection information to obtain the state of the road participants.

[0009] Furthermore, in one embodiment of the present invention, determining the corresponding light signal brightness value range based on the current light signal type specifically includes: Determine the corresponding standard value and brightness fluctuation ratio of the light signal based on the current light signal type; The brightness range of the light signal is determined based on the standard value of the light signal brightness and the brightness fluctuation ratio.

[0010] Furthermore, in one embodiment of the present invention, determining the optimal light signal brightness value within the light signal brightness value range based on the multi-objective decision model specifically includes: Multiple alternative light signal brightness values ​​are determined based on the preset brightness value interval and the light signal brightness value range; The current ambient light intensity, the status of the road participants, and the brightness value of the candidate light signals are input into the pre-trained light signal recognition probability prediction sub-model to obtain the light signal recognition probability corresponding to the brightness value of the candidate light signals. The current ambient light intensity, the status of the road participants, and the brightness value of the candidate light signals are input into the pre-trained visual discomfort probability prediction sub-model to obtain the visual discomfort probability corresponding to the brightness value of the candidate light signals. The light signal recognition probability and the visual discomfort probability are input into the multi-objective optimization function to obtain the priority score corresponding to the brightness value of the candidate light signal. The candidate light signal brightness value with the highest priority score is determined as the optimal light signal brightness value.

[0011] Furthermore, in one embodiment of the present invention, the multi-objective optimization function is:

[0012] in, Indicates priority score, and These represent the probability of light signal recognition and the probability of visual discomfort, respectively. and These represent the weights for light signal recognition and visual discomfort, determined based on the current driving scenario, respectively. .

[0013] Furthermore, in one embodiment of the present invention, the light signal recognition probability prediction sub-model is trained through the following steps: The sample ambient light intensity, sample road participant status, and sample light signal brightness are obtained in the test scenario, and the corresponding light signal recognition probability labels are determined through manual annotation. The ambient light intensity, the status of the road participants, and the brightness of the light signals are input into a pre-constructed convolutional neural network to obtain the predicted light signal recognition probability. The loss value is determined based on the predicted light signal recognition probability and the light signal recognition probability label; The parameters of the convolutional neural network are updated using the backpropagation algorithm based on the loss value to obtain the trained light signal recognition probability prediction sub-model.

[0014] Furthermore, in one embodiment of the present invention, the visual discomfort probability prediction sub-model is trained through the following steps: The sample ambient light intensity, sample road participant status, and sample light brightness were obtained in the test scenario, and the corresponding visual discomfort probability labels were determined through manual annotation. The ambient light intensity, the status of the road participants, and the brightness of the streetlights are input into an initialized gradient boosting decision tree to obtain the predicted probability of visual discomfort. The residual value is determined based on the predicted visual discomfort probability and the visual discomfort probability label; Train a target decision tree to fit the residual values, and update the gradient boosting decision tree based on the target decision tree; When the number of iterations exceeds a preset threshold or the residual value is lower than a preset residual threshold, training stops, and the current gradient boosting decision tree is used as the trained visual discomfort probability prediction sub-model.

[0015] On the other hand, embodiments of the present invention provide a vehicle headlight brightness adaptive adjustment device, comprising: The data acquisition module is used to acquire the current driving scene of the target vehicle, the current ambient light intensity, and the status of road participants outside the target vehicle; The brightness value range determination module is used to determine the current light signal type of the target vehicle and determine the corresponding light signal brightness value range based on the current light signal type. The optimal brightness value determination module is used to construct a multi-objective decision model with the optimization objectives of maximizing the probability of light language recognition and minimizing the probability of visual discomfort, and to determine the optimal light language brightness value within the light language brightness value range based on the multi-objective decision model. The light signal brightness adjustment module is used to adjust the current light signal brightness of the target vehicle according to the optimal light signal brightness value; The multi-objective decision-making model includes a light signal recognition probability prediction sub-model, a visual discomfort probability prediction sub-model, and a multi-objective optimization function. The light signal recognition probability prediction sub-model is used to predict the light signal recognition probability corresponding to different light signal brightness values ​​by combining the current ambient light intensity and the state of the road participants. The visual discomfort probability prediction sub-model is used to predict the visual discomfort probability corresponding to different light signal brightness values ​​by combining the current ambient light intensity and the state of the road participants. The multi-objective optimization function is used to perform a weighted summation of the light signal recognition probability and the visual discomfort probability according to a dynamic weight coefficient to obtain a priority score corresponding to different light signal brightness values. The dynamic weight coefficient is determined according to the current driving scenario.

[0016] On the other hand, embodiments of the present invention provide an electronic device, including: 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 above-described vehicle headlight brightness adaptive adjustment method.

[0017] On the other hand, embodiments of the present invention also provide a computer-readable storage medium storing a processor-executable computer program that, when executed by a processor, implements the above-described vehicle light brightness adaptive adjustment method.

[0018] On the other hand, embodiments of the present invention also provide a computer program product, including a computer program that, when executed by a processor, implements the above-described vehicle light brightness adaptive adjustment method.

[0019] The advantages and beneficial effects of the present invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention: This invention acquires the current driving scene of the target vehicle, the current ambient light intensity, and the status of road users outside the vehicle to determine the current light signal type. Based on the current light signal type, it determines the corresponding light signal brightness value range and constructs a multi-objective decision model with the optimization objectives of maximizing the light signal recognition probability and minimizing the visual discomfort probability. Based on the multi-objective decision model, it determines the optimal light signal brightness value within the light signal brightness value range and adjusts the current light signal brightness of the target vehicle according to the optimal light signal brightness value. This invention achieves precise adaptation of light signal brightness to the vehicle's surrounding environment, taking into account both the recognizability of the light signal and the visual experience of other road users, improving the accuracy of vehicle light signal brightness adjustment, and ensuring road traffic safety. Attached Figure Description

[0020] To more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings used in the embodiments of the present invention are described below. It should be understood that the drawings described 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 creative effort.

[0021] Figure 1 A flowchart illustrating the steps of a vehicle headlight brightness adaptive adjustment method provided in an embodiment of the present invention; Figure 2 This is a structural block diagram of a vehicle headlight brightness adaptive adjustment device provided in an embodiment of the present invention; Figure 3 This is a structural block diagram of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0022] To make the objectives, technical solutions, and advantages of this invention clearer, the invention 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 merely illustrative of the invention and are not intended to limit the invention. In the following description, when referring to the accompanying drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the embodiments of this invention; they are merely examples of apparatuses and methods consistent with some aspects of the embodiments of this invention as detailed in the appended claims.

[0023] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used herein is for the purpose of describing embodiments of the invention only and is not intended to limit the invention.

[0024] The vehicle light brightness adaptive adjustment method provided in this invention can be applied to a terminal, a server, or software running on either a terminal or a server. In some embodiments, the terminal can be a smartphone, tablet, laptop, desktop computer, smart speaker, smartwatch, or in-vehicle terminal, but is not limited to these. The server can be configured as an independent physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms. The server can also be a node server in a blockchain network. The software can be an application that implements the vehicle light brightness adaptive adjustment method, but is not limited to the above forms.

[0025] This invention can be used in a wide variety of general-purpose or special-purpose computer system environments or configurations. Examples include: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, and distributed computing environments including any of the above systems or devices. This invention can be described in the general context of computer-executable instructions, such as program modules, that are executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc., that perform specific tasks or implement specific abstract data types. This invention can also be practiced in distributed computing environments where tasks are performed by remote processing devices connected via a communication network. In distributed computing environments, program modules can reside in local and remote computer storage media, including storage devices.

[0026] It should be noted that in various specific embodiments of the present invention, when processing data related to user identity or characteristics, such as user information, user behavior data, user historical data, and user parking space location information, user permission or consent is obtained first. Furthermore, the collection, use, and processing of this data comply with relevant laws, regulations, and standards. In addition, when embodiments of the present invention require access to sensitive personal information of users, separate permission or consent from the user is obtained through pop-ups or redirection to a confirmation page. Only after obtaining the user's separate permission or consent is the necessary user-related data for the normal operation of the embodiments of the present invention acquired.

[0027] Reference Figure 1 This invention provides a method for adaptive adjustment of vehicle headlight brightness, specifically including the following steps: S101. Obtain the current driving scene of the target vehicle, the current ambient light intensity, and the status of road participants outside the target vehicle; S102. Determine the current light signal type of the target vehicle, and determine the corresponding light signal brightness value range based on the current light signal type; S103. Construct a multi-objective decision model with the optimization objectives of maximizing the probability of light signal recognition and minimizing the probability of visual discomfort, and determine the optimal light signal brightness value within the light signal brightness value range based on the multi-objective decision model. S104. Adjust the current light signal brightness of the target vehicle according to the optimal light signal brightness value; The multi-objective decision-making model includes a light signal recognition probability prediction sub-model, a visual discomfort probability prediction sub-model, and a multi-objective optimization function. The light signal recognition probability prediction sub-model is used to predict the light signal recognition probability corresponding to different light signal brightness values ​​by combining the current ambient light intensity and the state of road participants. The visual discomfort probability prediction sub-model is used to predict the visual discomfort probability corresponding to different light signal brightness values ​​by combining the current ambient light intensity and the state of road participants. The multi-objective optimization function is used to perform a weighted summation of the light signal recognition probability and the visual discomfort probability according to the dynamic weight coefficient to obtain the priority score corresponding to different light signal brightness values. The dynamic weight coefficient is determined according to the current driving scenario.

[0028] This invention determines the corresponding light signal brightness value range based on the current light signal type, and optimizes the light signal brightness value within this range using a multi-objective decision model to maximize the light signal recognition probability and minimize the visual discomfort probability. This achieves precise adaptation of light signal brightness to the vehicle's surrounding environment, takes into account both the recognizability of the light signal and the visual experience of other road users, improves the accuracy of vehicle light signal brightness adjustment, and ensures road traffic safety.

[0029] As a further optional implementation, the current driving scene of the target vehicle, the current ambient light intensity, and the status of road participants outside the target vehicle are obtained, specifically including: S1011. Obtain road image information around the target vehicle through the vehicle-mounted camera device, obtain radar detection information around the target vehicle through the vehicle-mounted radar device, and obtain the current ambient light intensity through the light sensor. S1012. Identify the current driving scenario based on road image information; S1013. Identify several road participants outside the target vehicle based on road image information, and determine the position, speed and attitude of each road participant based on radar detection information to obtain the road participant status.

[0030] Specifically, based on road image information, scenarios such as urban roads, highways, tunnels, and rural roads are identified. The scene granularity is refined by combining vehicle speed and traffic density to obtain the current driving scenario. The ambient light intensity is collected in real time by the vehicle's external light sensor, and cross-validation can be performed by combining the brightness data of the on-board camera. Based on road image information, the participant types (motor vehicles, non-motor vehicles, pedestrians) of several road participants are identified. Then, the position, speed, and attitude of each road participant are determined by combining radar detection information. If necessary, V2X communication data can be used to supplement the information, and finally the status of road participants around the target vehicle is obtained.

[0031] As a further optional implementation, the corresponding light signal brightness value range is determined based on the current light signal type, specifically including: S1021. Determine the corresponding standard value and brightness fluctuation ratio of the light signal according to the current light signal type; S1022. Determine the range of light signal brightness values ​​based on the standard value of light signal brightness and the brightness fluctuation ratio.

[0032] Specifically, a basic brightness standard and brightness fluctuation ratio corresponding to different light signal types need to be established in advance. This needs to be formulated in conjunction with existing traffic regulations, industry standards, and real vehicle test data. After determining the current light signal type, the upper limit and lower limit of brightness are calculated based on the corresponding light signal brightness standard value and brightness fluctuation ratio, respectively, to obtain the light signal brightness value range. For example, the standard value of brake light brightness is 220 cd / m², with an upward fluctuation ratio of 20% and a downward fluctuation ratio of 10%, resulting in a brightness range of 198 cd / m² to 264 cd / m². Finally, within the 198-264 cd / m² range, the optimal brightness value is selected through a multi-objective decision model.

[0033] As a further optional implementation, the optimal light signal brightness value is determined within the light signal brightness value range based on a multi-objective decision model, specifically including: S1031. Determine multiple alternative light signal brightness values ​​based on preset brightness value intervals and light signal brightness value ranges; S1032. Input the current ambient light intensity, the status of road participants and the brightness value of the candidate light signals into the pre-trained light signal recognition probability prediction sub-model to obtain the light signal recognition probability corresponding to the brightness value of the candidate light signals. S1033. Input the current ambient light intensity, the status of road participants and the brightness value of the candidate light signals into the pre-trained visual discomfort probability prediction sub-model to obtain the visual discomfort probability corresponding to the brightness value of the candidate light signals. S1034. Input the light signal recognition probability and the visual discomfort probability into the multi-objective optimization function to obtain the priority score corresponding to the brightness value of the candidate light signal. S1035. Determine the brightness value of the candidate light signal with the highest priority score as the optimal light signal brightness value.

[0034] Specifically, multiple candidate light signal brightness values ​​are determined based on preset brightness value intervals and light signal brightness value ranges. For example, for the range of 198-264 cd / m², if the brightness value interval is 1 cd / m², then all integer brightness values ​​within the 198-264 cd / m² range can be used as candidate light signal brightness values. If the brightness value interval is 2 cd / m², then all even-numbered brightness values ​​within the 198-264 cd / m² range can be used as candidate light signal brightness values. It should be noted that the brightness value interval can be determined based on the model's calculation speed. When the model's calculation speed is fast, a smaller brightness value interval can be used to improve the accuracy of the optimal light signal brightness value. When the model's calculation speed is slow, a larger brightness value interval can be used to improve the efficiency of light signal brightness adjustment.

[0035] The current ambient light intensity, the status of road participants, and the brightness values ​​of candidate light signals are input into pre-trained light signal recognition probability prediction sub-models and visual discomfort probability prediction sub-models, respectively, to obtain the light signal recognition probability and visual discomfort probability corresponding to the candidate light signal brightness values. Then, the light signal recognition probability and visual discomfort probability are input into a multi-objective optimization function, and the priority score corresponding to the candidate light signal brightness value is calculated by combining the light signal recognition weight and visual discomfort weight determined according to the current driving scenario. Finally, the candidate light signal brightness value with the highest priority score is determined as the optimal light signal brightness value.

[0036] As an optional implementation, the multi-objective optimization function is:

[0037] in, Indicates priority score, and These represent the probability of light signal recognition and the probability of visual discomfort, respectively. and These represent the weights for light signal recognition and visual discomfort, determined based on the current driving scenario, respectively. .

[0038] Specifically, the optimization objective of the multi-objective optimization function in this embodiment of the invention is to maximize the priority score of the light signal brightness value. The priority score is positively correlated with the light signal recognition probability and negatively correlated with the visual discomfort probability. Therefore, it can balance the recognizability of the light signal with the visual experience of other road users, achieving a balance between the two. The weights for light signal recognition and visual discomfort can be flexibly adjusted according to different scenarios. For example, in urban congestion scenarios, visual comfort is emphasized more, while in highway nighttime scenarios, the light signal recognition rate is emphasized more. For instance, in highway nighttime scenarios, the weight for light signal recognition is 0.7, and the weight for visual discomfort is -0.3; in urban congestion scenarios, the weight for light signal recognition is 0.3, and the weight for visual discomfort is -0.7.

[0039] The light signal recognition probability prediction sub-model and the visual discomfort probability prediction sub-model in this embodiment of the invention can both be implemented using various prediction models. For example, a neural network model can be used to fit the mapping relationship between ambient light intensity, road participant status, light signal brightness value, and light signal recognition probability; or a gradient boosting decision tree can be used to fit the visual discomfort probability under different combinations of variables such as ambient light intensity, road participant status, and light signal brightness value. Finally, the light signal recognition probability prediction sub-model and the visual discomfort probability prediction sub-model are obtained. The training process of the light signal recognition probability prediction sub-model and the visual discomfort probability prediction sub-model will be explained below using a neural network model and a gradient boosting decision tree as examples, respectively.

[0040] As an optional implementation, the light signal recognition probability prediction sub-model is trained through the following steps: S201. Obtain the ambient light intensity, participant status, and light signal brightness of the sample road in the test scenario, and determine the corresponding light signal recognition probability label through manual annotation. S202. Input the ambient light intensity, the status of road participants, and the brightness of the light signals into a pre-built convolutional neural network to obtain the predicted light signal recognition probability. S203. Determine the loss value based on the predicted light signal recognition probability and the light signal recognition probability label; S204. Update the parameters of the convolutional neural network based on the loss value using the backpropagation algorithm to obtain the trained light signal recognition probability prediction sub-model.

[0041] Specifically, the ambient light intensity, road participant status, and light signal brightness of the test scenario are obtained. The light signal recognition performance of each road participant in the scenario is statistically analyzed to determine the light signal recognition probability label. After obtaining a sufficient number of training samples, the ambient light intensity, road participant status, and light signal brightness are input into a pre-constructed convolutional neural network to obtain the predicted light signal recognition probability. The loss value is determined based on the predicted light signal recognition probability and the light signal recognition probability label. The parameters of the convolutional neural network are updated using the backpropagation algorithm based on the loss value to complete one iteration of training. Training stops when the number of iterations reaches a preset threshold or the loss value falls below a preset threshold, thus obtaining the trained light signal recognition probability prediction sub-model.

[0042] As an optional implementation, the visual discomfort probability prediction sub-model is trained through the following steps: S301. Obtain the ambient light intensity, participant status, and light brightness of the sample road in the test scenario, and determine the corresponding visual discomfort probability labels through manual annotation. S302. Input the ambient light intensity, the status of road participants, and the brightness of streetlights into the initialized gradient boosting decision tree to obtain the predicted probability of visual discomfort. S303. Determine the residual value based on the predicted visual discomfort probability and the visual discomfort probability label; S304. Train the target decision tree to fit the residual values, and update the gradient boosting decision tree based on the target decision tree; S305. When the number of iterations exceeds the preset threshold or the residual value is lower than the preset residual threshold, stop training and use the current gradient boosting decision tree as the trained visual discomfort probability prediction sub-model.

[0043] Specifically, the system acquires the ambient light intensity, participant status, and light brightness of the test scenario, and statistically analyzes the visual comfort of each participant to determine the visual discomfort probability label. After acquiring a sufficient number of training samples, the ambient light intensity, participant status, and light brightness are input into an initialized gradient boosting decision tree to obtain the predicted visual discomfort probability. The residual value is determined based on the predicted visual discomfort probability and the visual discomfort probability label. A target decision tree is trained to fit the residual value, and this target decision tree is multiplied by a preset learning rate and then superimposed onto the gradient boosting decision tree to update the gradient boosting decision tree and enter the next iteration. When the number of iterations exceeds a preset threshold or the residual value falls below a preset residual threshold, training stops, and the current gradient boosting decision tree is used as the trained visual discomfort probability prediction sub-model.

[0044] In some optional embodiments, when adjusting the current light signal brightness of the target vehicle according to the optimal light signal brightness value, a pre-warning buffer mechanism is used to achieve a smooth transition in the light signal brightness adjustment, that is, gradually increasing / decreasing from the current light signal brightness to the optimal light signal brightness value. Furthermore, embodiments of the present invention achieve real-time continuous adjustment of the light signal brightness based on the signal duty cycle using PWM control, thereby improving the reliability of vehicle light signal brightness adjustment.

[0045] The method steps of the embodiments of the present invention have been described above. It can be understood that the embodiments of the present invention determine the corresponding light signal brightness value range based on the current light signal type, and optimize the light signal brightness value within this brightness value range using a multi-objective decision model. This maximizes the light signal recognition probability and minimizes the probability of visual discomfort, achieving precise adaptation of light signal brightness to the vehicle's surrounding environment, taking into account both the recognizability of the light signal and the visual experience of other road users, improving the accuracy of vehicle light signal brightness adjustment, and ensuring road traffic safety.

[0046] Reference Figure 2 This invention provides a vehicle headlight brightness adaptive adjustment device, comprising: The data acquisition module is used to acquire the target vehicle's current driving scenario, current ambient light intensity, and the status of road users outside the target vehicle; The brightness value range determination module is used to determine the current light signal type of the target vehicle and determine the corresponding light signal brightness value range based on the current light signal type. The optimal brightness value determination module is used to construct a multi-objective decision model with the optimization objectives of maximizing the probability of light message recognition and minimizing the probability of visual discomfort, and to determine the optimal light message brightness value within the light message brightness value range based on the multi-objective decision model. The light signal brightness adjustment module is used to adjust the current light signal brightness of the target vehicle according to the optimal light signal brightness value; The multi-objective decision-making model includes a light signal recognition probability prediction sub-model, a visual discomfort probability prediction sub-model, and a multi-objective optimization function. The light signal recognition probability prediction sub-model is used to predict the light signal recognition probability corresponding to different light signal brightness values ​​by combining the current ambient light intensity and the state of road participants. The visual discomfort probability prediction sub-model is used to predict the visual discomfort probability corresponding to different light signal brightness values ​​by combining the current ambient light intensity and the state of road participants. The multi-objective optimization function is used to perform a weighted summation of the light signal recognition probability and the visual discomfort probability according to the dynamic weight coefficient to obtain the priority score corresponding to different light signal brightness values. The dynamic weight coefficient is determined according to the current driving scenario.

[0047] It is understood that the content of the above method embodiments is applicable to the present device embodiments. The specific functions 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.

[0048] Reference Figure 3 This invention provides an electronic device, comprising: At least one processor; At least one memory for storing at least one program; When the above-mentioned at least one program is executed by the above-mentioned at least one processor, the above-mentioned at least one processor implements the above-mentioned vehicle light brightness adaptive adjustment method.

[0049] It is understood that the content of the above method embodiments is applicable to this device embodiment. The specific functions implemented by this device embodiment 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.

[0050] This invention also provides a computer-readable storage medium storing a processor-executable computer program that, when executed by a processor, implements the aforementioned vehicle headlight brightness adaptive adjustment method.

[0051] This invention provides a computer-readable storage medium that can execute a vehicle headlight brightness adaptive adjustment method provided in the method embodiment of this invention. It can execute any combination of the implementation steps of the method embodiment and has the corresponding functions and beneficial effects of the method.

[0052] This invention also provides a computer program product, including a computer program that, when executed by a processor, implements the above-described vehicle headlight brightness adaptive adjustment method.

[0053] It is understood that the content of the above method embodiments is applicable to the embodiments of this program product. The specific functions implemented by the embodiments of this program product 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.

[0054] Memory, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs and non-transitory computer-executable programs. Furthermore, memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, memory may optionally include memory remotely located relative to the processor, and these remote memories can be connected to the processor via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0055] The embodiments described in this invention are for the purpose of more clearly illustrating the technical solutions of the embodiments of this invention, and do not constitute a limitation on the technical solutions provided by the embodiments of this invention. As those skilled in the art will know, with the evolution of technology and the emergence of new application scenarios, the technical solutions provided by the embodiments of this invention are also applicable to similar technical problems.

[0056] The terms "first," "second," "third," "fourth," etc. (if present) in the specification and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0057] In some alternative embodiments, the functions / operations mentioned in the block diagrams may not occur in the order shown in the operation diagrams. For example, depending on the functions / operations involved, two consecutively shown blocks may actually be executed substantially simultaneously, or the aforementioned blocks may sometimes be executed in reverse order. Furthermore, the embodiments presented and described in the flowcharts of this invention are provided by way of example to provide a more comprehensive understanding of the technology. The disclosed methods are not limited to the operations and logic flows presented herein. Alternative embodiments are contemplated in which the order of various operations is changed and sub-operations described as part of a larger operation are executed independently.

[0058] Furthermore, although the invention has been described in the context of functional modules, it should be understood that, unless otherwise stated, one or more of the aforementioned functions and / or features may be integrated into a single physical device and / or software module, or one or more functions and / or features may be implemented in a separate physical device or software module. It is also understood that a detailed discussion of the actual implementation of each module is unnecessary for understanding the invention. Rather, given the properties, functions, and internal relationships of the various functional modules in the apparatus disclosed herein, the actual implementation of the module will be understood within the scope of conventional skill of an engineer. Therefore, those skilled in the art can implement the invention as set forth in the claims using ordinary techniques without excessive experimentation. It is also understood that the specific concepts disclosed are merely illustrative and not intended to limit the scope of the invention, which is determined by the full scope of the appended claims and their equivalents.

[0059] If the aforementioned functions are implemented as 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 this invention, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0060] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing 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 (such as a computer-based system, a processor-including system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0061] More specific examples (a non-exhaustive list) of computer-readable media include: electrical connections (electronic devices) having one or more wires, portable computer disk drives (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, computer-readable media can even be paper or other suitable media on which the aforementioned program can be printed, because the aforementioned program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.

[0062] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0063] In the foregoing description of this specification, references to terms such as "one embodiment," "another embodiment," or "some embodiments" indicate that a specific feature, structure, material, or characteristic described in connection with an embodiment or example is included in at least one embodiment or example of the present invention. In this specification, illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0064] Although embodiments of the invention have been shown and described, those skilled in the art will understand that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the claims and their equivalents.

[0065] The above is a detailed description of the preferred embodiments of the present invention. However, the present invention is not limited to the above embodiments. Those skilled in the art can make various equivalent modifications or substitutions without departing from the spirit of the present invention. All such equivalent modifications or substitutions are included within the scope defined by the claims of the present invention.

Claims

1. A method for adaptively adjusting the brightness of vehicle headlights, characterized in that, Includes the following steps: The current driving scene of the target vehicle, the current ambient light intensity, and the status of road participants outside the target vehicle are obtained. Determine the current light signal type of the target vehicle, and determine the corresponding light signal brightness value range based on the current light signal type; A multi-objective decision model is constructed with the optimization objectives of maximizing the probability of light signal recognition and minimizing the probability of visual discomfort. Based on the multi-objective decision model, the optimal light signal brightness value is determined within the light signal brightness value range. The current light signal brightness of the target vehicle is adjusted according to the optimal light signal brightness value; The multi-objective decision-making model includes a light signal recognition probability prediction sub-model, a visual discomfort probability prediction sub-model, and a multi-objective optimization function. The light signal recognition probability prediction sub-model is used to predict the light signal recognition probability corresponding to different light signal brightness values ​​by combining the current ambient light intensity and the state of the road participants. The visual discomfort probability prediction sub-model is used to predict the visual discomfort probability corresponding to different light signal brightness values ​​by combining the current ambient light intensity and the state of the road participants. The multi-objective optimization function is used to perform a weighted summation of the light signal recognition probability and the visual discomfort probability according to a dynamic weight coefficient to obtain a priority score corresponding to different light signal brightness values. The dynamic weight coefficient is determined according to the current driving scenario.

2. The vehicle headlight brightness adaptive adjustment method according to claim 1, characterized in that, The acquisition of the target vehicle's current driving scenario, current ambient light intensity, and the status of road participants outside the target vehicle specifically includes: The vehicle acquires road image information around the target vehicle through an onboard camera device, acquires radar detection information around the target vehicle through an onboard radar device, and acquires the current ambient light intensity through a light sensor. The current driving scenario is identified based on the road image information; Based on the road image information, identify several road participants outside the target vehicle, and determine the position, speed, and attitude of each road participant based on the radar detection information to obtain the state of the road participants.

3. The vehicle headlight brightness adaptive adjustment method according to claim 1, characterized in that, The step of determining the corresponding light signal brightness value range based on the current light signal type specifically includes: Determine the corresponding standard value and brightness fluctuation ratio of the light signal based on the current light signal type; The brightness range of the light signal is determined based on the standard value of the light signal brightness and the brightness fluctuation ratio.

4. The vehicle headlight brightness adaptive adjustment method according to claim 1, characterized in that, The process of determining the optimal light signal brightness value within the light signal brightness value range based on the multi-objective decision model specifically includes: Multiple alternative light signal brightness values ​​are determined based on the preset brightness value interval and the light signal brightness value range; The current ambient light intensity, the status of the road participants, and the brightness value of the candidate light signals are input into the pre-trained light signal recognition probability prediction sub-model to obtain the light signal recognition probability corresponding to the brightness value of the candidate light signals. The current ambient light intensity, the status of the road participants, and the brightness value of the candidate light signals are input into the pre-trained visual discomfort probability prediction sub-model to obtain the visual discomfort probability corresponding to the brightness value of the candidate light signals. The light signal recognition probability and the visual discomfort probability are input into the multi-objective optimization function to obtain the priority score corresponding to the brightness value of the candidate light signal. The candidate light signal brightness value with the highest priority score is determined as the optimal light signal brightness value.

5. The vehicle headlight brightness adaptive adjustment method according to claim 1, characterized in that, The multi-objective optimization function is: in, Indicates priority score, and These represent the probability of light signal recognition and the probability of visual discomfort, respectively. and These represent the weights for light signal recognition and visual discomfort, determined based on the current driving scenario, respectively. .

6. The vehicle headlight brightness adaptive adjustment method according to claim 1, characterized in that, The light signal recognition probability prediction sub-model is trained through the following steps: The sample ambient light intensity, sample road participant status, and sample light signal brightness are obtained in the test scenario, and the corresponding light signal recognition probability labels are determined through manual annotation. The ambient light intensity, the status of the road participants, and the brightness of the light signals are input into a pre-constructed convolutional neural network to obtain the predicted light signal recognition probability. The loss value is determined based on the predicted light signal recognition probability and the light signal recognition probability label; The parameters of the convolutional neural network are updated using the backpropagation algorithm based on the loss value to obtain the trained light signal recognition probability prediction sub-model.

7. The vehicle headlight brightness adaptive adjustment method according to claim 1, characterized in that, The visual discomfort probability prediction sub-model is trained through the following steps: The sample ambient light intensity, sample road participant status, and sample light brightness were obtained in the test scenario, and the corresponding visual discomfort probability labels were determined through manual annotation. The ambient light intensity, the status of the road participants, and the brightness of the streetlights are input into an initialized gradient boosting decision tree to obtain the predicted probability of visual discomfort. The residual value is determined based on the predicted visual discomfort probability and the visual discomfort probability label; Train a target decision tree to fit the residual values, and update the gradient boosting decision tree based on the target decision tree; When the number of iterations exceeds a preset threshold or the residual value is lower than a preset residual threshold, training stops, and the current gradient boosting decision tree is used as the trained visual discomfort probability prediction sub-model.

8. A vehicle headlight brightness adaptive adjustment device, characterized in that, include: The data acquisition module is used to acquire the current driving scene of the target vehicle, the current ambient light intensity, and the status of road participants outside the target vehicle; The brightness value range determination module is used to determine the current light signal type of the target vehicle and determine the corresponding light signal brightness value range based on the current light signal type. The optimal brightness value determination module is used to construct a multi-objective decision model with the optimization objectives of maximizing the probability of light language recognition and minimizing the probability of visual discomfort, and to determine the optimal light language brightness value within the light language brightness value range based on the multi-objective decision model. The light signal brightness adjustment module is used to adjust the current light signal brightness of the target vehicle according to the optimal light signal brightness value; The multi-objective decision-making model includes a light signal recognition probability prediction sub-model, a visual discomfort probability prediction sub-model, and a multi-objective optimization function. The light signal recognition probability prediction sub-model is used to predict the light signal recognition probability corresponding to different light signal brightness values ​​by combining the current ambient light intensity and the state of the road participants. The visual discomfort probability prediction sub-model is used to predict the visual discomfort probability corresponding to different light signal brightness values ​​by combining the current ambient light intensity and the state of the road participants. The multi-objective optimization function is used to perform a weighted summation of the light signal recognition probability and the visual discomfort probability according to a dynamic weight coefficient to obtain a priority score corresponding to different light signal brightness values. The dynamic weight coefficient is determined according to the current driving scenario.

9. An electronic device, 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 a vehicle light brightness adaptive adjustment method as described in any one of claims 1 to 7.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements a vehicle lighting brightness adaptive adjustment method as described in any one of claims 1 to 7.

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