An integrated fog-detecting highway safety driving guidance device and method

Abstract

The present invention relates to the field of intelligent transportation technology, and in particular to an integrated fog-detecting highway safety driving induction device and method. The present invention proposes the following scheme: local visibility data is obtained based on multiple highway safety driving induction devices, and uploaded to the regional controller to generate induction information. The regional controller analyzes and calibrates the data, and by screening the calibration anchor points, using the exponential decay function and iterative gradient calculation, corrects the measurement error and generates road visibility spatial distribution information. Based on this information, the connectivity algorithm is used to identify fog areas, and combined with the road environment and historical accident data, the optimal induction strategy is matched and the induction information is dynamically adjusted. This application improves the accuracy of fog area detection, optimizes the matching of induction information, and improves driving safety and traffic efficiency under low visibility conditions.

Description

Technical Field

[0001] The present invention relates to the field of intelligent transportation technology, and in particular to an integrated fog-detecting highway safety driving induction device and method. Background Art

[0002] Existing fog detection technologies primarily rely on single-point fog measurement devices or regional measurement methods based on linear interpolation. However, these methods struggle to maintain accuracy and stability when the fog is unevenly distributed, measurement equipment exhibits significant errors, and environmental interference is complex. Single-point measurement methods cannot fully reflect the spatial variation of the fog area and are prone to misjudgment due to individual device failures or measurement errors. Traditional interpolation methods also have limited accuracy in identifying fog area boundaries, making it difficult to accurately determine the fog area's extent.

[0003] For example, Chinese patent publication number CN112885116B discloses a vehicle-road cooperative guidance system for rainy and foggy highway scenarios, comprising: an intelligent roadside system, including a roadside information acquisition subsystem, a roadside communication subsystem, and a traffic information release subsystem; an intelligent onboard system, including an onboard information acquisition subsystem, an onboard communication subsystem, and an onboard warning and control subsystem; and a vehicle-road cooperative system for communicating and transmitting data between the intelligent roadside system and the intelligent onboard system. This invention enables vehicles and personnel to obtain driving information and road condition information of other vehicles beyond visual range; obtains a speed prediction model for curves, ramps, and straight sections in rainy and foggy environments; and implements intelligent guidance for vehicle-road cooperative driving on highways in rainy and foggy environments, thereby improving the efficiency and safety of intelligent highways.

[0004] The above existing technologies all suffer from the problems raised in this background art: it is difficult to ensure detection accuracy and stability when the fog area is unevenly distributed, the measurement equipment has large errors, and the environmental interference is complex. To solve the above problems, this application designs an integrated fog-detecting highway safety driving guidance device and method. Summary of the Invention

[0005] The technical problem to be solved by the present invention is to address the deficiencies of the existing technology and provide an integrated fog-detecting highway safety driving induction device and method. Local visibility data is obtained based on multiple highway safety driving induction devices and uploaded to the regional controller to generate induction information. Each induction device is equipped with a fog-detecting module, which adopts a discrete time-sharing coding control rule to excite infrared signals and receive scattered signals through a multi-level power signal pulse train to obtain accurate visibility data. The regional controller calibrates and gradient-calibrates the data, and by screening the calibration anchor points, using an exponential decay function and iterative gradient calculation, corrects the measurement error and generates road visibility spatial distribution information. Based on this information, the connectivity algorithm is used to identify fog areas, and combined with the road environment and historical accident data, the optimal induction strategy is matched and the induction information is dynamically adjusted. In addition, the highway safety driving induction device is integrated with a collision detection sensor, which can monitor the collision status in real time and upload it to the regional controller.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] An integrated fog-detecting highway safety driving induction method is applied to a highway safety driving induction device, wherein the highway safety driving induction device is communicatively connected to a regional controller. The method comprises:

[0008] Acquiring a plurality of local visibility data through a plurality of highway safety driving induction devices, and uploading the local visibility data to the regional controller via corresponding wireless communication terminals, so that the regional controller generates guidance information based on the local visibility data, wherein each highway safety driving induction device is equipped with a fog detection module, which generates a multi-level power signal pulse train using a discrete time-sharing coding control rule, cyclically excites infrared signals of different levels in a target area, and receives scattered signals in real time to obtain local visibility data;

[0009] The guidance information transmitted by the zone controller is displayed.

[0010] The highway safety driving induction device is further equipped with a collision detection sensor, and the method further includes:

[0011] The collision status of the highway safety driving induction device is monitored by the collision detection sensor, and the collision event is reported to the regional controller through the wireless communication terminal.

[0012] The monitoring of the collision status of the highway safety driving induction device includes:

[0013] Collecting vibration signals of the device body;

[0014] Extracting features from the vibration signal to determine whether it is a valid impact event;

[0015] When a valid collision event is determined, the highway safety driving induction device generates event data including the collision location, time, collision intensity and operating status of the induction device, and uploads it to the regional controller via the wireless communication terminal.

[0016] The fog area detection method comprises:

[0017] Receive local visibility data uploaded by multiple highway safety driving induction devices, each of which is equipped with a fog detection module. The fog detection module uses a discrete time-sharing coding control rule to generate a multi-level power signal pulse train, cyclically excites infrared signals of different levels in the target area, and receives scattered signals in real time to obtain local visibility data;

[0018] Calibrate the local visibility data to generate road visibility spatial distribution information;

[0019] Generating fog area information according to the road visibility spatial distribution information, wherein the fog area information includes the fog area range and the fog area visibility;

[0020] Guidance information is generated based on the fog area information.

[0021] Calibrating the local visibility data includes:

[0022] Based on historical visibility and device reliability, calibration anchor points were selected from multiple highway safety driving guidance devices;

[0023] sending a calibration activation instruction to the calibration anchor point to generate an associated data packet, wherein the associated data packet includes local visibility data and temperature and humidity parameters;

[0024] According to the associated data packet, the local visibility data of the remaining highway safety driving induction device is calibrated by a gradient calculation method to generate local visibility data corrected by gradient calibration.

[0025] The calibrating of the local visibility data of the remaining road safety driving guidance device by the gradient calculation method includes:

[0026] Based on the position coordinates of the calibration anchor points and the corresponding local visibility data, a discretized visibility spatial gradient field is constructed. The initial visibility calibration value of each remaining highway safety driving guidance device is calculated using an exponential decay function based on its Euclidean distance and visibility difference from the calibration anchor point.

[0027] According to the visibility spatial gradient field, multiple iterative gradient calculations are performed on the visibility differences between nodes until the number of iterations is reached, wherein each iteration calibrates the visibility value of each node according to the gradient direction and distance weight of adjacent nodes.

[0028] The generating fog area information includes:

[0029] According to the spatial distribution information of the road visibility, target nodes having visibility less than the visibility threshold are screened according to a preset visibility threshold;

[0030] Connecting the target nodes by a connectivity algorithm to generate a low visibility area;

[0031] Boundary fitting is performed on each low visibility area, and fog area information is generated based on the area information within the boundary.

[0032] The generating inducing information includes:

[0033] Classifying the fog area into danger levels according to the fog area information;

[0034] According to the danger level of the fog area, combined with the road type, vehicle density and historical accident information, the corresponding guidance information is selected from the preset strategy library.

[0035] A highway driving safety induction device, comprising:

[0036] The fog measurement module is configured to generate a multi-level power signal pulse train through a discrete time-sharing coding control rule, cyclically drive the infrared transmitting tube, stimulate the infrared signal of the target area and receive the scattered signal in real time to calculate the local visibility data;

[0037] a wireless communication terminal, connected to the regional controller for uploading local visibility data, collision detection data, and receiving guidance information;

[0038] The guidance execution module includes an LED array with adjustable brightness and a direction indicator light, which is used to generate dynamic light bands or direction guidance according to the guidance information sent by the regional controller;

[0039] The impact detection module is equipped with a vibration sensor and an embedded processor. It is used to collect the vibration signal of the device body, determine whether it is a valid impact event through a time-frequency domain feature extraction algorithm, and generate event data including the impact location, time and impact intensity when it is determined to be a valid impact.

[0040] A zone controller, comprising:

[0041] A data receiving module is used to receive local visibility data, temperature and humidity parameters, and collision event data uploaded by multiple highway safety driving induction devices;

[0042] A processor configured to:

[0043] Calibration anchor points are selected based on historical visibility fluctuations and device reliability. A spatial gradient field is constructed based on the visibility data and temperature and humidity parameters of the anchor points. Visibility data from non-calibrated anchor point devices is calibrated using a nonlinear attenuation model and weighted fusion algorithm, and secondary infrared signal verification is triggered in areas with abnormal visibility.

[0044] Connectivity cluster analysis is performed on the calibrated visibility data. The dynamic fog area boundary is fitted by combining the real-time wind speed vector and road topology structure to generate fog area information including fog area range and visibility level.

[0045] The risk factor is calculated based on the visibility level, diffusion speed, and road type in the fog area. LED brightness, flashing frequency, and direction guidance rules are matched from the multimodal strategy library and synchronized to the external traffic management system through the collaborative control interface.

[0046] Collaborative control interface, used to synchronize fog area information and guidance strategies to external traffic management systems, and receive meteorological forecast data to optimize calibration parameters.

[0047] Compared with the prior art, the present invention has the following beneficial effects:

[0048] This method achieves high-precision correction of visibility data by selecting calibration anchor points, combining an exponential decay function and iterative gradient calculation, minimizing measurement errors globally and ensuring the reliability of fog zone identification. Furthermore, a connectivity algorithm is used to precisely delineate fog zone boundaries. Combined with historical accident data, road type, and traffic density, a dynamic matching guidance strategy is implemented to enhance the pertinence of guidance information. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Other features, objects and advantages of the present invention will become more apparent upon reading the detailed description of non-limiting embodiments made with reference to the following drawings:

[0050] Figure 1 This is a schematic flow chart of an integrated fog-detection-based highway safe driving guidance method according to Example 1 of the present invention;

[0051] Figure 2 This is a schematic diagram of data transmission in accordance with embodiment 1 of the present invention;

[0052] Figure 3 This is a flow chart of visibility detection according to Example 1 of the present invention;

[0053] Figure 4 This is a simplified schematic diagram of the visibility detection device emitting infrared rays according to Example 1 of the present invention;

[0054] Figure 5 This is a schematic flow chart of an integrated fog-detection-based highway safe driving guidance method according to Example 2 of the present invention;

[0055] Figure 6 This is a schematic diagram of induction information transmission in Example 1 of the present invention. DETAILED DESCRIPTION

[0056] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0057] Example 1

[0058] See also Figure 1 The present invention provides an embodiment of an integrated fog-detecting highway driving safety induction method, which is applied to a highway driving safety induction device. The highway driving safety induction device is communicatively connected to a regional controller. The specific steps of the method are as follows:

[0059] S1: Acquire multiple local visibility data through multiple highway safety driving induction devices;

[0060] In this embodiment, multiple highway safety guidance devices are deployed along specific sections of a highway. Each device is equipped with a fog detection module. This module utilizes discrete time-sharing coding control rules to generate a multi-level power signal pulse train based on a preset visibility detection device interval sequence, thereby synchronously stimulating infrared signals in the target area. The infrared signal's emission angle and light intensity level are dynamically adjusted for different intervals, and the scattered signal is collected at the receiving end to achieve precise local visibility measurement. This approach improves the stability and anti-interference capabilities of the measurement data, ensuring reliable detection results even in heavy fog or complex lighting conditions.

[0061] S2: uploading the local visibility data to the regional controller via the corresponding wireless communication terminal, so that the regional controller generates guidance information based on the local visibility data;

[0062] In this embodiment, the highway safety driving guidance device establishes a data connection with a regional controller via a wireless communication terminal, uploading real-time local visibility data. After receiving visibility data from multiple highway safety driving guidance devices, the regional controller performs spatiotemporal calibration and distributed gradient calibration on the data. Combining traffic flow data with historical meteorological information, the regional controller generates highly accurate spatial distribution information of road visibility. Based on this information, the regional controller further analyzes the coverage of the fog area and changes in visibility gradients. Based on pre-defined fog zone classification criteria, the regional controller determines the fog zone level and matches the corresponding guidance strategy. This effectively reduces single-point measurement errors, improves the accuracy of fog zone detection, and ensures that the guidance information is more consistent with actual road conditions.

[0063] S3: displays the induction information transmitted by the regional controller;

[0064] In this embodiment, based on the generated guidance information, the regional controller issues instructions to the highway safety driving guidance device via a wireless communication terminal, causing the guidance device to display the corresponding guidance information. Specific display methods include LED variable information boards displaying the fog level and recommended speed, dynamic flashing warning lights as reminders, voice broadcasts to inform the driver of the fog conditions ahead, or sending guidance signals to vehicle terminals via the Internet of Vehicles. The display method of guidance information is adapted based on the severity of the fog area, road structure, and traffic flow. For example, in severe fog, the guidance information will be updated more frequently, and more detailed driving instructions can be issued in conjunction with the on-board navigation system. This method can effectively enhance the driver's perception of the visibility conditions on the road ahead, reduce traffic accidents caused by insufficient visibility, and improve highway traffic efficiency and driving safety.

[0065] See also Figure 2 , a data transmission diagram of an embodiment of the present invention, which is composed of a plurality of highway safety driving induction devices. The visibility data is collected by the highway safety driving induction devices and then transmitted to the regional controller. The number of the devices is from small to large along the direction of the lane.

[0066] See also Figure 3 , the visibility detection flow chart of the embodiment of the present invention, the specific steps of S1 are as follows:

[0067] S1.1: Preset an interval sequence of the visibility detection device, and generate a coded pulse train corresponding to each interval based on the interval sequence of the visibility detection device and a preset discrete time-sharing coding control rule;

[0068] S1.2: Based on the coded pulse train corresponding to each interval, synchronously pass the signal power amplifier device to obtain a multi-level power signal pulse train corresponding to each interval;

[0069] Furthermore, in this embodiment, the process of acquiring the multi-level power signal pulse train corresponding to each interval includes:

[0070] The preset visibility detection device interval sequence includes N visibility detection device intervals and is combined with a control clock at an excitation frequency of 38 kHz to generate N reference clock pulse train sequences;

[0071] Furthermore, in this embodiment, the visibility detection device deployment interval is set with an interval length of 20 meters, and the visibility detection device is deployed according to the path direction to obtain the visibility detection device interval sequence.

[0072] Based on the N reference clock pulse train sequences combined with the counter, obtain the period length T corresponding to each reference clock pulse train and the high level window width and the adjacent low level window width in each period;

[0073] Based on the period length T corresponding to the N reference clock pulse train sequences and the high-level window width in each period, the standard duty cycle of the N reference clock pulse train sequences is obtained;

[0074] Set the signal excitation instantaneous power level range, and each instantaneous power level increases in sequence, and obtain the signal transmission information sequence corresponding to the maximum signal detection strength detected by each historical visibility detection ,in, It represents the instantaneous power corresponding to the pulse train transmitted by the nth visibility detection device at the current time t, Directly determines the emission intensity of infrared signals. In low-visibility scenarios, aerosol scattering and dielectric absorption significantly weaken signal strength. Therefore, it is necessary to increase the instantaneous power (for example, from 20mW to 80mW) to penetrate environmental interference and ensure that the receiver can capture valid echo signals. In this embodiment, by setting different power levels at different duty cycles, the probability of signal detection is increased while reducing the corresponding energy consumption points. Indicates the duty cycle of the pulse train transmitted by the nth visibility detection device at the current time t, which is used to adjust the balance between energy consumption and signal duration. The smaller the duty cycle, the lower the average power consumption; Indicates the energy conversion efficiency of the power amplifier of the nth visibility detection device, which characterizes the effectiveness of energy conversion. Improving efficiency can reduce energy loss and allow the same Use higher , It represents the average power corresponding to the pulse transmitted by the nth visibility detection device in a single cycle; represents the signal-to-noise ratio corresponding to the pulse train transmitted by the nth visibility detection device at time t, Represents the noise power spectral density of the ambient noise on the pulse train transmitted by the nth visibility detection device, which characterizes the intensity of environmental interference (such as rain and snow scattering, electromagnetic noise). For example, heavy rain can cause a sudden increase in noise power. In this case, it is necessary to dynamically increase the instantaneous power or adjust the transmission angle to maintain the signal-to-noise ratio above the detection threshold (such as SNR ≥ 10dB). If the noise mainly comes from heat sources (such as high temperature environments), it is necessary to shorten the noise sampling time of the low-level window to reduce the impact of noise accumulation. This variable can be used to more accurately balance the duty cycle and instantaneous power of the transmitted pulse train, while reducing energy consumption and improving the signal-to-noise ratio of the transmitted power, so that the average power consumption corresponding to each transmission cycle remains at a very low level. It represents the high-level window width corresponding to the pulse train transmitted by the nth visibility detection device at the current time t, Indicates the low-level window width corresponding to the pulse train transmitted by the nth visibility detection device at time t, This represents the emission angle gain factor corresponding to the infrared signal emitted by the nth visibility detection device at time t, which determines the concentration of infrared signal radiation in a specific direction. By dynamically adjusting the emission angle (for example, narrowing it from 60° to 30°), the system can focus energy on the target area (such as the road monitoring direction) and reduce ineffective scattering losses. For example, narrow-angle emission can concentrate energy to penetrate the fog layer in foggy weather, while wide-angle emission is suitable for large-scale, high-visibility monitoring. It represents the thermal noise value corresponding to the nth visibility detection device, the ambient thermal noise, which is related to the temperature; It represents the probability of the nth visibility detection device transmitting an infrared signal at the mth instantaneous power level being detected;

[0075] Furthermore, in this embodiment ,in Indicates the launch angle;

[0076] Based on the signal transmission information sequence and the instantaneous power level interval of the signal excitation, the average power threshold corresponding to the transmitted pulse in a single cycle, and the upper and lower limits of the high-level window width corresponding to the pulse train, a high-level width-instantaneous power-maximum signal-to-noise ratio search equation and corresponding search constraints are constructed. While satisfying the constraints, the probability of the infrared signal emitted by the visibility detection device being detected at each instantaneous power level is maximized.

[0077] Furthermore, a high-level width-instantaneous power-maximum signal-to-noise ratio search equation is constructed by those skilled in the art based on the above variables and a multi-objective linear algorithm;

[0078] Furthermore, the constraints corresponding to the present invention are specifically as follows: ,in 、 The upper and lower limits of the signal excitation instantaneous power level range are divided according to the density of the corresponding transmitted signal in the actual detection process. The specific division will be made by those skilled in the art according to specific needs; , ;

[0079] Furthermore, the average power threshold corresponding to the transmitted pulse in a single cycle in this embodiment is 0.009W;

[0080] Based on the high-level width-instantaneous power-maximum signal-to-noise ratio search equation and the corresponding search constraints, a multi-objective optimization discrimination algorithm and a two-way search rule are combined to search and solve, and the high-level window width that meets the conditions and the corresponding instantaneous power level are obtained, and the high-level window width that meets the conditions is distinguished and coded.

[0081] Furthermore, the process of distinguishing code marking in this embodiment includes:

[0082] An unpredictable pulse interval sequence is generated based on the improved Logistic map combined with the pulse train of corresponding duty cycle;

[0083] Furthermore, the improved Logistic mapping in this embodiment adopts a Cubic-Logistic hybrid mapping algorithm;

[0084] Orthogonal subcarriers are superimposed on the base frequency of the pulse interval sequence to form a spectrum fingerprint, and the distinguishing code and corresponding decoding information of the corresponding pulse string are obtained, and the corresponding decoding information is synchronously pre-stored in the receiving end.

[0085] Furthermore, the process of the bidirectional search rule in this embodiment includes:

[0086] Based on the upper and lower limits of the high-level window width and the instantaneous power level interval of the signal excitation, the upper limit of the high-level window width corresponding to the standard duty cycle is used as the search starting point, and each high-level window width value is searched downward, and the duty cycle corresponding to each high-level window width is obtained with each high-level window width;

[0087] Based on the duty cycle corresponding to each high-level window width, a search is performed from the lower limit to the upper limit of the signal excitation instantaneous power level interval and the emission angle interval, under the condition that the average power threshold corresponding to the emission pulse in a single cycle is met, to obtain the power level sequence that meets the average power threshold corresponding to each duty cycle and the emission angle corresponding to the maximum probability of the infrared signal being detected under the current duty cycle.

[0088] S1.3. Based on the multi-level power signal pulse train, a preset hierarchical power control model is used in conjunction with a preset mapping table of emission angle intervals and signal power levels to visibility, to cyclically stimulate infrared signals of different light intensities and emission angles for the target area within each interval;

[0089] Furthermore, the visibility detection device in this embodiment uses a fully digital CMOS logic circuit without using a single-chip processor, so the corresponding anti-interference ability is extremely strong, and it can accurately generate pulse signals and cyclically change the intensity of the modulated emitted infrared rays.

[0090] S1.4. The signal receiving device configured in the visibility detection device collects infrared signals of different light intensities and emission angles scattered from the target area in real time, and synchronously outputs the signal power level through a preset filtering discrimination evaluation model, and matches and judges whether there is an infrared signal of the corresponding power level in the infrared signals collected at each interval. The specific filtering discrimination evaluation model can be set through the existing filtering algorithm.

[0091] Furthermore, the preset emission angle interval in this embodiment is [39°, 41°], please refer to Figure 4 This is a simplified diagram of the visibility detection device emitting infrared rays, where A is the emission angle, the two circles corresponding to 1 and 2 are the corresponding infrared signal emission ports, and Q represents the target detection area;

[0092] The signal receiving device configured in the visibility detection device is a conical total reflection condenser, which has a large amount of light entering and a small overall size of the equipment, greatly reducing the cost of use.

[0093] If so, then the visibility corresponding to each interval is obtained by combining the infrared signal with the current power level of each interval detected and the signal power level-visibility mapping table, and the visibility is updated and displayed in real time;

[0094] Furthermore, in this embodiment, the step of obtaining the visibility corresponding to each interval includes:

[0095] Based on a power level sequence that satisfies an average power threshold corresponding to each duty cycle, infrared rays of a power level sequence corresponding to each duty cycle are generated from small to large using the hierarchical power control model, and infrared rays of different power levels with specific codes are cyclically emitted at the emission angle corresponding to the maximum probability of being detected under the current duty cycle.

[0096] The signal receiving device configured for each visibility detection device collects the corresponding target area signal in real time, and filters the real-time collected target area signal by combining the marked code and pre-stored decoding information through a filtering algorithm to determine whether the collected target area signal contains an infrared ray signal with a distinguishing code;

[0097] If it exists, the infrared ray signal information with the discriminative code obtained is input into the matching detection algorithm and combined with the signal power level-visibility mapping table to match the infrared ray signal with the discriminative code to the corresponding power level, and obtain the visibility of the current interval area corresponding to the matching level;

[0098] Furthermore, the signal power level-visibility mapping table in this embodiment is constructed by those skilled in the art based on historical weather conditions and specific experimental test data of road areas in combination with a hash table;

[0099] If it does not exist, then according to the infrared ray signal at the power level corresponding to each duty cycle, the transmission, collection and detection matching are carried out cyclically from low to high power levels until the visibility of the current interval area is detected;

[0100] Furthermore, in this embodiment, the process of matching the infrared ray signal with the distinctive code to the corresponding power level includes:

[0101] According to the infrared ray signal information obtained by discrimination, the non-target frequency band noise is filtered out through a Butterworth bandpass filter to obtain the infrared signal to be matched;

[0102] Based on the infrared signal to be matched and the synchronous pre-stored decoding information, the infrared ray signal matching the pre-stored decoding information (such as Logistic chaotic sequence) is extracted;

[0103] Mapping the distinguishing code corresponding to the matched infrared signal to the signal power level-visibility one-to-one mapping table in the hash bucket, and compensating the power level corresponding to the identified infrared signal by a preset power reduction coefficient;

[0104] The key in the distinguishing code here is the CRC-16 checksum of the encoded fingerprint, and the value is the power level;

[0105] Furthermore, the power reduction factor in this embodiment is obtained by analyzing historical measured data under different environments and visibility conditions, and is stored in the above-mentioned signal power level-visibility one-to-one mapping table in one-to-one correspondence with the corresponding power level.

[0106] Furthermore, the CRC-16 check value in this embodiment is generated by the improved Logistic mapping, including 256 groups of chaotic sequences (length 64), each group of chaotic sequences corresponding to a unique CRC-16 check value;

[0107] Based on the distinguishing codes corresponding to the compensated matching infrared ray signals, the signal power level-visibility mapping table is traversed. If there is a corresponding power level in the traversal, the corresponding visibility is obtained by inversion.

[0108] Furthermore, the cyclic emission in this embodiment is to continuously emit the corresponding infrared rays from low to high according to the power level sequence corresponding to each duty cycle, so as to detect the visibility of the corresponding area.

[0109] The highway safety driving induction device is further configured with a collision detection sensor, and the method further includes:

[0110] The collision status of the highway safety driving induction device is monitored by the collision detection sensor, and the collision event is reported to the regional controller through the wireless communication terminal.

[0111] Monitor the collision status of highway safety driving guidance devices, including:

[0112] Collecting vibration signals of the device body;

[0113] Extract features from vibration signals to determine whether they are valid impact events;

[0114] When a valid collision event is determined, the highway safety driving induction device generates event data including the collision location, time, collision intensity and operating status of the induction device, and uploads it to the regional controller via the wireless communication terminal.

[0115] Example 2

[0116] See also Figure 5 The present invention provides an embodiment: an integrated fog detection type highway safe driving guidance method, applied to a regional controller, the fog area detection method comprising:

[0117] A1: Receive local visibility data uploaded by multiple road safety driving guidance devices;

[0118] In this embodiment, multiple highway safety guidance devices are deployed along the highway and upload local visibility data to a regional controller in real time via wireless communication terminals. The fog measurement module within each device utilizes discrete time-sharing coding control rules to generate a multi-level power signal pulse train. This pulse train emits infrared signals of varying levels at set intervals and simultaneously receives scattered signals from the target area to measure local visibility. This ensures the temporal consistency of visibility data, avoiding overall data deviations caused by single-point errors or measurement lags, thereby improving the accuracy of real-time assessments of foggy areas.

[0119] A2: Calibrate local visibility data to generate road visibility spatial distribution information;

[0120] In this embodiment, to improve the credibility of visibility data, the regional controller performs calibration processing on the received local visibility data. First, based on historical visibility data and device reliability indicators, a calibration anchor point is selected, and a calibration activation instruction is sent to the calibration anchor point, causing it to generate an associated data packet containing local visibility data and ambient temperature and humidity parameters. Then, a gradient calculation method is used to construct a visibility spatial gradient field based on the visibility data of the calibration anchor point, and the visibility data of the remaining highway safety driving induction devices is calibrated. Through multiple iterations of gradient calculation, the visibility data of each highway safety driving induction device converges to a reasonable range, generating road visibility spatial distribution information that better reflects the actual road conditions. This method effectively reduces the impact of single-point measurement errors and improves the accuracy and stability of fog area detection.

[0121] A3: Generate fog area information based on the spatial distribution of road visibility;

[0122] The fog area information includes the fog area range and fog area visibility;

[0123] In this embodiment, the regional controller screens data from each measurement point based on calibrated road visibility spatial distribution information, extracting nodes with visibility below a set threshold. These nodes are then clustered using a connectivity algorithm to identify low-visibility areas. Subsequently, a boundary fitting algorithm is used to smooth the boundaries of the low-visibility areas to generate complete fog zone information, including the fog zone range, fog zone center, and average visibility. This approach effectively avoids fog zone shape distortion caused by gaps between measurement points or local outliers, ensuring that fog zone detection results more accurately reflect actual road conditions and improving fog zone identification accuracy.

[0124] A4: Generate guidance information based on fog area information;

[0125] In this embodiment, to effectively guide vehicles through safe passage, the regional controller matches the optimal guidance strategy from a preset strategy library based on the fog area's hazard level, combined with road type, vehicle density, and historical accident information. For light fog areas, guidance may be provided through speed limit reminders, lane change instructions, and other methods, while for heavy fog areas, graded warnings and traffic flow control measures may be activated. Based on the dynamic changes in the fog area and traffic flow, the regional controller can adjust the guidance information in real time and issue guidance instructions through various methods such as LED variable information boards, voice broadcast equipment, and V2X vehicle networks to ensure that drivers can obtain driving guidance information in a timely manner. Through this graded guidance and real-time adjustment, traffic accidents caused by low visibility can be effectively reduced, and road traffic efficiency under complex weather conditions can be improved.

[0126] Existing fog detection technologies typically use independently deployed fog measuring devices to measure road visibility and estimate the overall visibility distribution of the road through single-point or linear interpolation. However, because fog formation is significantly influenced by factors such as terrain, wind speed, and humidity, the data collected by a single fog measuring device often fails to truly reflect the overall fog situation, resulting in significant measurement errors. Furthermore, because fog measuring devices are affected by factors such as ambient light, temperature, and humidity, their measurement accuracy may deviate, and these deviations directly affect the final fog detection results.

[0127] In this embodiment, fog area detection is used as a specific application scenario, and calibration anchor points are selected based on historical visibility data and equipment reliability. These anchor point devices perform baseline calibration on local visibility data through specific algorithms. Compared with the prior art method of directly using the data of fog measuring devices to determine fog areas, this embodiment constructs a visibility space gradient field and uses a gradient calculation method to correct the data of the remaining fog measuring devices, so that the measurement errors between different devices are dynamically adjusted, thereby significantly improving the accuracy of local visibility data. This is equivalent to performing adaptive corrections during the measurement process. Even if individual fog measuring devices are interfered with by external factors, their data can still be integrated into the overall fog area detection system after being corrected by the gradient field, thus avoiding error accumulation.

[0128] See also Figure 6 , a schematic diagram of the induction information transmission according to an embodiment of the present invention, after the regional controller detects the fog area A, it transmits the induction information to the driving induction device within the fog area A.

[0129] The specific steps of A2 are as follows:

[0130] A2.1: Based on historical visibility and device reliability, select calibration anchor points from multiple highway safety driving guidance devices;

[0131] In this embodiment, in order to ensure the accuracy and stability of visibility data, a method based on historical visibility data and device reliability assessment is adopted to select calibration anchor points from multiple highway safety driving induction devices. The selection of calibration anchor points is not just random selection, but a comprehensive judgment based on multiple factors.

[0132] Specifically, first, analyze the historical measurement data of each highway safety driving induction device under different meteorological conditions, calculate the long-term measurement error deviation, and eliminate those devices with large measurement fluctuations or large errors to ensure the high credibility of the calibration anchor points. Secondly, evaluate the working stability of each device, including parameters such as equipment failure rate, data transmission stability, and communication delay, and select devices with stable data transmission, low false alarm rate and small long-term error as calibration anchor points. In addition, geographical factors must also be considered. For example, the calibration anchor points should be evenly distributed within the target monitoring area to avoid distortion of the overall calibration results due to over-concentration of the calibration anchor points. After determining the calibration anchor points, the regional controller will assign special identifiers to these devices so that the data of these devices can be given priority reference in the subsequent calibration process.

[0133] A2.2: Sending a calibration activation command to the calibration anchor point to generate an associated data packet, wherein the associated data packet includes local visibility data and temperature and humidity parameters;

[0134] In this embodiment, after the regional controller determines the calibration anchor points, it sends a calibration activation command to these calibration anchor devices, causing them to enter a dedicated calibration mode. In calibration mode, the calibration anchor points not only collect visibility data, but also synchronously collect environmental information such as temperature and humidity parameters to form a complete associated data package.

[0135] Specifically, because fog formation is significantly affected by temperature and humidity, fog density, coverage, and rate of change can vary under the same meteorological conditions. Ignoring temperature and humidity factors and relying solely on visibility data for calibration can lead to significant errors in fog area identification.

[0136] Furthermore, the present application synchronously collects visibility data and temperature and humidity parameters by associating data packets, and includes metadata such as measurement timestamp and device geographic location in the data packets to ensure the consistency of the data in time and space.

[0137] To prevent measurement errors due to individual device differences, the calibration anchor device, in calibration mode, repeatedly measures visibility, temperature, and humidity, and uses a weighted average method to obtain the final measurement value, reducing the impact of accidental factors on measurement accuracy. All calibration data is transmitted to the regional controller via a wireless communication terminal and simultaneously sent to adjacent non-calibration anchor devices to provide highly reliable reference data to support the subsequent gradient calibration process. This ensures the stability of visibility measurements and the spatiotemporal consistency of data, thereby improving the accuracy of subsequent fog zone identification.

[0138] A2.3: Calibrate the remaining local visibility data of the highway safety driving guidance device using a gradient calculation method based on the associated data packet to generate gradient-calibrated local visibility data;

[0139] In this embodiment, after receiving the associated data package uploaded by the calibration anchor point, the regional controller performs gradient calibration on the remaining road safety driving guidance devices based on the calibration data. The core concept of gradient calibration is to use the calibration anchor point data as a benchmark and combine it with the measurement data of non-calibration anchor point devices to gradually adjust their measurement values ​​to minimize the measurement error of all devices. The associated data package includes the calibration data.

[0140] Specifically, the regional controller first constructs a spatial visibility gradient field. The core variables of this gradient field include the location coordinates of the calibration anchor point, the measured visibility value, and the corresponding temperature and humidity parameters. For each non-calibrated anchor device, the regional controller calculates the spatial distance between it and the calibration anchor point, analyzes the measurement differences between the two in historical measurement data, and corrects the initial measurement value using an exponentially decaying weighting function to ensure that the visibility data does not deviate significantly due to individual device differences. Furthermore, after completing the initial round of gradient correction, an iterative optimization process is performed. After each round of calibration, the data of the non-calibrated anchor device is recalculated against the nearest calibration anchor point and its own measurement value is further adjusted until the overall measurement error falls below a set threshold, ensuring that the data for all devices is within a reasonable range. This not only considers the spatial distance relationship between devices but also combines historical data and meteorological conditions for multi-level error correction, making the calibration results more stable. For example, in traditional methods, if the visibility data measured by a device is significantly lower than that of other devices, that data may be directly discarded or averaged, which may result in the inaccurate identification of actual local low visibility areas. In this embodiment, by introducing the gradient correction model, the data of the device will not be simply eliminated, but will be dynamically adjusted in combination with the data of the surrounding calibration anchor points, so that the visibility measurement results are more reasonable.

[0141] Preferably, while performing gradient calibration, the regional controller also dynamically evaluates the reliability of non-calibrated anchor devices and adjusts their calibration weights based on the device's historical stability. For example, if a device has large fluctuations in past data records, the system will assign a lower weight to its calibration results to reduce the impact of its data on the overall fog zone identification results. This improves the credibility of the data, making fog zone identification more accurate and avoiding misjudgments caused by single device failures or abnormal measurements. Ultimately, all calibrated data will be integrated to generate road visibility spatial distribution information.

[0142] The specific steps of A2.3 are as follows:

[0143] A2.3.1: Based on the coordinates of the calibration anchor points and the corresponding local visibility data, a discretized visibility spatial gradient field is constructed. The initial visibility calibration value of each remaining highway safety driving guidance device is calculated using an exponential decay function based on its Euclidean distance from the calibration anchor point and the visibility difference.

[0144] In this embodiment, the purpose of constructing the visibility spatial gradient field is to achieve high-precision calibration of visibility data, so that the measurement data of all highway safety driving induction devices can be consistent on a global scale, reducing the overall measurement distortion caused by single-point equipment errors.

[0145] Specifically, the coordinates of the calibration anchor points are first determined, and then the visibility data measured at these anchor points is combined to establish the basic visibility reference points. The selection of the calibration anchor points is based on multiple factors, including historical measurement stability, equipment reliability, and geographical uniformity. The measurement data from these anchor points is considered the most reliable benchmark value.

[0146] Furthermore, it is necessary to properly calibrate the initial visibility values ​​of the remaining highway safety driving guidance devices that were not selected as calibration anchor points. Simply using mean interpolation or direct value assignment methods results in poor spatial consistency of the data, making it difficult to reflect the distribution of fog areas in different road areas. Therefore, this embodiment uses an exponential decay function to calculate the initial visibility calibration value for each uncalibrated anchor point device. The basic concept is to assign a dynamic correction weight based on the difference between the spatial distance between the device and the calibration anchor point and the measured value.

[0147] The core function of the exponential decay function is to ensure that devices closer to the calibration anchor point can obtain higher calibration reliability, while the calibration values ​​of devices farther away are gradually affected by the calibration anchor point. This ensures that the calibrated data will not be overly dependent on a specific anchor point, thereby maintaining the spatial smoothness of the overall data. At the same time, because the distribution of fog areas is greatly affected by environmental factors, traditional linear interpolation methods may cause excessive errors in the edge areas of fog areas. The exponential decay method can better adapt to the non-uniform diffusion characteristics of fog areas, making the calibrated visibility distribution more consistent with the actual shape of road fog areas. In this way, each highway safety driving induction device that is not selected as a calibration anchor point can obtain a preliminary visibility calibration value. This value is not only affected by the data of the neighboring calibration anchor points, but also takes into account the spatial gradient characteristics, making the overall calibration process more consistent with the actual distribution pattern of road fog areas.

[0148] A2.3.2: Perform multiple iterative gradient calculations on the visibility differences between nodes based on the visibility spatial gradient field until the number of iterations is reached, wherein each iteration calibrates the visibility value of each node based on the gradient direction and distance weight of adjacent nodes;

[0149] In this embodiment, the gradient calibration process does not just stay at the preliminary exponential decay calibration stage, but adopts an iterative gradient calculation method so that the visibility data of each node can be reasonably stable during the continuous optimization process.

[0150] Specifically, after completing the initial exponential decay calibration, the system performs a global calibration of all uncalibrated nodes based on the visibility spatial gradient field. This iterative gradient utilizes the visibility trends between adjacent nodes, combined with their spatial positional relationships, to dynamically correct the resulting visibility distribution. This ensures that the resulting visibility distribution not only conforms to the actual diffusion characteristics of the foggy area, but also effectively mitigates the impact of errors in individual measurement nodes on the overall data.

[0151] Furthermore, during each round of gradient calculation, the visibility value of each uncalibrated node is adjusted based on the gradient difference with multiple adjacent nodes. The gradient direction is calculated based on the visibility difference between adjacent nodes, and the gradient weight is determined by the spatial distance and measurement error of adjacent nodes. This ensures that the calibration value of each node does not rely solely on a single calibration anchor point, but is constrained by the entire spatial data, so that the calibrated visibility data changes smoothly in space and avoids sudden changes or discontinuities. At the same time, the iterative process is set based on the error convergence threshold or the maximum number of iterations. Once the calibration errors of all nodes converge to the set error range or the maximum number of iterations is reached, the gradient calibration process is terminated and the final visibility calibration value is output.

[0152] The specific steps of A3 are as follows:

[0153] A3.1: Based on the spatial distribution information of road visibility, select target nodes having visibility less than the preset visibility threshold according to the preset visibility threshold;

[0154] A3.2: Connect the target nodes using a connectivity algorithm to generate a low visibility area;

[0155] A3.3: Perform boundary fitting for each low visibility area and generate fog area information based on the area information within the boundary.

[0156] The specific steps of A4 are as follows:

[0157] A4.1: Based on the fog area information, classify the fog area into hazard levels;

[0158] A4.2: Based on the fog area hazard level, combined with road type, vehicle density, and historical accident information, select the corresponding guidance information from the preset strategy library.

[0159] Example 3

[0160] See also Figure 4 The present invention provides an embodiment: an integrated fog-detecting highway safety driving induction device, the highway safety driving induction device comprising:

[0161] The fog measurement module is configured to generate a multi-level power signal pulse train through a discrete time-sharing coding control rule, cyclically drive the infrared transmitting tube, stimulate the infrared signal of the target area and receive the scattered signal in real time to calculate the local visibility data;

[0162] a wireless communication terminal, connected to the regional controller for uploading local visibility data, collision detection data, and receiving guidance information;

[0163] The guidance execution module includes an LED array with adjustable brightness and a direction indicator light, which is used to generate dynamic light bands or direction guidance according to the guidance information sent by the regional controller;

[0164] The impact detection module is equipped with a vibration sensor and an embedded processor. It is used to collect vibration signals from the device body, determine whether it is a valid impact event through a time-frequency domain feature extraction algorithm, and generate event data including the impact location, time, and impact intensity when it is determined to be a valid impact;

[0165] In this embodiment, the total average power corresponding to the highway safety driving induction device per unit time length is less than 0.009W. Therefore, the highway safety driving induction device targeted by this application is also equipped with a solar power generation device, which can fully meet the power supply process of the corresponding device.

[0166] Example 4

[0167] See also Figure 4 The present invention provides an embodiment: a regional controller, the regional controller comprising:

[0168] A data receiving module is used to receive local visibility data, temperature and humidity parameters, and collision event data uploaded by multiple highway safety driving induction devices;

[0169] A processor configured to:

[0170] Calibration anchor points are selected based on historical visibility fluctuations and device reliability. A spatial gradient field is constructed based on the visibility data and temperature and humidity parameters of the anchor points. Visibility data from non-calibrated anchor point devices is calibrated using a nonlinear attenuation model and weighted fusion algorithm, and secondary infrared signal verification is triggered in areas with abnormal visibility.

[0171] Connectivity cluster analysis is performed on the calibrated visibility data. The dynamic fog area boundary is fitted by combining the real-time wind speed vector and road topology structure to generate fog area information including fog area range and visibility level.

[0172] The risk factor is calculated based on the visibility level, diffusion speed, and road type in the fog area. LED brightness, flashing frequency, and direction guidance rules are matched from the multimodal strategy library and synchronized to the external traffic management system through the collaborative control interface.

[0173] Collaborative control interface, used to synchronize fog area information and guidance strategies to external traffic management systems, and receive meteorological forecast data to optimize calibration parameters.

[0174] Although the embodiments of the present invention have been shown and described above, it will be understood that the above embodiments are illustrative and are not to be construed as limitations on the present invention. A person skilled in the art may change, modify, replace and modify the above embodiments within the scope of the present invention.

Claims

1. An integrated fog-detection-based highway safety driving guidance method, applied to a regional controller, comprising: Receive local visibility data uploaded by multiple highway safety driving induction devices, where each highway safety driving induction device is equipped with a fog detection module. The fog detection module uses a discrete time-sharing coding control rule to generate a multi-level power signal pulse train, cyclically drive an infrared emitting tube, stimulate infrared signals in the target area, and receive scattered signals in real time to obtain local visibility data; Calibrate the local visibility data to generate road visibility spatial distribution information; Generating fog area information according to the road visibility spatial distribution information, wherein the fog area information includes the fog area range and the fog area visibility; generating guidance information according to the fog area information; Calibrating the local visibility data includes: Based on historical visibility and device reliability, calibration anchor points were selected from multiple highway safety driving guidance devices; sending a calibration activation instruction to the calibration anchor point to generate an associated data packet, wherein the associated data packet includes local visibility data and temperature and humidity parameters; Calibrate the local visibility data of the remaining road safety driving guidance device using a gradient calculation method according to the associated data packet to generate gradient-calibrated and corrected local visibility data; The calibrating of the local visibility data of the remaining road safety driving guidance device by the gradient calculation method includes: Based on the position coordinates of the calibration anchor points and the corresponding local visibility data, a discretized visibility spatial gradient field is constructed. The initial visibility calibration value of each remaining highway safety driving guidance device is calculated using an exponential decay function based on its Euclidean distance and visibility difference from the calibration anchor point. According to the visibility spatial gradient field, multiple iterative gradient calculations are performed on the visibility differences between nodes until the number of iterations is reached, wherein each iteration calibrates the visibility value of each node according to the gradient direction and distance weight of adjacent nodes.

2. The integrated fog-detection type road safety driving guidance method according to claim 1, characterized in that: The generating fog area information includes: According to the spatial distribution information of the road visibility, target nodes having visibility less than the visibility threshold are screened according to a preset visibility threshold; Connecting the target nodes by a connectivity algorithm to generate a low visibility area; Boundary fitting is performed on each low visibility area, and fog area information is generated based on the area information within the boundary.

3. The integrated fog-detection-type highway safe driving guidance method according to claim 1, characterized in that: The generating inducing information includes: Classifying the fog area into danger levels according to the fog area information; According to the danger level of the fog area, combined with the road type, vehicle density and historical accident information, the corresponding guidance information is selected from the preset strategy library.

4. An integrated fog-detecting highway safety driving guidance method, applied to a highway safety driving guidance device, wherein the highway safety driving guidance device is communicatively connected to a regional controller, characterized in that: The method comprises: Acquiring a plurality of local visibility data through a plurality of highway safety driving induction devices, and uploading the local visibility data to the regional controller via corresponding wireless communication terminals, so that the regional controller generates guidance information based on the local visibility data, wherein each highway safety driving induction device is equipped with a fog detection module, which generates a multi-level power signal pulse train using a discrete time-sharing coding control rule, cyclically drives an infrared emitting tube, excites infrared signals in a target area, and receives scattered signals in real time to obtain local visibility data; displaying the guidance information transmitted by the regional controller; Wherein, enabling the regional controller to generate guidance information according to the local visibility data includes calibrating the local visibility data to generate road visibility spatial distribution information; Calibrating the local visibility data includes: Based on historical visibility and device reliability, calibration anchor points were selected from multiple highway safety driving guidance devices; sending a calibration activation instruction to the calibration anchor point to generate an associated data packet, wherein the associated data packet includes local visibility data and temperature and humidity parameters; Calibrate the local visibility data of the remaining road safety driving guidance device using a gradient calculation method according to the associated data packet to generate gradient-calibrated and corrected local visibility data; The calibrating of the local visibility data of the remaining road safety driving guidance device by the gradient calculation method includes: Based on the position coordinates of the calibration anchor points and the corresponding local visibility data, a discretized visibility spatial gradient field is constructed. The initial visibility calibration value of each remaining highway safety driving guidance device is calculated using an exponential decay function based on its Euclidean distance and visibility difference from the calibration anchor point. According to the visibility spatial gradient field, multiple iterative gradient calculations are performed on the visibility differences between nodes until the number of iterations is reached, wherein each iteration calibrates the visibility value of each node according to the gradient direction and distance weight of adjacent nodes.

5. The integrated fog-detection-type highway safe driving guidance method according to claim 4, characterized in that: The highway safety driving induction device is further equipped with a collision detection sensor, and the method further includes: The collision status of the highway safety driving induction device is monitored by the collision detection sensor, and the collision event is reported to the regional controller through the wireless communication terminal.

6. The integrated fog-detection-type highway safe driving guidance method according to claim 5, characterized in that: The monitoring of the collision status of the highway safety driving induction device includes: Collecting vibration signals of the device body; Extracting features from the vibration signal to determine whether it is a valid impact event; When a valid collision event is determined, the highway safety driving induction device generates event data including the collision location, time, collision intensity and operating status of the induction device, and uploads it to the regional controller via the wireless communication terminal.

7. An integrated fog-detecting road safety driving induction device, used to implement the integrated fog-detecting road safety driving induction method according to any one of claims 4 to 6, characterized in that: The highway safety driving induction device includes: The fog measurement module is configured to generate a multi-level power signal pulse train through a discrete time-sharing coding control rule, cyclically drive the infrared transmitting tube, stimulate the infrared signal of the target area and receive the scattered signal in real time to calculate the local visibility data; a wireless communication terminal, connected to the regional controller for uploading local visibility data and receiving guidance information; The guidance execution module includes an LED array with adjustable brightness and a direction indicator light, which is used to generate dynamic light bands or direction guidance according to the guidance information sent by the regional controller; The impact detection module is equipped with a vibration sensor and an embedded processor. It is used to collect the vibration signal of the device body, determine whether it is a valid impact event through a time-frequency domain feature extraction algorithm, and generate event data including the impact location, time and impact intensity when it is determined to be a valid impact.

8. A zone controller for implementing the integrated fog-detection-type highway safe driving guidance method according to any one of claims 1 to 3, characterized in that: The zone controller includes: A data receiving module is used to receive local visibility data, temperature and humidity parameters, and collision event data uploaded by multiple highway safety driving induction devices; A processor configured to: Calibration anchor points are selected based on historical visibility fluctuations and device reliability. A spatial gradient field is constructed based on the visibility data and temperature and humidity parameters of the anchor points. Visibility data from non-calibrated anchor point devices is calibrated using a nonlinear attenuation model and weighted fusion algorithm, and secondary infrared signal verification is triggered in areas with abnormal visibility. Connectivity cluster analysis is performed on the calibrated visibility data. The dynamic fog area boundary is fitted by combining the real-time wind speed vector and road topology structure to generate fog area information including fog area range and visibility level. The risk factor is calculated based on the visibility level, diffusion speed and road type in the fog area, and the LED brightness, flashing frequency and direction guidance rules are matched from the multimodal strategy library and synchronized to the external traffic management system through the collaborative control interface.

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