Driving traffic indication standard display method and system in severe weather

Abstract

The invention discloses a driving traffic indication standard display method and system in severe weather, and relates to the technical field of vehicle-mounted information display, and the method comprises the steps: collecting road environment parameters, carrying out the data preprocessing of the road environment parameters, and generating a standardized road environment parameter matrix; inputting the standardized road environment parameter matrix into a preset deep learning model, and mapping to generate weather danger level parameters; and calling a display parameter optimization algorithm according to the weather danger level parameter, calculating a display control parameter of the traffic indication sign, and dynamically adjusting a display mode on the driving instrument based on the display control parameter. According to the invention, the timeliness and reliability of traffic safety early warning can be effectively improved, real-time adaptation to information display in a complex traffic environment is realized, the attention of a driver and the driving safety are improved, the environmental perception precision and the display response speed are improved, and the driving safety guarantee is effectively enhanced.

Description

Technical Field

[0001] The present invention relates to the technical field of vehicle information display, and particularly to a driving traffic indication specification display method and system under bad weather conditions. Background Art

[0002] In recent years, with the rapid development of sensor technology, data processing technology and artificial intelligence algorithms, intelligent transportation systems have received extensive attention in the fields of vehicle safety and driving information display. However, existing technologies mainly rely on traditional rules or simple models to process and display and adjust road environment data, and there are problems such as insufficient response to complex and changeable environments, low data fusion accuracy, and poor adaptability of display modes.

[0003] Existing display control schemes usually adopt fixed parameter adjustment strategies, which are difficult to meet the display requirements under special environments such as bad weather, resulting in untimely and intuitive information prompts on the driving recorder at critical moments. Summary of the Invention

[0004] In view of the problems existing in the existing driving traffic indication specification display method under bad weather conditions, the present invention is proposed. Therefore, the problem to be solved by the present invention is how to provide a driving traffic indication specification display method and system under bad weather conditions.

[0005] To solve the above technical problems, the present invention provides the following technical solutions:

[0006] In a first aspect, the present invention provides a driving traffic indication specification display method under bad weather conditions, which includes collecting road environment parameters, performing data preprocessing on the road environment parameters to generate a standardized road environment parameter matrix;

[0007] Inputting the standardized road environment parameter matrix into a preset deep learning model to map and generate weather hazard level parameters;

[0008] Calling a display parameter optimization algorithm according to the weather hazard level parameters, calculating display control parameters of traffic indication signs, and dynamically adjusting the display mode on the driving recorder based on the display control parameters.

[0009] As a preferred solution of the driving traffic indication specification display method under bad weather conditions of the present invention, wherein: the road environment parameters include precipitation parameters, wind speed parameters, visibility parameters, road surface friction coefficient parameters, and ambient illuminance parameters.

[0010] As a preferred solution of the driving traffic indication specification display method under bad weather conditions of the present invention, wherein: the data preprocessing includes:

[0011] Perform outlier detection on the road environment parameter data to be processed, calculate the mean and standard deviation, and obtain the standardized value to determine whether the obtained road environment parameter data is an outlier. The formula is:

[0012]

[0013] Among them, X is the road environment parameter data, μ is the mean of the road environment parameter data, σ is the standard deviation of the road environment parameter data. When |Z|>3, it is determined that the corresponding road environment parameter data is an outlier and is excluded;

[0014] Based on the road environment parameter data after excluding outliers, use the moving average method to smooth the road environment parameter data, and perform moving average processing on the road environment parameter data at each moment. The formula is:

[0015]

[0016] Among them, n is the moving window size, where X i ' represents the data after smoothing at the i-th position, and X i-j represents the current and the previous n - 1 data;

[0017] After completing the smoothing process, perform normalization processing on the smoothed road environment parameter data. The formula is:

[0018]

[0019] In the formula, X” is the road environment parameter data after normalization processing, and X min ' and X max ' are the minimum and maximum values after data smoothing respectively.

[0020] As a preferred solution of the driving traffic indication specification display method under bad weather according to the present invention, wherein: the deep learning model includes a feature extraction module and a detection module;

[0021] The feature extraction module includes a convolutional component one, a residual structure one, a residual structure two, a residual structure three, a residual structure four, a residual structure five, a double - layer residual structure six, a residual structure seven, and a global average pooling layer arranged in sequence; the convolutional component one is connected to the residual structure one, and the residual structure one is sequentially connected to the residual structure five; the residual structure five is connected to the double - layer residual structure six, the double - layer residual structure six is connected to the residual structure seven, and the residual structure seven is connected to the global average pooling layer;

[0022] Among them, the first convolutional component uses 64 convolutional kernels of size 5×5, with a stride of 2, and uses ReLU as the activation function; the convolutional layers in the first residual structure and the second residual structure use 64 convolutional kernels of 3×3, with a stride of 1; the convolutional layers in the third residual structure and the fourth residual structure use 128 convolutional kernels of 3×3, with a stride of 1; the convolutional layers in the fifth residual structure and the sixth double-layer residual structure use 256 convolutional kernels of 3×3, with a stride of 1; the seventh residual structure uses 256 convolutional kernels of 3×3, with a stride of 1, and all residual structures use ReLU as the activation function;

[0023] The detection module includes a first fully-connected layer, a second fully-connected layer, and an output layer; the first fully-connected layer has 128 nodes, and the activation function is ReLU; the second fully-connected layer has 64 nodes, and the activation function is ReLU; the output layer has 1 node, and after the output is processed by the sigmoid activation function, a one-dimensional weather danger level parameter is generated.

[0024] As a preferred solution of the driving traffic indication specification display method under bad weather according to the present invention, wherein: the display parameter optimization algorithm includes the following:

[0025] Obtain the environmental illuminance parameter, visibility parameter, vehicle speed parameter, and weather danger level parameter. At the same time, preset the reference display brightness, reference font size, and reference blinking frequency, as well as various weight coefficients, adjustment coefficients, correction coefficients, and dynamic weight coefficients, and calculate the display brightness parameter, font size parameter, and blinking frequency parameter;

[0026] When the calculated display brightness parameter exceeds the defined range, calculate the display brightness adjustment value of the indication sign, perform compensatory correction on the brightness parameter, and correct the blinking frequency parameter to determine the corrected blinking frequency parameter;

[0027] Combine the calculated font size parameter with the corrected display brightness parameter and blinking frequency parameter to form the display control parameter of the traffic indication sign.

[0028] As a preferred solution of the driving traffic indication specification display method under bad weather according to the present invention, wherein: the calculation formulas for the display brightness parameter, font size parameter, and blinking frequency parameter are:

[0029] L = L0×(α1×E -1 +α2×V -1 +α3)

[0030] S = S0×(β1×v + β2×V -1 +β3)

[0031] F = F0×(γ1×D + γ2)

[0032] Among them, L is the display brightness parameter, L0 is the reference display brightness parameter, E is the ambient light intensity parameter, V is the visibility parameter, and α1, α2, and α3 are the weight coefficients; S is the font size parameter, S0 is the reference font size parameter, v is the vehicle speed parameter, and β1, β2, and β3 are the adjustment coefficients; F is the flicker frequency parameter, F0 is the reference flicker frequency parameter, D is the weather danger level parameter, and γ1 and γ2 are the correction coefficients.

[0033] In a second aspect, the present invention provides a driving traffic indication specification display system under bad weather, which includes: an environmental parameter acquisition module for acquiring road environmental parameters and sending them to the central processing module through the CAN bus;

[0034] A central processing module for processing the road environmental parameters and calculating the display control parameters;

[0035] A display control module for receiving the display control parameters and adjusting the display state of the traffic indication signs;

[0036] A communication module for data transmission between functional modules.

[0037] In a third aspect, the present invention provides a computer device, including a memory and a processor, where the memory stores a computer program, and: when the processor executes the computer program, the steps of the driving traffic indication specification display method under bad weather are implemented.

[0038] In a fourth aspect, the present invention provides a computer-readable storage medium, on which a computer program is stored, and: when the computer program is executed by the processor, the steps of the driving traffic indication specification display method under bad weather are implemented.

[0039] The beneficial effects of the present invention are that it can effectively improve the timeliness and reliability of traffic safety warnings, realize real-time adaptation to information display in complex traffic environments, improve the driver's attention and driving safety, improve the environmental perception accuracy and display response speed, and effectively enhance driving safety protection. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0041] Figure 1 It is a flowchart of the driving traffic indication specification display method under bad weather.

[0042] Figure 2Structural diagram of a driving traffic indication specification display system in bad weather. Detailed implementation manners

[0043] To make the above objects, features and advantages of the present invention more comprehensible, the following detailed description of the specific implementation manners of the present invention is provided in conjunction with the accompanying drawings of the specification. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0044] In the following description, many specific details are set forth to facilitate a thorough understanding of the present invention. However, the present invention may be implemented in other ways different from those described herein. Those skilled in the art can make similar extensions without departing from the connotation of the present invention. Therefore, the present invention is not limited by the specific embodiments disclosed below.

[0045] Secondly, the so-called "one embodiment" or "embodiment" herein refers to a specific feature, structure or characteristic that may be included in at least one implementation manner of the present invention. The "in one embodiment" appearing in different places in this specification does not all refer to the same embodiment, nor is it an independent or alternative embodiment that excludes other embodiments.

[0046] Embodiment 1

[0047] Referring to Figure 1 , this is the first embodiment of the present invention. This embodiment provides a driving traffic indication specification display method in bad weather, including:

[0048] S1: Collect road environment parameters, perform data preprocessing on the road environment parameters, and generate a standardized road environment parameter matrix;

[0049] Specifically, the road environment parameters include precipitation parameters, wind speed parameters, temperature parameters, humidity parameters, visibility parameters, road surface friction coefficient parameters, and ambient illuminance parameters; the optical sensor unit uses a laser scattering visibility meter to measure the visibility parameters and an illuminance sensor to measure the ambient illuminance parameters; the meteorological sensor unit uses a tipping bucket rain gauge to measure the precipitation parameters and an ultrasonic anemometer to measure the wind speed parameters; the road surface state sensor unit uses a friction coefficient detector to measure the road surface friction coefficient parameters, and dynamically adjusts the sampling period of the sensor unit.

[0050] The data preprocessing includes outlier removal, data smoothing, and data standardization; perform outlier detection on the to-be-processed road environment parameter data, calculate the mean and standard deviation of the road environment parameter data, and obtain the standardized value according to the following formula. The formula is:

[0051]

[0052] Among them, X is the road environment parameter data, μ is the mean value of the road environment parameter data, σ is the standard deviation of the road environment parameter data. When |Z| > 3, it is determined that the corresponding road environment parameter data is an outlier and is excluded;

[0053] Based on the road environment parameter data after excluding outliers, the sliding average method is used to smooth the road environment parameter data. Specifically, the size of the sliding window is set to n, and the sliding average processing is performed on the road environment parameter data at each moment according to the following formula:

[0054]

[0055] Among them, n is the size of the sliding window, where, X i ' represents the data after smoothing at the i-th position, X i-j represents the current and the previous n - 1 data points.

[0056] After completing the smoothing process, the normalized processing is performed on the smoothed road environment parameter data, and the formula is:

[0057]

[0058] In the formula, X” is the road environment parameter data after normalization processing, X min ' and X max ' are the minimum and maximum values respectively after data smoothing.

[0059] After completing the outlier detection, smoothing process, and normalization process of the road environment parameter data, the normalized road environment parameter data is obtained. One set of normalized road environment parameter data corresponding to each moment is used as a row of the standardized road environment parameter matrix. By arranging the data at multiple moments row by row, the construction of the standardized road environment parameter matrix is completed.

[0060] By collecting road environment parameters, it provides an objective and dynamic road environment basis for the entire system. It realizes the improvement of data quality and the guarantee of feature consistency, provides reliable data input for risk mapping, and improves the accuracy and robustness of weather hazard level prediction.

[0061] S2: Input the standardized road environment parameter matrix into a preset deep learning model to map and generate weather hazard level parameters;

[0062] Specifically, the deep learning model includes a feature extraction module and a detection module; the feature extraction module includes a convolution component 1, a residual structure 1, a residual structure 2, a residual structure 3, a residual structure 4, a residual structure 5, a double-layer residual structure 6, a residual structure 7, and a global average pooling layer arranged in sequence; the convolution component 1 is connected to the residual structure 1, and the residual structure 1 is sequentially connected to the residual structure 5; the residual structure 5 is connected to the double-layer residual structure 6, the double-layer residual structure 6 is connected to the residual structure 7, the residual structure 7 is connected to the global average pooling layer, and the global average pooling layer is used to compress the feature dimension into a vector of a fixed length as the input of the detection module;

[0063] Among them, the convolution component 1 uses 64 convolution kernels of size 5×5, with a stride of 2, and uses ReLU as the activation function; the convolution layers in the residual structure 1 and the residual structure 2 use 64 convolution kernels of 3×3, with a stride of 1; the convolution layers in the residual structure 3 and the residual structure 4 use 128 convolution kernels of 3×3, with a stride of 1; the convolution layers in the residual structure 5 and the double-layer residual structure 6 use 256 convolution kernels of 3×3, with a stride of 1; the residual structure 7 uses 256 convolution kernels of 3×3, with a stride of 1, and all residual structures use ReLU as the activation function;

[0064] The detection module includes a fully connected layer 1, a fully connected layer 2, and an output layer; the number of nodes in the fully connected layer 1 is 128, and the activation function is ReLU; the number of nodes in the fully connected layer 2 is 64, and the activation function is ReLU; the number of nodes in the output layer is 1, and after the output is processed by the sigmoid activation function, a one-dimensional weather danger level parameter is generated.

[0065] The present invention can comprehensively consider multi-dimensional data, realize the accurate quantification of the impact of bad weather, not only make the weather risk assessment more accurate, but also provide a quantitative basis for the dynamic optimization of display parameters, thereby effectively improving the timeliness and reliability of traffic safety warning.

[0066] S3: Call the display parameter optimization algorithm according to the weather danger level parameter, calculate the display control parameter of the traffic indication sign, and dynamically adjust the display mode on the driving recorder based on the display control parameter.

[0067] Specifically, the display control parameters include a display brightness parameter, a font size parameter, and a flashing frequency parameter.

[0068] Calculate the display brightness parameter based on the ambient illuminance parameter and the visibility parameter, calculate the font size parameter based on the vehicle speed parameter and the visibility parameter, and calculate the flashing frequency parameter based on the weather danger level parameter.

[0069] When the display brightness parameter exceeds the defined range, calculate the display brightness adjustment value of the indication sign according to the current visibility parameter, and the formula is:

[0070]

[0071] where L' is the display brightness adjustment value of the indication mark, and V max is the preset maximum visibility threshold;

[0072] Determine the corrected flicker frequency parameter, and the formula is:

[0073]

[0074] R = α × V -1 + β × ln(f + 1) + γ × W p

[0075] where F’ is the corrected flicker frequency parameter, k is the proportionality coefficient, M v is the density of surrounding vehicles, R is the vehicle driving risk coefficient, M v_max is the preset maximum vehicle density, R max is the preset maximum risk coefficient, R is the risk coefficient, f is the road surface friction coefficient parameter, W p is the precipitation parameter; α, β, and γ are dynamic weight coefficients, and the initial values are set according to historical accident data and are periodically adjusted through a feedback correction model.

[0076] Obtain the environmental illuminance parameter, visibility parameter, vehicle speed parameter, and weather hazard level parameter, and at the same time preset the reference display brightness, reference font size, and reference flicker frequency, as well as various weight coefficients, adjustment coefficients, correction coefficients, and dynamic weight coefficients (where the dynamic weight coefficients are set with initial values according to historical accident data and are periodically adjusted by feedback).

[0077] Based on the environmental illuminance and visibility parameters, the display brightness parameter is obtained through weighted operation; using the vehicle speed parameter and visibility parameter, the font size parameter is obtained through weighted calculation with a preset adjustment coefficient; and based on the weather hazard level parameter and the corresponding correction coefficient, the reference flicker frequency is adjusted to obtain the preliminary flicker frequency parameter, and the formula is:

[0078] L = L0 × (α1 × E -1 + α2 × V -1 + α3)

[0079] S = S0 × (β1 × v + β2 × V -1 + β3)

[0080] F = F0 × (γ1 × D + γ2)

[0081] Among them, L is the display brightness parameter, L0 is the reference display brightness parameter, E is the ambient light intensity parameter, V is the visibility parameter, α1, α2, and α3 are weight coefficients; S is the font size parameter, S0 is the reference font size parameter, v is the vehicle speed parameter, β1, β2, and β3 are adjustment coefficients; F is the flicker frequency parameter, F0 is the reference flicker frequency parameter, D is the weather danger level parameter, and γ1, γ2 are correction coefficients;

[0082] When the initially calculated display brightness parameter exceeds the defined range, the display brightness adjustment value of the indication sign is calculated through the current visibility parameter and the preset maximum visibility threshold, and the brightness parameter is compensatorily corrected. The formula is:

[0083]

[0084] Among them, L' is the display brightness adjustment value of the indication sign, and V max is the preset maximum visibility threshold;

[0085] The flicker frequency parameter is corrected by multi-factor weighting to determine the corrected flicker frequency parameter. The formula is:

[0086]

[0087] R = α × V -1 + β × ln(f + 1)+ γ × M p

[0088] Among them, F’ is the corrected flicker frequency parameter, k is the proportionality coefficient, and M v is the density of surrounding vehicles, R is the vehicle driving risk coefficient, and M v_max is the preset maximum vehicle density, and R max is the preset maximum risk coefficient, R is the risk coefficient, f is the road surface friction coefficient parameter, and W p is the precipitation parameter; α, β, and γ are dynamic weight coefficients, and the initial values are set according to historical accident data and are periodically adjusted through a feedback correction model.

[0089] The display brightness parameter, font size parameter, and the corrected flicker frequency parameter after the above calculations and corrections are combined to form the display control parameter of the traffic indication sign.

[0090] The brightness, font size, and flicker frequency of the indication sign are uniformly regulated. The driving recorder control unit periodically receives the display control parameter and dynamically adjusts the display mode accordingly, so that the traffic indication sign has an appropriate visual effect in different environments and driving states.

[0091] The driving recorder control module synthesizes various adjustment results and dynamically updates the entire display mode through real-time signal transmission with the hardware interface of the display screen. The updated display mode can synchronously reflect external environmental conditions, vehicle operating status, and weather risk information, enabling traffic indication signs to be in the best display state in terms of brightness, text size, and flashing rhythm, and continuously adjusting with changes in external factors.

[0092] Through the real-time feedback on the external environment and vehicle driving status, the display parameter optimization algorithm of the present invention can specifically adjust various parameters. Based on the obtained display control parameters, the display mode of traffic indication signs is dynamically adjusted on the driving recorder, so that the display content can achieve the best visual effect under different environmental conditions. The system not only realizes the real-time adaptation of information display in complex traffic environments but also highlights safety warning information at critical moments, thereby improving the driver's attention and driving safety.

[0093] Furthermore, this embodiment also provides a driving traffic indication specification display system in bad weather, including: an environmental parameter acquisition module, a central processing module, a display control module, and a communication module; the environmental parameter acquisition module acquires road environmental parameters and sends them to the central processing module through the CAN bus; the central processing module processes the road environmental parameters and calculates display control parameters; the display control module receives the display control parameters and adjusts the display status of traffic indication signs; the communication module is responsible for data transmission between each functional module; each module of the system is connected by the CAN bus, the communication rate of the CAN bus is 1 Mbps, and the processing delay of the system is less than 100 ms.

[0094] This embodiment also provides a computer device applicable to the situation of the driving traffic indication specification display method in bad weather, including: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement all or part of the steps of the method described in the embodiment of the present invention as proposed in the above embodiment.

[0095] This embodiment also provides a storage medium, on which a computer program is stored. When the computer program is executed by a processor, it executes the method in any optional implementation manner of the above embodiment. Among them, the storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (abbreviated as SRAM), electrically erasable programmable read-only memory (abbreviated as EEPROM), erasable programmable read-only memory (abbreviated as EPROM), programmable read-only memory (abbreviated as PROM), read-only memory (abbreviated as ROM), magnetic memory, flash memory, magnetic disk or optical disc.

[0096] The storage medium proposed in this embodiment and the data storage method proposed in the above embodiment belong to the same inventive concept. For the technical details not described in detail in this embodiment, reference can be made to the above embodiment, and this embodiment has the same beneficial effects as the above embodiment.

[0097] In summary, the present invention can effectively improve the timeliness and reliability of traffic safety warning, realize real-time adaptation to information display in a complex traffic environment, improve the driver's attention and driving safety, improve the environmental perception accuracy and display response speed, and effectively enhance driving safety guarantee.

[0098] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention, and they should all be covered by the scope of the claims of the present invention.

Claims

1. A method for displaying traffic instructions in bad weather, characterized in that: include, Collect road environment parameters, perform data preprocessing on the road environment parameters, and generate a standardized road environment parameter matrix; Inputting the standardized road environment parameter matrix into a preset deep learning model to map and generate weather hazard level parameters; The display parameter optimization algorithm is called according to the weather danger level parameters, the display control parameters of the traffic sign are calculated, and the display mode is dynamically adjusted on the driving instrument based on the display control parameters.

2. The method for displaying traffic instructions in bad weather as claimed in claim 1, characterized in that: The road environment parameters include precipitation parameters, wind speed parameters, visibility parameters, road surface friction coefficient parameters and ambient light illumination parameters.

3. The method for displaying traffic instructions in bad weather as claimed in claim 2, characterized in that: The data preprocessing includes: Perform outlier detection on the road environment parameter data to be processed, calculate the mean and standard deviation, and obtain the standardized value to determine whether the obtained road environment parameter data is an outlier. The formula is: Where X is the road environment parameter data, μ is the mean of the road environment parameter data, σ is the standard deviation of the road environment parameter data, and when |Z|>3, the corresponding road environment parameter data is determined to be an abnormal value and is removed; On the basis of the road environment parameter data after removing the outliers, the sliding average method is used to smooth the road environment parameter data, and the road environment parameter data at each moment is processed by sliding average. The formula is: Among them, n is the sliding window size, among them, X i ' represents the smoothed data at the i-th position, X i-j Indicates the current and previous n-1 data; After the smoothing process is completed, the smoothed road environment parameter data is normalized, and the formula is: In the formula, X” is the normalized road environment parameter data, X min ' and X max ' are the minimum and maximum values ​​after data smoothing.

4. The method for displaying traffic instructions in bad weather as claimed in claim 3, characterized in that: The deep learning model includes a feature extraction module and a detection module; The feature extraction module includes a convolution component 1, a residual structure 1, a residual structure 2, a residual structure 3, a residual structure 4, a residual structure 5, a double-layer residual structure 6, a residual structure 7 and a global average pooling layer, which are arranged in sequence; the convolution component 1 is connected to the residual structure 1, and the residual structure 1 is sequentially connected to the residual structure 5; the residual structure 5 is connected to the double-layer residual structure 6, the double-layer residual structure 6 is connected to the residual structure 7, and the residual structure 7 is connected to the global average pooling layer; Among them, convolution component 1 uses 64 convolution kernels of size 5×5, with a step size of 2, and uses ReLU as the activation function; the convolution layer in residual structure 1 and residual structure 2 uses 64 3×3 convolution kernels with a step size of 1; the convolution layer in residual structure 3 and residual structure 4 uses 128 3×3 convolution kernels with a step size of 1; the convolution layer in residual structure 5 and double-layer residual structure 6 uses 256 3×3 convolution kernels with a step size of 1; residual structure 7 uses 256 3×3 convolution kernels with a step size of 1, and all residual structures use ReLU as the activation function; The detection module includes a fully connected layer 1, a fully connected layer 2 and an output layer; the number of nodes in the fully connected layer 1 is 128, and the activation function is ReLU; the number of nodes in the connection layer 2 is 64, and the activation function is ReLU; the number of nodes in the output layer is 1, and the output is processed by the sigmoid activation function to generate a one-dimensional weather hazard level parameter.

5. The method for displaying traffic instructions in bad weather as claimed in claim 4, characterized in that: The display parameter optimization algorithm includes the following contents: Obtaining ambient light illumination parameters, visibility parameters, vehicle speed parameters and weather danger level parameters, and presetting reference display brightness, reference font size and reference flicker frequency, as well as various weight coefficients, adjustment coefficients, correction coefficients and dynamic weight coefficients, and calculating display brightness parameters, font size parameters and flicker frequency parameters; When the calculated display brightness parameter exceeds the limited range, the display brightness adjustment value of the indicator mark is calculated, the brightness parameter is compensated, and the flicker frequency parameter is corrected to determine the corrected flicker frequency parameter; The calculated font size parameter is combined with the corrected display brightness parameter and the flashing frequency parameter to form the display control parameter of the traffic sign.

6. The method for displaying traffic instructions in bad weather as claimed in claim 5, characterized in that: The calculation formulas for the display brightness parameter, font size parameter and flicker frequency parameter are: L=L0×(α1×E -1 +α2×V -1 +a3) S=S0×(β1×v+β2×V -1 +β3) F=F0×(γ1×D+γ2) Among them, L is the display brightness parameter, L0 is the benchmark display brightness parameter, E is the ambient light illumination parameter, V is the visibility parameter, α1, α2, and α3 are weight coefficients; S is the font size parameter, S0 is the benchmark font size parameter, v is the vehicle speed parameter, β1, β2, and β3 are adjustment coefficients; F is the flicker frequency parameter, F0 is the benchmark flicker frequency parameter, D is the weather hazard level parameter, and γ1 and γ2 are correction coefficients.

7. A system for displaying traffic signs in bad weather, based on the method for displaying traffic signs in bad weather according to any one of claims 1 to 6, characterized in that: include, Environmental parameter acquisition module, used to collect road environmental parameters and send them to the central processing module via the CAN bus; A central processing module, used to process road environment parameters and calculate display control parameters; A display control module, used to receive display control parameters and adjust the display status of the traffic sign; Communication module, used for data transmission between functional modules.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method for displaying traffic instructions in bad weather conditions according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method for displaying traffic instructions in bad weather conditions according to any one of claims 1 to 6 are implemented.

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