Visibility processing method and device, electronic equipment and storage medium

Abstract

The invention provides a visibility processing method and device, electronic equipment and a storage medium. The method comprises the following steps: acquiring weather information, driving information of a vehicle and a first threshold value corresponding to visibility; a first weight corresponding to the weather information and a second weight corresponding to the driving information are determined, the first weight is used for indicating the influence degree of the weather information on the visibility, and the second weight is used for indicating the influence degree of the driving information on the visibility; determining a first parameter based on the weather information, the driving information, the first weight and the second weight; and determining a second threshold value corresponding to the visibility based on the first threshold value and the first parameter, and classifying the visibility based on the second threshold value. According to the method, the change of the weather information and the change of the driving information can be dynamically responded to optimize the first threshold value of the visibility, the accuracy of visibility classification is effectively improved, and a more reliable basis is provided for traffic decision making of drivers.

Description

Technical Field

[0001] The present application relates to the field of traffic safety, and in particular to a visibility processing method, device, electronic device and storage medium. Background Art

[0002] In the field of traffic safety, changes in visibility will affect the driver's judgment of road conditions. Accurate prediction of visibility classification can effectively guide the driver to adjust driving behavior, such as controlling speed and maintaining a safe distance, thereby significantly reducing the incidence of traffic accidents.

[0003] In the related art, a plurality of visibility ranges are obtained through a preset fixed threshold, and the visibility range to which the visibility belongs is determined, thereby completing the classification of visibility. The preset fixed threshold is set by relevant technical personnel based on experience.

[0004] However, the preset fixed threshold cannot capture subtle changes in different visibility ranges, resulting in the visibility classification results being inconsistent with the actual risks or visual experience of the actual scene, thereby reducing the accuracy of the visibility classification results. Summary of the Invention

[0005] The present application provides a visibility processing method, device, electronic device and storage medium to improve the accuracy of visibility classification results.

[0006] In a first aspect, the present application provides a visibility processing method, the method comprising:

[0007] Obtaining weather information, vehicle driving information, and a first threshold corresponding to visibility;

[0008] determining a first weight corresponding to the weather information and a second weight corresponding to the driving information, the first weight being used to indicate the degree of influence of the weather information on the visibility, and the second weight being used to indicate the degree of influence of the driving information on the visibility;

[0009] determining a first parameter based on the weather information, the driving information, the first weight, and the second weight;

[0010] Based on the first threshold and the first parameter, a second threshold corresponding to the visibility is determined, and the visibility is classified based on the second threshold.

[0011] In this solution, meteorological information, vehicle driving information, and a first threshold corresponding to visibility are obtained; a first weight corresponding to the meteorological information and a second weight corresponding to the driving information are determined, wherein the first weight is used to indicate the degree of influence of the meteorological information on visibility, and the second weight is used to indicate the degree of influence of the driving information on visibility; a first parameter is determined based on the meteorological information, driving information, the first weight, and the second weight; a second threshold corresponding to visibility is determined based on the first threshold and the first parameter, and visibility is classified based on the second threshold. In the above method, by obtaining meteorological information and vehicle driving information, determining the weights of the two and calculating the first parameter accordingly, and then adjusting the first threshold of visibility, obtaining the second threshold and classifying accordingly, the method can dynamically respond to changes in meteorological information and driving information to optimize the fixed threshold of visibility, effectively improving the accuracy of visibility classification, and providing a more reliable basis for drivers' traffic decisions.

[0012] In one implementation, the weather information includes a plurality of weather information sub-items, and the driving information includes a plurality of driving information sub-items; and determining a first parameter based on the weather information, the driving information, the first weight, and the second weight includes:

[0013] Determine the first sub-weight corresponding to each sub-meteorological information in the first weight;

[0014] In the second weight, determining a second sub-weight corresponding to each sub-driving information;

[0015] The first parameter is determined based on the plurality of weather information sub-items, the plurality of first sub-weights, the plurality of driving information sub-items, and the plurality of second sub-weights.

[0016] In one implementation, determining the first parameter based on the plurality of weather information sub-items, the plurality of first sub-weights, the plurality of driving information sub-items, and the plurality of second sub-weights includes:

[0017] determining a product of the sub-meteorological information and a first sub-weight corresponding to the sub-meteorological information to obtain a plurality of second parameters;

[0018] determining a product of the sub-driving information and a second sub-weight corresponding to the sub-driving information to obtain a plurality of third parameters;

[0019] The first parameter is determined based on the plurality of second parameters and the plurality of third parameters.

[0020] In one implementation, determining a second threshold corresponding to the visibility based on the first threshold and the first parameter includes:

[0021] Obtaining a current vehicle speed from the driving information and a current humidity from the weather information;

[0022] The second threshold is determined based on the current vehicle speed, the current humidity, the first threshold, and the first parameter.

[0023] In this solution, the current vehicle speed is obtained from driving information, and the current humidity is obtained from weather information. A second threshold is determined based on the current vehicle speed, humidity, the first threshold, and the first parameter. This method further optimizes the fixed visibility threshold by using current vehicle speed and humidity, two key variables that significantly influence the visibility threshold. The resulting second threshold fully accounts for the driver's response characteristics, enabling more refined dynamic adjustment of the visibility threshold and further improving the accuracy of visibility classification.

[0024] In one implementation, determining the second threshold based on the current vehicle speed, the current humidity, the first threshold, and the first parameter includes:

[0025] determining a third threshold based on the current vehicle speed and the current humidity;

[0026] determining a fourth threshold based on the first threshold and the first parameter;

[0027] A second threshold corresponding to the visibility is determined based on the third threshold and the fourth threshold.

[0028] In this approach, the current environment is gridded based on the current vehicle speed, resulting in multiple grids within the vehicle's travel range. The visibility corresponding to each grid is then determined. The visibility of each grid is then classified based on a second threshold and the corresponding visibility. This method dynamically adjusts the grid width based on vehicle speed, balancing the need for refined perception of environmental details at low speeds with the need for efficient analysis of long-range vision at high speeds. This significantly improves the spatiotemporal adaptability of visibility data collection, and the grid-based driving guidance provides drivers with a more reliable basis for traffic decisions.

[0029] In one implementation, classifying the visibility based on the second threshold includes:

[0030] Based on the current vehicle speed, the current environment is divided into grids to obtain a plurality of grids within the vehicle's driving range;

[0031] Obtaining the visibility corresponding to each grid;

[0032] The visibility of each grid is classified based on the second threshold and the visibility corresponding to each grid.

[0033] In this approach, the current environment is gridded based on the current vehicle speed, resulting in multiple grids within the vehicle's travel range. The visibility corresponding to each grid is then determined. The visibility of each grid is then classified based on a second threshold and the corresponding visibility. This method dynamically adjusts the grid width based on vehicle speed, balancing the need for refined perception of environmental details at low speeds with the need for efficient analysis of long-range vision at high speeds. This significantly improves the spatiotemporal adaptability of visibility data collection, and the grid-based driving guidance provides drivers with a more reliable basis for traffic decisions.

[0034] In one implementation, for any two adjacent first and second grids, after classifying the visibility of the grids, the method further includes:

[0035] When the vehicle travels from the range of the first grid to the range of the second grid, determining whether the visibility type of the first grid is the same as the visibility type of the second grid;

[0036] If they are different, a prompt message indicating the visibility change is generated.

[0037] In this solution, for any two adjacent first and second grids, when a vehicle travels from the range of the first grid to the range of the second grid, it is determined whether the visibility type of the first grid is the same as the visibility type of the second grid. If they are different, a prompt message of the visibility change is generated, so that the driver can perceive the sudden change in visibility in time and gain valuable reaction time to adjust the driving strategy.

[0038] In a second aspect, an embodiment of the present application provides a visibility processing device, comprising:

[0039] an acquisition module, configured to acquire weather information, vehicle driving information, and a first threshold corresponding to visibility;

[0040] a determination module, configured to determine a first weight corresponding to the weather information and a second weight corresponding to the driving information, wherein the first weight is used to indicate the degree of influence of the weather information on the visibility, and the second weight is used to indicate the degree of influence of the driving information on the visibility;

[0041] The determining module is further configured to determine a first parameter based on the weather information, the driving information, the first weight, and the second weight;

[0042] A processing module is configured to determine a second threshold corresponding to the visibility based on the first threshold and the first parameter, and classify the visibility based on the second threshold.

[0043] The visibility processing device provided in the embodiment of the present application can execute the technical solution shown in the above method embodiment. Its implementation principle and beneficial effects are similar and will not be repeated here.

[0044] In one implementation, the determining module is specifically configured to:

[0045] Determine the first sub-weight corresponding to each sub-meteorological information in the first weight;

[0046] In the second weight, determining a second sub-weight corresponding to each sub-driving information;

[0047] The first parameter is determined based on the plurality of weather information sub-items, the plurality of first sub-weights, the plurality of driving information sub-items, and the plurality of second sub-weights.

[0048] In one implementation, the determining module is specifically configured to:

[0049] determining a product of the sub-meteorological information and a first sub-weight corresponding to the sub-meteorological information to obtain a plurality of second parameters;

[0050] determining a product of the sub-driving information and a second sub-weight corresponding to the sub-driving information to obtain a plurality of third parameters;

[0051] The first parameter is determined based on the plurality of second parameters and the plurality of third parameters.

[0052] In one implementation, the processing module is specifically configured to:

[0053] Obtaining a current vehicle speed from the driving information and a current humidity from the weather information;

[0054] The second threshold is determined based on the current vehicle speed, the current humidity, the first threshold, and the first parameter.

[0055] In one implementation, the processing module is specifically configured to:

[0056] determining a third threshold based on the current vehicle speed and the current humidity;

[0057] determining a fourth threshold based on the first threshold and the first parameter;

[0058] A second threshold corresponding to the visibility is determined based on the third threshold and the fourth threshold.

[0059] In one implementation, the processing module is specifically configured to:

[0060] Based on the current vehicle speed, the current environment is divided into grids to obtain a plurality of grids within the vehicle's driving range;

[0061] Obtaining the visibility corresponding to each grid;

[0062] The visibility of each grid is classified based on the second threshold and the visibility corresponding to each grid.

[0063] In one implementation, for any two adjacent first grids and second grids, the processing module is further configured to:

[0064] When the vehicle travels from the range of the first grid to the range of the second grid, determining whether the visibility type of the first grid is the same as the visibility type of the second grid;

[0065] If they are different, a prompt message indicating the visibility change is generated.

[0066] The visibility processing device provided in the embodiment of the present application can execute the technical solution shown in the above method embodiment. Its implementation principle and beneficial effects are similar and will not be repeated here.

[0067] In a third aspect, the present application provides an electronic device, comprising: a processor, and a memory communicatively connected to the processor;

[0068] Memory stores computer-executable instructions;

[0069] The processor executes the computer-executable instructions stored in the memory to implement the method according to the first aspect.

[0070] The electronic device provided in the embodiment of the present application can execute the technical solution shown in the above method embodiment, and its implementation principles and beneficial effects are similar, which will not be repeated here.

[0071] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, in which computer-executable instructions are stored. When the computer-executable instructions are executed by a processor, they are used to implement the method of the first aspect.

[0072] When the computer-executable instructions in the computer-readable storage medium provided in the embodiment of the present application are executed by the processor, the technical solution shown in the above method embodiment can be implemented. Its implementation principle and beneficial effects are similar and will not be repeated here.

[0073] In a fifth aspect, the present application provides a computer program product, comprising a computer program, which implements the method of the first aspect when executed by a processor.

[0074] When the computer program in the computer program product provided in the embodiment of the present application is executed by a processor, the technical solution shown in the above method embodiment can be implemented. Its implementation principles and beneficial effects are similar and will not be repeated here. BRIEF DESCRIPTION OF THE DRAWINGS

[0075] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0076] Figure 1 A flow chart of a visibility processing method provided in an embodiment of the present application;

[0077] Figure 2 A flowchart of a method for determining a second threshold value provided in this application;

[0078] Figure 3 Schematic diagram of the method for classifying visibility provided for this application;

[0079] Figure 4 A schematic flow chart of a method for determining a preset adaptive neural network provided in an embodiment of the present application;

[0080] Figure 5 A schematic diagram of a visibility processing device provided in an embodiment of the present application;

[0081] Figure 6 A structural diagram of an electronic device provided in an embodiment of the present application.

[0082] The above drawings illustrate specific embodiments of the present application, which will be described in more detail below. These drawings and the textual description are not intended to limit the scope of the present application in any way, but rather to illustrate the concepts of the present application to those skilled in the art by reference to specific embodiments. DETAILED DESCRIPTION

[0083] Exemplary embodiments will be described in detail herein, with examples illustrated in the accompanying drawings. In the following description, when referring to the drawings, identical numerals in different figures represent identical or similar elements, unless otherwise indicated. The embodiments described in the following exemplary embodiments are not intended to represent all embodiments consistent with the present application. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the present application, as detailed in the appended claims.

[0084] In the field of traffic safety, changes in visibility will affect the driver's judgment of road conditions. Accurate prediction of visibility classification can effectively guide the driver to adjust driving behavior, such as controlling speed and maintaining a safe distance, thereby significantly reducing the incidence of traffic accidents.

[0085] In the related art, a plurality of visibility ranges are obtained through a preset fixed threshold, and the visibility range to which the visibility belongs is determined, thereby completing the classification of visibility. The preset fixed threshold is set by relevant technical personnel based on experience.

[0086] However, the preset fixed threshold cannot capture subtle changes in different visibility ranges, resulting in the visibility classification results being inconsistent with the actual risks or visual experience of the actual scene, thereby reducing the accuracy of the visibility classification results and the effectiveness of the warning.

[0087] Based on the above technical problems, the technical concepts of the embodiments of the present application are as follows:

[0088] Obtaining meteorological information, vehicle driving information, and a first threshold corresponding to visibility; determining a first weight corresponding to the meteorological information and a second weight corresponding to the driving information, wherein the first weight is used to indicate the degree of influence of the meteorological information on visibility, and the second weight is used to indicate the degree of influence of the driving information on visibility; determining a first parameter based on the meteorological information, driving information, the first weight, and the second weight; determining a second threshold corresponding to visibility based on the first threshold and the first parameter, and classifying visibility based on the second threshold. In the above method, by obtaining meteorological information and vehicle driving information, determining the weights of the two and calculating the first parameter accordingly, and then adjusting the first threshold of visibility, obtaining the second threshold and classifying accordingly, the method can dynamically respond to changes in meteorological information and driving information to optimize the fixed threshold of visibility, effectively improving the accuracy of visibility classification, and providing a more reliable basis for drivers' traffic decisions.

[0089] The following specific embodiments describe in detail the technical solution of the present application and how the technical solution of the present application solves the above-mentioned technical problems. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments. The embodiments of the present application will be described below in conjunction with the accompanying drawings.

[0090] Figure 1 A flow chart of the visibility processing method provided in the embodiment of the present application is shown as follows: Figure 1 As shown, the method includes:

[0091] S101: Acquire weather information, vehicle driving information, and a first threshold corresponding to visibility.

[0092] The execution subject of the embodiment of the present application can be an electronic device, or a visibility processing device provided in the electronic device. The visibility processing device can be implemented by software, or by a combination of software and hardware.

[0093] The meteorological information includes a plurality of sub-meteorological information. For example, the plurality of sub-meteorological information are temperature, humidity, wind speed, air pressure, precipitation, time, terrain features, road signs, and the frequency of historical fog on the road section where the vehicle is traveling.

[0094] Terrain features include slope, curvature, and altitude, while road markings include solid and dashed lines and guardrails.

[0095] The vehicle driving information includes multiple sub-driving information. For example, the multiple sub-driving information are the current vehicle speed, the surrounding vehicle density, and the driving distance.

[0096] Optionally, obtain weather-related information through a vehicle-mounted weather station.

[0097] Optionally, obtain the terrain features of the current location through a high-precision map.

[0098] Optionally, the road sign is identified by image recognition technology. For example, when the road sign is identified as having a guardrail, it can be represented as 1, and when the road sign is identified as having no guardrail, it can be represented as 0.

[0099] The frequency of fog occurrence in the history of the vehicle's current road section is, for example, the frequency of fog occurrence in the past 30 days in the vehicle's current road section. Alternatively, the frequency of fog occurrence in the past 30 days in the vehicle's current road section is obtained by querying a historical meteorological database.

[0100] Optionally, the current vehicle speed is obtained via a Controller Area Network (CAN) bus.

[0101] Optionally, the surrounding vehicle density is obtained through vehicle-to-everything (V2X) technology, where the surrounding vehicle density is the number of vehicles per kilometer of the road section where the vehicle is currently traveling.

[0102] Optionally, the distance between the vehicle and the vehicle in front is obtained by using millimeter wave radar or visual sensor.

[0103] The first threshold corresponding to visibility is obtained based on manual experience, and is, for example, 2 meters.

[0104] In one implementation, obtaining weather information, vehicle driving information, and a first threshold corresponding to visibility includes: obtaining original weather information, vehicle driving information, and a first threshold corresponding to visibility; and normalizing the original weather information and vehicle driving information to obtain weather information and vehicle driving information.

[0105] The original meteorological information includes multiple sub-original meteorological information.

[0106] The original driving information of the vehicle includes a plurality of sub-original driving information.

[0107] Exemplarily, the corresponding sub-meteorological information may be obtained by normalizing the sub-original meteorological information.

[0108] Optionally, the sub-original meteorological information and the sub-meteorological information satisfy the following formula 1:

[0109]

[0110] in, represents the ith sub-meteorological information, x represents the ith sub-original meteorological information, max i Indicates the maximum value corresponding to the i-th sub-original meteorological information, min i Indicates the minimum value corresponding to the i-th sub-original meteorological information.

[0111] The maximum value corresponding to the ith sub-original meteorological information and the minimum value corresponding to the ith sub-original meteorological information are determined by a preset adaptive neural network, wherein the preset adaptive neural network is obtained through training.

[0112] It should be noted that the sub-original driving information and its corresponding relationship with the sub-driving information are similar to the corresponding relationship with the sub-original weather information and the sub-weather information, and will not be repeated here.

[0113] S102: Determine a first weight corresponding to the weather information and a second weight corresponding to the driving information.

[0114] The first weight is used to indicate the degree of influence of weather information on visibility, and the second weight is used to indicate the degree of influence of driving information on visibility.

[0115] In one implementation, a first weight corresponding to the weather information and a second weight corresponding to the driving information are determined by presetting an adaptive neural network model.

[0116] The first weight is used to indicate the degree of influence of meteorological information on visibility. It can be understood that the first weight is used to indicate the degree of influence of meteorological information on the first threshold corresponding to visibility.

[0117] In one implementation, the first weight includes N first sub-weights. Specifically, the i-th first sub-weight indicates the degree of influence of the i-th sub-meteorological information on the first threshold corresponding to visibility, where i=1, 2,…, N.

[0118] The second weight is used to indicate the degree of influence of the driving information on the visibility. It can be understood that the second weight is used to indicate the degree of influence of the driving information on the first threshold corresponding to the visibility.

[0119] In one implementation, the second weight includes M second sub-weights. Specifically, the j-th second sub-weight indicates the degree of influence of the j-th sub-driving information on the first threshold corresponding to visibility, where j=1, 2, ..., M.

[0120] S103: Determine a first parameter based on weather information, driving information, a first weight, and a second weight.

[0121] In one implementation, the first parameter is determined by presetting an adaptive neural network model based on weather information, driving information, a first weight, and a second weight.

[0122] In one implementation, a first parameter is determined based on weather information, driving information, a first weight, and a second weight, including: determining a first sub-weight corresponding to each sub-weather information in the first weight; determining a second sub-weight corresponding to each sub-driving information in the second weight; and determining the first parameter based on multiple sub-weather information, multiple first sub-weights, multiple sub-driving information, and multiple second sub-weights.

[0123] In one implementation, a first parameter is determined based on multiple sub-meteorological information, multiple first sub-weights, multiple sub-driving information, and multiple second sub-weights, including: determining the product of the sub-meteorological information and the first sub-weight corresponding to the sub-meteorological information to obtain multiple second parameters; determining the product of the sub-driving information and the second sub-weight corresponding to the sub-driving information to obtain multiple third parameters; and determining the first parameter based on the multiple second parameters and the multiple third parameters.

[0124] Optionally, the sub-meteorological information, the first sub-weight corresponding to the sub-meteorological information, and the second parameter satisfy the following formula 2:

[0125]

[0126] in, W1 represents the i-th second parameter determined by multiplying the sub-meteorological information by the first sub-weight corresponding to the sub-meteorological information. i Indicates the i-th first sub-weight, the i-th first sub-weight is the first sub-weight corresponding to the i-th sub-meteorological information, X1 i Represents the i-th sub-meteorological information.

[0127] Optionally, the sub-driving information, the second sub-weight corresponding to the sub-driving information, and the third parameter satisfy the following formula 3:

[0128]

[0129] in, represents the jth third parameter determined according to the sub-driving information and the second sub-weight corresponding to the sub-driving information, W2 j represents the jth second sub-weight, the jth second sub-weight is the second sub-weight corresponding to the jth sub-driving information, X2 j represents the j-th sub-driving information.

[0130] In one implementation, the first threshold may include multiple first sub-thresholds.

[0131] When the first threshold includes K first sub-thresholds, each first sub-weight includes K weather-threshold weights, and the kth weather-threshold weight in the i-th first sub-weight indicates the degree of influence of the i-th sub-weather information on the k-th first sub-threshold corresponding to visibility. Similarly, each second sub-weight includes K driving-threshold weights, and the kth driving-threshold weight in the j-th second sub-weight indicates the degree of influence of the j-th driving information on the k-th first sub-threshold corresponding to visibility. Here, k = 1, 2, …, K.

[0132] Optionally, the sub-meteorological information, the K meteorological-threshold weights corresponding to the sub-meteorological information, and the second parameter satisfy the following formula:

[0133] Formula 4:

[0134]

[0135] in, represents the i-th second parameter determined according to the sub-meteorological information and the K meteorological-threshold weights corresponding to the sub-meteorological information, w1 ik represents the kth weather-threshold weight in the i-th first sub-weight.

[0136] Optionally, the sub-driving information, the K driving-threshold weights corresponding to the sub-driving information, and the third parameter are expressed as follows in Formula 5:

[0137]

[0138] in, represents the jth third parameter determined based on the sub-driving information and the K driving-threshold weights corresponding to the sub-driving information, w2 jk represents the kth driving-threshold weight in the jth second sub-weight.

[0139] When the first threshold includes K first sub-thresholds, the first parameter includes K first sub-parameters.

[0140] Optionally, the plurality of second parameters, the plurality of third parameters, and the first sub-parameter satisfy the following formula 6:

[0141]

[0142] Where, ΔT k represents the kth first sub-parameter, λ k Indicates the scaling factor corresponding to the kth first sub-threshold, r takes the value 1 or 2, b k represents the preset bias term corresponding to the kth first sub-threshold, tanh represents the hyperbolic tangent function,

[0143] By presetting an adaptive neural network model, a scaling factor corresponding to the first threshold and a preset bias item corresponding to the first threshold are determined, specifically, a scaling factor corresponding to each first sub-threshold and a preset bias item corresponding to each first sub-threshold are determined.

[0144] S104: Determine a second threshold corresponding to the visibility based on the first threshold and the first parameter, and classify the visibility based on the second threshold.

[0145] In one implementation, when the first threshold includes K first sub-thresholds, the first parameter includes K first sub-parameters, and the second threshold includes K second sub-thresholds.

[0146] The following describes a method for determining a second threshold corresponding to visibility based on the first threshold and the first parameter:

[0147] When the first threshold includes K first sub-thresholds, the first parameter includes K first sub-parameters, and the second threshold includes K second sub-thresholds;

[0148] The first sub-threshold, the first sub-parameter, and the second sub-threshold may satisfy the following formula 7:

[0149]

[0150] in, represents the kth second sub-threshold, T k represents the kth first sub-threshold.

[0151] A method of classifying visibility based on the second threshold is described with the following example.

[0152] In one implementation, classifying visibility based on the second threshold may be: obtaining visibility within a vehicle's driving range; determining multiple visibility ranges based on multiple second sub-thresholds; determining the visibility range to which the visibility belongs based on the multiple visibility ranges; and classifying visibility based on the visibility range to which the visibility belongs to determine the visibility type.

[0153] Optionally, according to the visibility range to which the visibility belongs, the visibility type is obtained by querying a pre-stored first correspondence relationship, wherein the first correspondence relationship includes a plurality of visibility ranges, a plurality of visibility types, and correspondences between the plurality of visibility ranges and the plurality of visibility types.

[0154] Exemplarily, the second threshold includes four second sub-thresholds, where the first second sub-threshold is, for example, 50 meters, the second second sub-threshold is, for example, 100 meters, the third second sub-threshold is, for example, 200 meters, and the fourth second sub-threshold is, for example, 500 meters. Five visibility ranges can be determined, and the five visibility ranges are less than or equal to 50 meters, greater than 50 meters and less than or equal to 100 meters, greater than 100 meters and less than or equal to 200 meters, and greater than 200 meters and less than or equal to 500 meters. The first correspondence can be as shown in Table 1:

[0155] Table 1 First correspondence

[0156] Visibility range Visibility Type Less than or equal to 50 meters Level 4 Greater than 50 meters and less than or equal to 100 meters Level 3 Greater than 100 meters and less than or equal to 200 meters Level 2 Greater than 200 meters and less than or equal to 500 meters Level 1 More than 500 meters Level 0

[0157] For example, when the visibility range is less than or equal to 50 meters, based on Table 1, the visibility type can be determined to be level 4.

[0158] Furthermore, after the visibility type is determined, an alarm may be issued according to the visibility type.

[0159] The following is an exemplary description of issuing an alarm based on visibility type:

[0160] When the visibility type is Level 4, the driver of the vehicle is reminded: turn on the fog lights, low beams, side marker lights, front and rear position lights and hazard warning flashers, and choose a safe area nearby to park and avoid danger, such as a highway service area or emergency parking lane; if you cannot stop, be sure to reduce the speed to the minimum, maintain an extremely low speed, keep a distance of at least 200 meters from the vehicle in front, and avoid sudden braking or changing lanes.

[0161] When the visibility type is level 3, the driver of the vehicle is reminded to turn on the fog lights, low beam lights, side marker lights, front and rear position lights and hazard warning flashers; the speed must not exceed 20 kilometers per hour; if on a highway, exit the highway as soon as possible from the nearest exit.

[0162] When the visibility type is Level 2, vehicle drivers are reminded to turn on the fog lights, low beam lights, side marker lights, front and rear position lights, and hazard warning flashers; the speed must not exceed 40 kilometers per hour; and the driver must maintain a distance of more than 100 meters from the vehicle in front in the same lane.

[0163] When the visibility type is Level 1, vehicle drivers are reminded to turn on the fog lights, low beam lights, side marker lights, and front and rear position lights, the speed must not exceed 60 kilometers per hour, and a distance of more than 50 meters must be maintained from the vehicle in front in the same lane.

[0164] When the visibility type is level 0, no alarm is issued.

[0165] Beneficial effects of this embodiment: In this embodiment, meteorological information, vehicle driving information, and a first threshold corresponding to visibility are obtained; a first weight corresponding to the meteorological information and a second weight corresponding to the driving information are determined, wherein the first weight is used to indicate the degree of influence of the meteorological information on visibility, and the second weight is used to indicate the degree of influence of the driving information on visibility; a first parameter is determined based on the meteorological information, driving information, the first weight, and the second weight; a second threshold corresponding to visibility is determined based on the first threshold and the first parameter, and visibility is classified based on the second threshold. In the above method, by obtaining meteorological information and vehicle driving information, determining the weights of the two and calculating the first parameter accordingly, and then adjusting the first threshold of visibility, obtaining the second threshold and classifying accordingly, it can dynamically respond to changes in meteorological information and driving information to optimize the fixed threshold of visibility (i.e., the first threshold), effectively improving the accuracy of visibility classification, and providing a more reliable basis for drivers' traffic decisions.

[0166] Next, combine Figure 2 ,exist Figure 1 Based on the embodiment, another method of determining the second threshold corresponding to the visibility based on the first threshold and the first parameter is described in detail.

[0167] Figure 2 A flow chart of the method for determining the second threshold value provided in this application is shown as follows: Figure 2 As shown, the method includes:

[0168] S201: Obtain the current vehicle speed from the driving information, and obtain the current humidity from the weather information.

[0169] S202: Determine a second threshold based on the current vehicle speed, the current humidity, the first threshold, and the first parameter.

[0170] In one implementation, determining the second threshold based on the current vehicle speed, the current humidity, the first threshold, and the first parameter includes:

[0171] determining a third threshold based on the current vehicle speed and the current humidity;

[0172] determining a fourth threshold based on the first threshold and the first parameter;

[0173] A second threshold corresponding to the visibility is determined based on the third threshold and the fourth threshold.

[0174] In one implementation, the current vehicle speed, the current humidity, and the third threshold value satisfy the following formula 8:

[0175]

[0176] Among them, T fogrepresents the third threshold, v represents the current vehicle speed, t react represents the driver's braking reaction time, α represents the attenuation factor, and RH represents the current humidity.

[0177] The driver's braking reaction time is determined based on experience, for example, 2.5 seconds.

[0178] The attenuation factor can be determined based on experience or based on a preset adaptive neural network.

[0179] In one implementation, the fourth threshold includes K fourth sub-thresholds. The fourth threshold is determined based on the first threshold and the first parameter. This can be understood as determining the kth fourth sub-threshold based on the kth first sub-threshold and the kth first sub-parameter for the kth fourth sub-threshold, where k = 1, …, K.

[0180] In one implementation, the first sub-threshold, the first parameter, and the fourth sub-threshold satisfy the following formula 9:

[0181]

[0182] in, represents the kth fourth sub-threshold.

[0183] In one implementation, when the fourth threshold includes K fourth sub-thresholds, determining the second threshold corresponding to the visibility based on the third threshold and the fourth threshold can be understood as follows:

[0184] For the kth fourth sub-threshold;

[0185] Based on the third threshold and the kth fourth threshold, a kth second threshold corresponding to the visibility is determined.

[0186] In one implementation, the third threshold, the fourth threshold, and the second threshold satisfy the following formula 10:

[0187]

[0188] Among them, γ represents the dynamic weight coefficient.

[0189] The dynamic weight coefficient is greater than or equal to 0 or less than or equal to 1, and is determined according to a preset adaptive neural network.

[0190] Beneficial effects of this embodiment: In this embodiment, the current vehicle speed is obtained from driving information, and the current humidity is obtained from weather information; a second threshold is determined based on the current vehicle speed, current humidity, the first threshold, and the first parameter. In this method, the fixed visibility threshold is further optimized using the current vehicle speed and humidity, two key variables that significantly influence the visibility threshold. The resulting second threshold fully considers the driver's response characteristics, enabling more refined dynamic adjustment of the visibility threshold and further improving the accuracy of visibility classification.

[0191] Next, combine Figure 3 ,exist Figure 1 Based on the embodiment, another method of classifying visibility based on the second threshold is described in detail.

[0192] Figure 3 A schematic diagram of a method for classifying visibility provided in this application is shown in FIG. Figure 3 As shown, the method includes:

[0193] S301 . Based on the current vehicle speed, the current environment is divided into grids to obtain a plurality of grids within the vehicle's driving range.

[0194] For example, when the current vehicle speed is less than 60 kilometers per hour, the current environment is divided into 500-meter grids to obtain multiple 500-meter grids within the vehicle's driving range; when the current vehicle speed is greater than or equal to 60 kilometers per hour, the current environment is divided into 1000-meter grids to obtain multiple 1000-meter grids within the vehicle's driving range.

[0195] S302: Obtain the visibility corresponding to each grid.

[0196] S303: Classify the visibility of each grid based on the second threshold and the visibility corresponding to each grid.

[0197] In one implementation, classifying the visibility of each grid based on the second threshold and the visibility corresponding to each grid includes:

[0198] determining a plurality of visibility ranges based on the plurality of second sub-thresholds;

[0199] Determining the visibility range to which the visibility of each grid belongs according to the multiple visibility ranges;

[0200] According to the visibility range to which the visibility of each grid belongs, the visibility of each grid is classified to determine the visibility type of each grid.

[0201] It should be noted that the specific execution process of classifying the visibility of each grid based on the second threshold and the visibility corresponding to each grid is similar to the specific execution process of classifying the visibility based on the second threshold in S104, and will not be repeated here.

[0202] In one implementation, for any two adjacent first and second grids, after classifying the visibility of each grid, the following steps may be performed:

[0203] When the vehicle travels from the range of the first grid to the range of the second grid, determining whether the visibility type of the first grid is the same as the visibility type of the second grid;

[0204] If they are different, a prompt message indicating the visibility change is generated.

[0205] For example, the prompt message for visibility changes is: You are about to enter an area with visibility level 2. It is recommended to turn on the fog lights, low beam lights, side marker lights and front and rear position lights. The speed must not exceed 60 kilometers per hour, and keep a distance of more than 100 meters from the vehicle in front in the same lane.

[0206] In one implementation, after classifying the visibility of each grid, the following steps may also be performed:

[0207] Determine the color of each grid according to its visibility type;

[0208] According to the color of each grid, a visual fog distribution map is generated.

[0209] Optionally, the color of each grid is obtained by searching a pre-stored second correspondence relationship according to the visibility type of each grid. The second correspondence relationship includes a plurality of visibility types, a plurality of colors, and correspondences between the plurality of visibility types and the plurality of colors.

[0210] For example, the second correspondence may be as shown in Table 2:

[0211] Table 2 Second correspondence

[0212] Visibility Type color Level 4 deep red Level 3 light red Level 2 orange color Level 1 yellow Level 0 green

[0213] For example, when the visibility type of the grid is level 2, based on Table 2, the color of the grid may be determined to be orange.

[0214] The beneficial effects of this embodiment include: In this embodiment, based on the current vehicle speed, the current environment is gridded to obtain multiple grids within the vehicle's driving range; the visibility corresponding to each grid is obtained; and the visibility of each grid is classified based on a second threshold and the visibility corresponding to each grid. In the above method, the grid width is dynamically adjusted according to the vehicle speed, which not only takes into account the refined perception of environmental details in low-speed scenarios but also meets the need for efficient analysis of long-distance vision at high speeds. This significantly improves the spatiotemporal adaptability of visibility data collection, and the grid-based driving prompt information provides a more reliable basis for the driver's traffic decision-making. Furthermore, for any two adjacent first and second grids, when the vehicle travels from the range of the first grid to the range of the second grid, it is determined whether the visibility type of the first grid is the same as the visibility type of the second grid. If different, a prompt message indicating the visibility change is generated, allowing the driver to promptly perceive the sudden change in visibility and gain valuable reaction time to adjust driving strategy. For example, when a vehicle drives from a visibility level 0 grid into a visibility level 2 grid, the driver can slow down and turn on the fog lights in advance based on the prompts to avoid misjudgment and emergency braking caused by a sudden drop in visibility, effectively improving driving safety.

[0215] Next, combine Figure 4 , the process of training the adaptive neural network model to determine the preset adaptive neural network model is described in detail.

[0216] Figure 4 A flow chart of a method for determining a preset adaptive neural network provided in an embodiment of the present application is shown as follows: Figure 4 As shown, the method includes:

[0217] S401 : Determine multiple groups of samples, each group of samples including sample weather information, sample driving information, a first sample threshold, and a sample result.

[0218] Among them, the sample results are of real visibility type.

[0219] Optionally, multiple groups of samples are determined, including:

[0220] Determine the original multiple groups of samples;

[0221] For the missing data in the original multi-group samples, interpolation and mean filling methods are used to process them and obtain complete multi-group samples;

[0222] The complete multiple groups of samples are processed by the inverse distance weighted interpolation method to obtain multiple groups of samples.

[0223] Optionally, a correlation analysis method and / or a feature importance ranking method of xgboost can be used to determine multiple sub-information with a strong correlation with the first threshold value of the sample from the sample meteorological information and the sample driving information, and then determine the final multiple groups of samples.

[0224] S402. Obtain multiple sets of training results for this iteration based on multiple sets of sample weather information, sample driving information, and sample first thresholds through an adaptive neural network model.

[0225] Among them, the training result is the predicted visibility type.

[0226] S403. Based on the multiple sets of training results of this iteration, the loss function is repeatedly iterated until a preset number of iterations is reached to determine the final multiple sets of training results.

[0227] In one implementation, taking the iteration of the weather-threshold weight as an example, the update of the weather-threshold weight in the (t+1)th iteration is described as follows:

[0228] The update of the weather-threshold weight satisfies the following formula 11:

[0229]

[0230] in, represents the kth meteorological-threshold weight in the i-th first sub-weight obtained in the (t+1)-th iteration, represents the kth meteorological-threshold weight in the i-th first sub-weight obtained at the t-th iteration, η (t+1) represents the learning rate after the (t+1)th adjustment, represents the partial derivative of the loss function with respect to the kth meteorological-threshold weight in the i-th first sub-weight.

[0231] The learning rate is adjusted according to the adaptive learning rate strategy and can be adjusted based on the convergence of the adaptive learning model. The initial learning rate is determined based on experience.

[0232] The loss function is, for example, a cross entropy loss function.

[0233] S404: Determine whether the adaptive neural network model meets preset conditions based on the final multiple sets of training results and multiple sets of sample results.

[0234] If so, execute S406; otherwise, execute S405.

[0235] The following describes the process of determining whether an adaptive neural network model meets preset conditions based on multiple groups of final training results and multiple groups of sample results through Examples 1A, 1B, and 1C.

[0236] Example 1A, determining whether the adaptive neural network model meets preset conditions based on the final multiple sets of training results and multiple sets of sample results, includes:

[0237] Based on the final multiple sets of training results and multiple sets of sample results, the threat score (TS) corresponding to each visibility type is obtained;

[0238] According to the threat score corresponding to each visibility type, it is determined whether the adaptive neural network model meets the preset conditions.

[0239] Optionally, based on the final multiple sets of training results and multiple sets of sample results, a threat score corresponding to each visibility type is obtained, including:

[0240] The threat score corresponding to a visibility type;

[0241] Determine the number of hits, missed alerts, and false alarms for the visibility type based on the final multiple sets of training results and multiple sets of sample results;

[0242] The threat score corresponding to the visibility type is determined based on the number of hits, missed alerts, and false alarms for the visibility type.

[0243] The visibility type hit can be understood, for example, as follows: if the sample result of the group is level 4 and the final training result of the group is also level 4, then a level 4 hit is determined. The visibility type miss can be understood, for example, as follows: if the sample result of the group is level 4 and the final training result of the group is not level 4, then a level 4 miss is determined. The visibility type miss false alarm can be understood, for example, as follows: if the sample result of the group is not level 4 and the final training result of the group is level 4, then a level 4 false alarm is determined.

[0244] Optionally, the number of hits, the number of missed alarms, the number of false alarms for the visibility type, and the threat score corresponding to the visibility type satisfy the following formula 12:

[0245]

[0246] Where TS represents the threat score corresponding to the visibility type, H represents the number of hits for the visibility type, M represents the number of missed reports for the visibility type, and F represents the number of false alarms for the visibility type.

[0247] The following describes the execution process of determining whether the adaptive neural network model meets preset conditions based on the threat scores corresponding to each visibility type through Examples 2A and 2B.

[0248] Example 2A, determining whether the adaptive neural network model meets preset conditions based on the threat scores corresponding to each visibility type, includes:

[0249] Determine the arithmetic mean of the threat scores corresponding to all visibility types to obtain the mean threat score;

[0250] When the threat score mean is less than or equal to a first preset value, determining that the adaptive neural network model does not meet the preset condition;

[0251] When the threat score mean is greater than a first preset value, it is determined that the adaptive neural network model meets the preset condition.

[0252] When determining whether the adaptive neural network model meets a preset condition based on the threat score corresponding to each visibility type, the first preset value is, for example, 60%.

[0253] Example 2B, determining whether the adaptive neural network model meets preset conditions based on the threat scores corresponding to each visibility type, includes:

[0254] determining, based on the threat score corresponding to each visibility type and the second preset value of each visibility type, whether the threat score corresponding to each visibility type meets a preset condition;

[0255] When the threat scores corresponding to the various visibility types meet the preset conditions, determining that the adaptive neural network model meets the preset conditions;

[0256] When any one or more of the threat scores corresponding to the visibility types do not meet the preset conditions, it is determined that the adaptive neural network model does not meet the preset conditions.

[0257] Optionally, the smaller the visibility range value corresponding to the visibility type, the larger the second preset value of the visibility type.

[0258] Determining whether the threat score corresponding to each visibility type meets a preset condition based on the threat score corresponding to each visibility type and the second preset value of each visibility type includes:

[0259] For a visibility type;

[0260] When the threat score corresponding to the visibility type is less than or equal to a second preset value of the visibility type, determining that the threat score corresponding to the visibility type does not meet the preset condition;

[0261] When the threat score corresponding to the visibility type is greater than the second preset value of the visibility type, the threat score corresponding to the visibility type meets the preset condition.

[0262] Example 1B, determining whether the adaptive neural network model meets preset conditions based on the final multiple sets of training results and multiple sets of sample results, includes:

[0263] Based on the final multiple sets of training results and multiple sets of sample results, the F1 score (F1Score) corresponding to each visibility type is obtained;

[0264] According to the F1 scores corresponding to each visibility type, it is determined whether the adaptive neural network model meets the preset conditions.

[0265] Optionally, based on the final multiple sets of training results and multiple sets of sample results, the F1 score corresponding to each visibility type is obtained, including:

[0266] The F1 score corresponding to a visibility type;

[0267] Determine the number of hits, missed alerts, and false alarms for the visibility type based on the final multiple sets of training results and multiple sets of sample results;

[0268] Determine the accuracy rate corresponding to the visibility type based on the number of hits and the number of false alarms for the visibility type;

[0269] Determine the recall rate corresponding to the visibility type based on the number of hits and missed reports for the visibility type;

[0270] An F1 score corresponding to the visibility type is determined according to the precision corresponding to the visibility type and the recall corresponding to the visibility type.

[0271] Optionally, the number of hits for the visibility type, the number of false alarms, and the accuracy rate corresponding to the visibility type satisfy the following formula 13:

[0272]

[0273] Among them, Precision represents the accuracy corresponding to the visibility type.

[0274] Optionally, the number of hits, the number of missed hits, and the recall rate corresponding to the visibility type satisfy the following formula 14:

[0275]

[0276] Among them, Recall represents the recall rate corresponding to the visibility type.

[0277] Optionally, the precision corresponding to the visibility type, the recall corresponding to the visibility type, and the F1 score corresponding to the visibility type satisfy the following formula 15:

[0278]

[0279] Wherein, F1 represents the F1 score corresponding to the visibility type.

[0280] It should be noted that the execution process of determining whether the adaptive neural network model meets the preset conditions based on the F1 score corresponding to each visibility type is similar to the execution process of determining whether the adaptive neural network model meets the preset conditions based on the threat score corresponding to each visibility type, and will not be repeated here.

[0281] When determining whether the adaptive neural network model meets the preset conditions according to the threat scores corresponding to each visibility type, the first preset value is, for example, 50%.

[0282] Example 1C, determining whether the adaptive neural network model meets preset conditions based on the final multiple sets of training results and multiple sets of sample results, includes:

[0283] According to the final multiple training results and multiple sample results, the receiver operating characteristic curve (ROC) is obtained;

[0284] According to the ROC curve, determine the area under the ROC curve (AUC);

[0285] When the area under the curve is greater than a third preset value, determining that the adaptive neural network model meets the preset condition;

[0286] When the area under the curve is less than or equal to the third preset value, it is determined that the adaptive neural network model does not meet the preset condition.

[0287] Optionally, based on the final multiple sets of training results and multiple sets of sample results, an ROC curve is obtained, including:

[0288] For a visibility type; based on the final multiple sets of training results and multiple sets of sample results, determine the number of hits, the number of missed alerts, the number of false alerts, and the number of hits that are not of the visibility type for the visibility type; based on the number of hits and the number of missed alerts for the visibility type, determine the true positive rate (TPR) corresponding to the visibility type; based on the number of false alerts and the number of hits that are not of the visibility type for the visibility type, determine the false positive rate (FPR) corresponding to the visibility type; based on the true positive rate and the false positive rate corresponding to the visibility type, determine the curve point corresponding to the visibility type;

[0289] Draw the ROC curve according to the curve points corresponding to each visibility type.

[0290] For example, the true positive rate corresponding to the visibility type is 0.83, the false positive rate corresponding to the visibility type is 0.21, and the curve point corresponding to the visibility type is (0.21, 0.83).

[0291] The third preset value is, for example, 0.8.

[0292] In one implementation, Example 1B and Example 1C may be combined to determine whether the adaptive neural network model meets preset conditions, specifically:

[0293] If it is determined according to both Example 1B and Example 1C that the adaptive neural network model satisfies the preset conditions, then it is finally determined that the adaptive neural network model satisfies the preset conditions;

[0294] Otherwise, it is finally determined that the adaptive neural network model does not meet the preset conditions;

[0295] S405: After modifying the parameter values ​​of the adaptive neural network model, execute S402.

[0296] Optionally, the parameter values ​​of the adaptive neural network model include but are not limited to the number of attention heads, hidden layer dimension, initial learning rate, attenuation factor, and dynamic weight coefficient.

[0297] S406: Determine the current adaptive neural network model as a preset adaptive neural network model.

[0298] Furthermore, the adaptive neural network model is trained to determine the preset adaptive neural network model and a cross-validation method is used for verification.

[0299] The cross-validation method is, for example, a K-fold cross-validation method or a time series cross-validation method.

[0300] Beneficial effects of this embodiment: In related technologies, adaptive neural network models are trained based on Root Mean Square Error (RMSE) and Bias. As a metric based on squared error, RMSE has a strong amplification effect on errors with larger values. When processing visibility data with a large span, it will generate high-weight error feedback due to small fluctuations in high-visibility scenarios, causing model training to be overly biased towards high-visibility data, while weakening the learning of key data features in low visibility. Bias only focuses on the average difference direction between the forecast value and the actual value, and can neither quantify the severity of the error in a single prediction nor distinguish the degree of error impact under different visibility conditions. Especially in scenarios such as low visibility that have a decisive impact on traffic safety, it is very easy to misevaluate model performance due to ignoring the error magnitude, resulting in the model being difficult to accurately respond to complex meteorological conditions in actual applications. In this embodiment, an adaptive neural network model is trained based on the threat score, FI score, and / or ROC curve, effectively avoiding the limitations of the evaluation indicators (root mean square error and bias) in related technologies. The threat score, FI score, and ROC curve can accurately measure the forecast effectiveness of the preset adaptive neural network model under different visibility types, especially in scenarios such as low visibility that are critical to safety decision-making. It can intuitively reflect the ability of the preset adaptive neural network model to capture key meteorological changes, thereby improving the accuracy of visibility classification.

[0301] Figure 5 This is a schematic diagram of a visibility processing device provided in an embodiment of the present application. Figure 5 As shown, the visibility processing device 50 includes an acquisition module 501 , a determination module 502 and a processing module 503 .

[0302] An acquisition module 501 is configured to acquire weather information, vehicle driving information, and a first threshold corresponding to visibility;

[0303] a determination module 502 configured to determine a first weight corresponding to the weather information and a second weight corresponding to the driving information, wherein the first weight indicates the degree of influence of the weather information on the visibility and the second weight indicates the degree of influence of the driving information on the visibility;

[0304] The determining module 502 is further configured to determine a first parameter based on the weather information, the driving information, the first weight, and the second weight;

[0305] The processing module 503 is configured to determine a second threshold corresponding to the visibility based on the first threshold and the first parameter, and classify the visibility based on the second threshold.

[0306] The visibility processing device 50 provided in the embodiment of the present application can execute the technical solution shown in the above method embodiment. Its implementation principle and beneficial effects are similar and will not be repeated here.

[0307] In one implementation, the determining module 502 is specifically configured to:

[0308] Determine the first sub-weight corresponding to each sub-meteorological information in the first weight;

[0309] In the second weight, determining a second sub-weight corresponding to each sub-driving information;

[0310] The first parameter is determined based on the plurality of weather information sub-items, the plurality of first sub-weights, the plurality of driving information sub-items, and the plurality of second sub-weights.

[0311] In one implementation, the determining module 502 is specifically configured to:

[0312] determining a product of the sub-meteorological information and a first sub-weight corresponding to the sub-meteorological information to obtain a plurality of second parameters;

[0313] determining a product of the sub-driving information and a second sub-weight corresponding to the sub-driving information to obtain a plurality of third parameters;

[0314] The first parameter is determined based on the plurality of second parameters and the plurality of third parameters.

[0315] In one implementation, the processing module 503 is specifically configured to:

[0316] Obtaining a current vehicle speed from the driving information and a current humidity from the weather information;

[0317] The second threshold is determined based on the current vehicle speed, the current humidity, the first threshold, and the first parameter.

[0318] In one implementation, the processing module 503 is specifically configured to:

[0319] determining a third threshold based on the current vehicle speed and the current humidity;

[0320] determining a fourth threshold based on the first threshold and the first parameter;

[0321] A second threshold corresponding to the visibility is determined based on the third threshold and the fourth threshold.

[0322] In one implementation, the processing module 503 is specifically configured to:

[0323] Based on the current vehicle speed, the current environment is divided into grids to obtain a plurality of grids within the vehicle's driving range;

[0324] Obtaining the visibility corresponding to each grid;

[0325] The visibility of each grid is classified based on the second threshold and the visibility corresponding to each grid.

[0326] In one implementation, for any two adjacent first grids and second grids, the processing module 503 is further configured to:

[0327] When the vehicle travels from the range of the first grid to the range of the second grid, determining whether the visibility type of the first grid is the same as the visibility type of the second grid;

[0328] If they are different, a prompt message indicating the visibility change is generated.

[0329] The visibility processing device 50 provided in the embodiment of the present application can execute the technical solution shown in the above method embodiment. Its implementation principle and beneficial effects are similar and will not be repeated here.

[0330] Figure 6 This is a block diagram of an electronic device provided in an embodiment of the present application. As shown in the figure, the electronic device 60 includes a processor 601 and a memory 602. The processor 601 is in communication with the memory 602, which is used to store computer-executable instructions. The processor 601 is configured to execute the technical solution of any of the aforementioned method embodiments by executing the computer-executable instructions stored in the memory 602.

[0331] Optionally, the memory 602 may be independent or integrated with the processor 601. Optionally, when the memory 602 is a device independent of the processor 601, the electronic device 60 may further include a bus 603 for connecting the above devices.

[0332] The electronic device is used to execute the technical solution in any of the aforementioned method embodiments, and its implementation principles and technical effects are similar and will not be repeated here.

[0333] An embodiment of the present application also provides a computer-readable storage medium, in which computer-executable instructions are stored. When the computer-executable instructions are executed by a processor, they are used to implement the technical solution provided by any of the aforementioned method embodiments.

[0334] An embodiment of the present application also provides a computer program product, including a computer program, which is used to implement the technical solution provided by the aforementioned method embodiment when executed by a processor.

[0335] It should be noted that for the aforementioned method embodiments, for the sake of simplicity, they are all expressed as a series of action combinations, but those skilled in the art should be aware that this application is not limited by the order of the actions described, because according to this application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in this specification are all optional embodiments, and the actions and modules involved are not necessarily required by this application.

[0336] It should be further noted that, although the various steps in the flowchart are shown in sequence as indicated by the arrows, these steps are not necessarily performed in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and these steps may be performed in other orders. Moreover, at least a portion of the steps in the flowchart may include multiple sub-steps or multiple stages, and these sub-steps or stages are not necessarily performed at the same time, but may be performed at different times. The execution order of these sub-steps or stages is not necessarily to be performed in sequence, but may be performed in turn or alternately with other steps or at least a portion of the sub-steps or stages of other steps.

[0337] It should be understood that the above-described device embodiments are merely illustrative, and the device of the present application may also be implemented in other ways. For example, the division of units / modules in the above-described embodiments is merely a logical functional division, and actual implementations may employ other division methods. For example, multiple units, modules, or components may be combined or integrated into another system, or some features may be omitted or not implemented.

[0338] In addition, unless otherwise specified, the functional units / modules in the various embodiments of the present application may be integrated into a single unit / module, each unit / module may exist physically separately, or two or more units / modules may be integrated together. The aforementioned integrated units / modules may be implemented in the form of hardware or software program modules.

[0339] If the integrated unit / module is implemented in hardware, the hardware may be digital circuits, analog circuits, etc. The physical implementation of the hardware structure includes, but is not limited to, transistors, memristors, etc. Unless otherwise specified, the processor may be any appropriate hardware processor, such as a CPU, GPU, FPGA, DSP, and ASIC. Unless otherwise specified, the storage unit may be any appropriate magnetic storage medium or magneto-optical storage medium, such as resistive random access memory (RRAM), dynamic random access memory (DRAM), static random access memory (SRAM), enhanced dynamic random access memory (EDRAM), high-bandwidth memory (HBM), hybrid memory cube (HMC), etc.

[0340] If the integrated unit / module is implemented in the form of a software program module and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, which is stored in a memory and includes a number of instructions for enabling a computer device (which can be a personal computer, server or network device, etc.) to execute all or part of the steps of the various embodiments of the present application. The aforementioned memory includes various media that can store program codes, such as a USB flash drive, a read-only memory (ROM), a random access memory (RAM), a mobile hard disk, a magnetic disk or an optical disk.

[0341] In the above embodiments, the description of each embodiment has its own focus. For parts not described in detail in a certain embodiment, please refer to the relevant description of other embodiments. The technical features of the above embodiments can be combined arbitrarily. In order to make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0342] Those skilled in the art will readily appreciate other embodiments of the present application after considering the specification and practicing the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of the present application that follow the general principles of the present application and include common knowledge or customary techniques in the art not disclosed herein. The description and examples are to be considered as exemplary only, and the true scope and spirit of the present application are indicated by the following claims.

[0343] It should be understood that the present application is not limited to the exact structure described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present application is limited only by the appended claims.

Claims

1. A visibility processing method, characterized in that: include: Obtaining weather information, vehicle driving information, and a first threshold corresponding to visibility; determining a first weight corresponding to the weather information and a second weight corresponding to the driving information, the first weight being used to indicate the degree of influence of the weather information on the visibility, and the second weight being used to indicate the degree of influence of the driving information on the visibility; determining a first parameter based on the weather information, the driving information, the first weight, and the second weight; Based on the first threshold and the first parameter, a second threshold corresponding to the visibility is determined, and the visibility is classified based on the second threshold.

2. The method according to claim 1, characterized in that The weather information includes a plurality of weather information sub-items, and the driving information includes a plurality of driving information sub-items; and determining a first parameter based on the weather information, the driving information, the first weight, and the second weight includes: Determine the first sub-weight corresponding to each sub-meteorological information in the first weight; In the second weight, determining a second sub-weight corresponding to each sub-driving information; The first parameter is determined based on the plurality of weather information sub-items, the plurality of first sub-weights, the plurality of driving information sub-items, and the plurality of second sub-weights.

3. The method according to claim 2, characterized in that Determining the first parameter based on the plurality of weather information sub-items, the plurality of first sub-weights, the plurality of driving information sub-items, and the plurality of second sub-weights includes: determining a product of the sub-meteorological information and a first sub-weight corresponding to the sub-meteorological information to obtain a plurality of second parameters; determining a product of the sub-driving information and a second sub-weight corresponding to the sub-driving information to obtain a plurality of third parameters; The first parameter is determined based on the plurality of second parameters and the plurality of third parameters.

4. The method according to any one of claims 1 to 3, characterized in that Determining a second threshold corresponding to the visibility based on the first threshold and the first parameter includes: Obtaining a current vehicle speed from the driving information and a current humidity from the weather information; The second threshold is determined based on the current vehicle speed, the current humidity, the first threshold, and the first parameter.

5. The method according to claim 4, characterized in that Determining the second threshold based on the current vehicle speed, the current humidity, the first threshold, and the first parameter includes: determining a third threshold based on the current vehicle speed and the current humidity; determining a fourth threshold based on the first threshold and the first parameter; A second threshold corresponding to the visibility is determined based on the third threshold and the fourth threshold.

6. The method according to any one of claims 1 to 3, characterized in that Classifying the visibility based on the second threshold includes: Based on the current vehicle speed, the current environment is divided into grids to obtain multiple grids within the vehicle's driving range; Get the visibility corresponding to each grid; The visibility of each grid is classified based on the second threshold and the visibility corresponding to each grid.

7. The method according to claim 6, characterized in that For any two adjacent first and second grids, after classifying the visibility of the grids, the method further includes: When the vehicle travels from the range of the first grid to the range of the second grid, determining whether the visibility type of the first grid is the same as the visibility type of the second grid; If they are different, a prompt message indicating the visibility change is generated.

8. A visibility processing device, characterized in that: include: an acquisition module, configured to acquire weather information, vehicle driving information, and a first threshold corresponding to visibility; a determination module, configured to determine a first weight corresponding to the weather information and a second weight corresponding to the driving information, wherein the first weight is used to indicate the degree of influence of the weather information on the visibility, and the second weight is used to indicate the degree of influence of the driving information on the visibility; The determining module is further configured to determine a first parameter based on the weather information, the driving information, the first weight, and the second weight; A processing module is configured to determine a second threshold corresponding to the visibility based on the first threshold and the first parameter, and classify the visibility based on the second threshold.

9. An electronic device, characterized in that: include: Memory, processor; The memory stores computer-executable instructions; The processor executes the computer-executable instructions stored in the memory, so that the processor performs the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer-executable instructions, which are used to implement the method according to any one of claims 1 to 7 when executed by a processor.

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