407 results about "Visibility" patented technology

The invention relates to the field of channel management, in particular to an intelligent channel management method and system based on multi-source data fusion. The invention discloses an intelligent navigation channel management system based on multi-source data fusion. The system comprises a combined ship identification module, a positioning frequency updating module, a collision risk assessment module and an intervention management module. According to the invention, the system can sense multi-dimensional environmental parameters such as illumination intensity, visibility, AIS signal quality, radar echo signal-to-noise ratio and the like in real time by constructing a combined ship identification model containing AIS, radar, vision and infrared, and dynamically adjust the fusion weight of each sensor to compensate for the deficiency of a single sensor; according to the method, the robustness of ship dynamic monitoring is improved, high-precision tracking can be kept in severe weather, the data redundancy contradiction of a traditional multi-sensor system is avoided, and therefore continuous and stable target state input is provided for follow-up risk analysis.

The invention provides a low-visibility environment pedestrian detection method based on an improved YOLOv8n model. A double-branch fusion attention network is adopted in a backbone network of an original YOLOv8n model, and a CBFuse module and a CBLinear module are introduced for feature fusion between different branches; a feature aggregation and calibration pyramid network is introduced, multi-scale feature fusion is carried out through an up-sampling module, a down-sampling module and a feature aggregation and calibration module, and the feature aggregation and calibration module carries out feature calibration and enhancement through a local attention mechanism, a global attention mechanism and a pixel attention mechanism; an adaptive task alignment detection head is introduced to execute a dynamic convolution mechanism, a task decomposition mechanism and a dynamic feature alignment mechanism; an improved YOLOv8n model is formed based on the improvement and serves as a foggy day pedestrian detection network model; according to the method, the detection accuracy and stability of the network in processing shielded and background complex images can be enhanced, and the boundary and detail features of a fuzzy target can be extracted more accurately in low-visibility environments such as foggy days and the like.

The invention relates to a sea fog quantitative forecasting method based on a period enhanced Transform, and the method comprises the steps: firstly constructing a Transform model as a forecasting model, enabling the Transform model and a self-attention mechanism to better capture the complex nonlinear relation and long-distance time dependence related to the formation of sea fog, and being superior to a conventional statistical method and an early-stage RNN / LSTM model, a data set for model training is constructed based on a time period enhancement technology and visibility standardization mapping, and the explicit time period enhancement technology enables the model to more accurately learn and forecast seasonal and daily change rules of sea fog; and a sample amplification and training set balancing strategy is executed on the data set, so that the problem of sparse fog samples is effectively solved, and the forecasting capability of the model on key low-visibility events is improved. According to the sea fog quantitative forecasting method based on the period enhanced Transform, the precision, timeliness (forecasting per hour) and spatial resolution of sea fog forecasting of a target area can be improved, and sea fog data characteristics can be effectively processed.

The invention discloses a civil aviation wind shear and visibility early warning method, which comprises the following steps of: acquiring an echo signal by using a high-precision and high-temporal-spatial-resolution coherent laser radar, and simultaneously acquiring civil aviation meteorological conventional observation data; calculating wind field distribution through a wind field inversion algorithm, and calculating visibility distribution through a visibility inversion algorithm; positioning a wind shear region by using a wind shear recognition algorithm, analyzing a visibility space-time evolution trend by combining a space-time deduction algorithm, and generating a visibility profile; inputting the wind shear identification data, the visibility profile data and the meteorological routine observation data into a machine learning model for modeling training; generating a wind shear and visibility early warning scheme over the airport based on the trained prediction model; and generating a wind shear alarm signal and visibility early warning information based on the early warning scheme, and outputting the signals to a civil aviation meteorological early warning system. According to the invention, high-precision wind shear and visibility combined early warning can be realized, and the early warning accuracy under complex weather conditions is improved.

A haze visibility detection method and device based on depth and brightness information and a medium are used for foggy weather visibility detection, a multi-mode visibility detection model comprehensively utilizing image data and meteorological data is provided, the model takes the image data and the meteorological data as inputs at the same time, and the visibility detection accuracy is improved through a parallel network structure. The data of the two modes are respectively sent to different channels to extract respective features, and then the features of the two modes are fused to jointly invert the visibility value. According to the method, the visual information and the environmental parameters are combined by constructing the multi-mode visibility detection model, and the depth information and the brightness information are introduced to optimize the model design, so that the accuracy and the robustness of the model are improved, and the visibility can be detected more accurately and reliably.

The invention relates to the technical field of intelligent traffic, in particular to an integrated fog measurement type highway safe driving guidance device and method, and provides the following scheme that local visibility data is acquired based on a plurality of highway safe driving guidance devices and uploaded to a region controller to generate guidance information. And the regional controller performs research and judgment calibration and gradient calibration on the data, corrects measurement errors and generates road visibility space distribution information by screening calibration anchor points and adopting an exponential attenuation function and iterative gradient calculation. Based on the information, a connectivity algorithm is used to identify a fog area, and in combination with road environment and historical accident data, an optimal induction strategy is matched, and induction information is dynamically adjusted. The fog area detection accuracy is improved, the induction information matching is optimized, and the driving safety and the passing efficiency under the low-visibility condition are improved.

The invention discloses a fog boundary layer parameterization scheme correction method based on a multi-scale physical coupling network, relates to the crossing field of numerical weather forecast and artificial intelligence, and aims to improve the performance of different boundary layer parameterization schemes in a WRF mode, construct a fusion framework of a physical mode and deep learning, adopt a double-branch heterogeneous network structure, and improve the performance of a fog boundary layer parameterization scheme. Establishing a nonlinear mapping relation between different parameterization scheme deviations and large eddy simulation turbulence characteristics by combining a space-time attention mechanism; by correcting systematic deviation existing in a parameterization scheme, the analysis capability of a turbulence structure is improved, and physical description of a boundary layer process is optimized; compared with a traditional boundary layer scheme, the method has the advantages that the fog zone simulation precision is remarkably improved, the fog zone visibility, the liquid water content and the root-mean-square error of a liquid water path are reduced by 66.7%, 50% and 58.3% respectively, the problem that turbulence intermittent characterization is insufficient under a stable boundary layer in a traditional method is effectively solved, and a new normal form is provided for refined forecasting of the fog generation and elimination process.

The invention provides an automatic driving test scene generation method based on real traffic data, and solves the problems of low accident data utilization rate, SIL / HIL test splitting and insufficient boundary coverage in the prior art. Comprising the following steps: acquiring multi-source heterogeneous traffic accident data; cleaning data by adopting a joint interpolation-anomaly detection mechanism; vehicle dynamic sudden change characteristics within 0.5 second before braking are extracted through LSTM and DTW algorithms; constructing a three-dimensional scene pipeline driven by a physical engine, and dynamically associating the pavement slippery coefficient with the rainfall intensity; analyzing the accident text into simulation parameters by using a semantic-physical parameter converter; performing SIL-HIL cooperative verification: performing extreme illumination perception test and narrow road planning verification in an SIL environment, and realizing 1ms step length fault injection test in an HIL environment; positioning failure parameters based on Bayesian optimization; a GAN is adopted to generate a long-tail scene, and a test boundary is expanded by coupling extreme conditions such as rainstorm / low visibility; and outputting a standard scene library containing the collision probability thermodynamic diagram. The safety verification efficiency under the extreme working condition is remarkably improved.

The invention belongs to the technical field of aviation meteorological monitoring, and relates to an airport dominant visibility automatic measurement method and system based on a three-dimensional laser radar, and the method comprises the steps: carrying out the full-space scanning of an airport through the laser radar, so as to collect echo signals; preprocessing and calibrating the echo signal; performing atmospheric extinction coefficient inversion on the preprocessed echo signal based on an inversion algorithm to obtain an atmospheric extinction coefficient, and then converting the atmospheric extinction coefficient into meteorological visibility; carrying out quantile statistics on all meteorological visibility in a period, judging a dominant visibility value, and carrying out adaptive weighted fusion on the dominant visibility value, the visibility value measured by the point-type visibility meter and the visibility value observed manually to obtain a fused dominant visibility value. The system can break through the limitation of traditional manual and point type visibility meter observation, and realizes accurate and automatic measurement of all-region, all-weather and unattended dominant visibility of an airport.

The invention discloses an automatic driving evaluation system based on artificial intelligence, and relates to the technical field of automatic driving, and the system comprises the following components: a meteorological model coupling module, a sensor parameter adjustment module, an automatic driving simulation module and an evaluation analysis module. According to the invention, data connection is established between the meteorological model coupling module and an external meteorological model, comprehensive meteorological data including temperature, humidity, wind speed, rainfall, snowfall, visibility and the like are acquired in real time, and a simulated extreme weather scene is constructed based on the data. The sensor parameter adjusting module automatically adjusts performance parameters of a vehicle sensor according to different weather types to ensure the accuracy of sensor data under extreme weather conditions, and the automatic driving simulation module operates an automatic driving system model in a simulated extreme weather scene to simulate the vehicle driving process.

According to the severe weather adaptive road violation real-time monitoring method and system provided by the invention, the image quality is dynamically judged by using the visibility index, and accurate traffic target identification and violation behavior detection are realized by combining image enhancement and a gating feature fusion mechanism. The system comprises a video acquisition module, a visibility evaluation module, an image enhancement module, a feature extraction and fusion module, a target detection module and a violation recognition module, can maintain high-accuracy recognition performance in various severe weathers (such as rain and fog), and is suitable for the field of intelligent traffic and urban safety management.

The invention discloses a visibility inversion method based on millimeter wave cloud radar and laser radar data fusion, and relates to the technical field of meteorology, and the method comprises the steps: guiding a millimeter wave cloud radar and a laser radar to carry out data correction and equivalent relation construction through employing a fog drop particle size distribution function measured by a fog drop spectrometer in a sample scene in advance; and then applying the correction coefficient and the equivalent function relationship to a target scene to perform multi-source data correction and fusion on the millimeter wave cloud radar and the laser radar and invert visibility. The method can fuse complementary advantages of the laser radar and the millimeter wave cloud radar; the introduction of the fogdrop particle size distribution function effectively avoids the distribution hypothesis of fogdrop shapes in the traditional model, can weaken the seasonal drift of the traditional empirical coefficient, significantly improves the accuracy and reliability of visibility inversion, and achieves the high-precision visibility monitoring under various weather conditions from clear sky to dense fog.

According to the expressway self-adaptive agglomerate fog early warning and response control method and system, meteorological data and traffic flow data of an expressway are collected in real time, and a reinforcement learning algorithm is utilized to predict a space-time mode of occurrence of agglomerate fog. By calculating the agglomerate fog risk index, if the risk exceeds the standard, the system triggers agglomerate fog early warning and dynamically adjusts the traffic management strategy, so that the driving safety is improved to the maximum extent and the traffic jam is reduced to the maximum extent. The system continuously optimizes decisions through traffic flow and visibility information fed back in real time, realizes adaptive adjustment through reinforcement learning, and adapts to changes under different weather and traffic conditions. The method can accurately predict the occurrence of agglomerate fog, optimizes the traffic flow, reduces the occurrence rate of traffic accidents, improves the passing efficiency and safety of the expressway, and has a wide application prospect.

The invention relates to sensing and tracking of intelligent driving in rain, snow, fog, night and large shielding scenes. In order to solve the problems of target invisibility, track interruption and misconnection caused by low visibility, a shielded dynamic target identification and tracking method based on deep learning is provided; according to the method, under a unified spatial index, sparse point cloud and road topology, passable and traverse areas, static shielding volume coding, ray marking visibility boundary and shielding entrance and exit are carried out; generating a motion voting field with consistent visibility through multi-agent situation reasoning, performing normal directional enhancement according to a passable boundary, and extracting a risk corridor; voxelization is carried out on the multi-frame point cloud in the corridor, a dynamic sparse voxel map is constructed, a voting field is used for gating cross-frame edge connection, occupation changes and infinitesimal displacement are aggregated, and three-dimensional verification candidates, limited state estimation and a time continuous track are obtained; outputting the target and the corridor to which the target belongs, and forming a potential conflict zone according to the intersection of the target and the own vehicle path; the reproduction rate and the advance are improved, and cross-lane misconnection is reduced.

The invention discloses an automatic driving vehicle speed control method based on environmental complexity, which sequentially executes the following four steps in a cycle period less than 50ms: step 1, synchronously acquiring road geometric morphology, traffic flow density, visual distance, road surface attachment condition and meteorological data, and acquiring the road geometric morphology, the traffic flow density, the visual distance, the road surface attachment condition and the meteorological data; performing fusion by using a Kalman filtering algorithm to obtain an environmental parameter vector; step 2, inputting the environment complexity label into a double-layer evaluation network, and outputting four levels of environment complexity labels; step 3, inputting the vehicle speed into a control model constructed based on a hierarchical adaptive complexity vehicle speed control algorithm, and generating a target vehicle speed; 4, if the target vehicle speed is larger than or equal to the collision risk index, the environmental complexity level is increased in time, the step 3 is returned to recalculate the target vehicle speed, and a vehicle speed execution instruction is output until the target vehicle speed is smaller than the collision risk index. According to the invention, the safety and the driving efficiency of the vehicle are obviously improved, and the stability and the reliability of the automatic driving system are guaranteed.

The invention relates to the field of beacon light control, in particular to a beacon light intelligent control method and system based on visible light communication. The method comprises the following steps: acquiring a weather type, a weather type grade, ambient light brightness, visibility and a wind speed of a target area; monitoring the target area based on the ambient light brightness to obtain a target ship; constructing a beacon light power prediction model, and predicting the beacon light power by using the beacon light power prediction model according to the weather type, the weather type grade, the ambient light brightness, the visibility and the wind speed; and obtaining a visible light communication protocol, and controlling the beacon light to flicker at the beacon light power based on the visible light communication protocol and the target ship. According to the invention, the problem that the beacon light is difficult to automatically adjust according to the environmental information in the prior art is solved, the real-time performance, the reliability and the self-adaptability of the working of the beacon light in a complex environment are improved, and the safety of ship navigation is further improved.

The invention discloses a visibility prediction method based on machine learning, and belongs to the technical field of visibility prediction, and the method comprises the following steps: obtaining historical meteorological data, carrying out the preprocessing of the historical meteorological data, and carrying out the feature selection to obtain a time sequence feature and a meteorological data feature set; constructing a visibility prediction model, inputting the time sequence features and the meteorological data feature set into the visibility prediction model for training, and performing optimization by using a loss function and gradient descent to obtain a trained visibility prediction model; performing visibility prediction based on the trained visibility prediction model to obtain a prediction result; the visibility prediction model obtained through the method is used for predicting actual data, it is found that the prediction result of the model is well matched with actual observation data, and it is indicated that visibility can be effectively predicted.

A vehicle including a windshield, a flexible display screen, a flexible display screen attachment component, at least one processor, and at least one memory configured to store instructions is disclosed. The instructions, when executed by the at least one processor, cause the at least one processor to (i) generate a set of images from sensor data received from one or more sensors mounted on a body of the vehicle; and (ii) display the set of images on the flexible display screen attached to the flexible display screen attachment component while covering the windshield. The set of images has effects of adverse weather conditions or poor lighting conditions removed.

The invention discloses a sea fog visibility prediction method based on an SLSTM-SVM model, and aims to overcome the defects of an existing sea fog visibility prediction method, the sea fog visibility prediction method comprises the following steps: firstly carrying out smooth stabilization processing on an LSTM network to obtain an SLSTM, and then replacing a softmax layer in the SLSTM with an SVM classifier to construct a model. Four types of hydrological data such as rainfall and water level and five types of meteorological data such as air temperature and air pressure form a sequence input model, an SVM classifier predicts and outputs a value through SLSTM layer smoothing and stabilization processing and data deep mining, parameters are updated through back propagation, and iterative training is carried out until errors reach the standard. Meanwhile, an autocorrelation function is adopted to evaluate data stability, and a Z-score method is utilized to normalize the data. The method can quickly converge the network weight to a globally optimal solution, is high in prediction accuracy, and has great application potential in long-term sea fog visibility prediction.

The invention discloses a visibility grade identification method based on multi-modal data fusion, and relates to the technical field of meteorology, and the method employs a meteorological modal feature extraction method which takes element dimensions as a main part and time dimensions as an auxiliary part, fully considers the interactive coupling characteristics between meteorological variables, excavates the nonlinear interactive relationship between different meteorological elements, and improves the visibility grade identification accuracy. According to the method, the cooperative influence of different meteorological variable combinations on the visibility can be effectively learned, the overall evolution mode of a meteorological time sequence can be reserved, and then cross-modal joint attention dynamic weight allocation is performed on the image modal features of the visible light image and the meteorological modal features to improve the feature utilization efficiency. According to the method, the time sequence stability of the meteorological data is used for reliably supplementing the image features, so that the method is more stable, and high recognition accuracy and robustness are achieved even in complex environments such as illumination abrupt change and dynamic interference.

The present application relates to the technical field of aviation meteorological support, and provides a meteorological display and alarm method, system, device and medium for aircraft navigation, which comprises receiving an aircraft route plan and collecting numerical weather forecasts of each place at the takeoff time of the aircraft; extracting wind speed, temperature, relative humidity, liquid water content and cloud droplet particle number density, and calculating the horizontal visibility, aircraft icing index, total cloud cover and aircraft turbulence index at the forecast time through adaptive attenuation interpolation based on feature perception; constructing an information box and a floating box of the route points on the aircraft route map; and performing meteorological display and alarm for aircraft navigation through the information box and the floating box according to the aircraft route plan. The present application uses regional meteorological forecast results to form meteorological forecast results for flight routes, can intuitively display the comprehensive meteorology on the route and form an alarm prompt, reduces the risk of information omission, increases the safety margin, and reduces the meteorological information processing workload of flight support personnel.

An LED illuminated buoy system is disclosed, designed to enhance maritime safety and visibility. The system includes a weighted base with a conical shape for stability and a semi-translucent flotation part that extends above the waterline. The buoy houses an LED light source within the base, programmable for various colors and patterns. A sealing connection between the base and flotation part prevents water ingress. The system also features a communication interface for data transmission and remote operation. The buoy can be anchored securely and is adaptable for use as a lamp on land. The system's design allows for easy assembly and maintenance, with environmental resistance features integrated for durability. The buoy serves as a navigational aid, safety marker, and versatile lighting solution in aquatic environments.

The invention discloses a visual audio multi-mode monitoring system and method for identifying fan blade defects. A panoramic video monitoring mechanism can obtain a back panoramic image of a blade of a current wind driven generator and a surrounding environment image of the wind driven generator; a blade root pickup is arranged on the panoramic video monitoring mechanism, and a blade tip pickup is arranged on a tower drum of the wind driven generator, so that blade tip section audios and blade root section audios of the wind driven generator can be obtained. Firstly, the environment visibility under the current weather condition is recognized through the panoramic video monitoring mechanism, and then different real-time monitoring strategies are executed according to the environment visibility, so that the blade defect monitoring is not limited by the weather environment any more, the application scene is improved, and the blade defect monitoring accuracy is improved no matter in the high-visibility environment or the low-visibility environment. The false alarm risk can be reduced as much as possible in a visual and audio mutual verification mode, and the accuracy of blade surface defect identification is greatly improved.

The invention discloses a foggy weather visibility real-time monitoring method based on multi-source data fusion, and the method comprises the following steps: S1, linking a front scattering visibility measuring instrument and a camera, and obtaining visibility data and image data in a corresponding region at the same time; s2, pairing visibility data provided by the front scattering visibility measuring instrument and image data captured by the camera in the same time window; s3, classifying and marking visibility grades by adopting a ConvNeXt classification model and fusing an inverted residual module of adaptive channel attention; and S4, deploying the improved ConvNeXt classification model to an edge computing device, performing feature extraction and classification calculation by using a real-time image acquired by a camera, and outputting a corresponding seaborne visibility grade. According to the invention, real-time acquisition, intelligent classification and automatic marking of visibility data can be realized, and the accuracy and efficiency of maritime visibility monitoring are improved.

The invention relates to the technical field of fan hoisting, and discloses a vision-enhanced low-visibility fan auxiliary hoisting system and method, and the system comprises an environment acquisition module, an intelligent processing and decision module and a hoisting installation module. The method comprises the following steps: acquiring image data of a working site, and transmitting the image data to the intelligent processing and decision module through wireless communication; constructing a hoisting equipment fault prediction model, carrying out early warning on abnormal conditions of the image data, and identifying sudden weather changes affecting hoisting operation through meteorological data analysis; according to a prediction result of the hoisting equipment fault prediction model, a hoisting scheme is adjusted, and an emergency plan is formulated; and carrying out real-time monitoring by using an unmanned aerial vehicle, comparing real-time monitored image data with a prediction result, and adjusting the hoisting equipment fault prediction model based on a comparison result. Through a data comparison and feedback mechanism, the hoisting equipment fault prediction model is continuously optimized, and the prediction precision and reliability are improved.

The invention discloses a visibility inversion method and device of a laser radar, equipment and a storage medium, and relates to the technical field of laser, and the method comprises the steps: determining a detection range boundary based on a laser radar voltage value and a preset threshold value; synchronously measuring the visibility in a segmented manner by using a ground single-point measurement visibility meter and a laser radar visibility meter, and performing inversion to obtain a function value; constructing a joint relation model by combining a laser radar equation according to the average error of the function value and the radar measurement signal, and generating a parameter mapping relation lookup table; acquiring signal intensity by means of a near-field visibility measuring device, and determining a target laser radar ratio and an extinction coefficient through the look-up table; and finally, resolving an extinction coefficient profile and inverting a visibility profile by adopting a Fernald inversion algorithm in combination with the laser echo signal path, the target extinction coefficient and the laser radar ratio. The efficiency of laser radar visibility inversion is improved.

The invention relates to the technical field of intelligent ship driving, and discloses an intelligent ship driving assistance system based on multi-source information fusion and AI decision, which comprises a multi-source information acquisition module, an information data processing module, an information fusion processing module, an AI decision calculation module and a man-machine interaction module, according to the ship intelligent driving assistance system based on multi-source information fusion and AI decision making, perception blind areas of scenes with low visibility and the like are eliminated through a multi-source information collection and vision enhancement technology, and a target is accurately recognized; through data processing and three-level fusion, multi-source information is efficiently integrated, and the manual analysis pressure is reduced; the AI decision calculation module provides accurate guarantee for berthing, stranding, collision avoidance and other scenes, and accident risks are reduced; the system can also optimize the route and speed in combination with data such as weather, reduce energy consumption, shorten the navigation time, take into account the safety and economy of ship driving, and greatly improve the intelligent level of shipping.

The invention discloses a one-stage multi-modal bad weather fruit detection method, which solves the problems of low real-time performance, poor generalization and the like during bad weather fruit detection by constructing a complete convolutional neural network framework, and comprises the following steps: firstly, carrying out multi-scale coding on an image and constructing a multi-scale receptive field; the method ensures that the detection method can detect targets of different sizes in an agricultural scene, encodes severe weather features in multiple scales, designs a multi-modal feature fusion module, highlights fruit-related parts in image features by using point cloud through an attention mechanism, and performs feature fusion after feature fusion. The decoder specially used for severe weather is designed to explore the features of the target under low visibility locally and fully and adaptively adjust the model, so that more attention is paid to the features of the target; accurate detection can be carried out without pre-processing the image, the training time and tedious steps are reduced, a good result is achieved in the aspects of precision and speed, and a reliable scheme is provided for agricultural scene fruit detection in severe weather.

The invention discloses an offshore floating type low-altitude waveguide all-weather detection method and system, and relates to the technical field of marine environment monitoring and electronic information engineering.The method comprises the steps that the current weather condition is judged according to a rain intensity sensor, a miniature weather station, a visibility sensor and an infrared cloud measurement sensor; obtaining a temperature and humidity value range according to the current weather condition, and adding liquid water for correction according to the weather condition; a simulated brightness temperature is obtained according to a MonoRTM model by combining an air pressure profile constructed by sea surface air pressure and a pressure height formula; designing a multi-objective genetic inversion algorithm, constructing an objective function, and when the objective function obtains a minimum value, obtaining an inverted atmospheric parameter profile group; and according to the atmospheric parameter profile group, calculating an atmospheric refractive index profile, judging whether a low-altitude waveguide occurs or not, and uploading a result to a shore station data management center. According to the invention, the atmospheric absorption coefficient is corrected according to different weathers, and all-weather and high-precision monitoring of the low-altitude waveguide on the offshore floating platform is realized.

The present invention provides a driver assistance system, as well as a system and method for extending the limit of the permissible operating range (ODD). This system (10) comprises an ODD module (12) configured to receive a visibility parameter representing an actual visibility level of a sensor for an automated driving environment and to determine an ODD limit based on a comparison between the actual visibility level represented by the visibility parameter and a predetermined visibility level.This system further comprises an extension module (13) configured to monitor the actual visibility level represented by the visibility parameter and the predetermined visibility level; and extending at least a portion of the ODD limit when it is monitored that the actual visibility level increases to a value greater than or equal to the predetermined visibility level and / or the predetermined visibility level is configured lower, such that the actual visibility level is greater than or equal to the configured predetermined visibility level.