Fog lamp induction system, method, device, product and storage medium based on fusion of forward scattering visibility detection and millimeter wave radar
Patent Information
- Application Number
- CN202611535671.7
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2026-09-30
- Publication Date
- 2026-11-03
AI Technical Summary
Traditional fog guidance systems rely on visibility monitoring data from sparse weather stations, making it difficult to capture local visibility changes along highways. This results in a low degree of matching between the guidance mode and actual road conditions, affecting driving safety.
The fog light guidance system, which integrates forward scattering visibility detection with millimeter-wave radar, collects visibility information through forward scattering modules and millimeter-wave radar modules on the fog light nodes. It then combines this information with a calculation module to evaluate and determine the guidance mode, thereby achieving accurate assessment and matching guidance for highway visibility.
It improves the accuracy and reliability of visibility detection in foggy areas, reduces delays in induced decision-making and execution, and enhances highway traffic safety.
Smart Images
Figure CN123033375A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of highway traffic safety technology, and in particular to a fog light guidance system, method, device, product and storage medium based on the fusion of forward scatter visibility detection and millimeter-wave radar. Background Technology
[0002] Driving safety in foggy areas is a major challenge in the transportation sector. Fog patches are characterized by their sudden onset, strong locality, and small scale, seriously threatening driving safety. Traditional fog guidance systems mainly rely on visibility monitoring data from meteorological stations. However, these stations are sparsely distributed (usually spaced several kilometers apart), making it difficult to capture local changes in visibility along the highway, resulting in a low degree of matching between the guidance model and actual road conditions. Summary of the Invention
[0003] This application provides a fog light guidance system, method, device, equipment, product, and storage medium based on the fusion of forward scatter visibility detection and millimeter-wave radar, which can accurately assess the visibility of highways and execute a matching guidance mode based on the assessment results.
[0004] To achieve the above objectives, this application adopts the following technical solution: In a first aspect, a fog light guidance system based on the fusion of forward scattering visibility detection and millimeter-wave radar is provided. The fog light guidance system includes multiple fog light nodes, which are spaced apart on both sides of the highway. Each fog light node includes a forward scattering module, a millimeter-wave radar module, a computing module, and a guidance module. The forward scattering module is used to acquire the first visibility information of the target area of the highway using forward scattering technology; The millimeter-wave radar module is used to collect second visibility information of the target area of the highway and vehicle information of vehicles in the target area using millimeter-wave radar technology. The calculation module is used to determine the visibility assessment value of the target area based on the first visibility information and the second visibility information, and to determine the first guidance mode based on the visibility assessment value and the vehicle information; The induction module is used to perform corresponding induction actions based on the first induction mode.
[0005] Optionally, the calculation module is further configured to: determine a first value based on the first visibility information and the first weight, determine a second value based on the second visibility information and the second weight, and determine a visibility assessment value for the target area based on the first value and the second value; The first weight is determined based on a first ratio, which is the ratio of the standard deviation of the background light noise to the mean of the scattering signal of the forward scattering module. The second weight is determined based on a second ratio, which is the ratio of the signal power to the noise power. The first ratio is negatively correlated with the first weight, and the second ratio is positively correlated with the second weight.
[0006] Optionally, the calculation module is further configured to: obtain a meteorological estimate, determine a third value based on the meteorological estimate and a third weight, and determine the visibility assessment value based on the first value, the second value, and the third value, wherein the third weight is determined based on the temperature-dew difference, and the temperature-dew difference is negatively correlated with the third weight.
[0007] Optionally, the system further includes a control device, which is configured to receive visibility assessment values sent by multiple fog light nodes, determine a second guidance mode for each target area based on the visibility assessment values sent by the multiple fog light nodes, and send the second guidance mode to the multiple fog light nodes; The fog light node's guidance module is used to execute a corresponding guidance action based on the second guidance mode, wherein the priority of the first guidance mode is lower than the priority of the second guidance mode.
[0008] Optionally, the control device is further configured to: determine the visibility estimate value corresponding to each fog light node based on the inverse distance weighted interpolation method; if the absolute value of the difference between the visibility estimate value and the visibility assessment value of the fog light node is greater than a preset difference threshold, then determine that the visibility assessment value of the fog light node is an abnormal value.
[0009] A second guidance mode is determined based on the visibility estimate corresponding to the fog light node and the vehicle information corresponding to the vehicle, and the second guidance mode is sent to the fog light node. The fog light node is used to receive the second guidance mode and execute the guidance action corresponding to the second guidance mode through its own guidance module.
[0010] Secondly, a fog light guidance method based on the fusion of forward scattering visibility detection and millimeter-wave radar is provided, the method comprising: Forward scattering technology was used to collect the first visibility information of the target area of the highway; Millimeter-wave radar technology is used to collect second visibility information of the target area of the highway and vehicle information of vehicles in the target area; Based on the first visibility information and the second visibility information, a visibility assessment value for the target area is determined, and based on the visibility assessment value and the vehicle information, a first guidance mode is determined; Execute the corresponding induction action based on the first induction mode.
[0011] Optionally, determining the visibility assessment value of the target area based on the first visibility information and the second visibility information includes: A first value is determined based on the first visibility information and the first weight; a second value is determined based on the second visibility information and the second weight; and a visibility assessment value for the target area is determined based on the first value and the second value. The first weight is determined based on a first ratio, which is the ratio of the standard deviation of the background light noise to the mean of the scattering signal of the forward scattering module. The second weight is determined based on a second ratio, which is the ratio of the signal power to the noise power. The first ratio is negatively correlated with the first weight, and the second ratio is positively correlated with the second weight.
[0012] Thirdly, an electronic device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the computer program, implements the steps of the fog light guiding method based on forward scattering visibility detection and millimeter-wave radar fusion as in any optional implementation of the second aspect.
[0013] Fourthly, a computer-readable storage medium is provided, the computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the fog light guidance method based on the fusion of forward scattering visibility detection and millimeter-wave radar as described in any of the second aspects.
[0014] Fifthly, a computer program product is provided, comprising a computer program that, when executed by a processor, implements the steps of the fog light guidance method based on forward scattering visibility detection and millimeter-wave radar fusion as described in any of the second aspects.
[0015] The fog light guidance method, apparatus, equipment, product, and storage medium based on the fusion of forward scattering visibility detection and millimeter-wave radar provided in this application have the following beneficial effects: By combining forward scattering technology and millimeter-wave radar technology, the visibility of the target area on the highway is detected through both detection methods, achieving reliable and accurate detection of atmospheric visibility on the highway. Compared to visual sensors, the forward scattering module and millimeter-wave radar module have high-accuracy visibility detection capabilities in extreme environments such as dense fog and heavy rain. Since the computing module and guidance module are deployed in the fog light node, the decision-making and execution of the guidance mode can be completed locally, reducing the implementation delay of guidance decision-making and execution. Attached Figure Description
[0016] Figure 1A schematic diagram of a fog light guidance system based on the fusion of forward scattering visibility detection and millimeter-wave radar, provided for an embodiment of this application; Figure 2 A schematic diagram of a fog light node provided in an embodiment of this application; Figure 3 This is a flowchart illustrating a fog light guidance method based on the fusion of forward scattering visibility detection and millimeter-wave radar, provided as an embodiment of this application.
[0017] Figure 4 This is a schematic diagram of the structure of an electronic device according to an embodiment of this application; Detailed Implementation It should be noted that the terminology used in the embodiments of this application is only for explaining specific embodiments of this application and is not intended to limit this application. In the description of the embodiments of this application, unless otherwise stated, "multiple" means two or more, "at least one" or "one or more" means one, two or more. The terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature.
[0018] References to "one embodiment" or "some embodiments" as described in this specification mean that one or more embodiments of this application include a specific feature, structure, or characteristic described in connection with that embodiment. Therefore, the phrases "in one embodiment," "in some embodiments," "in other embodiments," "in still other embodiments," etc., appearing in different parts of this specification do not necessarily refer to the same embodiment, but rather mean "one or more, but not all, embodiments," unless otherwise specifically emphasized. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless otherwise specifically emphasized.
[0019] Please see Figure 1 , Figure 1 This is a schematic diagram of a fog light guidance system based on the fusion of forward scattering visibility detection and millimeter-wave radar, provided in an embodiment of this application. Figure 1 The fog light guidance system includes multiple fog light nodes, which are spaced apart on both sides of the road. For example, multiple fog light nodes are spaced at preset intervals on both sides of the road, where the preset interval can be 50 meters to 200 meters.
[0020] Please see Figure 2 , Figure 2 This is a schematic diagram of a fog light node provided in an embodiment of this application.
[0021] Figure 2 Each fog light node includes a forward scattering module, a millimeter-wave radar module, a computing module, and a guidance module; Among them, the forward scattering module is used to collect the first visibility information of the target area of the highway using forward scattering technology; The millimeter-wave radar module is used to collect second visibility information of the target area of the highway and vehicle information of vehicles in the target area using millimeter-wave radar technology. The vehicle information includes the vehicle's position information and speed information. The calculation module is used to determine the visibility assessment value of the target area based on the first visibility information and the second visibility information, and to determine the first guidance mode based on the visibility assessment value and vehicle information; The induction module is used to execute the corresponding induction action based on the first induction mode.
[0022] Thus, by combining forward scattering technology and millimeter-wave radar technology, visibility in target areas along highways is detected using both methods, achieving reliable and accurate detection of atmospheric visibility on highways. Compared to visual sensors, the forward scattering module and millimeter-wave radar module have higher visibility detection capabilities in extreme environments such as dense fog and heavy rain. Since the computing module and guidance module are deployed within the fog light node, guidance mode decisions and execution can be completed locally, reducing the latency in guidance decision-making and execution.
[0023] It should be noted that the fog light guidance system of this application embodiment can be applied to ordinary road scenarios as well as highway scenarios.
[0024] In one example, the forward scattering module can be a forward scattering detector that emits a 940nm wavelength laser infrared beam toward the target area of the highway and receives the scattered light signal at a 35° scattering angle, determining the first visibility information based on the Koschmieder principle.
[0025] In one example, the first visibility information includes meteorological optical range.
[0026] The formula for calculating meteorological optical visibility is as follows: ; in, Meteorological optical sight distance (unit: m). The atmospheric extinction coefficient (unit: Np / m) is calculated as the ratio of scattered light intensity to emitted light intensity.
[0027] In one example, the forward scattering module includes: a laser infrared emitting unit, a scattered light receiving unit, a signal processing unit, and a heating and dew-removing unit. The laser infrared emitting unit uses a 940nm laser infrared LED as a light source to emit a 50mW signal with a beam divergence angle of 2° and a frequency of 2.3kHz towards the target area. The scattered light receiving unit uses a high-sensitivity silicon photodiode as a detector to receive the scattered light signal from a 35° scattering angle. The signal processing unit amplifies, filters, and demodulates the received scattered light signal, extracts the scattered light intensity, and calculates the extinction coefficient using instrument constants. The heating and dew-removing unit automatically activates the heater to remove condensation on the lens surface, ensuring the optical window remains clean.
[0028] In one example, a millimeter-wave radar module is used to transmit a linear frequency-modulated continuous wave signal toward a target area, measure the received power attenuation, and then invert visibility based on a fog attenuation model. Millimeter waves attenuate when propagating in fog, and the attenuation coefficient is positively correlated with the liquid water content of the fog.
[0029] As an example, the second visibility information includes meteorological optical range.
[0030] Among them, the 77GHz band attenuation in dense fog can reach 2~4dB / km. Visibility can be retrieved by measuring power attenuation. ; in, The meteorological optical range retrieved by millimeter-wave radar (i.e., the second visibility information mentioned above). The fog attenuation coefficient is... This is an empirical constant, calibrated experimentally.
[0031] As an example, millimeter-wave radar modules experience attenuation when propagating in fog. According to the International Telecommunication Union (ITU) standard ITU-R P.840, the attenuation of the 77 GHz band in dense fog (liquid water content 0.5 g / m³) is approximately 2–4 dB / km. The fog attenuation coefficient is calculated by measuring the difference in received power under clear sky and foggy conditions. ; in, This refers to the receiving power under clear sky conditions. This represents the actual received power. For detection distance.
[0032] Empirical formula for retrieving visibility based on fog attenuation coefficient: ; in, This is an empirical constant, calibrated experimentally.
[0033] Millimeter-wave radar modules can also be used to detect vehicle information within a target area. For example, the millimeter-wave radar module emits a signal whose frequency increases linearly with time towards the target area. This signal reflects back after encountering a vehicle in the target area and is received by the millimeter-wave radar module. Because electromagnetic signals require time to travel from transmission to reception, at the instant of reception, the frequency of the currently transmitted signal is already slightly higher than the frequency of the received signal. The farther the target is, the longer the round-trip time and the greater the frequency difference. Therefore, the distance to the vehicle can be determined by measuring the frequency difference.
[0034] When a vehicle moves radially relative to a millimeter-wave radar module, the frequency of the reflected electromagnetic waves changes. As the vehicle approaches the radar, the frequency of the reflected waves increases; as the vehicle moves away from the radar, the frequency decreases. This change in frequency, known as the "Doppler shift," is proportional to the vehicle's relative speed. The vehicle's speed can be determined by measuring this shift in the frequency of the reflected waves.
[0035] In one example, the calculation module determines a first value based on first visibility information and a first weight, determines a second value based on second visibility information and a second weight, and determines a visibility assessment value for the target area based on the first value and the second value.
[0036] The first weight is determined based on a first ratio, which is the ratio of the standard deviation of the background light noise to the mean of the scattered signal. The second weight is determined based on a second ratio, which is the ratio of the signal power to the noise power. The larger the first ratio, the smaller the first weight; the smaller the first ratio, the larger the first weight, i.e., the first weight and the first ratio are negatively correlated. The larger the second ratio, the larger the second weight; the smaller the second ratio, the smaller the second weight, i.e., the second ratio and the second weight are positively correlated.
[0037] This application assesses visibility using visibility information obtained from a millimeter-wave radar module and a forward scattering module. However, different environments have varying impacts on forward scattering and millimeter-wave radar. For example, heavy rain or snow can directly form water films, ice crystals, or snow accumulation on the optical lens, severely obstructing the emission and reception of infrared beams, significantly affecting the accuracy of visibility information from the forward scattering module. In contrast, the millimeter waves emitted by the millimeter-wave radar have wavelengths much larger than raindrops or snowflakes, exhibiting excellent diffraction and penetration capabilities, thus being less affected by obstructions to the optical lens. Therefore, the first and second weights can be adaptively adjusted based on specific environments. For instance, during clear days, the impact of forward scattering is relatively small, allowing for an increase in the first weight and a decrease in the second weight, meaning the visibility assessment value for the target area of the highway is primarily determined by the first visibility information. In dense fog, due to the significant impact of fog on infrared radiation, the first weight is decreased and the second weight is increased, meaning the visibility assessment value for the target area of the highway is primarily determined by the second visibility information. If one of the detection methods has a problem, its weight is adaptively reduced, and the weight of the other detection method in the visibility assessment is increased accordingly. By adaptively adjusting the weight of the two detection methods in the visibility assessment based on the specific scenario, the reliability of the visibility assessment for the actual scenario is improved. Furthermore, based on the impact of the environment on the two data sources, the weights of the two data sources are adaptively adjusted to improve the accuracy of the visibility assessment.
[0038] In one example, the visibility assessment value is the sum of a first value and a second value, where the first value is the product of the value indicated by the first visibility information and a first weight, and the second value is the product of the second visibility information and a second weight.
[0039] In one example, the first weight satisfies: ; in, As the first weight, The standard deviation of background light noise. This represents the mean of the scattered signal.
[0040] The second weight satisfies: ; in, As the second weight, For signal power, This is signal noise.
[0041] In real-world scenarios, the mean value of the scattered signal will decrease significantly due to physical obstruction of the optical path. The noise level will increase significantly due to the irregular movement of particles such as rain and snow. Therefore, the first weight can be adjusted according to the actual scene by obtaining the mean value of the scattering signal and the standard deviation of the background light noise in the actual environment and based on the changes of the two, so that the first weight can be adapted to the actual scene.
[0042] The mean of the scattered signal refers to the arithmetic mean of the amplitude of the scattered light signal continuously sampled by the receiver of the forward scattering module within a preset time period when the emitting light source is turned on. This value represents the intensity of the "useful signal" received by the receiver.
[0043] The background light noise standard deviation is the statistical fluctuation amplitude of the noise signal intensity in the forward scattering module caused by a combination of factors such as sunlight, stray light, and circuit thermal noise, when the transmitting light source is turned off. The background light noise standard deviation can be determined by using an analog-to-digital converter to perform high-speed continuous sampling of the receiver circuit when the transmitting light source of the forward scattering module is turned off or completely blocked, and then based on the multiple acquired signal amplitude values.
[0044] In one example, the background light noise can be sampled multiple times when the emission light source of the forward scattering module is blocked, and the standard deviation of the background light noise can be determined based on the background light noise obtained from the multiple samplings; correspondingly, the scattering signal can be sampled multiple times when the emission light source of the forward scattering module is turned on, and the mean of the scattering signal can be determined based on the scattering signal obtained from the multiple samplings.
[0045] In one example, when acquiring background light noise corresponding to multiple samples, the background light noise corresponding to multiple samples can be processed by averaging through a sliding window, and the standard deviation of the background light noise can be determined based on the processed background light noise. Correspondingly, when acquiring scattering signals corresponding to multiple samples, the mean of the scattering signal can be determined based on the processed scattering signal.
[0046] In order to adapt to different lighting conditions, the standard deviation of background light noise can be obtained periodically.
[0047] In one example, after the calculation module obtains the first weight, the second weight, and the third weight, the obtained weights can be smoothed, and the visibility assessment value can be determined based on the smoothed weights to avoid frequent weight jumps.
[0048] One example is the smoothing of the weights using the following formula: ; Where α is the smoothing coefficient, taking a value of 0.2 to 0.5. Let be the first, second, or third weight at time t.
[0049] The signal power is the power of the useful echo signal received by the millimeter-wave radar module from the target fog droplets.
[0050] Signal noise refers to the background noise power measured at the output of a millimeter-wave radar module when there is no external target echo signal input.
[0051] Temperature-dew point difference, also known as temperature-dew point difference, refers to the difference between the current ambient air temperature (T) and the dew point temperature (Td). When the difference is close to 0°C (or <2°C), the air is nearly saturated, and water vapor condenses easily, increasing the probability of fog or clouds forming, and consequently, low visibility. If the difference is larger, such as greater than 5°C, the air is drier, the probability of fog or clouds forming is smaller, and consequently, high visibility is more likely. Therefore, the accuracy of visibility assessment can be verified through temperature-dew point difference.
[0052] In one example, the calculation module is also used to obtain meteorological estimates of the target area of the highway, determine a third value based on the meteorological estimates and a third weight, and determine the visibility assessment value of the target area based on the first value, the second value and the third value. The third weight is determined based on the temperature-dew difference. The larger the temperature-dew difference, the smaller the third weight, and the smaller the temperature-dew difference, the larger the third weight. That is, the third weight is negatively correlated with the temperature-dew difference.
[0053] In one example, the visibility assessment value is the sum of the first, second, and third values.
[0054] In one example, the third value is equal to the product of the meteorological estimate and the third weight.
[0055] Forward scattering radar performs well in clear, nighttime, and light fog conditions, but poorly in strong light, lens contamination, and heavy rain / snow. Millimeter-wave radar performs well in dense fog and heavy rain / snow, but poorly in clear weather. Meteorological estimates are used to verify fog formation conditions. Measurements are based on forward scattering and millimeter-wave radar, and meteorological estimates are used to verify the results. For example, a small temperature-dew difference easily leads to dense fog and a lower visibility assessment value; conversely, a large visibility assessment value may result in false alarms. Combining these three methods improves the accuracy of visibility assessment values.
[0056] In one example, the third weight satisfies: ,in, As the third weight, Due to temperature and dew difference, The ambient temperature (T) is the ambient temperature. This is the dew point temperature.
[0057] One example is a visibility assessment value that satisfies: ; in, This is the visibility assessment value. For first visibility information, This is the second visibility information. These are meteorological estimates; As the first weight, As the second weight, It is the third weight.
[0058] In one example, after the calculation module obtains the first weight, the second weight, and the third weight, it normalizes the first weight, the second weight, and the third weight, and determines the visibility assessment value based on the three weights, the first visibility information, the second visibility information, and the meteorological estimate.
[0059] One example, ; ; ; in, , and These are the first, second, and third weights after normalization.
[0060] In one example, the fog light node also includes a sensor module for measuring the temperature and humidity information of the surrounding environment of the fog light node. After receiving the temperature and humidity information, the calculation module determines the meteorological estimate based on the temperature and humidity information.
[0061] In one example, the fog light node is connected to the measuring equipment of a weather station to obtain initial meteorological information measured by the weather station. Then, based on the initial meteorological information, temperature information, and humidity information, a meteorological estimate is determined. Since weather stations are usually sparsely distributed (with a spacing of several kilometers), it is difficult to capture local changes in visibility along the highway. This application combines the meteorological information provided by the weather station with the temperature and humidity information obtained by the fog light node to achieve accurate prediction of the meteorological estimate.
[0062] In one possible implementation, the calculation module compares the first visibility information and the second visibility information, and determines the larger of the two as the visibility assessment value for the target area. For example, when the millimeter-wave radar module is less affected by rainfall, the second visibility information can be used as the visibility assessment value; when the forward scattering module is working normally, the first visibility information can be used as the visibility assessment value.
[0063] In one possible implementation, the calculation module determines the first confidence level of the first visibility information and the second confidence level of the second visibility information, and determines the visibility assessment value based on the visibility information with the higher confidence level; if both confidence levels are lower than the threshold, the average of the two is taken.
[0064] In one possible implementation, the computation module uses Kalman filtering to fuse the first and second visibility information. Specifically, using the visibility assessment value as the state variable and the first and second visibility information as the observations, state equations and observation equations are established, and the visibility assessment value is obtained through recursive calculation.
[0065] In one possible implementation, the computing module uses a pre-trained neural network model, taking first visibility information, second visibility information, and environmental parameters (such as temperature, humidity, and light) as inputs to the neural network model, and the neural network model outputs a visibility assessment value.
[0066] In one example, the calculation module determines a first guidance mode based on visibility assessment values, vehicle information, and a first mapping relationship, wherein the first mapping relationship describes the alarm conditions satisfied by each first guidance mode, and different alarm conditions correspond to different first guidance modes.
[0067] In one example, the calculation module can determine the distance between multiple vehicles based on vehicle information of multiple vehicles in the target area, determine the limit distance between two vehicles based on the visibility assessment value, and if the distance between vehicles is less than the limit distance, the guidance module executes the guidance action corresponding to the first guidance mode to remind the vehicles to maintain a distance.
[0068] The vehicle's current speed is obtained. If the vehicle's current speed is greater than the speed limit, the guidance module executes the guidance action corresponding to the first guidance mode to remind the vehicle to maintain the speed limit.
[0069] One example is that the functions required by the first guidance mode, such as road contour enhancement, rear-end collision warning, fatigue guidance, emergency warning, or curve assist, can be achieved through guided actions.
[0070] In one example, the alarm conditions include visibility assessment value conditions. For instance, if the visibility assessment value indicates that the visibility is less than a first visibility threshold, then the first guidance mode is a road contour enhancement mode. Based on this mode, the guidance module performs the action of flashing a yellow LED and displaying the word "slow".
[0071] In one example, the guidance module includes multiple LED lights, spaced apart or continuously distributed along both sides of the road. Guidance actions are executed by controlling the color and on / off state of the multiple LED lights.
[0072] In one example, the alarm conditions include visibility assessment conditions and vehicle spacing conditions. For instance, if the visibility is less than a second visibility threshold (e.g., 100 meters) and the distance between two vehicles on the road is less than a distance threshold, then the first guidance mode is a rear-end collision prevention warning mode. The action performed by the guidance module based on this mode is to turn the LED red at a specified distance.
[0073] In one example, the calculation module determines multiple candidate first guidance modes based on visibility assessment values, vehicle information, and a first mapping relationship, and then determines the first guidance mode to be executed by the guidance module based on the priority of the multiple candidate first guidance modes, wherein the priority of the first guidance mode executed by the guidance module is higher than the priority of other first guidance modes among the multiple candidate first guidance modes.
[0074] As visibility assessment values and vehicle information change constantly, the first guidance mode determined by the calculation module may also change to meet the needs of the actual scenario.
[0075] In one example, the calculation module is further configured to determine the possible safety status of the vehicle based on vehicle information, determine a first guidance mode based on the possible safety status of the vehicle, and cause the guidance module to execute the guidance action corresponding to the first guidance mode.
[0076] Possible states include vehicle speeding, distance between vehicles being less than a distance threshold, absolute deceleration being greater than a threshold (e.g., 5 m / s²), and time to collision (TTC) being less than a preset time (e.g., 3 seconds). The distance threshold is determined based on visibility assessment values.
[0077] in, ;in, To maintain distance from the vehicle in front, This refers to relative velocity.
[0078] One example, see again Figure 1 The fog light guidance system based on the fusion of forward scattering visibility detection and millimeter-wave radar also includes a control device. Multiple fog light nodes send visibility assessment values and vehicle information to the control device. The control device determines a traffic situation risk index based on the visibility and vehicle information provided by the multiple fog light nodes, determines a second guidance mode based on the traffic situation risk index, and sends the second guidance mode to multiple fog light nodes in the target area. The guidance modules of the multiple fog light nodes cooperate to execute the actions corresponding to the second guidance mode.
[0079] The traffic situation risk index is used to indicate the probability of traffic accidents occurring on highways.
[0080] In order to execute the action corresponding to the second guidance mode, the guidance modules of multiple fog light nodes can execute the same action or different actions.
[0081] Please see again Figure 2 The fog light node also includes a communication module, through which the fog light node communicates with the control device.
[0082] After obtaining visibility assessment values and vehicle information at the fog light node, the visibility assessment values and vehicle information are sent to the control equipment through the communication module.
[0083] The communication module of the fog light node is also used to receive the second induction mode sent by the control device.
[0084] In one example, the first guidance mode has a lower priority than the second guidance mode. The guidance module receives both the first and second guidance modes, prioritizing the execution of the second mode. Furthermore, if, during the execution of the first guidance mode, an instruction to execute the second guidance mode is received, the execution of the action corresponding to the first guidance mode is interrupted, and the execution of the action corresponding to the second guidance mode begins. Because the control device acquires visibility information for multiple areas of the highway based on visibility data provided by multiple fog light nodes, and determines a more targeted guidance mode based on this visibility information, and associates guidance modes across multiple areas, the applicability of the guidance modes is improved.
[0085] In one example, the control device calls a traffic situation risk prediction model based on visibility and vehicle information provided by multiple fog light nodes to obtain a traffic situation risk index. The traffic situation risk prediction model is a pre-trained model that can be used to predict the traffic situation risk index.
[0086] One example is that the traffic situation risk index meets the following criteria: ; in, This is a traffic situation risk index. For the first The weight coefficients of each fog light node, It is related to traffic flow on highways and can be determined based on highway vehicle information. This is a visibility assessment value. Risk Index. The larger the value, the more dangerous the traffic situation.
[0087] In one example, the control device determines a second guidance mode based on a traffic situation risk index and a second mapping relationship, which describes the range of the traffic situation risk index corresponding to each guidance mode.
[0088] For example, when R<1, the second guidance mode is the normal guidance mode; when 1≤R<2, the second guidance mode is used to increase the guidance brightness, and the rear-end collision warning distance should usually be shortened when the risk increases; when R≥2, the second guidance mode is used to push alarms to the remote control platform, suggesting speed limits or road closures.
[0089] In one example, the control device receives visibility assessment values sent by multiple fog light nodes. It can detect these visibility assessment values to identify outliers. Then, based on the visibility assessment values of multiple adjacent fog light nodes, it determines the visibility assessment value of the fog light node corresponding to the outlier. Based on the determined visibility assessment value, it determines a second guidance mode and sends the second guidance mode to the fog light node. The guidance module of the fog light node executes the action corresponding to the second guidance mode. This allows the control device to verify the detection results of the fog light node and correct the actions performed by the fog light node based on the verification results.
[0090] Among them, the Inverse Distance Weighting (IDW) method is based on a core assumption: the closer a known point is to the target location, the greater its influence on the target location. By assigning a weight inversely proportional to the distance to each known fog light node, the visibility value of the target fog light node is estimated (as shown in the visibility estimate below).
[0091] In one example, after receiving visibility assessment values from multiple fog light nodes, the control device determines the visibility estimate value corresponding to each fog light node based on the inverse distance weighted interpolation method; if the absolute value of the difference between the visibility estimate value and the visibility assessment value of the fog light node is greater than a preset difference threshold, then the visibility assessment value of the fog light node is determined to be an abnormal value.
[0092] Based on the visibility estimate of the fog light node and the vehicle information, a second guidance mode is determined and sent to the fog light node. The fog light node receives the second guidance mode and executes the guidance action corresponding to the second guidance mode through the guidance module.
[0093] In one example, the difference threshold is the product of the visibility estimate and a threshold ratio of 0.3.
[0094] One example uses inverse distance-weighted interpolation (IDW) to calculate the visibility estimate for each fog light node: ; Where: V(x) is the position Visibility interpolation corresponding to the fog light node at that location. For the first Visibility assessment value for each node; For position and number The distance between nodes; It is a power parameter, with a value of 2.
[0095] For example, if there are 3 fog light nodes on the road, with a spacing of 50 meters between them, the visibility assessment values detected by the forward scattering module and millimeter-wave radar module of each fog light node are as follows: Fog light node 1 (position 0 meters): = Fog light node 2 (location 50 meters): = Fog light node 3 (located 100 meters away): = The visibility at a location 25 meters away is: = , = , = ; .
[0096] If the visibility assessment value at the location 25 meters from fog light node 2 is 120 meters, then the difference between the two is 15. 0.3 = 40.5, and 15 is less than 40.5, meaning the visibility assessment value corresponding to this fog light node is normal.
[0097] In one example, after acquiring visibility assessment values and vehicle information sent by multiple fog light nodes, the control device constructs a road visibility spatial distribution map based on the visibility assessment values and vehicle information. This road visibility spatial distribution map indicates the visibility assessment information of multiple target areas corresponding to the multiple fog light nodes on the highway, and a second guidance mode is generated based on the road visibility spatial distribution map. For example, based on the road visibility spatial distribution map, the location of fog patches on the highway, the start and end boundaries of the fog patches, and the characteristics of the fog patches determine the second guidance mode corresponding to the multiple fog light nodes.
[0098] Please see Figure 3 , Figure 3 This is a flowchart illustrating a fog light guidance method based on the fusion of forward scattering visibility detection and millimeter-wave radar, provided as an embodiment of this application.
[0099] S301. The first visibility information of the target area of the highway is collected using forward scattering technology.
[0100] For a detailed explanation of the implementation method, please refer to the implementation process of the forward scattering module in the above embodiments.
[0101] S302. Millimeter-wave radar technology is used to collect second visibility information of the target area of the highway and vehicle information of vehicles in the target area.
[0102] For a detailed explanation of the implementation method, please refer to the implementation process of the millimeter-wave radar module in the above embodiment.
[0103] S303. Determine the visibility assessment value of the target area based on the first visibility information and the second visibility information, and determine the first guidance mode based on the visibility assessment value and vehicle information.
[0104] For details on the implementation method, please refer to the implementation process of the calculation module in the above embodiment.
[0105] S304. Execute the corresponding induction action based on the first induction mode.
[0106] For a detailed explanation of the implementation method, please refer to the implementation process of the induction module in the above embodiments.
[0107] Optionally, determining the visibility assessment value of the target area based on the first visibility information and the second visibility information in S303 includes: A first value is determined based on first visibility information and a first weight; a second value is determined based on second visibility information and a second weight; and a visibility assessment value for the target area is determined based on the first value and the second value. The first weight is determined based on a first ratio, which is the ratio of the standard deviation of the background light noise to the mean of the scattered signal of the forward scattering module. The second weight is determined based on a second ratio, which is the ratio of the signal power to the noise power. The first ratio is negatively correlated with the first weight, and the second ratio is positively correlated with the second weight.
[0108] The fog light guidance method based on the fusion of forward scattering visibility detection and millimeter-wave radar provided in this application embodiment can be applied to electronic devices, such as smartphones, tablets, personal computers, and servers. In this application embodiment, the device used to implement the function of the electronic device can be called an electronic device. Alternatively, a device that enables the electronic device to implement this function can be installed in the electronic device, such as a chip system. The chip system can be composed of chips or include chips and other discrete components.
[0109] Figure 4 This is a schematic diagram of the structure of an electronic device shown in an embodiment of this application, such as... Figure 4 As shown in the illustration, an electronic device 40 is provided in this application embodiment. This electronic device 40 can be specifically a terminal device or a server. In some embodiments, the electronic device 40 can be specifically a central control module in a server, or its functions can be implemented by the central control module in the server. In some embodiments, the electronic device 40 can be specifically a central control module in a terminal device, or its functions can be implemented by the central control module in the terminal device. For example... Figure 4As shown, the electronic device may include a processor 401, a memory 402, a communication bus 403, and a communication interface 404. The processor 401 is connected to the memory 402 and the communication interface 404 via the communication bus 403. It should be noted that... Figure 4 The illustrated device structure does not constitute a limitation on the electronic device 40. The electronic device 40 may include more or fewer components than illustrated, or combine certain components, or have different component arrangements. This application embodiment does not limit this. The following, in conjunction with... Figure 4 A detailed description of each component of the electronic device 40 is provided.
[0110] The processor 401 can be a general-purpose central processing unit (CPU), microprocessor, application-specific integrated circuit (ASIC), or one or more integrated circuits, used to execute relevant programs to perform the steps executed by the electronic device in the fog light guidance method based on forward scattering visibility detection and millimeter-wave radar fusion described in the embodiments of this application. The processor 401 can also be an integrated circuit chip with signal processing capabilities. In implementation, each step of the fog light guidance method based on forward scattering visibility detection and millimeter-wave radar fusion of this application can be completed by the integrated logic circuits in the hardware of the processor 401 or by instructions in software form. The processor 401 can also be a general-purpose processor, digital signal processor (DSP), application-specific integrated circuit (ASIC), field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this application. The general-purpose processor can be a microprocessor or any conventional processor, etc. The steps of the method disclosed in the embodiments of this application can be directly implemented by a hardware decoding processor, or implemented by a combination of hardware and software modules in the decoding processor. The software modules can reside in random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, or other mature storage media in the art. This storage medium is located in memory 402. The processor 401 reads the information in memory 402 and, in conjunction with its hardware, executes the steps performed by the electronic device in the fog light guidance method based on forward scattering visibility detection and millimeter-wave radar fusion in the embodiments of this application. Memory 402 can be read-only memory (ROM), static storage medium, dynamic storage medium, or random access memory (RAM). Memory 402 can store programs and data, such as the program of the fog light guidance method based on forward scattering visibility detection and millimeter-wave radar fusion in the embodiments of this application. When the program stored in memory 402 is executed by processor 401, processor 401 performs the various steps executed by the electronic device in the fog light guidance method based on forward scattering visibility detection and millimeter-wave radar fusion according to embodiments of this application. Communication interface 404 uses transceiver devices, such as, but not limited to, transceivers, to enable communication between electronic device 40 and other devices or communication networks.
[0111] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units is merely an example. In practical applications, the functions described above can be assigned to different functional units as needed, that is, the internal structure of the highway guidance system can be divided into different functional units to complete all or part of the functions described above. The functional units in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units are only for easy differentiation and are not intended to limit the scope of protection of this application. The specific working process of the units in the above system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.
[0112] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, can implement the steps in the various method embodiments described above.
[0113] This application provides a computer program product that, when run on an electronic device, enables the electronic device to perform the steps described in the various method embodiments above.
[0114] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, refer to the relevant descriptions of other embodiments.
[0115] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.
[0116] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.
[0117] Finally, it should be noted that the above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions within the technical scope disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.
Claims
1. A fog light guidance system based on the fusion of forward scattering visibility detection and millimeter-wave radar, characterized in that, The fog light guidance system includes multiple fog light nodes, which are spaced apart on both sides of the road. Each fog light node includes a forward scattering module, a millimeter-wave radar module, a computing module, and a guidance module. The forward scattering module is used to acquire the first visibility information of the target area of the highway using forward scattering technology; The millimeter-wave radar module is used to collect second visibility information of the target area of the highway and vehicle information of vehicles in the target area using millimeter-wave radar technology. The calculation module is used to determine a first value based on the first visibility information and the first weight, determine a second value based on the second visibility information and the second weight, determine a visibility assessment value of the target area based on the first value and the second value, and determine a first guidance mode based on the visibility assessment value and the vehicle information; The first weight is determined based on a first ratio, which is the ratio of the standard deviation of the background light noise to the mean of the scattered signal of the forward scattering module, and the first ratio is negatively correlated with the first weight; the second weight is determined based on a second ratio, which is the ratio of the signal power to the noise power, and the second ratio is positively correlated with the second weight. The induction module is used to perform corresponding induction actions based on the first induction mode.
2. The system according to claim 1, characterized in that, The calculation module is further configured to: obtain a meteorological estimate, determine a third value based on the meteorological estimate and a third weight, and determine the visibility assessment value based on the first value, the second value, and the third value, wherein the third weight is determined based on the temperature-dew difference, and the temperature-dew difference is negatively correlated with the third weight.
3. The system according to claim 1, characterized in that, The system also includes a control device, which is used to receive visibility assessment values sent by multiple fog light nodes, determine a traffic situation risk index based on the visibility assessment values sent by the multiple fog light nodes, determine a second guidance mode for each target area based on the traffic situation risk index, and send the second guidance mode to the multiple fog light nodes. The traffic situation risk index is used to indicate the probability of a traffic accident occurring on the highway. The fog light node's guidance module is used to execute a corresponding guidance action based on the second guidance mode, wherein the priority of the first guidance mode is lower than the priority of the second guidance mode.
4. The system according to claim 3, characterized in that, The control device is further configured to: determine the visibility estimate value corresponding to each fog light node based on the inverse distance weighted interpolation method; if the absolute value of the difference between the visibility estimate value and the visibility assessment value of the fog light node is greater than a preset difference threshold, then determine that the visibility assessment value of the fog light node is an abnormal value. A second guidance mode is determined based on the visibility estimate corresponding to the fog light node and the vehicle information of the vehicles in the target area, and the second guidance mode is sent to the fog light node.
5. The system according to claim 1, characterized in that, The first weight satisfies: ; in, As the first weight, The standard deviation of background light noise. The mean of the scattered signal; The second weight satisfies: ; in, As the second weight, For signal power, This is signal noise.
6. A fog light guidance method based on the fusion of forward scattering visibility detection and millimeter-wave radar, characterized in that, The method includes: Forward scattering technology was used to collect the first visibility information of the target area of the highway; Millimeter-wave radar technology is used to collect second visibility information of the target area of the highway and vehicle information of vehicles in the target area; A first value is determined based on the first visibility information and the first weight; a second value is determined based on the second visibility information and the second weight; a visibility assessment value for the target area is determined based on the first value and the second value; and a first guidance mode is determined based on the visibility assessment value and the vehicle information. The first weight is determined based on a first ratio, which is the ratio of the standard deviation of the background light noise to the mean of the scattered signal of the forward scattering module, and the first ratio is negatively correlated with the first weight; the second weight is determined based on a second ratio, which is the ratio of the signal power to the noise power, and the second ratio is positively correlated with the second weight. Execute the corresponding induction action based on the first induction mode.
7. The method according to claim 6, characterized in that, The first weight satisfies: ; in, As the first weight, The standard deviation of background light noise. The mean of the scattered signal; The second weight satisfies: ; in, As the second weight, For signal power, This is signal noise.
8. An electronic device, characterized in that, It includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the computer program, implements the steps of the method as described in claim 6 or 7.
9. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method as described in claim 6 or 7.
10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method as described in claim 6 or 7.