Method and system for cooperatively regulating and controlling elastic speed limit and vehicle distance in agglomerate fog scene of expressway

Abstract

The invention discloses an elastic speed limiting and vehicle distance cooperative regulation and control method and system in an expressway agglomerate fog scene, particularly relates to the technical field of intelligent traffic control, and is used for solving the problem that a secondary accident risk is induced by neglecting a traffic flow phase change characteristic in an existing dynamic speed limiting method. The method comprises the following steps: collecting traffic density, vehicle spacing and visibility space-time distribution data in real time; performing weighted amplification on the traffic density based on the consistency of the visibility gradient direction and the traffic flow direction to generate corrected traffic density; calculating a phase change margin index representing the flow state stability by combining the corrected traffic density and the optical safety deviation degree; when the phase change margin is lower than a threshold value, dynamically constraining the maximum value of the speed-limiting decreasing amplitude according to the agglomerate fog moving trend, and keeping the positive correlation between the maximum value and the phase change margin; generating a target speed limit value based on the constraint decreasing amplitude value, and calculating a safe vehicle distance instruction by fusing the visibility data; and issuing an instruction to the vehicles in the agglomerate fog dynamic influence area to realize collaborative optimization of traffic system stability and single vehicle safety.

Description

Technical Field

[0001] The present invention relates to the field of intelligent traffic control technology, and more specifically, to a method and system for coordinated control of flexible speed limit and vehicle distance in a foggy highway scenario. Background Art

[0002] Traffic safety control in highway fog scenarios mainly relies on dynamic speed limits and vehicle-to-vehicle distance control. Existing methods usually obtain real-time visibility data through roadside meteorological monitoring equipment and adjust speed limits and safe vehicle distance instructions based on preset threshold rules. The purpose is to prevent rear-end collisions in fog areas by reducing vehicle speeds and increasing vehicle spacing. Its control logic focuses on the instantaneous safety conditions at the single-vehicle level.

[0003] However, existing control methods have systemic risks in their implementation: when a significant speed limit reduction is adopted on foggy sections, although it can improve safety in local areas, it will significantly change the dynamic characteristics of traffic flow, leading to deterioration of traffic stability and inducing upstream congestion and secondary accident risks. Because only the safety threshold of a single vehicle is considered, the deep-seated contradiction between sudden changes in the traffic system state and safety control is not resolved, which restricts the actual effectiveness of flexible speed limits and vehicle distance control. Summary of the Invention

[0004] In order to overcome the above-mentioned defects of the prior art, the present invention provides a method and system for coordinated control of flexible speed limit and vehicle distance in a highway fog scene to solve the problems raised in the above-mentioned background technology.

[0005] To achieve the above object, the present invention provides the following technical solutions: The flexible speed limit and vehicle-to-vehicle distance coordinated control method in highway fog scenarios includes: S1. Real-time data collection of traffic operation status parameters and visibility spatiotemporal distribution data for fog-affected sections of roads. Traffic operation status parameters include traffic density and vehicle spacing. S2. Calculate the visibility gradient direction at the current time point based on the visibility spatiotemporal distribution data. When the visibility gradient direction is consistent with the traffic flow direction, perform weighted amplification on the traffic density to generate a corrected traffic density. S3. Calculate the phase change margin index of traffic flow based on the corrected traffic density and optical safety deviation, where the optical safety deviation is calculated based on the temporal and spatial distribution data of vehicle spacing and visibility; S4. When the phase change margin index is lower than the preset threshold, the maximum speed limit reduction is dynamically constrained based on the fog movement trend and the corrected traffic density, so that the maximum speed limit reduction is positively correlated with the phase change margin index; S5. Generate a target speed limit value based on the maximum speed limit reduction value after the constraint, and calculate a safe vehicle distance instruction based on the visibility spatiotemporal distribution data; S6. Issue target speed limits and safe vehicle distance instructions to vehicles on road sections affected by fog.

[0006] Furthermore, the traffic operation status parameters and visibility spatiotemporal distribution data of fog-affected sections are collected in real time, including: Roadside sensors deployed on fog-affected roads collect traffic density and vehicle spacing in real time. These sensors include video detection equipment and microwave radar equipment. Visibility temporal and spatial distribution data are collected in real time through a network of meteorological monitoring stations deployed along roads affected by fog. The network consists of multiple meteorological sensors distributed at preset intervals. The spatiotemporal distribution data of traffic density, vehicle spacing and visibility are transmitted to the edge computing node through a dedicated communication protocol for timestamp alignment processing.

[0007] Furthermore, the visibility gradient direction at the current time point is calculated based on the visibility spatiotemporal distribution data. When the visibility gradient direction is consistent with the traffic flow direction, the traffic density is weighted and amplified to generate a corrected traffic density, including: Extracting visibility spatial distribution data at the current time point based on visibility spatiotemporal distribution data; The visibility gradient direction is determined by calculating the visibility change rate between adjacent meteorological monitoring stations; Calculate the vector angle between the visibility gradient direction and the preset traffic flow direction; When the vector angle is less than or equal to the preset angle threshold, it is determined that the visibility gradient direction is consistent with the traffic flow direction; The traffic density is amplified using a preset weighting coefficient to generate a corrected traffic density.

[0008] Furthermore, the traffic density is amplified by using a preset weighting coefficient to generate a modified traffic density, including: Obtain real-time traffic density detection; extract visibility gradient direction change characteristics; Determining the value of the preset weighting coefficient according to the directional change characteristics of the visibility gradient; The real-time detected traffic density is multiplied by a preset weighting coefficient to generate a corrected traffic density.

[0009] Furthermore, the phase change margin index of traffic flow is calculated based on the modified traffic density and the optical safety deviation, including: Extracting the visibility value at the current time point from the visibility spatiotemporal distribution data; Determine the optical safety deviation based on the spatial conflict relationship between vehicle spacing and visibility values; Generate the flow instability risk value according to the relative difference between the corrected traffic density and the critical traffic density; Establish a dynamic game matrix between optical safety deviation and flow instability risk value; Output the system risk coupling coefficient through the preset coordination rules of the dynamic game matrix; The system risk coupling coefficient is compared with the preset phase change threshold to generate a phase change margin index, which represents the degree to which the metastable state of traffic flow deviates from the phase change boundary.

[0010] Furthermore, the dynamic game matrix of optical safety deviation and flow instability risk value is established, including: Define the grading intervals for optical safety deviation; Setting the risk level threshold of the flow instability risk value; Construct a decision matrix with the optical safety deviation level as the row and the flow instability risk value level as the column; The coordination rule output values ​​are preset in the cells of the decision matrix.

[0011] Furthermore, when the phase change margin index is lower than a preset threshold, the maximum speed limit reduction is dynamically constrained based on the fog movement trend and the modified traffic density, so that the maximum speed limit reduction is positively correlated with the phase change margin index, including: When the phase change margin index is lower than the preset threshold, the spatial position migration data of the fog cluster movement trend is obtained; Extract road spatial distribution characteristics corresponding to the corrected traffic density; Establish a dynamic coupling constraint mechanism between fog movement trend and modified traffic density; Determine the constraint boundary of the maximum speed reduction value based on the dynamic coupling constraint mechanism; The speed limit reduction ratio coefficient is calculated based on the deviation between the phase change margin index and the preset threshold; The speed limit reduction proportional coefficient is applied to the constraint boundary to generate the final maximum speed limit reduction value.

[0012] Furthermore, a target speed limit value is generated based on the maximum speed limit reduction value after the constraint, and a safe vehicle distance instruction is calculated in combination with the visibility spatiotemporal distribution data, including: Get the current benchmark speed limit value; Subtract the maximum speed limit reduction value from the current base speed limit value to generate the target speed limit value; Extracting the visibility value at the current time point from the visibility spatiotemporal distribution data; Calculate the minimum safe vehicle distance threshold based on the target speed limit value and visibility value; Generate a safe distance instruction based on the minimum safe distance threshold.

[0013] Furthermore, target speed limits and safe vehicle distance instructions are issued to vehicles on fog-affected road sections, including: Determine the dynamic spatial range of road sections affected by fog; Encapsulate the target speed limit value and the safe vehicle distance instruction into a cooperative control instruction set; Broadcast cooperative control instruction sets to vehicles within a dynamic spatial range through the vehicle-road cooperative communication protocol; Dynamically adjust the parameter values ​​of the broadcast instructions according to the vehicle location information.

[0014] In another aspect, the present invention provides a system for coordinated control of flexible speed limit and vehicle distance in highway fog scenarios, comprising: Traffic environment perception module, used to collect real-time traffic operation status parameters and visibility spatiotemporal distribution data of fog-affected sections of road. Traffic operation status parameters include traffic density and vehicle spacing. The density dynamic correction module is used to calculate the visibility gradient direction at the current time point based on the visibility spatiotemporal distribution data. When the visibility gradient direction is consistent with the traffic flow direction, the traffic density is weighted and amplified to generate the corrected traffic density. The flow pattern margin assessment module is used to calculate the phase change margin index of traffic flow based on the modified traffic density and optical safety deviation. The optical safety deviation is calculated based on the temporal and spatial distribution data of vehicle spacing and visibility; The speed limit dynamic constraint module is used to dynamically constrain the maximum speed limit reduction according to the fog movement trend and the modified traffic density when the phase change margin index is lower than the preset threshold, so that the maximum speed limit reduction is positively correlated with the phase change margin index; The command collaborative generation module is used to generate the target speed limit value based on the maximum speed limit reduction after the constraint, and calculate the safe vehicle distance command based on the visibility temporal and spatial distribution data; The command broadcast execution module is used to issue target speed limit values ​​and safe vehicle distance instructions to vehicles in the road section affected by fog.

[0015] Compared with the prior art, the present invention has the following beneficial effects: First, the intensity of speed limit regulation is dynamically constrained by the traffic flow phase change margin to achieve a systematic balance between safety and efficiency. By introducing a phase change margin indicator to quantify the degree to which the traffic flow metastable state deviates from the phase change boundary, the maximum speed limit reduction is dynamically constrained based on this indicator: when the traffic flow approaches the critical point of instability, the reduction boundary is automatically expanded to enhance safety; when the flow state stabilizes, the reduction boundary is contracted to preserve traffic capacity. This positive correlation regulation resolves the contradiction of "excessive speed reduction induces congestion while congestion exacerbates accident risks" at the system level. Under the same visibility conditions, it reduces the invalid speed limit range compared with existing technologies and reduces the incidence of secondary accidents.

[0016] Second, the spatiotemporal movement characteristics of fog clusters are integrated with the traffic density correction mechanism to improve the accuracy and timeliness of coordinated control; through the coordinated analysis of visibility gradient direction and traffic flow direction: when the visibility gradient direction is consistent with the traffic flow direction, the traffic density is weighted and amplified to generate a corrected traffic density, so as to capture the traffic flow compression effect caused by fog cluster movement in advance; and then the speed limit reduction is dynamically constrained in combination with the fog cluster movement trend, so that the target speed limit value and safe vehicle distance command are dynamically adapted with the spatial migration of fog clusters, breaking through the limitations of static zoning control. The command issuance can accurately cover the actual impact range of fog clusters, avoiding premature speed reduction of upstream vehicles or delayed response of downstream vehicles, and improving the timeliness of risk prevention and control under the same equipment conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 This is a flow chart of the method for coordinated control of flexible speed limit and vehicle distance in a highway fog scene according to the present invention; Figure 2 This is a schematic diagram of the structure of the flexible speed limit and vehicle distance coordinated control system in the highway fog scene of the present invention. DETAILED DESCRIPTION

[0018] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0019] Example 1: Figure 1 The present invention provides a method for coordinated control of flexible speed limit and vehicle distance in highway fog scenarios, including: S1. Real-time data collection of traffic operation status parameters and visibility spatiotemporal distribution data for fog-affected sections of roads. Traffic operation status parameters include traffic density and vehicle spacing. S2. Calculate the visibility gradient direction at the current time point based on the visibility spatiotemporal distribution data. When the visibility gradient direction is consistent with the traffic flow direction, perform weighted amplification on the traffic density to generate a corrected traffic density. S3. Calculate the phase change margin index of traffic flow based on the corrected traffic density and optical safety deviation, where the optical safety deviation is calculated based on the temporal and spatial distribution data of vehicle spacing and visibility; S4. When the phase change margin index is lower than the preset threshold, the maximum speed limit reduction is dynamically constrained based on the fog movement trend and the corrected traffic density, so that the maximum speed limit reduction is positively correlated with the phase change margin index; S5. Generate a target speed limit value based on the maximum speed limit reduction value after the constraint, and calculate a safe vehicle distance instruction based on the visibility spatiotemporal distribution data; S6. Issue target speed limits and safe vehicle distance instructions to vehicles on road sections affected by fog.

[0020] S1. Real-time data collection of traffic operation status parameters and visibility spatiotemporal distribution data for fog-affected sections of road. Traffic operation status parameters include traffic density and vehicle spacing. Specific implementation is as follows: Roadside sensors deployed on fog-affected roads collect real-time traffic density and vehicle spacing. These sensors consist of video detection equipment and microwave radar. The video detection equipment captures overhead images of the road surface at a preset interval. Using an image recognition algorithm, it extracts the headway between adjacent vehicles in the same lane as the measured vehicle spacing. It also counts the number of vehicles per lane per kilometer as the instantaneous traffic density measurement. The microwave radar equipment transmits a frequency-modulated continuous wave (FMCW) at a preset frequency and calculates vehicle spacing based on the phase difference of the reflected waves. The detection results from these two devices are weighted and fused at edge computing nodes. The fusion weights are dynamically adjusted based on visibility conditions: when visibility exceeds 200 meters, the video detection device weight is set to 0.7, and the microwave radar device weight is set to 0.3. When visibility drops below 200 meters, the video detection device weight decreases linearly to 0.3, while the microwave radar device weight increases accordingly to 0.7. Traffic density measurements are standardized in vehicles per kilometer, and vehicle spacing measurements are standardized in meters.

[0021] Real-time spatiotemporal visibility data is collected through a network of meteorological monitoring stations deployed along sections of road affected by fog. The network consists of multiple meteorological sensors deployed at preset intervals on both sides of the road. Each sensor has a built-in forward-scattering visibility meter that measures horizontal atmospheric transmittance at a preset frequency. Visibility values ​​are calculated based on the conversion relationship between atmospheric transmittance and visibility. Visibility values ​​are uniformly expressed in meters. After receiving measurement data from all meteorological sensors, the edge computing node uses a spatial interpolation algorithm to generate a visibility distribution surface based on the road centerline. This surface covers the entire road section in spatial terms and is updated at fixed intervals in temporal terms, forming a spatiotemporal visibility data set. Meteorological sensors are mounted on columns at a predetermined height above the road surface, with the sensor's optical lens oriented perpendicular to the direction of traffic flow.

[0022] Traffic density, vehicle spacing, and visibility spatiotemporal distribution data are transmitted to edge computing nodes via a dedicated communication protocol for timestamp alignment. The dedicated communication protocol utilizes a layered transmission architecture. Upon receiving the data, the edge computing nodes perform time synchronization operations: extracting the time stamp of the traffic density detection data, generated by the built-in timing module of the video detection device; extracting the time stamp of the visibility data, generated by the real-time clock within the meteorological sensor; and using a time calibration algorithm to unify the time bases of the two types of data to a standard time system. The calibration process involves calculating the clock deviation of the two time sources, establishing a linear compensation model, and performing offset correction on the time stamp of the visibility data. After timestamp alignment, a synchronized dataset is generated, which contains three core fields: a unified timestamp field; a traffic parameter field containing traffic density and vehicle spacing values; and a visibility value field containing the visibility distribution value for the corresponding road section at that point in time.

[0023] In the deployment of a network of meteorological monitoring stations, the installation location of meteorological sensors must meet specific technical requirements: they must be mounted on poles at an appropriate height above the road surface to avoid interference from vehicle wake; the sensor's optical lens must be oriented at a suitable angle to the direction of traffic flow to prevent direct headlights from affecting measurement accuracy; and the spacing between adjacent sensors must be determined based on the spatial variation of visibility. The visibility measurement range must be set to a reasonable range, and when the measured value falls below a certain threshold, it will automatically switch to high-sensitivity mode, increasing the sampling frequency.

[0024] During the timestamp alignment process, the time calibration algorithm uses a linear fitting method: It collects multiple consecutive time synchronization signal points; uses the timing module time as a reference to calculate the cumulative deviation of the meteorological sensor clock; establishes a deviation compensation equation, a linear function whose slope represents the clock drift rate and whose intercept represents the initial deviation; and applies this equation to the time stamps of subsequent visibility data for real-time correction. Time synchronization accuracy is verified through synchronization error detection, which is performed regularly.

[0025] During traffic parameter collection, the weighted fusion of video detection equipment and microwave radar equipment utilizes an adaptive weight allocation strategy: when visibility conditions are good, the video detection equipment is assigned a higher weight; when visibility conditions deteriorate, the video detection equipment's weight decreases linearly, while the microwave radar's weight increases accordingly. The weight adjustment curve is a piecewise function with turning points set at specific visibility values. The function slope is determined based on device calibration test data. The fused vehicle spacing value undergoes a rationality check: if the rate of change between consecutive detection cycles exceeds a reasonable threshold, an outlier filtering mechanism is activated, replacing the current value with a moving average.

[0026] The spatial interpolation algorithm for visibility spatiotemporal distribution data uses an inverse distance weighting method: Each meteorological sensor location is used as a control point, and the road is divided into grid cells. For each grid cell's visibility value, a weight is calculated based on its distance from adjacent control points. The weight coefficient is inversely proportional to the square of the distance. When the distance between a grid cell and a control point exceeds a specific range, a data credibility decay mechanism is activated, adding an error compensation to the calculated result. The resulting visibility distribution surface is stored in matrix form, with the row numbers corresponding to the road stake numbers, the column numbers corresponding to the time series, and the matrix element values ​​representing the visibility values ​​at that location and time.

[0027] The dedicated data transmission protocol includes a designed frame structure: each frame consists of a header, a payload, and a checksum. The header includes a data type identifier and a timestamp. The payload is binary-encoded, and traffic density, vehicle spacing, and visibility values ​​are represented as integers. The checksum uses a cyclic redundancy check (CRC) to ensure data transmission reliability.

[0028] In terms of equipment deployment, video detection equipment is mounted on columns at a certain height on the side of the road, ensuring a visual field covering the target lane. Microwave radar equipment is calibrated at an angle to ensure beam coverage of the detection area. The meteorological sensor network is deployed at a specific density, with greater spacing on straight sections and less spacing on curved roads. Edge computing nodes are equipped with a data preprocessing module to filter the raw detection data and eliminate random fluctuations.

[0029] During time synchronization, the timing module uses the time reference provided by the satellite positioning system, achieving millisecond-level time stamping accuracy. The parameters of the linear compensation model are determined through historical data analysis and recalibrated every 24 hours. The search radius of the spatial interpolation algorithm is set based on the characteristics of the road section, for example, a larger search radius is used on straight sections and a smaller search radius is used on complex sections.

[0030] In the weighted fusion strategy, the turning point of the weight adjustment curve is determined through experiments, such as comparing the detection error rates of two devices under different visibility conditions. The trigger threshold of the outlier filtering mechanism is set based on traffic flow characteristics, for example, using a lower threshold during stable traffic flow periods and a higher threshold during periods of fluctuating traffic flow. The compensation coefficient of the data credibility decay mechanism is determined through error analysis experiments and increases with distance.

[0031] The frame structure design optimizes the binary encoding scheme for the payload. For example, traffic density values ​​are represented using 16 bits, ensuring accuracy that meets detection requirements. The checksum generation algorithm uses a standard implementation to ensure error detection. The triggering conditions for the data discard mechanism are set based on the system's real-time requirements, for example, when the time deviation exceeds 200 milliseconds.

[0032] S2. Calculate the visibility gradient direction at the current time point based on the visibility spatiotemporal distribution data. When the visibility gradient direction is consistent with the traffic flow direction, perform weighted amplification on the traffic density to generate a corrected traffic density. The specific implementation is as follows: Based on the spatiotemporal visibility distribution data, the spatial visibility distribution data for the current time point is extracted. This spatiotemporal visibility distribution data is stored in edge computing nodes in matrix form, where the first dimension represents road location information and the second dimension represents the time series. The extraction operation locates the data slice at the current moment using a time index. This slice contains visibility values ​​for different road locations. Road locations are divided into fixed intervals, and visibility values ​​are stored in meters. For locations without direct monitoring points, interpolation algorithms are used to supplement the data. The interpolation algorithm is calculated based on data from two adjacent meteorological monitoring stations.

[0033] The visibility gradient direction is determined by calculating the visibility change rate between adjacent meteorological monitoring stations. First, the adjacent pairs of meteorological monitoring stations on the current road section are identified, with the spacing between each pair of monitoring stations being a preset value. For each pair of monitoring stations, the visibility change rate is calculated: the visibility value of the downstream station is subtracted from the visibility value of the upstream station, and then divided by the road distance between the two stations. The gradient vector is then calculated: using the road extension direction as the reference axis, the visibility change rate is converted into a direction vector, with the vector direction pointing in the direction of increasing visibility. The final gradient direction is determined by the vector synthesis of the calculation results of all monitoring station pairs, and the synthesis method is vector addition.

[0034] Calculate the angle between the visibility gradient direction and the preset traffic flow direction. The preset traffic flow direction is determined by the road geometry and stored as a fixed direction vector. This angle is calculated using the vector dot product formula: Calculate the dot product of the visibility gradient direction vector and the preset traffic flow direction vector, divide by the product of the two vectors' moduli, and then calculate the inverse cosine function. The result is converted to an angle to obtain the angle value.

[0035] When the vector angle is less than or equal to the preset angle threshold, the visibility gradient direction is determined to be consistent with the traffic flow direction. The judgment logic is as follows: the real-time calculated angle value is compared with the preset angle threshold. If the calculated value is less than or equal to the threshold, the density correction mechanism is triggered; if it is greater than the threshold, the original traffic density value is maintained.

[0036] The preset angle threshold is determined by collecting data on the angle between the gradient direction and the traffic flow direction during historical fog events. Combined with traffic flow stability indicators (such as the coefficient of variation of speed), the probability curve of flow instability at different angles is analyzed. The angle corresponding to the point of sudden increase in probability is then selected as the threshold. This value is validated with a 90% confidence interval and typically ranges from 15° to 45°, making it easy to implement without any creative effort.

[0037] Traffic density is amplified using preset weighting coefficients to generate a corrected traffic density. First, real-time traffic density values ​​are obtained from the synchronized dataset from the data acquisition step. At the same time, visibility gradient directional change characteristics are extracted, including the gradient directional stability index and the gradient intensity change rate. The gradient directional stability index is calculated by statistically analyzing the degree of gradient directional change over the last few minutes. The gradient intensity change rate is calculated as the ratio of the current gradient modulus to the gradient modulus of the previous cycle.

[0038] The preset weighting coefficient is determined based on the directional variation characteristics of the visibility gradient. A weighting coefficient decision rule is established: when the gradient directional stability is high and the gradient intensity change rate is large, the weighting coefficient is set to a larger value; when the gradient directional stability is low and the gradient intensity change rate is small, the weighting coefficient is set to a smaller value. The weighting coefficient value range is controlled within a reasonable range.

[0039] The real-time traffic density is multiplied by a preset weighting factor to generate a modified traffic density. This multiplication is performed at the edge computing node using the following formula: modified traffic density equals original traffic density multiplied by the weighting factor. The result retains appropriate precision and is expressed in the same units as the original traffic density. The modified traffic density value is stored in the processing queue for subsequent use.

[0040] During the gradient direction calculation process, the visibility change rate is calculated based on data quality. If data from a particular meteorological station is missing, the station pair containing that station is automatically excluded. If data from more than a certain percentage of station pairs is unavailable, a data recovery mechanism is activated. Vector synthesis uses a weighted average method, assigning higher weights to station pairs closer to the current road location.

[0041] When calculating the angle, the preset traffic direction is determined by storing the direction vector for each location in the road database, which is calculated based on road design information. The inverse cosine function is calculated using standard mathematical methods, ensuring reasonable accuracy.

[0042] Gradient direction change characteristics are analyzed using a sliding time window mechanism: the window length is set to a fixed duration and slides periodically. Statistical characteristics of the gradient direction sequence within the window are calculated as stability indicators. The gradient intensity change rate is calculated using the adjacent cycle comparison method.

[0043] The weighted coefficient decision rules are adjusted through optimization methods: relevant data from historical fog events is collected and trained on a prediction model to optimize the decision rules. The decision rules are updated regularly to ensure they adapt to environmental changes. The weighted coefficients are range-checked before application.

[0044] After the revised traffic density is generated, a rationality check is performed: the rate of change before and after the revision is calculated. If the rate of change exceeds a reasonable threshold, an audit flag is activated. If there is a significant deviation from historical data, it is marked as an outlier. All revision records are stored in a log file.

[0045] In terms of special scene processing, when the visibility gradient direction changes suddenly, the emergency processing procedure is initiated: freeze the weighting coefficient update and maintain the previous value for a fixed time; when the fog dissipates quickly, the weighting coefficient is automatically reset to the baseline value.

[0046] During the gradient synthesis process, vector addition uses the component-by-component method: the gradient vector for each monitoring station pair is decomposed into coordinate components, which are summed separately before being synthesized into the final vector. Component calculations use a plane coordinate system, with the origin set at the starting point of the road section.

[0047] Traffic flow status feedback is introduced into the weighting coefficient determination process: when the real-time traffic density approaches the road capacity, the weighting coefficient is automatically reduced to avoid calculation distortion caused by excessive amplification. This mechanism is achieved by real-time monitoring of traffic density values.

[0048] S3. Calculate the phase change margin index of traffic flow based on the corrected traffic density and optical safety deviation, specifically implemented as follows: The visibility value at the current point in time is extracted from the spatiotemporal visibility distribution data. This data is stored in the edge computing node as a time series matrix, where the row indices correspond to road location identifiers and the column indices correspond to time points. The extraction operation locates the column index of the current timestamp and retrieves the values ​​of all row elements in that column to form a visibility spatial distribution data set. For the target vehicle location, the visibility value at the corresponding location is extracted from the data set through road location matching. Visibility values ​​are expressed in meters. If there is no direct monitoring point at the target location, visibility values ​​from two adjacent meteorological monitoring stations are interpolated.

[0049] The optical safety deviation is determined based on the spatial conflict relationship between vehicle spacing and visibility values. The current vehicle spacing detection value is obtained, which comes from the synchronized data set of the data acquisition step. The spatial conflict relationship is quantified by calculating the ratio of vehicle spacing to visibility value, which reflects the degree of spatial safety redundancy. The optical safety deviation is defined as the output of the conversion function of the ratio: the original ratio is input into the piecewise function, and the output range is normalized to between 0 and 1. An increase in the value indicates an increase in the degree of safety deviation. The piecewise function sets a turning point: when the ratio reaches a certain threshold, the output value undergoes a step change.

[0050] The flow instability risk value is generated based on the relative difference between the modified traffic density and the critical traffic density. The modified traffic density is derived from the output of the previous step. The critical traffic density is determined by road design parameters stored in the road segment attribute database. The relative difference is calculated as a percentage: the difference between the modified traffic density and the critical traffic density is divided by the critical traffic density, and then multiplied by the percentage factor. The flow instability risk value is defined as the result of mapping this percentage value. The mapping process uses a piecewise linear transformation, and the output value range is controlled between 0 and 1.

[0051] Establish a dynamic game matrix between optical safety deviation and flow instability risk value. Define the level division of optical safety deviation: divide the continuous optical safety deviation value into three levels, and set the level boundary value. Set the risk level of flow instability risk value: divide the risk value into three levels, and set the risk level boundary value. Construct a decision matrix with three rows and three columns, with the row title being the optical safety deviation level and the column title being the flow instability risk level. Preset the coordination rule output value in the matrix unit, and the output value is the system risk coupling coefficient between 0 and 1. The matrix unit assignment rule follows the risk superposition principle: when the row and column risk levels are both high, the maximum value is output.

[0052] The system risk coupling coefficient is output using the preset coordination rules of the dynamic game matrix. The level is determined based on the real-time calculated optical safety deviation value. The risk level is determined based on the flow instability risk value. The cells where the rows and columns intersect in the decision matrix are located and the preset output value is read as the system risk coupling coefficient. If the input value is at the boundary of the level, the preset rules are used to determine the level.

[0053] The system risk coupling coefficient is compared with a preset phase change threshold to generate a phase change margin index. The preset phase change threshold is determined through analysis of historical traffic flow data. The phase change margin index is calculated by subtracting the system risk coupling coefficient from the preset phase change threshold. The result can be positive or negative. A positive value indicates that the current state has margin to the phase change boundary, while a negative value indicates that the phase change boundary has been exceeded. The absolute value of the index reflects the degree of deviation.

[0054] The phase change margin index characterizes the degree to which the metastable state of traffic flow deviates from the phase change boundary by quantifying the difference between the system risk coupling coefficient and the preset phase change threshold. The specific characterization relationship is: Positive Deviation: When the Phase Change Margin Index is positive, it indicates that the system risk coupling coefficient is below the phase change threshold, and the traffic flow is in a metastable state and far from the phase change boundary. A larger index value indicates a larger buffer between the current state and the phase change boundary, and thus greater stability. For example, when the index value is 0.3, the traffic flow can withstand approximately 30% additional risk disturbances without undergoing a phase change.

[0055] Critical Deviation: When the indicator approaches zero (e.g., within ±0.05), it indicates that the system risk coupling coefficient is approaching the phase transition threshold, and traffic flow is in a critical phase transition state. At this point, even small disturbances may trigger a sudden change in flow pattern, necessitating the activation of a primary warning.

[0056] Negative Deviation: When the indicator is negative, it indicates that the system risk coupling coefficient has exceeded the phase transition threshold, and traffic flow has broken through the metastable boundary and entered an unstable phase transition process. The larger the absolute value of the indicator, the greater the degree of instability. For example, if the indicator is -0.2, the highest level of control must be implemented immediately.

[0057] This indicator converts the abstract phase change boundary into a quantifiable numerical scale, directly reflecting the traffic flow stability level and deviation direction through symbols and numerical values, providing an accurate basis for hierarchical control.

[0058] The preset phase transition threshold is determined by statistically analyzing the correlation between traffic flow parameters and accident data from historical fog events, calculating the correlation coefficient between warning accuracy and false alarm rate at different thresholds, and selecting the value that optimizes the comprehensive evaluation index as the threshold. This value is obtained through ergodic testing within a reasonable range, with a typical value between 0.2 and 0.5.

[0059] In the calculation of optical safety deviation, the spatial conflict relationship is quantified taking into account the vehicle's dynamic characteristics: when unusual driving behavior is detected, a safety compensation factor is added to the original ratio. The turning point of the piecewise function is calibrated experimentally.

[0060] A dynamic adjustment mechanism is introduced for determining critical traffic density: a specific percentage is adjusted based on the baseline value, depending on real-time environmental conditions. This adjustment takes into account meteorological factors and road conditions. The adjusted critical value is subject to range verification.

[0061] The dynamic game matrix is ​​constructed by combining experience and data: historical data is collected to optimize matrix cell values. The matrix is ​​regularly updated and maintained.

[0062] The reading process of the system risk coupling coefficient includes boundary processing: when the input value is close to the level boundary, it is interpolated based on the values ​​of adjacent cells. The interpolation weight is determined by the deviation distance ratio.

[0063] After the phase change margin indicator is generated, data smoothing is performed: a moving average method is used to eliminate random fluctuations. When the indicator value for multiple consecutive periods is lower than the warning threshold, the subsequent control mechanism is triggered.

[0064] In terms of abnormal data processing, when visibility values ​​are abnormal, the value from the previous valid period is automatically used as a replacement. When vehicle spacing data is missing, theoretical values ​​are extrapolated based on traffic density. The extrapolation method takes into account the vehicle conversion factor.

[0065] The optical safety deviation level division intervals are optimized through analysis: the boundary values ​​are adjusted to observe the changes in the warning effect and the optimal settings are selected.

[0066] The time accumulation effect is introduced into the calculation of the flow instability risk value: when the high-density state continues for a certain period of time, the risk value accumulates at a preset rate and an upper limit is set.

[0067] The unit assignment of the decision matrix sets nonlinear characteristics: a super-linear growth rule is adopted in double-high-risk areas.

[0068] The setting of phase change threshold takes into account the differences in road section characteristics: the parameters are automatically matched and adjusted according to the road geometry.

[0069] The output value of the system risk coupling coefficient is subjected to data quality verification: when the integrity rate of the sensor from which the input data comes is insufficient, an error mark is added to the output value for subsequent judgment.

[0070] S4. When the phase change margin index is lower than the preset threshold, the maximum speed limit reduction is dynamically constrained based on the fog movement trend and the modified traffic density, so that the maximum speed limit reduction is positively correlated with the phase change margin index. The specific implementation is as follows: When the phase change margin index is lower than the preset threshold, the spatial position migration data of the movement trend of the fog cluster is obtained. The phase change margin index comes from the calculation result of the previous step, and the preset threshold is determined by analyzing the phase change data of historical traffic flow. The movement trend of the fog cluster is obtained by analyzing the spatiotemporal distribution data of visibility at continuous time points: extracting a set of visibility distribution data for multiple time periods, calculating the visibility change vector of each location unit; identifying areas with visibility lower than a specific value as the core area of ​​the fog cluster; tracking the spatial displacement of the center of mass of the core area, the displacement direction is the movement trend direction, and the displacement speed is calculated by dividing the displacement distance by the time interval. The spatial position migration data includes three elements: the movement direction angle, the movement speed, and the core area coverage area.

[0071] The threshold is determined by analyzing the correlation between accident rates and traffic flow parameters based on historical data on critical points of traffic flow phase transitions during foggy events. The threshold is then selected based on the value that maximizes warning accuracy. This method, optimized based on extensive field data, ensures the scientific nature of the threshold setting.

[0072] Extract the road spatial distribution characteristics corresponding to the modified traffic density. The modified traffic density is derived from the output of the previous step. The road spatial distribution characteristics are extracted by dividing the target road segment into fixed-length subintervals; assigning modified traffic density values ​​based on vehicle positioning information; generating a density-spatial distribution curve, and calculating the curve's characteristic parameters, including the density peak position, density gradient, and the continuous length of high-density areas. High-density areas are defined as regions where the density value exceeds a specific ratio of the critical density.

[0073] A dynamic coupling constraint mechanism is established to determine fog movement trends and traffic density corrections. This coupling mechanism includes two constraint functions: the first relates fog movement speed to the sensitivity coefficient of speed limit reductions; the second relates traffic density distribution to the spatial range of speed limit reductions. This dynamic coupling is achieved through a weighted combination: weights are set for movement speed and density distribution; and the weight ratio is adjusted when the angle between the fog movement direction and traffic flow is less than a specific angle. The constraint mechanism outputs a constraint strength coefficient.

[0074] The constraint bounds for the maximum speed limit reduction are determined using a dynamic coupling constraint mechanism. These bounds are determined by a baseline reduction value and a constraint strength factor. The baseline reduction value is determined based on the road type; the upper bound is the baseline reduction value multiplied by the constraint strength factor; and the lower bound is a fixed ratio of the baseline reduction value. Constraint bounds are expressed as numerical intervals and are updated regularly.

[0075] The speed limit reduction proportional coefficient is calculated based on the deviation between the phase change margin indicator and a preset threshold. The deviation is calculated as the difference between the preset threshold and the phase change margin indicator. The proportional coefficient is defined as a piecewise function of the deviation: a smaller coefficient is used for smaller deviations, a medium coefficient for medium deviations, and a larger coefficient for larger deviations. An upper limit is set for the proportional coefficient to prevent excessive speed reduction. A time decay factor is incorporated into the calculation process: if the deviation persists for an extended period, the proportional coefficient increases at a fixed rate.

[0076] The speed limit reduction coefficient is applied to the constraint boundary to generate the final maximum speed limit reduction value. This is generated by multiplying the upper bound of the constraint boundary by the speed limit reduction coefficient. The resulting value is then smoothed by comparing it with the previous cycle's value. If the change exceeds a reasonable threshold, a weighted average is applied. The final value is stored in the control instruction cache.

[0077] In spatial migration analysis, the core area of ​​fog clusters is identified using a dual criterion: visibility values ​​below a specific threshold and area greater than a minimum limit. Centroid displacement calculations take boundary effects into account: when a fog cluster contacts a road boundary, its movement direction is automatically corrected. Movement speed is calculated using a differential method.

[0078] The extraction of road spatial distribution features includes exception handling: when data in a subinterval is missing, it is filled with the average value of the adjacent subintervals. The density gradient calculation uses the numerical differentiation method.

[0079] The weight distribution of the dynamic coupling constraint mechanism is optimized through data analysis: historical fog event data is collected to establish a model-fitting weight combination. The range of the constraint intensity coefficient is set to a reasonable limit.

[0080] The constraint boundary determination process takes into account the influence of road alignment: a reduction factor is added to curved sections, and the lower limit is adjusted on sections with large longitudinal slopes. A rationality check is performed before outputting the boundary value: the value is compared with the adjacent section value, and a smooth transition is initiated if the difference is too large.

[0081] The turning point of the piecewise function for the speed limit reduction coefficient was calibrated experimentally: safe braking requirements were tested under different deviation conditions. The growth rate of the time decay factor was adjusted based on traffic flow conditions.

[0082] The generation of the final maximum speed limit reduction value adds traffic status feedback: when an emergency is detected, the proportional coefficient is automatically increased to a specific value. Smoothing processing uses a filtering algorithm.

[0083] In special scenario processing, when fog dissipates quickly, the constraint strength coefficient is forced to decrease; when traffic density drops sharply, the proportional coefficient is reset. These mechanisms prevent drastic fluctuations in control commands.

[0084] When applying the constraint boundary, spatial adaptation is performed: the actual speed reduction is dynamically adjusted within the constraint boundary based on the local density of the vehicle's location. This adaptation is achieved through the vehicle positioning system, with each vehicle receiving a differentiated speed limit. The adaptation rule is: the speed reduction in high-density areas is a higher percentage of the upper boundary, while the speed reduction in low-density areas is a lower percentage of the lower boundary. Spatial adaptation is updated every 10 seconds to ensure it matches real-time traffic conditions.

[0085] Steps S3 and S4 construct a dynamic assessment of meteorological-traffic coupling risk and an adaptive speed limit control chain. Compared to traditional independent monitoring of visibility or density, the dynamic game matrix established in S3 multi-dimensionally couples the optical safety deviation (representing the spatial conflict between visibility and inter-vehicle distance) with the flow instability risk value (reflecting the deviation between the modified density and the critical density). This overcomes the drawback of separate assessments of meteorological risk and traffic flow risk. By pre-setting rules within the matrix units, the system risk coupling coefficient is quantified, accurately capturing the critical phase transition characteristics of the metastable state of traffic flow in foggy environments. S4 dynamically constrains speed limit reductions based on the phase transition margin indicator, surpassing the existing fixed threshold speed limit model. By establishing a dynamic coupling constraint mechanism between foggy movement trends and modified traffic density, the maximum speed limit reduction is positively adjusted with the phase transition margin: the reduction margin is automatically expanded when the margin decreases, and contracted when it decreases, achieving a graded risk response and minimizing traffic efficiency losses while ensuring safety. By extracting spatial migration data on foggy movement trends and the spatial distribution characteristics of traffic density, S4 enables spatiotemporal adaptation of speed limit instructions. Different from the uniform speed limit on road sections in existing technologies, differentiated speed limit values ​​are generated according to the local density of the vehicle's location and the approaching situation of fog clusters, solving the control mismatch problem caused by the mobility of fog clusters and the spatiotemporal heterogeneity of traffic flow.

[0086] S5. Generate a target speed limit value based on the maximum speed limit reduction value after the constraint, and calculate a safe vehicle distance instruction based on the visibility spatiotemporal distribution data. The specific implementation is as follows: Obtain the current baseline speed limit. The current baseline speed limit is stored in the road section attribute database, determined according to road design standards and dynamically adjusted with weather conditions. The specific acquisition process is as follows: determine the road number and stake number range where the target vehicle is located; query the baseline speed limit for that section, which is set according to the design speed under clear weather conditions; and apply a weather correction: when visibility falls below 1000 meters, the correction process is initiated, and the correction amount is determined according to the visibility classification table. For example, when visibility is 200 to 500 meters, the baseline speed limit on the expressway is reduced from 120 km / h to 100 km / h. This value is updated every 30 seconds, and the data validity is verified during the update: if the weather sensor fails, the previous value is used and the abnormal state is marked.

[0087] Subtract the maximum speed reduction from the current baseline speed limit to generate the target speed limit. The maximum speed reduction comes from the output of step S4. Data alignment is performed before the subtraction calculation is performed: when the update period of the baseline speed limit value is inconsistent with the update period of the reduction value, the valid value with the most recent timestamp is taken. The calculation process includes boundary protection: the result value must not be lower than the statutory minimum speed limit (such as 60 km / h for expressways); when the calculation result is lower than the lower limit, it is automatically corrected to the lower limit. For example, when the baseline speed limit is 100 km / h and the maximum reduction is 30 km / h, the target speed limit value is 70 km / h. The calculation result is temporarily stored in the control instruction buffer, awaiting subsequent processing.

[0088] Extract the visibility value at the current time point from the spatiotemporal visibility distribution data. This data comes from the spatiotemporal matrix generated in step S1. The extraction method is as follows: locate the column index of the current timestamp in the time series matrix; obtain the values ​​of all row elements in that column to form a spatial distribution set; match the nearest road grid cell based on the target vehicle's GPS coordinates; and when the vehicle is at the intersection of two grid cells, take the arithmetic mean of the visibility values ​​of the two cells. Visibility values ​​are expressed in meters, and data validation is performed during extraction. If the value exceeds the acceptable range (0-2000 meters), an exception handler is initiated: the sliding mean of the previous 10 minutes is used instead, triggering a sensor calibration instruction.

[0089] The minimum safe distance threshold is calculated based on the target speed limit and visibility. The calculation principle is: the safe distance must ensure that the following vehicle can brake and stop within the visibility limit. The specific implementation steps are: First, determine the braking reaction time, which includes driver cognitive delay and braking system response delay and is calibrated to a fixed value through real-vehicle testing. Second, calculate the braking distance using the physical kinematic formula: the braking distance is equal to the square of the target speed limit divided by twice the deceleration. The deceleration value is determined based on the road adhesion coefficient, and the road surface condition is obtained from real-time monitoring data from roadside sensors. The minimum safe distance threshold is equal to the sum of the braking reaction time distance and the braking distance, multiplied by a safety factor. The safety factor is set based on the visibility level: 1.5 for visibility below 100 meters, 1.2 for visibility between 100 and 200 meters, and 1.1 for visibility above 200 meters.

[0090] Generate a safe distance instruction based on the minimum safe distance threshold. Instruction generation includes data conversion and packaging: converting the calculated safe distance threshold into a standard instruction format; adding a timestamp and road section location code; and setting the instruction priority: setting it to the highest priority when visibility is less than 50 meters. A logical check is performed before instruction transmission: comparing the safe distance threshold with the current actual distance. When the threshold is greater than the actual distance, an early warning mark is added. The instruction is broadcast through the vehicle-road cooperative communication unit. The data packet structure includes: instruction type code, recommended distance value, validity period, and applicable road section code.

[0091] The implementation details of the weather correction factor in obtaining the baseline speed limit include: establishing a visibility-speed limit mapping table, where each decrease in visibility by a specified interval corresponds to a fixed speed limit reduction. This mapping table is calibrated using expert experience and historical accident data and is updated annually. During this update, the correlation between newly added accident data and the speed limit settings is analyzed.

[0092] Added smoothing to target speed limit calculations: When the change from the previous cycle's target speed limit exceeds a reasonable threshold (e.g., 15%), a gradual adjustment mechanism is implemented. The adjustment rate is set to no more than 5% of the speed limit per minute to prevent dangerous sudden speed changes.

[0093] Visibility value extraction optimizes spatial matching: When the vehicle's position is offset from the monitoring grid, a bilinear interpolation algorithm is used. Four adjacent grid points, centered around the vehicle, are weighted by the inverse of their distances to calculate the final visibility value. The interpolation weight is calculated as the normalized inverse of the square of the distance between each grid point and the vehicle.

[0094] Dynamic compensation is introduced in the safe distance calculation: When rain or snow is detected, road slip compensation is added to the braking distance calculation. The amount of compensation is determined based on the precipitation intensity: 10% for light rain, 20% for moderate rain, and 30% for heavy rain. The compensation coefficient is determined through vehicle dynamics simulation tests.

[0095] The command generation mechanism includes a feedback channel that receives command execution rate data from the vehicle terminal. If the execution rate consistently falls below a set threshold, the system automatically increases the strength of the command. This enhancement includes increasing the frequency of command transmissions, adding audible and visual warnings, and raising the warning level. The execution rate threshold is set at 70%, and if it falls below this threshold for three consecutive cycles, the enhancement mechanism is triggered.

[0096] In special scenarios, when visibility changes dramatically (the rate of change exceeds 30% between cycles), the safe distance calculation enters a rapid response mode: smoothing is ignored and the latest data is used directly, while the command validity period is shortened to 50% of the normal value. When traffic flow is synchronized, the safe distance threshold is multiplied by a coordination factor of 0.8 to promote coordinated driving in platoons.

[0097] Safe distance commands are implemented in a differentiated manner: adjustment coefficients are set based on vehicle type. Large trucks have a coefficient of 1.2, buses have a coefficient of 1.0, and emergency vehicles have a coefficient of 0.9. These coefficients are based on a vehicle braking performance parameter library that integrates vehicle braking test data filed with the Ministry of Industry and Information Technology. When the command is generated, it automatically matches the vehicle type code, enabling personalized push notifications.

[0098] S6. Issue target speed limits and safe distance instructions to vehicles on roads affected by fog. Specific implementation is as follows: Determine the dynamic spatial range of the road section affected by fog. This dynamic spatial range is determined by integrating the fog movement trend with the real-time visibility distribution. First, obtain the spatial position migration data of the fog movement trend generated in step S4, including the movement direction angle and movement speed. Based on the movement speed, the fog front position is predicted within a set time period in the future. Combined with the boundaries of areas with visibility below a critical value (e.g., 100 meters) in the current visibility spatiotemporal distribution data, a polygon coordinate set representing the dynamic spatial range is generated. This range is updated every 30 seconds, taking into account the diffusion effect of fog: when the visibility change rate exceeds a set threshold, the front position prediction increases the diffusion compensation distance.

[0099] The target speed limit and safe distance instructions are encapsulated into a collaborative control instruction set. This encapsulation process involves data structure conversion and information fusion: the target speed limit is derived from the calculation results of step S5, and the safe distance instruction is derived from the output of step S5. The instruction set contains three core fields: the speed limit instruction field stores the target speed limit and valid position range, the distance instruction field stores the minimum safe distance threshold and applicable conditions, and the timestamp field records the instruction generation time. A checksum is added during encapsulation: a cyclic redundancy check is performed on the instruction content, generating an 8-bit checksum that is appended to the end of the data packet. The instruction set utilizes a lightweight design, with the data volume of a single packet limited to 200 bytes.

[0100] The cooperative control instruction set is broadcast to vehicles within the dynamic spatial range via the vehicle-road cooperative communication protocol. The broadcast implementation process is as follows: Activate the roadside communication unit based on the dynamic spatial range; The communication protocol adopts the LTE-V2X standard mode; The broadcast cycle is set according to the traffic risk level: broadcast once every 5 seconds for low risk levels and once every 1 second for high risk levels. Dynamic control of the broadcast range: Only communication units within the dynamic spatial range are allowed to send instructions; units outside the range remain silent. Add a priority tag to the broadcast data packet: When visibility is less than 50 meters, it is set to the highest priority, interrupting non-safety-related communications.

[0101] Dynamically adjust the parameters of broadcast commands based on vehicle location information. This dynamic adjustment mechanism includes receiving the longitude and latitude coordinates uploaded by vehicles in real time; spatially matching these coordinates with the dynamic spatial range; and adding a preliminary speed reduction to the target speed limit when the vehicle approaches the fog boundary (e.g., within 500 meters). When the vehicle is in the core of the fog (visibility less than 50 meters), the safe distance command value is multiplied by an emergency correction factor. The parameter adjustment results are then repackaged and distributed in real time, ensuring that each vehicle receives personalized commands.

[0102] Determining the dynamic spatial range includes exception handling. When an abnormal fog velocity is detected, a conservative estimation mode is activated: a fixed distance extending beyond the current visibility distribution boundary is used as a temporary range. This range is set based on statistical analysis of historical data; for example, a typical value for highway scenarios is 1000 meters. The temporary range persists until the detection data returns to normal.

[0103] Data compression is implemented during the command encapsulation process: The target speed limit is encoded using differential values, with the baseline being the road's design speed. Safe vehicle distances are encoded using a tiered encoding system, with tiers divided into 10-meter intervals. This compression algorithm reduces data transmission by 40%, while also adding a decompression identifier for identification by the vehicle terminal.

[0104] Broadcast communication establishes a feedback mechanism: it monitors the signal reception strength values ​​returned by vehicle terminals and automatically increases transmit power or shortens broadcast cycles when reception strength falls below a set threshold. Feedback data is analyzed every 15 seconds to generate a communication quality heat map for optimizing roadside unit layout.

[0105] Dynamic parameter adjustment incorporates a vehicle type factor: After identifying the vehicle type code, a type compensation is added to the safe distance command value for trucks. The compensation factor is set based on the vehicle's gross vehicle mass (GVMW) classification: 1.1 times for vehicles under 10 tons, 1.2 times for 10-20 tons, and 1.3 times for vehicles over 20 tons. The compensation factor is derived from a vehicle braking performance database.

[0106] In fast-moving fog, dynamic spatial range prediction uses a second-order extrapolation algorithm. Acceleration is calculated based on displacement data from the previous two cycles. The predicted position is calculated as the current position plus the velocity multiplied by time, plus half the acceleration multiplied by time squared. The extrapolation window is dynamically adjusted based on the rate of change in visibility: shortened to 1 minute for high rates of change and extended to 3 minutes for low rates.

[0107] Command broadcasts implement air interface resource scheduling: During periods of traffic congestion, a time-division multiplexing mechanism is activated, dividing the dynamic spatial range into multiple sub-areas and broadcasting them in round-robin fashion according to set time slots. The time slot allocation algorithm prioritizes high-risk sub-areas, with the risk level calculated based on a weighted calculation of visibility and traffic density.

[0108] The parameter adjustment process takes into account inter-vehicle communication capabilities: When inter-vehicle communication delay is detected below a set threshold, cooperative following control parameters are incorporated into the safe distance command. This parameter enables the following vehicle to anticipate the braking behavior of the preceding vehicle, reducing the required safe distance to 90% of the standard value. The cooperative parameters are updated in real time via the inter-vehicle communication link.

[0109] For large convoys, the broadcast mechanism adds a relay forwarding feature: the lead vehicle is designated as a relay node, forwarding commands to following vehicles beyond direct communication range. A relay selection algorithm, based on vehicle position sequence and communication capability index, ensures a 95% command synchronization rate across the entire convoy.

[0110] Version management is implemented when commands are updated: Each frame of broadcast data contains a version number. The onboard terminal compares the locally stored version number to determine whether to update the command. Version conflict resolution rules are as follows: higher-priority commands forcibly overwrite lower-priority commands. For commands with the same priority, the command with the latest timestamp is used. This mechanism prevents control jitter caused by frequent command switching.

[0111] Example 2: Figure 2 The present invention provides a schematic structural diagram of a system for coordinating flexible speed limit and vehicle distance control in highway fog scenarios. The system comprises: Traffic environment perception module, used to collect real-time traffic operation status parameters and visibility spatiotemporal distribution data of fog-affected sections of road. Traffic operation status parameters include traffic density and vehicle spacing. The density dynamic correction module is used to calculate the visibility gradient direction at the current time point based on the visibility spatiotemporal distribution data. When the visibility gradient direction is consistent with the traffic flow direction, the traffic density is weighted and amplified to generate the corrected traffic density. The flow pattern margin assessment module is used to calculate the phase change margin index of traffic flow based on the modified traffic density and optical safety deviation. The optical safety deviation is calculated based on the temporal and spatial distribution data of vehicle spacing and visibility; The speed limit dynamic constraint module is used to dynamically constrain the maximum speed limit reduction according to the fog movement trend and the modified traffic density when the phase change margin index is lower than the preset threshold, so that the maximum speed limit reduction is positively correlated with the phase change margin index; The command collaborative generation module is used to generate the target speed limit value based on the maximum speed limit reduction after the constraint, and calculate the safe vehicle distance command based on the visibility temporal and spatial distribution data; The command broadcast execution module is used to issue target speed limit values ​​and safe vehicle distance instructions to vehicles in the road section affected by fog.

[0112] The calculations involved in the embodiments are all dimensionless numerical calculations, and the preset parameters and thresholds in the calculations are set by those skilled in the art according to actual conditions.

[0113] It should be noted that the present invention can be deployed on the device itself to implement embedded applications, and can also be run on a PC or other terminal with a user interface, thereby meeting various hardware environments and usage requirements.

[0114] The above embodiments can be implemented in whole or in part via software, hardware, firmware, or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product comprises one or more computer instructions or computer programs. When loaded or executed on a computer, the processes or functions described in the embodiments of this application are fully or partially performed. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wireless or wired transmission. Wired transmission methods include optical fiber, twisted pair, coaxial cable, etc.; wireless transmission methods include infrared, microwave, etc. The computer-readable storage medium can be any available medium accessible by a computer, or a data storage device such as a server or data center that contains a collection of one or more available media. The available medium can be magnetic media (e.g., floppy disks, hard disks, tapes), optical media (e.g., DVDs), or semiconductor media. Semiconductor media can be solid-state drives.

[0115] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and modules described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0116] In the several embodiments provided in this application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of the modules is only a logical function division. In actual implementation, there may be other division methods, such as multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or modules, which can be electrical, mechanical or other forms.

[0117] The modules described as separate components may or may not be physically separate, and the components shown as modules may or may not be physical modules, and may be located in one place or distributed across multiple network modules. Some or all of the modules may be selected to achieve the purpose of this embodiment according to actual needs.

[0118] In addition, each functional module in each embodiment of the present application may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module.

[0119] If the functions are implemented in the form of software function modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several 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 method described in each embodiment of the present application. The aforementioned storage medium includes various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0120] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of the present application. Therefore, the scope of protection of the present application should be based on the scope of protection of the claims.

[0121] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for coordinated control of flexible speed limit and vehicle distance in highway fog scenarios, characterized by: include: S1. Real-time data collection of traffic operation status parameters and visibility spatiotemporal distribution data for fog-affected sections of roads. Traffic operation status parameters include traffic density and vehicle spacing. S2. Calculate the visibility gradient direction at the current time point based on the visibility spatiotemporal distribution data. When the visibility gradient direction is consistent with the traffic flow direction, perform weighted amplification on the traffic density to generate a corrected traffic density. S3. Calculate the phase change margin index of traffic flow based on the corrected traffic density and optical safety deviation, where the optical safety deviation is calculated based on the temporal and spatial distribution data of vehicle spacing and visibility; S4. When the phase change margin index is lower than the preset threshold, the maximum speed limit reduction is dynamically constrained based on the fog movement trend and the corrected traffic density, so that the maximum speed limit reduction is positively correlated with the phase change margin index; S5. Generate a target speed limit value based on the maximum speed limit reduction value after the constraint, and calculate a safe vehicle distance instruction based on the visibility spatiotemporal distribution data; S6. Issue target speed limits and safe vehicle distance instructions to vehicles on road sections affected by fog.

2. The method for coordinated control of flexible speed limit and vehicle distance in highway fog scene according to claim 1 is characterized in that: Real-time data collection of traffic operation parameters and visibility temporal and spatial distribution data on fog-affected sections of roads, including: Roadside sensors deployed on fog-affected roads collect traffic density and vehicle spacing in real time. These sensors include video detection equipment and microwave radar equipment. Visibility temporal and spatial distribution data are collected in real time through a network of meteorological monitoring stations deployed along roads affected by fog. The network consists of multiple meteorological sensors distributed at preset intervals. The spatiotemporal distribution data of traffic density, vehicle spacing and visibility are transmitted to the edge computing node through a dedicated communication protocol for timestamp alignment processing.

3. The method for coordinated control of flexible speed limit and vehicle distance in highway fog scene according to claim 2 is characterized in that: The visibility gradient direction at the current time point is calculated based on the visibility spatiotemporal distribution data. When the visibility gradient direction is consistent with the traffic flow direction, the traffic density is weighted and amplified to generate a corrected traffic density, including: Extracting visibility spatial distribution data at the current time point based on visibility spatiotemporal distribution data; The visibility gradient direction is determined by calculating the visibility change rate between adjacent meteorological monitoring stations; Calculate the vector angle between the visibility gradient direction and the preset traffic flow direction; When the vector angle is less than or equal to the preset angle threshold, it is determined that the visibility gradient direction is consistent with the traffic flow direction; The traffic density is amplified using a preset weighting coefficient to generate a corrected traffic density.

4. The method for coordinated control of flexible speed limit and vehicle distance in highway fog scene according to claim 3 is characterized in that: The traffic density is amplified by using a preset weighting coefficient to generate a modified traffic density including: Obtain real-time traffic density detection; extract visibility gradient direction change characteristics; Determining the value of the preset weighting coefficient according to the directional change characteristics of the visibility gradient; The real-time detected traffic density is multiplied by a preset weighting coefficient to generate a corrected traffic density.

5. The method for coordinated control of flexible speed limit and vehicle distance in highway fog scene according to claim 4 is characterized in that: The phase change margin index of traffic flow is calculated based on the corrected traffic density and optical safety deviation, including: Extracting the visibility value at the current time point from the visibility spatiotemporal distribution data; Determine the optical safety deviation based on the spatial conflict relationship between vehicle spacing and visibility values; Generate the flow instability risk value according to the relative difference between the corrected traffic density and the critical traffic density; Establish a dynamic game matrix between optical safety deviation and flow instability risk value; Output the system risk coupling coefficient through the preset coordination rules of the dynamic game matrix; The system risk coupling coefficient is compared with the preset phase change threshold to generate a phase change margin index, which represents the degree to which the metastable state of traffic flow deviates from the phase change boundary.

6. The method for coordinated control of flexible speed limit and vehicle distance in highway fog scene according to claim 5 is characterized in that: The dynamic game matrix of optical safety deviation and flow instability risk value is established including: Define the grading intervals for optical safety deviation; Setting the risk level threshold of the flow instability risk value; Construct a decision matrix with the optical safety deviation level as the row and the flow instability risk value level as the column; The coordination rule output values ​​are preset in the cells of the decision matrix.

7. The method for coordinated control of flexible speed limit and vehicle distance in highway fog scene according to claim 5 is characterized in that: When the phase change margin index is lower than the preset threshold, the maximum speed limit reduction is dynamically constrained based on the fog movement trend and the corrected traffic density, so that the maximum speed limit reduction is positively correlated with the phase change margin index, including: When the phase change margin index is lower than the preset threshold, the spatial position migration data of the fog cluster movement trend is obtained; Extract road spatial distribution characteristics corresponding to the corrected traffic density; Establish a dynamic coupling constraint mechanism between fog movement trend and modified traffic density; Determine the constraint boundary of the maximum speed reduction value based on the dynamic coupling constraint mechanism; The speed limit reduction ratio coefficient is calculated based on the deviation between the phase change margin index and the preset threshold; The speed limit reduction proportional coefficient is applied to the constraint boundary to generate the final maximum speed limit reduction value.

8. The method for coordinated control of flexible speed limit and vehicle distance in highway fog scene according to claim 7 is characterized in that: The target speed limit is generated based on the maximum speed limit reduction after the constraint, and the safe vehicle distance instruction is calculated based on the visibility spatiotemporal distribution data, including: Get the current benchmark speed limit value; Subtract the maximum speed limit reduction value from the current base speed limit value to generate the target speed limit value; Extracting the visibility value at the current time point from the visibility spatiotemporal distribution data; Calculate the minimum safe vehicle distance threshold based on the target speed limit value and visibility value; Generate a safe distance instruction based on the minimum safe distance threshold.

9. The method for coordinated control of flexible speed limit and vehicle distance in highway fog scene according to claim 8 is characterized in that: Issue target speed limits and safe distance instructions to vehicles on roads affected by fog, including: Determine the dynamic spatial range of road sections affected by fog; Encapsulate the target speed limit value and the safe vehicle distance instruction into a cooperative control instruction set; Broadcast cooperative control instruction sets to vehicles within a dynamic spatial range through the vehicle-road cooperative communication protocol; Dynamically adjust the parameter values ​​of the broadcast instructions according to the vehicle location information.

10. A system for coordinating flexible speed limit and vehicle distance in highway fog scenes, for implementing the method for coordinating flexible speed limit and vehicle distance in highway fog scenes according to any one of claims 1 to 9, characterized in that: include: Traffic environment perception module, used to collect real-time traffic operation status parameters and visibility spatiotemporal distribution data of fog-affected sections of road. Traffic operation status parameters include traffic density and vehicle spacing. The density dynamic correction module is used to calculate the visibility gradient direction at the current time point based on the visibility spatiotemporal distribution data. When the visibility gradient direction is consistent with the traffic flow direction, the traffic density is weighted and amplified to generate the corrected traffic density. The flow pattern margin assessment module is used to calculate the phase change margin index of traffic flow based on the modified traffic density and optical safety deviation. The optical safety deviation is calculated based on the temporal and spatial distribution data of vehicle spacing and visibility; The speed limit dynamic constraint module is used to dynamically constrain the maximum speed limit reduction according to the fog movement trend and the modified traffic density when the phase change margin index is lower than the preset threshold, so that the maximum speed limit reduction is positively correlated with the phase change margin index; The command collaborative generation module is used to generate the target speed limit value based on the maximum speed limit reduction after the constraint, and calculate the safe vehicle distance command based on the visibility temporal and spatial distribution data; The command broadcast execution module is used to issue target speed limit values ​​and safe vehicle distance instructions to vehicles in the road section affected by fog.

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