Illumination parameter determination method and device, equipment, medium, program product and vehicle

Abstract

The invention relates to an illumination parameter determination method and device, equipment, a medium, a program product and a vehicle, the method is applied to a first vehicle, and the method comprises the steps that illumination parameters of the first vehicle are determined according to first illumination data collected by a sensing device in the first vehicle and second illumination data of a second vehicle. The illumination parameters of the vehicle are determined through the first illumination data acquired by the vehicle sensing device and the second illumination data acquired by other vehicles, and the accuracy of measurement of the illumination parameters is improved.

Description

Technical Field

[0001] This application relates to the field of data processing technology, and in particular to a method, apparatus, equipment, medium, program product, and vehicle for determining illumination parameters. Background Technology

[0002] The vehicle's air conditioning system is an important component for maintaining vehicle comfort. Light sensors measure the light intensity on the vehicle, providing information to the air conditioning system.

[0003] However, since light sensors are usually installed on the dashboard or near the rearview mirror inside the windshield, the measurement of light parameters can easily become inaccurate if the light sensor is obstructed or at a suboptimal angle. Summary of the Invention

[0004] This application provides a method for determining the illumination parameters of a vehicle by using first illumination data collected by a vehicle sensor and second illumination data obtained from other vehicles, thereby improving the accuracy of illumination parameter measurement and at least partially solving the aforementioned technical problems.

[0005] To achieve the above objectives, according to a first aspect of this application, a method for determining illumination parameters is provided, the method being applied to a first vehicle, the method comprising:

[0006] The illumination parameters of the first vehicle are determined based on the first illumination data collected by the sensor in the first vehicle and the second illumination data of the second vehicle.

[0007] Optionally, determining the illumination parameters of the first vehicle based on the first illumination data collected by the sensing device in the first vehicle and the second illumination data of the second vehicle includes:

[0008] The first illumination data collected by the sensing device in the first vehicle is corrected based on the second illumination data of the second vehicle to determine the illumination parameters of the first vehicle.

[0009] Optionally, the step of correcting the first illumination data collected by the sensing device in the first vehicle based on the second illumination data of the second vehicle to determine the illumination parameters of the first vehicle includes:

[0010] Determine the probability distribution corresponding to the second illumination data;

[0011] Based on the probability distribution, the feature value corresponding to the second illumination data is determined;

[0012] The first illumination data is corrected based on the feature values ​​to determine the illumination parameters of the first vehicle.

[0013] Optionally, the feature value includes one of the following: mode, mean, standard deviation, covariance, extreme value, and peak value.

[0014] Optionally, determining the illumination parameters of the first vehicle based on the first illumination data collected by the sensing device in the first vehicle and the second illumination data of the second vehicle includes:

[0015] Abnormal lighting parameters are removed from the second lighting data to obtain the third lighting data;

[0016] The first illumination data is corrected based on the third illumination data to determine the illumination parameters of the first vehicle.

[0017] Optionally, the method further includes:

[0018] Based on the second illumination data, the reference illumination range is determined;

[0019] The second illumination data that is outside the reference illumination range is determined as the abnormal illumination parameter.

[0020] Optionally, the number of the second vehicles is multiple, and the step of correcting the first illumination data based on the third illumination data to determine the illumination parameters of the first vehicles includes:

[0021] Determine the mean value of the third illumination data for each of the second vehicles;

[0022] The first illumination data is corrected based on the average of the third illumination data for each of the second vehicles to determine the illumination parameters of the first vehicles.

[0023] Optionally, the step of correcting the first illumination data based on the average of the third illumination data for each of the second vehicles to determine the illumination parameters of the first vehicle includes:

[0024] Determine the total number of vehicles in the second vehicle;

[0025] The illumination parameters of the first vehicle are determined based on the total number of vehicles and the average of the third illumination data of each of the second vehicles.

[0026] Optionally, the second illumination data is obtained by a sensor in the second vehicle.

[0027] Optionally, both the first vehicle and the second vehicle are equipped with an interactive device, and the interactive device in the first vehicle can interact with the interactive device in the second vehicle, so that the first vehicle can obtain the second illumination data of the second vehicle.

[0028] Optionally, the sensing device in the first vehicle and / or the second vehicle includes at least one of a light sensor and an ultraviolet sensor.

[0029] Optionally, the sensing devices in the first vehicle and / or the second vehicle are arranged on the dashboard, inside the windshield, and / or adjacent to the rearview mirror.

[0030] Optionally, the method further includes:

[0031] If the amount of data corresponding to the second illumination data is less than a preset threshold, the illumination parameters of the first vehicle are determined based on the preset correction model and the first illumination data.

[0032] Optionally, determining the illumination parameters of the first vehicle based on a preset correction model and the first illumination data includes:

[0033] Obtain vehicle driving data, vehicle location information, and / or weather information corresponding to the first illumination data;

[0034] The vehicle driving data, the vehicle location information, and / or the weather information are input into the correction model to determine the illumination parameters of the first vehicle.

[0035] Optionally, inputting the vehicle driving data, the vehicle location information, and / or the weather information into the correction model to determine the illumination parameters of the first vehicle includes:

[0036] Based on the correction model, feature data corresponding to the vehicle driving data, the vehicle location information, and / or the weather information are extracted;

[0037] Based on the correction model and the feature data, the illumination parameters of the first vehicle are output.

[0038] Optionally, the correction model is an artificial intelligence model.

[0039] Optionally, the correction model includes either the LightGBM model or the DeepFM model.

[0040] Optionally, the method further includes:

[0041] Obtain the location information of the first vehicle;

[0042] Based on the location information, the second vehicle is determined from target vehicles whose distance from the first vehicle is within a preset distance range.

[0043] Optionally, the location information includes at least one of latitude and longitude information, map coordinate system positioning information, and local coordinate system positioning information.

[0044] Optionally, determining the second vehicle from vehicles within a preset distance range from the first vehicle based on the location information includes:

[0045] Based on the location information, from the target vehicles whose distance from the first vehicle is within the preset distance range, a second vehicle that meets at least one target condition is determined;

[0046] The target conditions include: the accuracy of the illumination parameters collected by the target vehicle can be determined, and / or the target vehicle and the first vehicle meet the same driving conditions.

[0047] Optionally, the method further includes:

[0048] Obtain the second illumination data within a preset time range from the second vehicle.

[0049] Optionally, before determining the illumination parameters of the first vehicle based on the first illumination data collected by the sensing device in the first vehicle and the second illumination data of the second vehicle, the method further includes:

[0050] Determine the accuracy of the first illumination data.

[0051] Optionally, determining the accuracy of the first illumination data includes:

[0052] If the sensor is not operating properly, the first illumination data is determined to be inaccurate.

[0053] Optionally, determining the accuracy of the first illumination data includes:

[0054] Obtain the driving information of the first vehicle and / or the corresponding environmental information of the first vehicle;

[0055] The accuracy of the first illumination data is determined based on the driving information and / or the environmental information.

[0056] Optionally, the driving information includes at least one of: vehicle status, and the angle between the driving direction and the direction of sunlight; the environmental information includes at least one of: weather conditions, and information on obstructions; determining the accuracy of the first illumination data based on the driving information and / or the environmental information includes:

[0057] The first illumination data is inaccurate if the vehicle status does not conform to the preset status, and / or the included angle is not within the preset included angle range, and / or the weather conditions do not conform to the preset weather conditions, and / or the obstruction information conforms to the preset obstruction information.

[0058] Optionally, the angle between the driving direction and the direction of sunlight is determined based on the direction of sunlight and the driving direction, wherein the direction of sunlight is determined based on time information and the position information of the first vehicle.

[0059] Optionally, the method further includes:

[0060] If the first illumination data is inaccurate, the illumination parameters of the first vehicle are determined based on the first illumination data collected by the sensing device in the first vehicle and the second illumination data of the second vehicle.

[0061] Optionally, determining the accuracy of the first illumination data includes:

[0062] When the sensing device is operating normally, and the vehicle status meets the preset conditions, the angle between the driving direction and the direction of sunlight is within the preset angle range, the weather conditions are within the preset weather conditions, and the obstruction information does not meet the preset obstruction information, the first illumination data is accurate.

[0063] Optionally, the method further includes:

[0064] If the first illumination data is accurate, the first illumination data is determined as the illumination parameters of the first vehicle.

[0065] According to a second aspect of this application, embodiments of this application also provide an illumination parameter determination apparatus, which includes one or more processing units. Each processing unit stores a computer program, and when the computer program is executed by the processing unit, the processing unit performs the steps of any illumination parameter determination method provided in the embodiments of this application.

[0066] According to a third aspect of this application, embodiments of this application also provide an electronic device, the electronic device including: one or more processors and a memory; and one or more application programs, wherein the one or more application programs are stored in the memory and configured to be executed by the processor to perform the steps of any of the illumination parameter determination methods provided in the embodiments of this application.

[0067] According to a fourth aspect of this application, embodiments of this application also provide a computer-readable storage medium storing a plurality of computer programs adapted for loading by a processor to perform the steps of any of the illumination parameter determination methods provided in embodiments of this application.

[0068] According to a fifth aspect of this application, embodiments of this application also provide a computer program product, including a computer program or computer program that, when executed by a processor, implements the steps in any of the illumination parameter determination methods provided in embodiments of this application.

[0069] According to a sixth aspect of this application, an embodiment of this application also provides a vehicle, which includes the electronic device provided in the embodiment of this application, or the illumination parameter determination device provided in the embodiment of this application, or performs the steps in any illumination parameter determination method provided in the embodiment of this application.

[0070] The illumination parameter determination method of this application determines the illumination parameters of the first vehicle based on first illumination data collected by a sensor in the first vehicle and second illumination data from a second vehicle. By determining the illumination parameters of the vehicle using first illumination data collected by the vehicle's sensor and second illumination data obtained from other vehicles, the accuracy of illumination parameter measurement is improved.

[0071] Other features and advantages of this application will be described in detail in the following detailed description section. Attached Figure Description

[0072] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0073] Figure 1 A schematic flowchart of the first embodiment of the method provided in this application;

[0074] Figure 2 A schematic flowchart of a second embodiment of the method provided in this application;

[0075] Figure 3 A schematic flowchart of the third embodiment of the method provided in this application;

[0076] Figure 4 A schematic flowchart of the fourth embodiment of the method provided in this application;

[0077] Figure 5A schematic flowchart of the fifth embodiment of the method provided in this application;

[0078] Figure 6 A schematic flowchart of the sixth embodiment of the method provided in this application;

[0079] Figure 7 A schematic flowchart of the seventh embodiment of the method provided in this application;

[0080] Figure 8 A schematic flowchart of the eighth embodiment of the method provided in this application;

[0081] Figure 9 This is a schematic diagram of the illumination parameter determination device provided in the embodiments of this application;

[0082] Figure 10 This is a schematic diagram of the vehicle structure provided in an embodiment of this application. Detailed Implementation

[0083] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the protection scope of this application.

[0084] This application provides a method, apparatus, device, medium, program product, and vehicle for determining illumination parameters.

[0085] The method provided in this application can be applied to vehicles, which may be fuel vehicles, electric vehicles, plug-in hybrid electric vehicles or other new energy vehicles, etc., and this disclosure does not specifically limit them.

[0086] The following is a detailed description in conjunction with the accompanying drawings. It should be noted that the order of description of the following embodiments is not intended to limit the preferred order of the embodiments. Although a logical order is shown in the flowcharts, in some cases, the steps shown or described may be performed in a different order than that shown in the drawings.

[0087] Please refer to Figure 1 , Figure 1 A flowchart illustrating a first embodiment of the method provided in this application. The method is applied to a first vehicle and includes:

[0088] Step 101: Determine the illumination parameters of the first vehicle based on the first illumination data collected by the sensor in the first vehicle and the second illumination data of the second vehicle.

[0089] In this step, when the first vehicle collects the first illumination data using its own sensors, it obtains the second illumination data collected by the second vehicle's sensors. The second vehicle is located within a preset distance range corresponding to the first vehicle, and the second illumination data represents the accurate illumination parameters collected by the second vehicle's sensors. The first vehicle determines its own illumination parameters based on the first illumination data collected by its own sensors and the second illumination data obtained from the second vehicle.

[0090] Optionally, there may be multiple second vehicles, specifically two or more second vehicles. The second illumination data for each second vehicle includes multiple illumination parameters, and the first illumination data may be illumination parameters that the first vehicle has not accurately determined. The first vehicle corrects the first illumination data based on the acquired second illumination data to determine the corresponding illumination parameters.

[0091] Optionally, there may be multiple second vehicles, specifically two or more second vehicles. The second illumination data for each second vehicle includes multiple illumination parameters. The first illumination data may be accurate illumination parameters collected by the first vehicle, but with a relatively large error. The first vehicle processes the acquired second illumination data and the first illumination data to determine more accurate illumination parameters.

[0092] Specifically, the sensing devices in the first vehicle and / or the second vehicle include at least one of a light sensor and an ultraviolet sensor, wherein the light sensor may be a sunlight sensor. The sensing devices in the first vehicle and / or the second vehicle are arranged on the dashboard, inside the windshield, and / or adjacent to the rearview mirror.

[0093] In this embodiment, the illumination parameters of the first vehicle are determined based on first illumination data collected by sensors in the first vehicle and second illumination data from the second vehicle. Determining the illumination parameters of the vehicle by using first illumination data collected by its own sensors and second illumination data obtained from other vehicles improves the accuracy of illumination parameter measurement.

[0094] Please refer to Figure 2 , Figure 2 A flowchart illustrating a second embodiment of the method provided in this application. The step of determining the illumination parameters of the first vehicle based on first illumination data collected by a sensor in the first vehicle and second illumination data from the second vehicle includes:

[0095] Step 201: Based on the second illumination data of the second vehicle, the first illumination data collected by the sensing device in the first vehicle is corrected to determine the illumination parameters of the first vehicle.

[0096] In this step, the first vehicle corrects the first illumination data based on the second illumination data to determine the illumination parameters of the first vehicle. It should be noted that there may be multiple second vehicles, and the second illumination data of each second vehicle includes multiple illumination parameters. The first illumination data represents the illumination parameters that the first vehicle has not accurately determined. Specifically, the first vehicle performs statistical analysis on the acquired second illumination data, and then corrects the first illumination data based on the results of the statistical analysis to determine the corresponding illumination parameters.

[0097] Specifically, step 201 includes:

[0098] Step 2011: Determine the probability distribution corresponding to the second illumination data;

[0099] In this step, the first vehicle performs statistical analysis on the second illumination data to determine the data distribution corresponding to the second illumination data. For example, the second illumination data follows a Gaussian distribution.

[0100] Step 2012: Based on the probability distribution, determine the feature value corresponding to the second illumination data;

[0101] In this step, the first vehicle determines the feature value corresponding to the second illumination data based on the probability distribution; wherein the feature value includes one of the mode, mean, standard deviation, covariance, extreme value, and peak value.

[0102] Step 2013: Correct the first illumination data based on the feature values ​​to determine the illumination parameters of the first vehicle.

[0103] In this step, the first vehicle corrects the first illumination data based on the feature values ​​to determine the corresponding illumination parameters. Optionally, the first vehicle determines the mode corresponding to the second illumination data as the illumination parameter; optionally, the first vehicle determines the mean corresponding to the second illumination data as the illumination parameter; optionally, the first vehicle determines the standard deviation corresponding to the second illumination data as the illumination parameter; optionally, the first vehicle determines the covariance corresponding to the second illumination data as the illumination parameter; optionally, the first vehicle determines the extreme value corresponding to the second illumination data as the illumination parameter; optionally, the first vehicle determines the peak value corresponding to the second illumination data as the illumination parameter.

[0104] In this embodiment, the first vehicle performs statistical analysis on the acquired second illumination data, and then corrects the first illumination data based on the results of the statistical analysis to determine the corresponding illumination parameters. By using its own first illumination data and the second illumination data acquired from other vehicles to determine its own illumination parameters, the vehicle can determine its own illumination parameters even when the illumination sensor is blocked or at a suboptimal angle, by combining the illumination parameters of other vehicles, thereby improving the accuracy of illumination parameter measurement.

[0105] Please refer to Figure 3 , Figure 3 A schematic flowchart of a third embodiment of the method provided in this application. The step of determining the illumination parameters of the first vehicle based on first illumination data collected by a sensor in the first vehicle and second illumination data from the second vehicle includes:

[0106] Step 301: Remove abnormal lighting parameters from the second lighting data to obtain the third lighting data;

[0107] In this step, the first vehicle identifies abnormal lighting parameters in the second lighting data and removes the abnormal lighting parameters from the second lighting data to obtain the third lighting data.

[0108] For example, the first vehicle calculates the mean and standard deviation of the second illumination data. Second illumination data falling outside the range of the mean plus or minus one standard deviation are identified as anomalous illumination parameters. These anomalous parameters are then removed from the second illumination data to obtain the third illumination data. This process reduces the impact of outliers, making the calculated average sunlight value more reliable.

[0109] Let X t This is the second set of illumination data, where t represents different time points within the past 300 seconds. First, the average μ and standard deviation σ of the sunlight value for each vehicle are calculated, using the following formula:

[0110]

[0111] Where T is the total number of records in 300 seconds (for example, if records are made once per second, then T = 300).

[0112] The second illumination data point, falling within the range of the mean plus or minus one standard deviation, is defined as the third illumination data point. That is, the third illumination data point satisfies the following formula condition:

[0113] Y = {X} t |μ-σ≤X t ≤μ+σ}

[0114] Where Y represents the third illumination data.

[0115] Specifically, methods for determining abnormal lighting parameters include:

[0116] Step 3011: Analyze the second illumination data to determine the reference illumination range;

[0117] Step 3012: The second illumination data that is outside the reference illumination range is determined as abnormal illumination parameters.

[0118] In steps 3011 and 3012, the first vehicle analyzes the second illumination data to determine a reference illumination range, and identifies second illumination data outside the reference illumination range as abnormal illumination parameters. Specifically, for each second illumination data corresponding to the second vehicle, the first vehicle calculates the mean and standard deviation of the second illumination data, and determines the reference illumination range based on the mean and standard deviation; then, each second illumination data is compared with the reference illumination range to identify target second illumination data outside the reference illumination range, and the target second illumination data is identified as an abnormal illumination parameter. The reference illumination range is [μ-σ, μ+σ].

[0119] It should be noted that by identifying and removing abnormal lighting parameters from the second lighting data to obtain the third lighting data, and then correcting the first lighting data based on the third lighting data to determine the lighting parameters of the first vehicle, the first vehicle can determine its lighting parameters based on the more accurate third lighting data, thereby further improving the accuracy of the determined lighting parameters.

[0120] Step 302: Correct the first illumination data based on the third illumination data to determine the illumination parameters of the first vehicle.

[0121] In this step, the first vehicle corrects the first illumination data based on the third illumination data to determine the corresponding illumination parameters. Specifically, the first vehicle performs statistical analysis on the acquired third illumination data, and then corrects the first illumination data based on the results of the statistical analysis to determine the corresponding illumination parameters.

[0122] Specifically, the number of the second vehicles is multiple, and step 302 includes:

[0123] Step 3021: Determine the mean value of the third illumination data for each of the second vehicles;

[0124] Step 3022: Based on the average value of the third illumination data for each of the second vehicles, the first illumination data is corrected to determine the illumination parameters of the first vehicle.

[0125] In steps 3021 and 3022, the first vehicle determines the mean of the third illumination data corresponding to each second vehicle; based on the mean of the third illumination data corresponding to each second vehicle, the first illumination data is corrected to determine the illumination parameters of the first vehicle. Specifically, for each second vehicle's third illumination data, the first vehicle performs a statistical analysis of the third illumination data to determine the corresponding mean; optionally, the first vehicle selects the mode of the mean of all second vehicle's third illumination data as the illumination parameter; optionally, the first vehicle sums and averages the mean of all second vehicle's third illumination data to determine the illumination parameter.

[0126] Specifically, the mean value of the third illumination data for each second vehicle is calculated using the following formula:

[0127]

[0128] in, X is the mean of the third illumination data for each second vehicle. t For each second vehicle, the third illumination data is t, which represents different time points within the past 300 seconds, and N is the amount of third illumination data for each second vehicle.

[0129] Specifically, step 3022 includes:

[0130] Step 30221: Determine the total number of vehicles in the second vehicle;

[0131] Step 30222: Based on the total number of vehicles and the average of the third illumination data of each second vehicle, determine the illumination parameters of the first vehicle.

[0132] In steps 30221 to 30222, the first vehicle counts the total number of second vehicles. Based on the total number of vehicles and the average of the third illumination data corresponding to each second vehicle, target illumination data is determined. The first illumination data is then replaced with the target illumination data to determine the illumination parameters of the first vehicle. For example, the total number of second vehicles is M, and the average of the third illumination data corresponding to each second vehicle is y. i (i = 1, 2, 3...M), the first vehicle is determined according to the formula: Calculate the illumination parameters, where Y is the illumination parameter.

[0133] In this embodiment, for the first vehicle, abnormal illumination parameters are removed from the second illumination data to obtain third illumination data. The first illumination data is then corrected based on the third illumination data to determine the illumination parameters of the first vehicle. This allows the first vehicle to determine its illumination parameters based on more accurate third illumination data, further improving the accuracy of the determined illumination parameters.

[0134] Please refer to Figure 4 , Figure 4 A schematic flowchart of a fourth embodiment of the method provided in this application. The method further includes:

[0135] Step 401: If the amount of data corresponding to the second illumination data is less than a preset threshold, determine the illumination parameters of the first vehicle based on the preset correction model and the first illumination data.

[0136] In this step, after acquiring the second illumination data, the first vehicle counts the amount of data corresponding to the second illumination data. If the amount of data corresponding to the second illumination data is less than a preset threshold, the corresponding illumination parameters are determined based on a preset correction model and the first illumination data. It should be noted that when the amount of data corresponding to the second illumination data is less than the preset threshold, using the second illumination data to correct the first illumination data will result in poor accuracy. Therefore, a preset correction model and the first illumination data are used to determine the corresponding illumination parameters to ensure the accuracy of the determined illumination parameters.

[0137] Specifically, step 401 includes:

[0138] Step 4011: Obtain vehicle driving data, vehicle location information and / or weather information corresponding to the first illumination data;

[0139] Step 4012: Input the vehicle driving data, the vehicle location information and / or the weather information into the correction model to determine the illumination parameters of the first vehicle.

[0140] In steps 4011 and 4012, the first vehicle acquisition sensor collects vehicle driving data, vehicle location information, and weather information corresponding to the first illumination data; the vehicle driving data, vehicle location information, and weather information are input into a preset calibration model, and the illumination parameters are output through the calibration model. The calibration model is constructed using vehicle driving data, vehicle location information, and weather information labeled with accurate illumination parameters as sample values, and employs advanced algorithms (such as LightGBM and DeepFM). The calibration effect is evaluated through metrics such as accuracy, F1 score, and confusion matrix, and the effectiveness of the scheme is verified through real-vehicle testing.

[0141] Specifically, step 4012 includes:

[0142] Step 40121: Extract feature data corresponding to the vehicle driving data, the vehicle location information, and / or the weather information based on the correction model;

[0143] Step 40122: Based on the correction model and the feature data, output the illumination parameters of the first vehicle.

[0144] In steps 40121 to 40122, the first vehicle extracts feature data corresponding to vehicle driving data, vehicle location information and weather information through the calibration model, and outputs the illumination parameters of the first vehicle based on the corresponding feature data through the calibration model.

[0145] Specifically, the calibration model is an artificial intelligence model, including one of the LightGBM model and the DeepFM model.

[0146] Specifically, when the calibration model is the LightGBM model, it includes an ensemble model layer, a decision tree structure layer, a splitting layer, a gradient boosting process layer, a feature importance layer, and a leaf node prediction layer. The LightGBM model is based on Gradient Boosting Decision Tree (GBDT), an ensemble learning method, primarily composed of multiple decision trees. Therefore, the hierarchical structure of the LightGBM model can be decomposed from multiple perspectives, typically including the following main layers: 1. The top layer is the ensemble model layer, which consists of multiple decision trees. The weights of each tree are usually equal or adjusted according to some rule. By progressively training multiple decision trees, each tree fits the residual of the previous tree, and finally, the predictions of these trees are weighted and summed (or combined in other ways) to obtain the final prediction. 2. The decision tree structure layer: Each decision tree in the LightGBM model has a tree structure, with a hierarchical structure including a root node, internal nodes, and leaf nodes. The construction and training of each tree is one of the core processes of the model. 3. Splitting Layer: The splitting operation in each decision tree is crucial to the LightGBM model. The splitting of each node determines the tree's structure and prediction results. The splitting operation is based on sample data and features. 4. Gradient Boosting Layer: During gradient boosting, the LightGBM model progressively builds the tree model. Each newly built tree attempts to improve the model's accuracy by fitting the residuals of the previous tree. 5. Feature Importance Layer: The LightGBM model can also output feature importance, which is a metric used to measure the contribution of each feature to the model's predictions. 6. Leaf Node Prediction Results Layer: The leaf nodes of each tree contain the predicted values ​​for the samples within that node.

[0147] Specifically, when the calibration model is a DeepFM model, it includes an input layer, an embedding layer, a factorization machine layer, a deep neural network layer, a concatenation layer, and an output layer. The DeepFM (Deep Factorization Machine) model combines the advantages of factorization machines (FM) and deep neural networks (DNN) to handle recommendation systems and other sparse feature prediction tasks. Its model structure has two main parts: the factorization machine layer (FM layer) and the deep neural network layer (DNN layer). These two parts jointly learn high-order interactions of features to capture non-linear relationships. The following is a hierarchical structure analysis of the DeepFM model: 1. Input Layer: The input layer is typically used to input sparse and continuous features. Input data is generally converted into a low-dimensional vector representation through the embedding layer. 2. Embedding Layer: This layer converts discrete features from a sparse high-dimensional representation into a dense low-dimensional vector, enabling the model to better capture the relationships between features. 3. Factorization Machine Layer (FM Layer): The goal of the FM layer is to model second-order interactions between features through low-order feature interactions. This layer effectively models the interactions between features by calculating the inner product of all feature pairs, especially in the case of high-dimensional sparse data, where FM can fully utilize the latent patterns of feature interactions. 4. Deep Neural Network Layer (DNN Layer): The goal of the DNN layer is to model more complex nonlinear relationships through higher-order feature interactions. Deep neural networks learn more complex and higher-order feature interaction relationships through their hierarchical structure, capturing nonlinear feature combination patterns. 5. Concatenation Layer: The concatenation layer combines the outputs of the FM layer and the DNN layer. 6. Output Layer: The output layer is typically a fully connected layer used to map the concatenated result to the final predicted value.

[0148] In this embodiment, the first vehicle determines that the amount of data corresponding to the second illumination data is less than a preset threshold. At this time, using the second illumination data to correct the first illumination data will result in poor accuracy. Therefore, a preset correction model and the first illumination data are used to determine the corresponding illumination parameters to ensure the accuracy of determining the corresponding illumination parameters.

[0149] Please refer to Figure 5 , Figure 5 A schematic flowchart of the fifth embodiment of the method provided in this application. The method further includes:

[0150] Step 501: Obtain the location information of the first vehicle;

[0151] Step 502: Based on the location information, determine the second vehicle from target vehicles whose distance from the first vehicle is within a preset distance range.

[0152] In steps 501 and 502, the first vehicle acquires its own location information, and then, based on the location information, determines the second vehicle from target vehicles within a preset distance range. The location information includes at least one of latitude and longitude information, map coordinate system positioning information, and local coordinate system positioning information.

[0153] For example, the location information is latitude and longitude information. The first vehicle determines the origin based on the latitude and longitude information, determines the target vehicles within a 100-meter radius of the origin, and selects the second vehicle from these target vehicles according to preset target conditions.

[0154] Specifically, step 502 includes:

[0155] Step 5021: Based on the location information, determine a second vehicle that meets at least one target condition from target vehicles whose distance from the first vehicle is within a preset distance range; the target condition includes: the accuracy of the illumination parameters collected by the target vehicle can be determined, and / or, the target vehicle and the first vehicle meet the same driving conditions.

[0156] In this step, the first vehicle acquires its own location information, and then, based on this location information, determines a second vehicle from target vehicles within a preset distance range according to preset target conditions. These target conditions include: the accuracy of the illumination parameters collected by the target vehicle is certain; and the target vehicle and the first vehicle meet the same driving conditions. For example, the location information is latitude and longitude information. The first vehicle determines an origin point based on the latitude and longitude information and identifies vehicles within a 100-meter radius of the origin. The first vehicle then determines whether the accuracy of the illumination parameters collected by the vehicles within a 100-meter radius of the origin is certain, and identifies the vehicles whose illumination parameters are certain as the second vehicle. For example, the first vehicle also determines whether the driving conditions of the vehicles within a 100-meter radius of the origin are the same as its own driving conditions, and identifies the vehicles that meet the same driving conditions as the second vehicle.

[0157] Furthermore, after identifying the second vehicle, the process includes:

[0158] Step 503: Obtain the second illumination data within a preset time range from the second vehicle.

[0159] In this step, after identifying the second vehicle, the first vehicle acquires second illumination data from the second vehicle within a preset time range. For example, the first vehicle acquires second illumination data from the second vehicle within a 300-second range.

[0160] In this embodiment, the location information of the first vehicle is acquired. Based on the location information, a second vehicle that meets at least one target condition is determined from vehicles within a preset distance range from the first vehicle. The target condition includes: the accuracy of the illumination parameters collected by the vehicle can be determined, and / or, the vehicle and the first vehicle meet the same driving conditions. By combining the actual situation of surrounding vehicles to determine the second vehicle, the correlation between the second vehicle and the first vehicle can be improved, thereby improving the correlation between the acquired second illumination data and the first vehicle, which helps to improve the accuracy of the illumination parameters of the subsequently determined first vehicle.

[0161] Please refer to Figure 6 , Figure 6 A schematic flowchart of the sixth embodiment of the method provided in this application. Before determining the illumination parameters of the first vehicle based on the first illumination data collected by the sensing device in the first vehicle and the second illumination data of the second vehicle, the method further includes:

[0162] Step 601: Determine the accuracy of the first illumination data.

[0163] In this step, the first vehicle determines the accuracy of the first illumination data collected by the sensor based on driving information and / or environmental information.

[0164] Furthermore, the method also includes:

[0165] Step 602: If the first illumination data is inaccurate, determine the illumination parameters of the first vehicle based on the first illumination data collected by the sensing device in the first vehicle and the second illumination data of the second vehicle.

[0166] In this step, when the first vehicle determines that the first illumination data collected by its own illumination sensor is inaccurate, it obtains second illumination data from the second vehicle. Then, based on the first illumination data collected by the sensor in the first vehicle and the second illumination data from the second vehicle, the illumination parameters of the first vehicle are determined. The second vehicle can be a vehicle whose sensor has collected accurate illumination parameters within a preset distance range of the first vehicle; the second vehicle can also be a vehicle within a preset distance range of the first vehicle that has the same driving conditions as the first vehicle (e.g., the vehicle is in motion, or the driving direction is similar). The second illumination data is the illumination parameter determined to be accurately measured from the second vehicle.

[0167] Optionally, both the first and second vehicles are equipped with an interactive device in advance. The interactive device can upload the location information of the first and second vehicles and the collected illumination data to a unified server. When the first vehicle determines that the first illumination data collected by its own illumination sensor is inaccurate, it can locate the second vehicle within a preset distance range on the server and then obtain the second illumination data of the second vehicle from the server.

[0168] Optionally, both the first vehicle and the second vehicle are equipped with an interactive device in advance. When the first vehicle determines that the first illumination data collected by its own illumination sensor is inaccurate, it can communicate with the interactive device of the second vehicle within a preset distance range through its own interactive device, and then obtain the second illumination data from the second vehicle.

[0169] Step 603: If the first illumination data is accurate, determine the first illumination data as the illumination parameters of the first vehicle.

[0170] In this step, if the first vehicle determines that the first illumination data is accurate, it uses the first illumination data collected by the sensor as its illumination parameter. That is, if the accuracy of the first illumination data collected by the sensor is confirmed, there is no need to obtain the second illumination data from surrounding second vehicles; instead, the first illumination data collected by the sensor is directly used as the final illumination parameter.

[0171] In this embodiment, when the first illumination data is inaccurate, the illumination parameters of the first vehicle are determined based on the first illumination data collected by the sensors in the first vehicle and the second illumination data from the second vehicle. This allows for the determination of the vehicle's illumination parameters using the first illumination data collected by the vehicle's sensors and the second illumination data obtained from other vehicles when the first illumination data is inaccurate, thus correcting the first illumination data and improving the accuracy of illumination parameter measurement.

[0172] Please refer to Figure 7 , Figure 7 A schematic flowchart of the seventh embodiment of the method provided in this application. Determining that the first illumination data is inaccurate includes the following steps:

[0173] Step 701: If the sensor is not operating properly, determine that the first illumination data is inaccurate.

[0174] In this step, the first vehicle detects the operating status of the sensor device that collects the first illumination data. If it is determined that the sensor device is not operating properly, the first illumination data is inaccurate. Specifically, if the sensor device's measurement value is empty or "NULL", the sensor device is determined to be not operating properly.

[0175] In this embodiment, if the sensor device for collecting the first illumination data is malfunctioning, the first illumination data of the first vehicle will be inaccurate. The vehicle can determine the accuracy of the first illumination data based on the operating status of the sensor device, thereby improving the accuracy of the judgment.

[0176] Please refer to Figure 8 , Figure 8 This is a schematic flowchart of the eighth embodiment of the method provided in this application. Determining that the first illumination data is inaccurate includes the following steps:

[0177] Step 801: Obtain the driving information of the first vehicle and / or the corresponding environmental information of the first vehicle;

[0178] In this step, the first vehicle acquires its own driving information and / or collects its corresponding environmental information; for example, the first vehicle is equipped with an on-board computer, which records the driving information of the first vehicle in real time; the first vehicle is equipped with a camera device, which can capture environmental images, including images inside and outside the vehicle, thereby determining the environmental information.

[0179] Step 802: Determine the accuracy of the first illumination data based on the driving information and / or the environmental information.

[0180] In this step, the first vehicle determines the accuracy of its first illumination data based on driving information and / or environmental information. Optionally, if the first vehicle determines that the driving information does not meet preset conditions, it determines that the first illumination data of the first vehicle is inaccurate; alternatively, if the first vehicle determines that the environmental information does not meet preset conditions, it determines that the first illumination data of the first vehicle is inaccurate; alternatively, if the first vehicle determines that both the driving information and the environmental information do not meet preset conditions, it determines that the first illumination data of the first vehicle is inaccurate.

[0181] Specifically, the driving information includes at least one of the following: vehicle status and the angle between the driving direction and the direction of sunlight; the environmental information includes at least one of the following: weather conditions and information on obstructions.

[0182] If the vehicle status does not conform to the preset status, and / or the included angle is not within the preset included angle range, and / or the weather condition does not belong to the preset weather condition, and / or the obstruction information conforms to the preset obstruction information, the first illumination data of the first vehicle is determined to be inaccurate.

[0183] Understandably, vehicle status includes whether the vehicle is moving or stationary. Weather refers to the specific state of the atmosphere near the Earth's surface in a given area over a short period of time. It includes various weather phenomena, such as the comprehensive spatial distribution of meteorological elements like temperature, air pressure, humidity, wind, clouds, fog, rain, snow, frost, thunder, hail, and haze. Obstruction information includes the shade of trees along roads, obstruction from objects placed on sensor surfaces, and obstruction when a vehicle enters a building.

[0184] Optionally, if any one of the following conditions is not met by the first vehicle: vehicle status, the angle between its driving direction and the direction of sunlight, weather conditions, or information about obstructions, the first vehicle's first illumination data can be determined to be inaccurate. Optionally, the first vehicle's first illumination data can only be determined to be inaccurate if all of the following conditions are not met: vehicle status, the angle between its driving direction and the direction of sunlight, weather conditions, or information about obstructions.

[0185] For example, if the first vehicle is not in a daytime, sunny weather environment, is not in a driving state, and the angle between the driving direction and the direction of sunlight is not between 145 and 225 degrees, or if the first vehicle is parked in an underground garage or is blocked by trees, then the first vehicle determines that the first light data collected by the sensor is inaccurate.

[0186] Furthermore, if the sensor is not operating normally, or if the vehicle is not in a preset state, the angle between the driving direction and the direction of sunlight is not within a preset angle range, the weather is not in a preset weather condition, or the obstruction information is in line with the preset obstruction information, the first illumination data will be inaccurate.

[0187] Furthermore, when the sensor is operating normally, and the vehicle condition meets the preset conditions, the angle between the driving direction and the direction of sunlight is within the preset angle range, the weather condition is the preset weather condition, and the obstruction information does not meet the preset obstruction information, the first illumination data is accurate.

[0188] Furthermore, the angle between the driving direction and the direction of sunlight is determined based on the direction of sunlight and the driving direction, wherein the direction of sunlight is determined based on time information and the position information of the first vehicle.

[0189] Specifically, the first vehicle acquires time information and corresponding location information. Based on the current time information and the vehicle's location information, it determines the sun's position. Specifically, the location information is the vehicle's latitude and longitude. The first vehicle calculates the sun's position based on the current time information and latitude / longitude, which includes the solar altitude angle and solar azimuth angle. The solar altitude angle is the angle of sunlight relative to the ground plane, affecting the sun's "height." The solar azimuth angle is the horizontal angle of the sun relative to north, affecting where the sun rises and sets. The first vehicle determines the angle between itself and the direction of sunlight based on the sun's position and its direction of travel. Specifically, the first vehicle determines the direction of sunlight based on the solar altitude angle and solar azimuth angle, and then calculates the angle between its direction of travel and the direction of sunlight.

[0190] It should be noted that the first vehicle determines the sun's position based on the time information and the corresponding location information. Based on the sun's position and the direction of travel, it determines the angle between the first vehicle and the direction of sunlight. The actual driving position and time of the vehicle can be combined to determine the angle between the first vehicle and the direction of sunlight, making the determined angle more consistent with the actual situation and improving the accuracy of the determined angle.

[0191] Furthermore, if the sensor device for collecting the first illumination data is found to be malfunctioning, and the vehicle is not in daylight or in a sunny weather environment, is not in motion, and the angle between the direction of travel and the direction of sunlight is not between 145 and 225 degrees, or if the vehicle is parked in an underground garage or is blocked by trees, then the vehicle determines that the first illumination data collected by the sensor device is inaccurate.

[0192] In this embodiment, the first vehicle acquires its driving information and / or corresponding environmental information. Based on the driving information and / or the environmental information, it is determined that the first illumination data of the first vehicle is inaccurate. The vehicle can combine actual driving conditions and the operating status of the sensing device to determine the accuracy of the first illumination data, thereby improving the accuracy of the judgment.

[0193] Accordingly, refer to Figure 9 This application also provides a lighting parameter determination device, which is installed in a vehicle 1100. The lighting parameter determination device includes one or more processing units, each storing a computer program. When the computer program is executed by the processing unit, the processing unit performs the steps in the lighting parameter determination method. Specifically:

[0194] The processing unit 1001 is used to determine the illumination parameters of the first vehicle based on the first illumination data collected by the sensing device in the first vehicle and the second illumination data of the second vehicle.

[0195] Optionally, the processing unit is also used for:

[0196] The first illumination data collected by the sensing device in the first vehicle is corrected based on the second illumination data of the second vehicle to determine the illumination parameters of the first vehicle.

[0197] Optionally, the processing unit is also used for:

[0198] Determine the probability distribution corresponding to the second illumination data;

[0199] Based on the probability distribution, the feature value corresponding to the second illumination data is determined;

[0200] The first illumination data is corrected based on the feature values ​​to determine the illumination parameters of the first vehicle.

[0201] Optionally, the processing unit is also used for:

[0202] Abnormal lighting parameters are removed from the second lighting data to obtain the third lighting data;

[0203] The first illumination data is corrected based on the third illumination data to determine the illumination parameters of the first vehicle.

[0204] Optionally, the processing unit is also used for:

[0205] Based on the second illumination data, the reference illumination range is determined;

[0206] The second illumination data that is outside the reference illumination range is determined as the abnormal illumination parameter.

[0207] Optionally, the processing unit is also used for:

[0208] Determine the mean value of the third illumination data for each of the second vehicles;

[0209] The first illumination data is corrected based on the average of the third illumination data for each of the second vehicles to determine the illumination parameters of the first vehicles.

[0210] Optionally, the processing unit is also used for:

[0211] Determine the total number of vehicles in the second vehicle;

[0212] The illumination parameters of the first vehicle are determined based on the total number of vehicles and the average of the third illumination data for each of the second vehicles.

[0213] Optionally, the processing unit is also used for:

[0214] If the amount of data corresponding to the second illumination data is less than a preset threshold, the illumination parameters of the first vehicle are determined based on the preset correction model and the first illumination data.

[0215] Optionally, the processing unit is also used for:

[0216] Obtain vehicle driving data, vehicle location information, and / or weather information corresponding to the first illumination data;

[0217] The vehicle driving data, the vehicle location information, and / or the weather information are input into the correction model to determine the illumination parameters of the first vehicle.

[0218] Optionally, the processing unit is also used for:

[0219] Based on the correction model, feature data corresponding to the vehicle driving data, the vehicle location information, and / or the weather information are extracted;

[0220] Based on the correction model and the feature data, the illumination parameters of the first vehicle are output.

[0221] Optionally, the processing unit is also used for:

[0222] Obtain the location information of the first vehicle;

[0223] Based on the location information, the second vehicle is determined from target vehicles whose distance from the first vehicle is within a preset distance range.

[0224] Optionally, the processing unit is also used for:

[0225] Based on the location information, from the vehicles whose distance from the first vehicle is within the preset distance range, a second vehicle that meets at least one target condition is determined;

[0226] The target conditions include: the accuracy of the illumination parameters collected by the target vehicle can be determined, and / or the target vehicle and the first vehicle meet the same driving conditions.

[0227] Optionally, the processing unit is also used for:

[0228] Obtain the second illumination data within a preset time range from the second vehicle.

[0229] Optionally, the processing unit is also used for:

[0230] Determine the accuracy of the first illumination data.

[0231] Optionally, the processing unit is also used for:

[0232] If the sensor is not operating properly, the first illumination data is determined to be inaccurate.

[0233] Optionally, the processing unit is also used for:

[0234] Obtain the driving information of the first vehicle and / or the corresponding environmental information of the first vehicle;

[0235] The accuracy of the first illumination data is determined based on the driving information and / or the environmental information.

[0236] Optionally, the processing unit is also used for:

[0237] The first illumination data is inaccurate if the vehicle status does not conform to the preset status, and / or the included angle is not within the preset included angle range, and / or the weather conditions do not conform to the preset weather conditions, and / or the obstruction information conforms to the preset obstruction information.

[0238] Optionally, the processing unit is also used for:

[0239] If the first illumination data is inaccurate, the illumination parameters of the first vehicle are determined based on the first illumination data collected by the sensing device in the first vehicle and the second illumination data of the second vehicle.

[0240] Optionally, the processing unit is also used for:

[0241] When the sensing device is operating normally, and the vehicle status meets the preset conditions, the angle between the driving direction and the direction of sunlight is within the preset angle range, the weather conditions are within the preset weather conditions, and the obstruction information meets the preset obstruction information, the first illumination data is accurate.

[0242] Optionally, the processing unit is also used for:

[0243] If the first illumination data is accurate, the first illumination data is determined as the illumination parameters of the first vehicle.

[0244] The illumination parameter determination device of this embodiment determines the illumination parameters of the first vehicle based on first illumination data collected by sensors in the first vehicle and second illumination data from the second vehicle. Determining the illumination parameters of the vehicle by using first illumination data collected by the vehicle's sensors and second illumination data obtained from other vehicles improves the accuracy of illumination parameter measurement.

[0245] Accordingly, embodiments of this application also provide a vehicle, such as Figure 10 As shown, Figure 10This is a schematic diagram of the structure of a vehicle provided in an embodiment of this application. The vehicle 1100 includes a lighting parameter determination device or electronic device that performs the steps of the lighting parameter determination method described in this application. The vehicle 1100 includes a processor 1101 with one or more processing cores, a memory 1102 with one or more computer-readable storage media, and a computer program stored on the memory 1102 and executable on the processor. The processor 1101 is electrically connected to the memory 1102. Those skilled in the art will understand that the vehicle structure shown in the figure does not constitute a limitation on the vehicle and may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0246] The processor 1101 is the control center of the vehicle 1100. It connects to various parts of the vehicle 1100 via various interfaces and lines. By running or loading software programs and / or units stored in the memory 1102, and by calling data stored in the memory 1102, it executes various functions of the vehicle 1100 and processes data, thereby performing overall monitoring of the vehicle 1100. The processor 1101 can be a CPU, GPU, network processor (NP), etc., and can implement or execute the methods, steps, and logic diagrams disclosed in the embodiments of this application.

[0247] In this embodiment of the application, the processor 1101 in the vehicle 1100 will load the computer program corresponding to the process of one or more applications into the memory 1102 according to the following steps, and the processor 1101 will run the applications stored in the memory 1102 to execute the method.

[0248] For details on the implementation of each of the above operations, please refer to the previous examples, which will not be repeated here.

[0249] Optional, such as Figure 10 As shown, the vehicle 1100 also includes: a touch screen display 1103, a radio frequency circuit 1104, an audio circuit 1105, an input unit 1106, and a power supply 1107. The processor 1101 is electrically connected to the touch screen display 1103, the radio frequency circuit 1104, the audio circuit 1105, the input unit 1106, and the power supply 1107. Those skilled in the art will understand that... Figure 10 The vehicle structure shown does not constitute a limitation on the vehicle and may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0250] The touch display screen 1103 can be used to display a graphical user interface (GUI) and receive operation commands generated by the user interacting with the GUI. The touch display screen 1103 may include a display panel and a touch panel. The display panel can be used to display information input by the user or information provided to the user, as well as various graphical user interfaces of the vehicle. These graphical user interfaces can be composed of graphics, text, icons, video, and any combination thereof. Optionally, the display panel can be configured using a liquid crystal display (LCD), organic light-emitting diode (OLED), or other similar technologies. The touch panel can be used to collect touch operations performed by the user on or near it (such as operations performed by the user using a finger, stylus, or any suitable object or accessory on or near the touch panel), generate corresponding operation commands, and execute the corresponding program according to the operation commands. Optionally, the touch panel may include two parts: a touch detection device and a touch controller. The touch detection device detects the user's touch location and the signal generated by the touch operation, transmitting the signal to the touch controller. The touch controller receives touch information from the touch detection device, converts it into touch point coordinates, and sends it to the processor 1101. It can also receive and execute commands from the processor 1101. The touch panel can cover the display panel. When the touch panel detects a touch operation on or near it, it transmits the information to the processor 1101 to determine the type of touch event. Subsequently, the processor 1101 provides corresponding visual output on the display panel based on the type of touch event. In this embodiment, the touch panel and the display panel can be integrated into the touch display screen 1103 to achieve input and output functions. However, in some embodiments, the touch panel and the touch display screen 1103 can be implemented as two independent components to achieve input and output functions. That is, the touch display screen 1103 can also be used as part of the input unit 1106 to achieve input functions.

[0251] The radio frequency circuit 1104 can be used to transmit and receive radio frequency signals to establish wireless communication with network devices or other vehicles, and to transmit and receive signals with network devices or other vehicles.

[0252] Audio circuit 1105 can be used to provide an audio interface between the user and the vehicle via a speaker and a microphone. Audio circuit 1105 can convert received audio data into electrical signals and transmit them to the speaker, where the speaker converts them into sound signals for output. Conversely, the microphone converts collected sound signals into electrical signals, which are then received by audio circuit 1105, converted back into audio data, and processed by processor 1101 before being transmitted via radio frequency circuit 1104 to, for example, another vehicle, or output to memory 1102 for further processing. Audio circuit 1105 may also include an earphone jack to provide communication between external headphones and the vehicle.

[0253] The input unit 1106 can be used to receive input numbers, characters, or user characteristic information (such as fingerprints, iris, facial information, etc.), and to generate keyboard, mouse, joystick, optical, or trackball signal inputs related to user settings and function control.

[0254] Power supply 1107 is used to supply power to various components of vehicle 1100. Optionally, power supply 1107 can be logically connected to processor 1101 through a power management device, thereby enabling functions such as charging, discharging, and power consumption management through the power management device. Power supply 1107 may also include one or more DC or AC power supplies, recharging devices, power fault detection circuits, power converters or inverters, power status indicators, and other arbitrary components.

[0255] although Figure 10 As not shown in the diagram, vehicle 1100 may also include cameras, sensors, wireless fidelity modules, Bluetooth modules, etc., which will not be described in detail here.

[0256] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0257] Those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be performed by a computer program, or by a computer program controlling related hardware. The computer program can be stored in a computer-readable storage medium and loaded and executed by a processor.

[0258] In the description of this application, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include one or more features. In the description of this application, "multiple" means two or more, unless otherwise explicitly specified.

[0259] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0260] The embodiments, implementation methods, and related technical features of this application can be combined and substituted for each other without conflict.

[0261] The above are merely preferred embodiments of this application and are not intended to limit this application in any way. Any simple modifications, equivalent changes, and alterations made to the above embodiments based on the technical essence of this application without departing from the scope of the technical solution of this application shall still fall within the scope of the technical solution of this application.

Claims

1. A method for determining illumination parameters, characterized in that, The method is applied to a first vehicle, and the method includes: The illumination parameters of the first vehicle are determined based on the first illumination data collected by the sensor in the first vehicle and the second illumination data of the second vehicle.

2. The method according to claim 1, characterized in that, The step of determining the illumination parameters of the first vehicle based on the first illumination data collected by the sensor in the first vehicle and the second illumination data of the second vehicle includes: The first illumination data collected by the sensing device in the first vehicle is corrected based on the second illumination data of the second vehicle to determine the illumination parameters of the first vehicle.

3. The method according to claim 2, characterized in that, The step of correcting the first illumination data collected by the sensing device in the first vehicle based on the second illumination data of the second vehicle, and determining the illumination parameters of the first vehicle, includes: Determine the probability distribution corresponding to the second illumination data; Based on the probability distribution, the feature value corresponding to the second illumination data is determined; The first illumination data is corrected based on the feature values ​​to determine the illumination parameters of the first vehicle.

4. The method according to claim 3, characterized in that, The eigenvalues ​​include one of the following: mode, mean, standard deviation, covariance, extreme values, and peak values.

5. The method according to claim 1, characterized in that, The step of determining the illumination parameters of the first vehicle based on the first illumination data collected by the sensor in the first vehicle and the second illumination data of the second vehicle includes: Abnormal lighting parameters are removed from the second lighting data to obtain the third lighting data; The first illumination data is corrected based on the third illumination data to determine the illumination parameters of the first vehicle.

6. The method according to claim 5, characterized in that, The method further includes: Based on the second illumination data, the reference illumination range is determined; The second illumination data that is outside the reference illumination range is determined as the abnormal illumination parameter.

7. The method according to claim 5, characterized in that, The number of the second vehicles is multiple, and the step of correcting the first illumination data based on the third illumination data to determine the illumination parameters of the first vehicles includes: Determine the mean value of the third illumination data for each of the second vehicles; The first illumination data is corrected based on the average of the third illumination data for each of the second vehicles to determine the illumination parameters of the first vehicles.

8. The method according to claim 7, characterized in that, The step of correcting the first illumination data based on the average of the third illumination data for each of the second vehicles to determine the illumination parameters of the first vehicles includes: Determine the total number of vehicles in the second vehicle; The illumination parameters of the first vehicle are determined based on the total number of vehicles and the average of the third illumination data of each of the second vehicles.

9. The method according to claim 1, characterized in that, The second illumination data was obtained by the sensors in the second vehicle.

10. The method according to any one of claims 1-9, characterized in that, Both the first vehicle and the second vehicle are equipped with an interactive device. The interactive device in the first vehicle can interact with the interactive device in the second vehicle, enabling the first vehicle to obtain the second illumination data of the second vehicle.

11. The method according to any one of claims 1-9, characterized in that, The sensing devices in the first vehicle and / or the second vehicle include at least one of a light sensor and an ultraviolet sensor.

12. The method according to claim 11, characterized in that, The sensing devices in the first vehicle and / or the second vehicle are arranged on the dashboard, inside the windshield, and / or adjacent to the rearview mirror.

13. The method according to any one of claims 1-9, characterized in that, The method further includes: If the amount of data corresponding to the second illumination data is less than a preset threshold, the illumination parameters of the first vehicle are determined based on the preset correction model and the first illumination data.

14. The method according to claim 13, characterized in that, The determination of the illumination parameters of the first vehicle based on the preset correction model and the first illumination data includes: Obtain vehicle driving data, vehicle location information, and / or weather information corresponding to the first illumination data; The vehicle driving data, the vehicle location information, and / or the weather information are input into the correction model to determine the illumination parameters of the first vehicle.

15. The method according to claim 14, characterized in that, The step of inputting the vehicle driving data, the vehicle location information, and / or the weather information into the correction model to determine the illumination parameters of the first vehicle includes: Based on the correction model, feature data corresponding to the vehicle driving data, the vehicle location information, and / or the weather information are extracted; Based on the correction model and the feature data, the illumination parameters of the first vehicle are output.

16. The method according to claim 13, characterized in that, The correction model is an artificial intelligence model.

17. The method according to claim 13, characterized in that, The correction model includes either the LightGBM model or the DeepFM model.

18. The method according to any one of claims 1-9, characterized in that, The method further includes: Obtain the location information of the first vehicle; Based on the location information, the second vehicle is determined from target vehicles whose distance from the first vehicle is within a preset distance range.

19. The method according to claim 18, characterized in that, The location information includes at least one of latitude and longitude information, map coordinate system positioning information, and local coordinate system positioning information.

20. The method according to claim 18, characterized in that, The step of determining the second vehicle from target vehicles whose distance from the first vehicle is within a preset distance range based on the location information includes: Based on the location information, from the target vehicles whose distance from the first vehicle is within the preset distance range, a second vehicle that meets at least one target condition is determined; The target conditions include: the accuracy of the illumination parameters collected by the target vehicle can be determined, and / or the target vehicle and the first vehicle meet the same driving conditions.

21. The method according to claim 18, characterized in that, The method further includes: Obtain the second illumination data within a preset time range from the second vehicle.

22. The method according to any one of claims 1-9, characterized in that, Before determining the illumination parameters of the first vehicle based on the first illumination data collected by the sensor in the first vehicle and the second illumination data of the second vehicle, the method further includes: Determine the accuracy of the first illumination data.

23. The method according to claim 22, characterized in that, Determining the accuracy of the first illumination data includes: If the sensor is not operating properly, the first illumination data is determined to be inaccurate.

24. The method according to claim 22, characterized in that, Determining the accuracy of the first illumination data includes: Obtain the driving information of the first vehicle and / or the corresponding environmental information of the first vehicle; The accuracy of the first illumination data is determined based on the driving information and / or the environmental information.

25. The method according to claim 24, characterized in that, The driving information includes at least one of: vehicle status and the angle between the driving direction and the direction of sunlight; the environmental information includes at least one of: weather conditions and information on obstructions; determining the accuracy of the first illumination data based on the driving information and / or the environmental information includes: The first illumination data is inaccurate if the vehicle status does not conform to the preset status, and / or the included angle is not within the preset included angle range, and / or the weather conditions do not conform to the preset weather conditions, and / or the obstruction information conforms to the preset obstruction information.

26. The method according to claim 25, characterized in that, The angle between the driving direction and the direction of sunlight is determined based on the direction of sunlight and the driving direction, wherein the direction of sunlight is determined based on time information and the position information of the first vehicle.

27. The method according to any one of claims 23-26, characterized in that, The method further includes: If the first illumination data is inaccurate, the illumination parameters of the first vehicle are determined based on the first illumination data collected by the sensing device in the first vehicle and the second illumination data of the second vehicle.

28. The method according to claim 22, characterized in that, Determining the accuracy of the first illumination data includes: When the sensing device is operating normally, and the vehicle status meets the preset conditions, the angle between the driving direction and the direction of sunlight is within the preset angle range, the weather conditions are within the preset weather conditions, and the obstruction information does not meet the preset obstruction information, the first illumination data is accurate.

29. The method according to claim 22, characterized in that, The method further includes: If the first illumination data is accurate, the first illumination data is determined as the illumination parameters of the first vehicle.

30. A device for determining illumination parameters, characterized in that, It includes one or more processing units, each storing a computer program that, when executed by the processing unit, causes the processing unit to perform the steps in the illumination parameter determination method according to any one of claims 1-29.

31. An electronic device, characterized in that, It includes one or more processors and a memory, the memory storing a computer program that, when executed by the processor, causes the processor to perform the steps of the illumination parameter determination method according to any one of claims 1-29.

32. A computer-readable storage medium, characterized in that, It stores a computer program, which is loaded by a processor to execute the steps in the illumination parameter determination method according to any one of claims 1-29.

33. A computer program product, characterized in that, The computer program product includes a computer program that, when executed by a processor, causes the computer program product to perform the steps in the illumination parameter determination method as described in any one of claims 1-29.

34. A vehicle, characterized in that, Includes the illumination parameter determination device as described in claim 30, or includes the electronic device as described in claim 31.

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