Road illumination detection method and system based on lightweight dynamic acquisition

Abstract

The invention discloses a road illumination detection method and system based on lightweight dynamic acquisition. The method comprises the following steps: acquiring illumination data acquired by an illumination sensor mounted in a vehicle and positioning data acquired by a positioning module; filtering the contrast data to obtain a processing result; judging whether the vehicle is in a static state; if yes, the change frequency of the processing result along with time is analyzed, street lamp flicker detection is carried out, and a detection result is obtained; performing illumination conversion on the processing result to obtain average road surface illumination; and if not, removing interference from the processing result by adopting a statistical outlier detection mode so as to update the processing result, and performing illumination conversion on the processing result so as to obtain the average road surface illumination. By implementing the method, the illumination data can be collected in real time in the driving process, the actual visual experience of a driver is reflected, the defects of insufficient real-time performance and perception deviation of a traditional method are overcome, and a real-time and accurate illumination measurement result is provided.

Description

Technical Field

[0001] The invention relates to a road illumination detection method, and more specifically to a road illumination detection method and system based on lightweight dynamic acquisition. Background Art

[0002] Road safety depends largely on good lighting conditions, so one of the key methods to evaluate road lighting is to measure the coverage and illumination level of road lights. Currently, static sensors and mobile illumination detection vehicles are common road lighting detection technologies, but they each have some limitations.

[0003] Although static sensors can monitor the illumination of a specific area at regular intervals, they are usually installed on lamp poles or roadsides, with fixed positions and limited coverage, and cannot dynamically reflect changes in road lighting. In addition, static sensors often require a large amount of equipment to ensure comprehensive monitoring, resulting in high installation and maintenance costs. Moreover, under complex road conditions, static sensors are difficult to fully reflect changes in road illumination, which not only affects the real-time maintenance of road lighting, but may also increase the risk of traffic accidents. Although mobile illumination detection vehicles can cover a large area of ​​roads through inspections, the number of vehicles is limited, and it is difficult to conduct real-time monitoring over a large area. In addition, the measurement data of traditional detection vehicles mainly comes from roof equipment, which is inconsistent with the illumination inside the car from the driver's perspective, which may lead to a deviation between the illumination evaluation and the driver's subjective experience.

[0004] Therefore, it is necessary to design a new method to realize the real-time collection of illuminance data during driving and reflect the driver's actual visual experience, overcome the lack of real-time performance and perceptual bias of traditional methods, and provide real-time and accurate illuminance measurement results. Summary of the invention

[0005] The purpose of the present invention is to overcome the defects of the prior art and provide a road illumination detection method and system based on lightweight dynamic acquisition.

[0006] To achieve the above object, the present invention adopts the following technical solution: a road illumination detection method based on lightweight dynamic acquisition, comprising: Acquire positioning data collected by a positioning module installed in the vehicle and illumination data collected by an illumination sensor installed in the vehicle; filtering the illumination data to obtain a processing result; determining whether the vehicle is in a stationary state according to the positioning data; If the vehicle is in a stationary state, analyzing the frequency of change of the processing result over time to perform street light flicker detection and obtain a detection result; Performing illumination conversion on the processing result to obtain average road surface illumination; If the vehicle is not in a stationary state, the processing result is subjected to statistical outlier detection to remove interference so as to update the processing result, and the illumination conversion of the processing result is performed to obtain an average road surface illumination.

[0007] A further technical solution is: the illumination sensor is installed toward the windshield of the vehicle, and the orientation of the illumination sensor is consistent with the driving direction.

[0008] A further technical solution is: before acquiring the positioning data collected by the positioning module installed in the vehicle and the illumination data collected by the illumination sensor installed in the vehicle, it also includes: Initialize the illumination sensor and positioning module.

[0009] A further technical solution is: filtering the illumination data to obtain a processing result includes: The illumination data is filtered using a sliding average filter to obtain a processing result.

[0010] A further technical solution is: judging whether the vehicle is in a stationary state according to the positioning data comprises: Calculating vehicle displacement according to the positioning data; Determining whether the vehicle displacement is less than a set threshold; If the vehicle displacement is less than a set threshold, it is determined that the vehicle is in a stationary state; If the vehicle displacement is not less than a set threshold, it is determined that the vehicle is not in a stationary state.

[0011] A further technical solution is: analyzing the frequency of changes in the processing results over time to perform street light flicker detection and obtain detection results, including: The frequency spectrum of the processing result is analyzed by fast Fourier transform. When a peak appears in the frequency spectrum, it is determined that there is flickering street lamp in the current road section to obtain a detection result.

[0012] A further technical solution is: converting the processing result into illumination to obtain an average road illumination, including: The processing result and the illumination conversion coefficient are weighted and averaged to obtain the average road surface illumination.

[0013] A further technical solution is as follows: the illumination conversion coefficient is obtained by collecting the actual road illumination under different environmental conditions and the illumination data collected by the illumination sensor and fitting the coefficient using the least square method.

[0014] A further technical solution is: the processing result is subjected to statistical outlier detection to remove interference, so as to update the processing result, including: Calculating the illumination change rate between every two data in the processing result; Determining whether the illumination change rate is greater than a set change rate threshold; If the illumination change rate is greater than a set change rate threshold, the corresponding data is filtered out from the processing result to update the processing result.

[0015] The present invention also provides a road illumination detection system based on lightweight dynamic acquisition, comprising: A data acquisition unit, used to acquire positioning data collected by a positioning module installed in the vehicle and illumination data collected by an illumination sensor installed in the vehicle; A filtering unit, used for filtering the illumination data to obtain a processing result; A judging unit, configured to judge whether the vehicle is in a stationary state according to the positioning data; A flicker detection unit, for analyzing the frequency of change of the processing result over time if the vehicle is stationary, so as to perform street lamp flicker detection and obtain a detection result; an illumination conversion unit, used for performing illumination conversion on the processing result to obtain an average road surface illumination; The interference removal unit is used to remove interference from the processing result by using a statistical outlier detection method if the vehicle is not in a stationary state, so as to update the processing result, and perform illumination conversion on the processing result to obtain an average road surface illumination.

[0016] Compared with the prior art, the beneficial effects of the present invention are as follows: the present invention collects vehicle position and illumination data in real time through a positioning module and an illumination sensor installed in the vehicle; filters the illumination data to remove noise and interference to ensure data accuracy; determines whether the vehicle is in a stationary state, and further analyzes the time variation frequency of the illumination data in the stationary state to detect the flickering phenomenon of street lights; converts the processed illumination data into average road illumination to reflect the driver's visual experience; if the vehicle is in a driving state, a statistical outlier detection method is used to remove interference during driving to ensure data accuracy, and the removed data is converted into average road illumination to provide continuous and accurate road illumination measurement results, overcoming the shortcomings of traditional methods in real time and perception.

[0017] The present invention is further described below in conjunction with the accompanying drawings and specific embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying any creative work.

[0019] Figure 1 A schematic diagram of a process flow of a road illumination detection method based on lightweight dynamic acquisition provided by an embodiment of the present invention; Figure 2 A schematic diagram of a sub-process of a road illumination detection method based on lightweight dynamic acquisition provided by an embodiment of the present invention Figure 1 ; Figure 3 A schematic diagram of a sub-process of a road illumination detection method based on lightweight dynamic acquisition provided by an embodiment of the present invention Figure 2 ; Figure 4 A schematic diagram of data before and after statistical outlier detection filtering provided by an embodiment of the present invention; Figure 5 A schematic flow chart of a road illumination detection method based on lightweight dynamic acquisition provided by another embodiment of the present invention; Figure 6 A schematic block diagram of a road illumination detection system based on lightweight dynamic acquisition provided by an embodiment of the present invention; Figure 7 A schematic block diagram of a judgment unit of a road illumination detection system based on lightweight dynamic acquisition provided by an embodiment of the present invention; Figure 8 A schematic block diagram of an interference removal unit of a road illumination detection system based on lightweight dynamic acquisition provided by an embodiment of the present invention; Fig. 9 A schematic block diagram of a road illumination detection system based on lightweight dynamic acquisition provided by another embodiment of the present invention; Fig.10 A schematic block diagram of a computer device provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0020] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0021] It should be understood that when used in this specification and the appended claims, the terms "include" and "comprises" indicate the presence of described features, integers, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or combinations thereof.

[0022] It should also be understood that the terms used in this specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. As used in the specification of the present invention and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an" and "the" are intended to include plural forms.

[0023] It should be further understood that the term "and / or" used in the present description and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.

[0024] See also Figure 1 , Figure 1 A schematic flow chart of a road illumination detection method based on lightweight dynamic acquisition provided by an embodiment of the present invention. The road illumination detection method based on lightweight dynamic acquisition is applied to a server, a terminal, an illumination sensor installed in a vehicle, and a positioning module for data interaction. The method of this embodiment is different from the traditional method of target recognition through a camera. Specifically, the road illumination data is collected in real time through an illumination sensor installed on an ordinary vehicle. This method can not only achieve wide coverage illumination monitoring, but also transmit the collected illumination information, vehicle location information, and time data in real time to a back-end server for processing and analysis. The illumination sensor can accurately measure the light intensity to meet the actual application requirements. Compared with the traditional method of complex image processing and analysis after the camera captures the image, this sensor-based solution avoids the influence of factors such as image quality and light conditions, thereby reducing the complexity of data processing and improving the accuracy of measurement. Through the illumination sensor installed on the vehicle, the vehicle-mounted equipment can measure the road illumination in real time at any time and any place, ensuring the comprehensive collection of dynamic data, thereby achieving high coverage of road illumination monitoring. In addition, the method of this embodiment also combines the driver's driving path and driving behavior, closely linking the measurement results with the actual driving experience, thereby more realistically reflecting the driver's visual perception under complex conditions such as different weather, time, and traffic flow.

[0025] Figure 1 FIG. 1 is a flow chart of a road illumination detection method based on lightweight dynamic acquisition provided by an embodiment of the present invention. Figure 1 As shown, the method includes the following steps S110 to S160.

[0026] S110, acquiring positioning data collected by a positioning module installed in the vehicle and illumination data collected by an illumination sensor installed in the vehicle.

[0027] In this embodiment, the illumination sensor is installed toward the windshield of the vehicle, and the orientation of the illumination sensor is consistent with the driving direction.

[0028] Install the illuminance sensor on the vehicle and ensure that the direction of the illuminance sensor is consistent with the driving direction of the vehicle. Specifically, the direction of the illuminance sensor is toward the windshield of the vehicle, which can best meet the driver's visual experience. In other words, the road illuminance data collected by the illuminance sensor can more accurately reflect the driver's visual experience during actual driving, thereby improving the authenticity and practicality of the measurement results.

[0029] The design of installing the illumination sensor and positioning module inside the vehicle has multiple advantages. First, internal installation can effectively reduce the interference of external environmental factors on sensor measurement. For example, natural factors such as dust, rain, snow, wind and sand may have an adverse effect on the performance of the sensor, and the protection inside the car can greatly reduce the interference of these external factors, thereby improving the stability and accuracy of the measurement. Secondly, this internal installation method is not only more adaptable to the environment, but also can meet the installation requirements of different types of vehicles. Whether it is a car, truck, or other type of vehicle, it can be easily installed. Therefore, this design has a wide range of applicability, can cover more types of vehicles, and meet the needs of different road illumination monitoring.

[0030] S120: Filter the illumination data to obtain a processing result.

[0031] In this embodiment, the processing result refers to the result obtained after the illumination data is filtered.

[0032] Specifically, a sliding average filter is used to filter the illumination data to obtain a processing result.

[0033] First, get the raw illumination data from the sensor , and GPS location data ; The collected original illumination data Filtering is performed to eliminate noise interference. Using a sliding average filter is an effective method, and its formula is: ;in, is the sliding window width, It is half of the window length. The sliding average filter smoothes the data by taking the average value of data within a certain range to reduce the impact of random noise.

[0034] In addition to the moving average filter, there are several other commonly used data filtering methods that can be selected according to specific needs: Median filter: Suitable for removing impulse noise and smoothing data by taking the median of the data in the sliding window.

[0035] Kalman filter: Suitable for data smoothing in dynamic systems, it can combine the dynamic model of the system and the observed data to provide the optimal estimate.

[0036] Wavelet transform: Suitable for multi-scale analysis, it can remove high-frequency noise and retain the detailed features of the signal at the same time.

[0037] Through these methods, the quality of illumination data can be effectively improved, providing a more reliable basis for subsequent analysis and application.

[0038] S130: Determine whether the vehicle is stationary according to the positioning data.

[0039] In one embodiment, see Figure 2 , the above-mentioned step S130 may include steps S131~S134.

[0040] S131, calculating the vehicle displacement according to the positioning data; S132, determining whether the vehicle displacement is less than a set threshold; S133, if the vehicle displacement is less than a set threshold, determining that the vehicle is in a stationary state; S134: If the vehicle displacement is not less than a set threshold, it is determined that the vehicle is not in a stationary state.

[0041] In this embodiment, the displacement d and velocity v(t) of the vehicle are calculated based on the positioning data to determine whether the vehicle is in a stationary state. The calculation method is as follows: Setting the displacement threshold Meters. When the vehicle displacement d is less than 0.5 meters per second, that is, d< , enter the "static illumination detection" mode; otherwise, enter the "dynamic illumination detection" mode.

[0042] The process of static illumination detection mode includes: When the vehicle is stationary, continuous measurement can be performed at the same location for a long time. By analyzing the spectrum changes, it can be determined whether the street light flickers. Spectral analysis can be achieved through methods such as Fourier transform to identify fluctuations in specific frequencies and thus determine the stability of the light.

[0043] The process of dynamic illumination detection includes: when the vehicle moves, it can cover a wider range of roads and quickly obtain illumination data of different road sections. This helps to achieve comprehensive monitoring of the road network and reflect the dynamic changes of road illumination.

[0044] S140: If the vehicle is stationary, analyzing the frequency of change of the processing result over time to perform street lamp flicker detection and obtain a detection result.

[0045] In this embodiment, the detection result refers to whether there is a flashing street light on the current road section.

[0046] Specifically, the frequency spectrum of the processing result is analyzed by fast Fourier transform, and when a peak appears in the frequency spectrum, it is determined that there is flickering street lamps in the current road section, so as to obtain a detection result.

[0047] The pre-processed light signal is analyzed in the frequency domain using Fast Fourier Transform (FFT). FFT is an efficient algorithm used to convert time domain signals into frequency domain signals, that is, to convert light signals from a time-varying form into representations of different frequency components.

[0048] In the frequency domain, the signal is decomposed into multiple frequency components. If a street lamp flickers, the periodic changes in its light intensity will appear as components of a specific frequency. This means that when the signal contains periodic changes, the corresponding frequency component will form a peak in the spectrum, indicating that the signal of this frequency dominates the overall signal.

[0049] When analyzing the spectrum graph in the FFT result, observe whether there are peaks at certain frequency positions. Generally speaking, street lamp flickering occurs periodically at a certain frequency, and this flickering frequency may be affected by different factors (such as street lamp failure, power supply fluctuations, etc.). If a peak is observed at a certain frequency in the spectrum, this usually means that there are periodic fluctuations in the signal, indicating that the street lamps on the road section may be flickering. For example, if a clear peak appears at a specific frequency position in the spectrum, and this frequency corresponds to the periodic changes in street lamp flickering (for example, the frequency of some street lamp flickering is around 20 Hz), then it can be determined that street lamps are flickering on the current road section.

[0050] A threshold is defined to distinguish significant peaks from other frequency components. If a significant peak is detected in the spectrum graph, it can be determined that there is a street light flickering phenomenon on the current road section. Specifically, if the amplitude of a certain frequency is greater than the threshold, it is considered that the street light flickers at this frequency.

[0051] Based on the results of spectrum analysis, when an obvious peak is detected, the set threshold can be used to determine whether there is a street light flickering phenomenon. If the peak in the spectrum exceeds the preset judgment threshold, it can be confirmed that the street lights on this road section have a flickering problem.

[0052] By performing a fast Fourier transform (FFT) on the processed light signal and analyzing the peaks in its spectrum, it is possible to accurately detect whether there is street light flickering on the road section. This method can effectively separate periodic flickering signals from other noise interference, and has the advantages of high efficiency, real-time and accuracy, which is of great significance for improving the efficiency of road lighting management and maintenance.

[0053] S150: Perform illumination conversion on the processing result to obtain average road surface illumination.

[0054] In this embodiment, the average road surface illumination refers to the average value of the illumination intensity at each point on the road surface area calculated by processing the collected illumination data. This indicator can reflect the overall illumination condition of the road section and is usually used to evaluate the uniformity and quality of road lighting and help determine whether there is uneven illumination or other potential problems in the lighting system.

[0055] Specifically, the processing result and the illumination conversion coefficient are weighted and averaged to obtain the average road surface illumination.

[0056] The illumination conversion coefficient is obtained by collecting the actual road illumination under different environmental conditions and the illumination data collected by the illumination sensor and fitting them using the least square method.

[0057] In this embodiment, there is a significant difference between the illuminance perceived by the driver (the illuminance from the driver's perspective) and the illuminance of the actual road surface. These differences are mainly caused by the following factors: Limited field of vision: The driver’s field of vision is limited and cannot fully cover the entire road surface.

[0058] Measuring angle: The measurement angle of the driver's perspective is different from the angle of the road illumination sensor.

[0059] Light source distance: The distance between the driver and the light source is different from the distance between the road illumination sensor and the light source.

[0060] In order to accurately evaluate the road lighting conditions, it is necessary to convert the illuminance from the driver's perspective into the actual road illuminance. This not only helps to improve the scientific nature of road lighting design, but also provides reliable data support for intelligent transportation systems.

[0061] Therefore, the driver's viewing angle illumination is required Need to be converted into actual road illuminance Specifically, the actual illuminance value of the road surface is estimated based on the illuminance data from the driver's perspective by determining the conversion coefficient k. The conversion relationship is: ; where k is the conversion coefficient obtained through experiments and data testing.

[0062] In order to obtain an accurate conversion coefficient, it is first necessary to conduct measurements and data collection under different environmental conditions (such as rainy and non-rainy days, different road materials, different road structures, etc.). The specific steps are as follows: Arrange road illumination sensors on known roads to directly measure the actual road illumination .

[0063] Collect illumination data from the driver’s perspective at the same location at the same time , the data is filtered data.

[0064] Record data points under different weather conditions (rainy / non-rainy), different road materials (asphalt, cement), and different road structures (straight, curved). Suppose there are n sets of data, marked as .

[0065] The conversion coefficient k is fitted by the least squares method. The goal is to minimize the measurement error, and the formula is: ;in, Indicates the illuminance from the driver's perspective .

[0066] The least squares method is a commonly used mathematical optimization method for fitting linear models. By minimizing the measurement error, the optimal conversion coefficient k can be found, making the relationship between the illuminance from the driver's perspective and the actual road illuminance more accurate.

[0067] The conversion coefficient k reflects the proportional relationship between the driver's visual angle illuminance and the road surface illuminance. By measuring and fitting at multiple different locations, the obtained k can be applied to other locations, which improves the versatility and applicability of the method.

[0068] By introducing the conversion coefficient, this embodiment can more accurately evaluate the road lighting conditions and improve the scientificity and reliability of the evaluation. Accurate road lighting data is crucial to the operation of the intelligent transportation system. By converting the illumination from the driver's perspective into the actual road illumination, more accurate data support can be provided for the intelligent transportation system, thereby optimizing traffic management and improving driving safety. Different weather, road surface materials and road structures have a significant impact on the scattering and reflection characteristics of light. By testing and fitting in a variety of environments, the conversion coefficient k obtained can better adapt to different environmental conditions and improve the robustness of the system.

[0069] S160: If the vehicle is not in a stationary state, a statistical outlier detection method is used to remove interference from the processing result to update the processing result, and the step S150 is executed.

[0070] During vehicle driving, the illuminance from the driver's perspective will be affected by a variety of factors, which may cause abnormal fluctuations in illuminance measurements, thus affecting the accurate assessment of road lighting conditions. Common interference factors include: The headlights of oncoming vehicles, especially high beams, can cause strong glare, resulting in an instantaneous increase in illumination values.

[0071] Reflections from buildings, roads, and water surfaces can affect the driver's visual experience and cause deviations in illuminance measurements.

[0072] Dynamic light sources from surrounding buildings, such as billboards, neon lights, etc., can also cause fluctuations in illumination values.

[0073] In order to ensure the accuracy of road lighting assessment, effective data processing methods are needed to remove these interferences.

[0074] In one embodiment, see Figure 3 The above-mentioned step S160 may include steps S161~S162.

[0075] S161, calculating the illumination change rate between every two data in the processing result.

[0076] In this embodiment, the processing result actually includes the result obtained after the continuously collected illumination data is filtered.

[0077] Continuously collects illumination data from the driver's perspective and forms data after filtering ,in Indicates the time of the i-th sampling point; calculates the rate of change of illumination between two consecutive sampling points ; The rate of change reflects the magnitude of the change in illumination between adjacent sampling points.

[0078] S162: Filter out data corresponding to the illumination change rate being greater than a set change rate threshold from the processing result to update the processing result.

[0079] Set a threshold , the threshold is obtained based on test experiments. Exceeding the threshold , then it is believed that There was a sudden change in the moment, probably due to glare or interference from the high beam.

[0080] Under normal circumstances, the change in brightness should be gentle, so points exceeding the threshold are considered outliers.

[0081] By performing outlier detection and filtering on the driver's perspective illumination data, the influence of interference factors such as oncoming vehicle lights, reflections, and dynamic light sources can be effectively removed, thereby improving the accuracy of illumination data. Accurate illumination data is essential for evaluating road lighting conditions and helps improve the scientificity and safety of road lighting design. There are many uncertain factors in the actual driving environment. By introducing outlier detection methods, the robustness of the system can be enhanced, allowing it to operate stably in complex and changing environments. For example, on urban roads, dynamic light sources such as billboards and neon lights appear frequently. Outlier detection methods can effectively deal with these interferences and ensure the reliability of data.

[0082] The driver's visual comfort directly affects driving safety. By removing interference factors, more realistic illumination data can be provided to help drivers better judge road lighting conditions and improve driving experience and safety. For example, when oncoming vehicles use high beams, the system can detect and filter out these interferences in a timely manner to prevent the driver from feeling uncomfortable or having their vision blocked due to glare.

[0083] like Figure 4 As shown in the figure, during the driving process, two vehicles passed by the opposite lane in succession. The original data (light value measured by the sensor) was obviously disturbed, and a significant peak appeared. The data filtered by statistical outlier detection effectively reduced the impact of the opposite vehicle, and the illumination change was smoother, which was more in line with the actual situation.

[0084] The method of this embodiment effectively removes interference factors in the driver's visual angle illumination by introducing a statistical outlier detection method, thereby improving the accuracy of illumination data and the robustness of the system. This method not only helps to evaluate road lighting conditions, but also optimizes the driving experience and improves driving safety. The effectiveness and reliability of this method have been demonstrated through its application in actual driving environments.

[0085] In addition, after filtering the data, which is equivalent to the processing result at this time having been updated, the illumination conversion is then performed to determine the average road surface illumination.

[0086] In the method of this embodiment, it should be noted that only positioning and illumination sensors are equipped, and the goal is to collect the illumination data of the road and transmit it to the server for analysis. Since the device itself lacks a lens, it cannot directly identify the existence of the street lamp, so it is impossible to determine the location of the street lamp by visual recognition. However, the device can indirectly infer the location of the street lamp by monitoring the trend of illumination changes. When a vehicle passes a street lamp, the illumination usually increases first and then decreases, which is a feature that conforms to the law of light propagation. The increase in illumination reflects that the vehicle is approaching the street lamp area, while the decrease in illumination indicates that the vehicle has left the illumination range of the street lamp. Between two street lamps, the illumination will gradually weaken, and this illumination change trend can be used as a reference for determining the location of the street lamp. After receiving the illumination data from the device, the server will analyze the road in sections in combination with the vehicle's positioning information and time information, and calculate the average illumination value of each section. Then, the system will determine whether these average illumination values ​​meet the requirements of the lighting design standards. It should be emphasized that the lighting design standards require the average illumination of the road section, not the illumination of a single location. Therefore, even if there are fluctuations or errors in the illumination data at certain locations, as long as the average illumination of the entire road section meets the standard, the lighting of the road section is considered to meet the requirements.

[0087] Considering that there may be multiple vehicles equipped with illuminance sensors passing through the same road section, the illuminance data may have certain errors due to factors such as sensor accuracy, installation location, and measurement time of different vehicles. However, this error does not affect the overall evaluation of road lighting conditions. Because if the road lighting conditions themselves meet the standards, then the illuminance data collected at different times and from different vehicles should all fall within the requirements of the lighting design standards, even if these data have slight errors.

[0088] In summary, this embodiment collects road illumination data and combines it with vehicle positioning information to evaluate the average illumination of different road sections. As long as the average illumination of the road section meets the lighting design standard, even if there is a certain error in the illumination data at a specific location, it can still be considered that the lighting conditions of the road section meet the standard. The changing trend of illumination data can effectively help infer the location of street lights, and the data errors of multiple vehicles will not affect the overall evaluation results.

[0089] The above-mentioned road illumination detection method based on lightweight dynamic acquisition collects vehicle position and illumination data in real time through the positioning module and illumination sensor installed in the vehicle; filters the illumination data to remove noise and interference to ensure the accuracy of the data; determines whether the vehicle is in a stationary state, and further analyzes the time change frequency of the illumination data in the stationary state to detect the flickering phenomenon of street lights; converts the processed illumination data into the average illumination of the road surface to reflect the driver's visual experience; if the vehicle is in a driving state, a statistical outlier detection method is used to remove interference during driving to ensure data accuracy, and the removed data is converted into the average illumination of the road surface, providing continuous and accurate road illumination measurement results, overcoming the shortcomings of traditional methods in real time and perception.

[0090] Figure 5 FIG. 1 is a flow chart of a road illumination detection method based on lightweight dynamic acquisition provided by another embodiment of the present invention. Figure 5 As shown, the road illumination detection method based on lightweight dynamic acquisition in this embodiment includes steps S210-S270. Steps S220-S270 are similar to steps S110-S160 in the above embodiment and are not described in detail. The added step S210 in this embodiment is described in detail below.

[0091] S210: Initialize the illumination sensor and positioning module.

[0092] In this embodiment, when the device is started, the illuminance sensor needs to undergo self-test and calibration to ensure that the illuminance data it measures is accurate and reliable. This step can eliminate any errors accumulated due to long-term non-use and ensure that the sensor can accurately capture the illuminance information of the current environment.

[0093] The positioning module needs to perform self-test at startup to ensure that it can correctly obtain the current vehicle location information. This process includes receiving satellite signals and confirming positioning accuracy so that the device can provide accurate location information for subsequent illumination data analysis.

[0094] Through the initialization operation, both the illuminance sensor and the positioning module can ensure that there is no deviation during the measurement process, thus providing a reliable data basis. This is crucial for subsequent illuminance analysis and positioning judgment. The initialization operation helps detect whether the hardware equipment is working properly, avoids system crashes or abnormal operations caused by equipment failure or incorrect configuration, and improves the stability and reliability of the system. Equipment that has not been used for a long time may be affected by environmental changes. The initialization step can effectively eliminate the negative impact of external factors on equipment performance, ensuring that the equipment can adapt to the current environment and provide accurate data when it starts. After the equipment is initialized, it can quickly enter the working state, ensuring real-time acquisition and processing of illuminance data during driving, meeting the system's requirements for real-time performance.

[0095] Figure 6 is a schematic block diagram of a road illumination detection system 300 based on lightweight dynamic acquisition provided by an embodiment of the present invention. Figure 6 As shown, corresponding to the above road illumination detection method based on lightweight dynamic acquisition, the present invention also provides a road illumination detection system 300 based on lightweight dynamic acquisition. The road illumination detection system 300 based on lightweight dynamic acquisition includes a unit for executing the above road illumination detection method based on lightweight dynamic acquisition, and the system can be configured in a server. Specifically, please refer to Figure 6 The road illumination detection system 300 based on lightweight dynamic acquisition includes a data acquisition unit 302, a filtering unit 303, a judgment unit 304, a flicker detection unit 305, an illumination conversion unit 306 and an interference removal unit 307.

[0096] The data acquisition unit 302 is used to acquire the positioning data collected by the positioning module installed in the vehicle and the illuminance data collected by the illuminance sensor installed in the vehicle; the filtering unit 303 is used to filter the illuminance data to obtain a processing result; the judgment unit 304 is used to judge whether the vehicle is in a stationary state based on the positioning data; the flicker detection unit 305 is used to analyze the frequency of change of the processing result over time if the vehicle is in a stationary state, so as to perform street lamp flicker detection and obtain a detection result; the illuminance conversion unit 306 is used to perform illuminance conversion on the processing result to obtain an average road surface illuminance; the interference removal unit 307 is used to remove interference from the processing result by using a statistical outlier detection method if the vehicle is not in a stationary state, so as to update the processing result, and perform the illuminance conversion on the processing result to obtain an average road surface illuminance.

[0097] In one embodiment, the filtering unit 303 is used to filter the illumination data using a sliding average filter to obtain a processing result.

[0098] In one embodiment, see Figure 7 , the judging unit 304 comprises: The displacement calculation subunit 3041 is used to calculate the vehicle displacement according to the positioning data; The displacement judgment subunit 3042 is used to judge whether the vehicle displacement is less than a set threshold; A first determination subunit 3043, configured to determine that the vehicle is in a stationary state if the displacement of the vehicle is less than a set threshold; The second determination subunit 3044 is configured to determine that the vehicle is not in a stationary state if the displacement of the vehicle is not less than a set threshold.

[0099] In one embodiment, the flicker detection unit 305 is used to analyze the frequency spectrum of the processing result by using fast Fourier transform, and when a peak appears in the frequency spectrum, it is determined that there is street light flickering in the current road section to obtain a detection result.

[0100] In one embodiment, the illumination conversion unit 306 is used to perform weighted average calculation on the processing result and the illumination conversion coefficient to obtain an average road surface illumination.

[0101] In one embodiment, see Figure 8 , the interference removal unit 307 includes: The change rate calculation subunit 3071 is used to calculate the illumination change rate between every two data in the processing result; The filtering subunit 3072 is used to filter out the data corresponding to the illumination change rate greater than the set change rate threshold from the processing result, so as to update the processing result.

[0102] Fig. 9 FIG. 3 is a schematic block diagram of a road illumination detection system 300 based on lightweight dynamic acquisition provided by another embodiment of the present invention. Fig. 9 As shown, the road illumination detection system 300 based on lightweight dynamic acquisition in this embodiment is based on the above embodiment and an initialization unit is added.

[0103] The initialization unit 301 is used to initialize the illumination sensor and the positioning module.

[0104] It should be noted that technicians in the relevant field can clearly understand that the specific implementation process of the above-mentioned road illumination detection system 300 based on lightweight dynamic acquisition and each unit can refer to the corresponding description in the aforementioned method embodiment, and for the convenience and conciseness of the description, it will not be repeated here.

[0105] The road illumination detection system 300 based on lightweight dynamic acquisition can be implemented in the form of a computer program. The computer program can be used in Fig.10 Runs on the computer device shown.

[0106] See also Fig.10 , Fig.10 5 is a schematic block diagram of a computer device provided in an embodiment of the present application. The computer device 500 may be a server, wherein the server may be an independent server or a server cluster composed of multiple servers.

[0107] See also Fig.10 The computer device 500 includes a processor 502 , a memory and a network interface 505 connected via a system bus 501 , wherein the memory may include a non-volatile storage medium 503 and an internal memory 504 .

[0108] The non-volatile storage medium 503 can store an operating system 5031 and a computer program 5032. The computer program 5032 includes program instructions, and when the program instructions are executed, the processor 502 can execute a road illumination detection method based on lightweight dynamic acquisition.

[0109] The processor 502 is used to provide computing and control capabilities to support the operation of the entire computer device 500 .

[0110] The internal memory 504 provides an environment for the operation of the computer program 5032 in the non-volatile storage medium 503. When the computer program 5032 is executed by the processor 502, the processor 502 can execute a road illumination detection method based on lightweight dynamic acquisition.

[0111] The network interface 505 is used to communicate with other devices over the network. Fig.10 The structure shown in the figure is merely a block diagram of a portion of the structure related to the solution of the present application, and does not constitute a limitation on the computer device 500 to which the solution of the present application is applied. The specific computer device 500 may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0112] The processor 502 is used to run the computer program 5032 stored in the memory to implement the following steps: Acquire positioning data collected by a positioning module installed in a vehicle and illuminance data collected by an illuminance sensor installed in the vehicle; filter the illuminance data to obtain a processing result; determine whether the vehicle is in a stationary state based on the positioning data; if the vehicle is in a stationary state, analyze the frequency of change of the processing result over time to perform street lamp flicker detection and obtain a detection result; perform illuminance conversion on the processing result to obtain an average road surface illuminance; if the vehicle is not in a stationary state, remove interference from the processing result using a statistical outlier detection method to update the processing result, and perform the illuminance conversion on the processing result to obtain an average road surface illuminance.

[0113] Wherein, the illumination sensor is installed toward the windshield of the vehicle, and the orientation of the illumination sensor is consistent with the driving direction.

[0114] In one embodiment, before implementing the step of acquiring the positioning data collected by the positioning module installed in the vehicle and the illuminance data collected by the illuminance sensor installed in the vehicle, the processor 502 further implements the following steps: Initialize the illumination sensor and positioning module.

[0115] In one embodiment, when the processor 502 implements the step of filtering the illumination data to obtain a processing result, the processor 502 specifically implements the following steps: The illumination data is filtered using a sliding average filter to obtain a processing result.

[0116] In one embodiment, when the processor 502 implements the step of determining whether the vehicle is in a stationary state according to the positioning data, the processor 502 specifically implements the following steps: The vehicle displacement is calculated according to the positioning data; whether the vehicle displacement is less than a set threshold is determined; if the vehicle displacement is less than the set threshold, it is determined that the vehicle is in a stationary state; if the vehicle displacement is not less than the set threshold, it is determined that the vehicle is not in a stationary state.

[0117] In one embodiment, when the processor 502 implements the step of analyzing the frequency of changes in the processing result over time to perform street light flicker detection and obtain the detection result, the processor 502 specifically implements the following steps: The frequency spectrum of the processing result is analyzed by fast Fourier transform. When a peak appears in the frequency spectrum, it is determined that there is flickering street lamp in the current road section to obtain a detection result.

[0118] In one embodiment, when the processor 502 implements the step of converting the processing result into illuminance to obtain the average road surface illuminance, the processor 502 specifically implements the following steps: The processing result and the illumination conversion coefficient are weighted and averaged to obtain the average road surface illumination.

[0119] The illumination conversion coefficient is obtained by collecting the actual road illumination under different environmental conditions and the illumination data collected by the illumination sensor and fitting them using the least square method.

[0120] In one embodiment, when the processor 502 implements the step of removing interference from the processing result by using a statistical outlier detection method to update the processing result, the processor 502 specifically implements the following steps: Calculate the illumination change rate between every two data in the processing result; filter out the data corresponding to the illumination change rate greater than the set change rate threshold from the processing result to update the processing result.

[0121] It should be understood that in the embodiment of the present application, the processor 502 may be a central processing unit (CPU), and the processor 502 may also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. Among them, the general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0122] It can be understood by those skilled in the art that all or part of the processes in the method for implementing the above embodiment can be completed by instructing the relevant hardware through a computer program. The computer program includes program instructions, and the computer program can be stored in a storage medium, which is a computer-readable storage medium. The program instructions are executed by at least one processor in the computer system to implement the process steps of the embodiment of the above method.

[0123] Therefore, the present invention also provides a storage medium. The storage medium may be a computer-readable storage medium. The storage medium stores a computer program, wherein when the computer program is executed by a processor, the processor executes the following steps: Acquire positioning data collected by a positioning module installed in a vehicle and illuminance data collected by an illuminance sensor installed in the vehicle; filter the illuminance data to obtain a processing result; determine whether the vehicle is in a stationary state based on the positioning data; if the vehicle is in a stationary state, analyze the frequency of change of the processing result over time to perform street lamp flicker detection and obtain a detection result; perform illuminance conversion on the processing result to obtain an average road surface illuminance; if the vehicle is not in a stationary state, remove interference from the processing result using a statistical outlier detection method to update the processing result, and perform the illuminance conversion on the processing result to obtain an average road surface illuminance.

[0124] Wherein, the illumination sensor is installed toward the windshield of the vehicle, and the orientation of the illumination sensor is consistent with the driving direction.

[0125] In one embodiment, before executing the computer program to implement the step of acquiring the positioning data collected by the positioning module installed in the vehicle and the illuminance data collected by the illuminance sensor installed in the vehicle, the processor further implements the following steps: Initialize the illumination sensor and positioning module.

[0126] In one embodiment, when the processor executes the computer program to implement the step of filtering the illumination data to obtain a processing result, the processor specifically implements the following steps: The illumination data is filtered using a sliding average filter to obtain a processing result.

[0127] In one embodiment, when the processor executes the computer program to implement the step of determining whether the vehicle is in a stationary state according to the positioning data, the processor specifically implements the following steps: The vehicle displacement is calculated according to the positioning data; whether the vehicle displacement is less than a set threshold is determined; if the vehicle displacement is less than the set threshold, it is determined that the vehicle is in a stationary state; if the vehicle displacement is not less than the set threshold, it is determined that the vehicle is not in a stationary state.

[0128] In one embodiment, when the processor executes the computer program to implement the step of analyzing the frequency of changes in the processing result over time to perform street light flicker detection and obtain the detection result, the processor specifically implements the following steps: The frequency spectrum of the processing result is analyzed by fast Fourier transform. When a peak appears in the frequency spectrum, it is determined that there is flickering street lamp in the current road section to obtain a detection result.

[0129] In one embodiment, when the processor executes the computer program to implement the step of converting the processing result into illuminance to obtain the average road surface illuminance, the processor specifically implements the following steps: The processing result and the illumination conversion coefficient are weighted and averaged to obtain the average road surface illumination.

[0130] The illumination conversion coefficient is obtained by collecting the actual road illumination under different environmental conditions and the illumination data collected by the illumination sensor and fitting them using the least square method.

[0131] In one embodiment, when the processor executes the computer program to implement the step of removing interference from the processing result by using a statistical outlier detection method to update the processing result, the processor specifically implements the following steps: Calculate the illumination change rate between every two data in the processing result; filter out the data corresponding to the illumination change rate greater than the set change rate threshold from the processing result to update the processing result.

[0132] The storage medium may be a USB flash drive, a mobile hard disk, a read-only memory (ROM), a magnetic disk, or an optical disk, etc., which are computer-readable storage media that can store program codes.

[0133] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in terms of function in the above description. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0134] In the several embodiments provided by the present invention, it should be understood that the disclosed systems and methods can be implemented in other ways. For example, the system embodiments described above are only schematic. For example, the division of each unit is only a logical function division, and there may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed.

[0135] The steps in the method of the embodiment of the present invention can be adjusted in order, combined and deleted according to actual needs. The units in the system of the embodiment of the present invention can be combined, divided and deleted according to actual needs. In addition, the functional units in the various embodiments of the present invention 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.

[0136] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a storage medium. Based on this understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, terminal, or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention.

[0137] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can easily think of various equivalent modifications or replacements within the technical scope disclosed by the present invention, and these modifications or replacements should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention shall be based on the protection scope of the claims.

Claims

1. A road illumination detection method based on lightweight dynamic acquisition, characterized in that: include: Acquire positioning data collected by a positioning module installed in the vehicle and illumination data collected by an illumination sensor installed in the vehicle; filtering the illumination data to obtain a processing result; determining whether the vehicle is in a stationary state according to the positioning data; If the vehicle is in a stationary state, analyzing the frequency of change of the processing result over time to perform street light flicker detection and obtain a detection result; Performing illumination conversion on the processing result to obtain average road surface illumination; If the vehicle is not in a stationary state, the processing result is subjected to statistical outlier detection to remove interference so as to update the processing result, and the illumination conversion of the processing result is performed to obtain an average road surface illumination.

2. The road illumination detection method based on lightweight dynamic acquisition according to claim 1 is characterized in that: The illumination sensor is installed toward the windshield of the vehicle, and the orientation of the illumination sensor is consistent with the driving direction.

3. The road illumination detection method based on lightweight dynamic acquisition according to claim 1 is characterized in that: Before acquiring the positioning data collected by the positioning module installed in the vehicle and the illumination data collected by the illumination sensor installed in the vehicle, the method further includes: Initialize the illumination sensor and positioning module.

4. The road illumination detection method based on lightweight dynamic acquisition according to claim 1 is characterized in that: The filtering of the illumination data to obtain a processing result includes: The illumination data is filtered using a sliding average filter to obtain a processing result.

5. The road illumination detection method based on lightweight dynamic acquisition according to claim 1 is characterized in that: The determining whether the vehicle is in a stationary state according to the positioning data includes: Calculating vehicle displacement according to the positioning data; Determining whether the vehicle displacement is less than a set threshold; If the vehicle displacement is less than a set threshold, it is determined that the vehicle is in a stationary state; If the vehicle displacement is not less than a set threshold, it is determined that the vehicle is not in a stationary state.

6. The road illumination detection method based on lightweight dynamic acquisition according to claim 1 is characterized in that: The analyzing the frequency of changes of the processing results over time to perform street light flicker detection and obtain the detection results includes: The frequency spectrum of the processing result is analyzed by fast Fourier transform. When a peak appears in the frequency spectrum, it is determined that there is flickering street lamp in the current road section to obtain a detection result.

7. The road illumination detection method based on lightweight dynamic acquisition according to claim 1 is characterized in that: The step of converting the processing result into an illuminance to obtain an average road surface illuminance includes: The processing result and the illumination conversion coefficient are weighted and averaged to obtain the average road surface illumination.

8. The road illumination detection method based on lightweight dynamic acquisition according to claim 7 is characterized in that: The illumination conversion coefficient is obtained by collecting the actual road illumination under different environmental conditions and the illumination data collected by the illumination sensor and fitting them using the least square method.

9. The road illumination detection method based on lightweight dynamic acquisition according to claim 1 is characterized in that: The process of removing interference from the processing result by using a statistical outlier detection method to update the processing result includes: Calculating the illumination change rate between every two data in the processing result; The data corresponding to the illumination change rate being greater than a set change rate threshold is filtered out from the processing result to update the processing result.

10. The road illumination detection system based on lightweight dynamic acquisition is characterized by: include: A data acquisition unit, used to acquire positioning data collected by a positioning module installed in the vehicle and illumination data collected by an illumination sensor installed in the vehicle; A filtering unit, used for filtering the illumination data to obtain a processing result; A judging unit, configured to judge whether the vehicle is in a stationary state according to the positioning data; A flicker detection unit, for analyzing the frequency of change of the processing result over time if the vehicle is stationary, so as to perform street lamp flicker detection and obtain a detection result; an illumination conversion unit, used for performing illumination conversion on the processing result to obtain an average road surface illumination; The interference removal unit is used to remove interference from the processing result by using a statistical outlier detection method if the vehicle is not in a stationary state, so as to update the processing result, and perform illumination conversion on the processing result to obtain an average road surface illumination.

Images