WO2023039620A1 - Procédé de mesure du volume de trafic à l'aide de das - Google Patents

Procédé de mesure du volume de trafic à l'aide de das Download PDF

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Publication number
WO2023039620A1
WO2023039620A1 PCT/AT2022/060245 AT2022060245W WO2023039620A1 WO 2023039620 A1 WO2023039620 A1 WO 2023039620A1 AT 2022060245 W AT2022060245 W AT 2022060245W WO 2023039620 A1 WO2023039620 A1 WO 2023039620A1
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Prior art keywords
vehicles
determined
vehicle
waveguide
time
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PCT/AT2022/060245
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German (de)
English (en)
Inventor
Martin Litzenberger
Christoph Wiesmeyr
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AIT Austrian Institute of Technology GmbH
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Publication of WO2023039620A1 publication Critical patent/WO2023039620A1/fr

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    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/01—Detecting movement of traffic to be counted or controlled
    • G08G1/04—Detecting movement of traffic to be counted or controlled using optical or ultrasonic detectors
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01H—MEASUREMENT OF MECHANICAL VIBRATIONS OR ULTRASONIC, SONIC OR INFRASONIC WAVES
    • G01H9/00—Measuring mechanical vibrations or ultrasonic, sonic or infrasonic waves by using radiation-sensitive means, e.g. optical means
    • G01H9/004—Measuring mechanical vibrations or ultrasonic, sonic or infrasonic waves by using radiation-sensitive means, e.g. optical means using fibre optic sensors
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/01—Detecting movement of traffic to be counted or controlled
    • G08G1/052—Detecting movement of traffic to be counted or controlled with provision for determining speed or overspeed
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/01—Detecting movement of traffic to be counted or controlled
    • G08G1/056—Detecting movement of traffic to be counted or controlled with provision for distinguishing direction of travel

Definitions

  • the invention relates to a method for measuring the traffic density and/or speed of a number of vehicles moving on a driving section, in particular a road, according to patent claim 1, comprising a method for measuring the traffic density and/or the speed on a road a number of driving sections for vehicles, according to claim 12, and an arrangement according to claim 13.
  • Roads and highways are essential to modern transportation, and reliable traffic monitoring systems are required to ensure a continuous, unobstructed flow of traffic.
  • traffic monitoring systems provide information about the traffic conditions currently prevailing on a road or a freeway, such as the number and speed of vehicles on a specific stretch of road. This data can provide information regarding the occurrence of traffic obstacles such as traffic jams or accidents on the streets or highways and thus contribute to the rapid elimination of these traffic obstacles. Traffic monitoring systems also help to promptly take action to avoid a complete standstill, such as closing a lane or allowing traffic to use the hard shoulder.
  • Traffic monitoring systems are known from the prior art, in which sensors are arranged either overhead, under or next to the roadway in order to detect the flow of traffic.
  • sensors can be, for example, laser scanners, infrared, radar or ultrasonic sensors, but magnetic or acoustic sensors or video cameras can also be used.
  • passing vehicles cause changes in the magnetic field that can be used to measure traffic flow.
  • Monitoring based on acoustic sensors can be implemented using a microphone array, for example.
  • smartphone connection data or vehicle GPS data for example, can also be evaluated in order to analyze the traffic flow.
  • DAS Distributed Acoustic Sensing
  • DAS systems use a series of electromagnetic waves transmitted over a waveguide, such as an optical fiber such as a fiber optic cable.
  • the electromagnetic wave backscattered from the waveguide which is influenced by deformation of the waveguide by e.g. ground vibrations, is measured and analyzed.
  • ground vibrations induced by passing vehicles affect the DAS signal, and traffic information related to the road section can be derived based on the DAS signal.
  • cables in the form of fiber optic cables are laid anyway for the operationally required communication applications.
  • the pressure changes or acoustic vibrations emitted by vehicles can be measured in space and time.
  • the object of the invention is therefore to provide a specific method with which traffic information such as traffic density and/or the speed of a number of vehicles can be determined based on DAS measurements.
  • the invention solves this problem with a method for measuring the traffic density and/or speed of a number of vehicles moving along a route, in particular a road, according to patent claim 1.
  • a) along the route by means of a waveguide , in particular a fiber optic cable, measured values for characterizing vibrations or pressure changes are determined at a large number of locations along the route, b) the waveguide is arranged along the route and is affected by the shocks, vibrations or pressure changes emanating from the route, c) at specified levels Points in time, in particular with a frequency between 100 Hz and 10 kHz, preferably between 1 and 4 kHz, in each case an electromagnetic pulse is emitted into the waveguide, and the electromagnetic wave returning from the waveguide is measured, with the time delay corresponding to d) the strength and/or phase and/or energy of the returning electromagnetic wave is used as a measured value to characterize vibrations or pressure changes in the relevant location, and e) for a Number of location points along the driving section and for a number of times a measured value, in particular a measurement signal, is provided to characterize the vibration or a pressure change.
  • a probability is determined as to whether a respective measured value corresponds to the presence of a vehicle, with the probabilities being mapped via the location points in a diagram, in particular in a waterfall diagram, in which the lanes contained therein Correspond to trajectories of the vehicles, with a trajectory describing the time-path profile of a vehicle, in particular the movement of a vehicle in relation to the location points for a number of points in time, it being provided in particular that the lanes correspond to straight lines with width, g) the number of lanes and/or their gradient being determined from the values of the diagram, in particular the waterfall diagram, and h) the number of lanes and/or their gradient being used to determine the speed of the vehicles and/or the number of vehicles is used.
  • one waveguide such as an optical fiber or a glass fiber cable and one measuring and processing unit, eg an opto-electronic interrogator device, are advantageously required in order to be able to reliably derive traffic information.
  • This is cheaper and requires less maintenance than conventional traffic monitoring systems, which require a large number of sensors along the roadway and are therefore associated with higher installation, maintenance and energy costs.
  • a further advantage of the procedure according to the invention is that the waveguide can be installed parallel to the roadway and not above or below the roadway surface, so that road construction work, for example, does not impair the method according to the invention. Furthermore, the passive nature of the waveguide makes it immune to electromagnetic interference and lightning and therefore requires less maintenance than, for example, copper-based cables.
  • a trajectory of a vehicle is understood to be the path-time profile of the respective vehicle in world coordinates, which is represented as a track in the waterfall diagram in the form of an x-y profile in image coordinates. Most preferably, these tracks are in the form of straight lines with a width corresponding to the length of the vehicle.
  • a number of time windows is specified for location points separately in the area of the vehicle and/or its trajectory in the respective location point
  • the signal energy is determined separately by Fourier transformation within predetermined frequency bands, and this signal energy is assigned to a point in time assigned to the respective time window, so that a discrete signal that assigns the associated signal energies to each location point for individual points in time is available and the probability of the presence of a vehicle is derived by calculating and combining the signal energies for different frequency bands, where a) if a single frequency band is specified, this frequency band, in particular after carrying out a standardization step, as a measure for the probability of the presence of a vehicle is considered, and / or b) the signal energies are combined by using a machine learning method, it being provided in particular that the combination of individual frequency bands is learned in a training step.
  • the signal energy in a frequency band is understood to mean the sum of the squares of the absolute values of the Fourier coefficients from the respective frequency band.
  • An alternative procedure for a particularly reliable determination of the probability of whether a respective measured value corresponds to a vehicle can be provided if selected, in particular all, measured values are subjected to a pattern analysis in detection step f), the similarity to a predetermined pattern being determined in the pattern analysis is used, and the similarity to the predetermined pattern is used as the probability of the presence of a vehicle.
  • a particularly exact pattern analysis is possible if a pattern matching method is used for the pattern analysis.
  • a further alternative procedure for a particularly reliable determination of the probability of whether a respective measured value corresponds to a vehicle can be provided if selected, in particular all, measured values are classified in detection step f) using a machine learning method using a classifier, in particular a previously trained classifier and in this way the probability of the presence of a vehicle is determined.
  • a particularly precise determination of the number of traces and/or their gradient from the values of the diagram can be achieved if in step g) a binary image is generated from selected, in particular all, values of the diagram, in particular the waterfall diagram, using a predetermined threshold value is, and in the respective binary image the number of tracks and / or their respective slope are determined by means of Hough transformation.
  • a binary image is understood below to mean an image matrix whose elements can only have two different values, namely 0 or 1.
  • step g) An alternative procedure for a particularly precise determination of the number of tracks and/or their gradient from the values in the diagram can be provided if - in step g) a binary image is generated in each case from selected, in particular all, values of the diagram, in particular the waterfall diagram, by means of a predetermined threshold value, and
  • orientation of the local gradients of selected tracks, in particular all tracks are determined in the respective binary image by means of an image processing method, in particular by means of Sobel, Prewitt and/or Roberts operators, in the form of an orientation image,
  • a histogram is formed from the orientation image of the orientations of the local gradients and the maximum values are determined in the histogram, where pairs of angles with a direction difference of 180 degrees are determined from the maximum values, and where the velocities (v) are determined from the pairs of angles of the vehicles is determined.
  • the position and/or the height of the maximum values in the histogram is determined, with the position in the histogram of the speed of the vehicles and the height in the histogram of the number of vehicles corresponds, which have a respective speed.
  • a particularly precise procedure for determining the volume of traffic and/or the traffic density can be provided that
  • the traffic volume is determined by forming the sum of all vehicles in a particular area under consideration and in relation to the duration of the time segment represented by the histogram, and/or
  • the traffic density is determined by forming the sum of all vehicles in a particular area under consideration and in relation to the size of the local section represented by the histogram.
  • the length of the vehicles is deduced by means of a filter based on the widths of the lanes, the width of a lane corresponding to a section in the diagram at a respective point in time (t) along the location,
  • the driving section is divided into a number of subsections, the subsections being designed in such a way that speeds can be determined from the trajectories of the vehicles, with a diagram in each case Subsection is formed, it being provided in particular that the subsections are chosen so large that the tracks are completely included and the slope of the tracks can be determined.
  • a road can advantageously be divided into a number of driving sections in order to determine the traffic condition over the entire length of a road.
  • the length of which is suitably chosen, typically 500 m long.
  • a measurement of the traffic density and/or the speed on a road comprising a number of driving sections is advantageously possible if the measurement of the traffic density on the individual driving sections is carried out using a method according to the invention.
  • the object of the invention is also to provide an arrangement for measuring the traffic density and/or speed of a number of vehicles that are moving on a road section, in particular a road.
  • the invention solves this problem with the features of claim 14. According to the invention, it is provided that the arrangement comprises the following:
  • a waveguide in particular a fiber optic cable, arranged along a section of travel, in particular a road, which is affected by the vibrations emanating from the section of travel and
  • FIG. 1 shows a schematic representation of a DAS system for traffic monitoring
  • Fig. 3 shows a schematic example of a line in the Hough transform defined by angle 0 and radius r
  • FIG. 4 shows a schematic example of a transformed binary image based on a pre-processed DAS signal
  • Fig. 5 shows a Hough transform image of the binary image in Fig. 4,
  • 8b is a binary image of the lanes of three vehicles, with the x-axis representing location along the driving segment and the y-axis representing time;
  • FIG. 9 shows an orientation image with the evaluated local orientations of the gradients of the lanes in FIG. 8b, the x-axis representing the location along the driving section and the y-axis representing the time,
  • FIG. 10 shows an example of a histogram of the local orientation of the gradients from FIG. 9,
  • FIG. 11 shows an example for the evaluation of the local orientations over an entire driving section.
  • a waveguide in the form of a glass fiber cable 1 is laid parallel to the travel section 3 .
  • a measurement and Processing unit in the form of an interrogator device 2 is connected to one end of the fiber optic cable 1 and delivers a series of electromagnetic pulses to the fiber optic cable 1 in the form of laser light pulses. Portions of the emitted light are backscattered and are measured with the same interrogator device 2 as is indicated schematically in FIG.
  • Shocks, vibrations or pressure changes generated by passing cars or trucks stretch and/or compress the fiber optic cable 1 and therefore affect the length of the optical path. This induces a measurable phase shift in the backscattered light, which can be determined using interferometric methods.
  • a light pulse is emitted into the glass fiber cable 1 and the light returning from the glass fiber cable 1 is measured.
  • the signal is assigned to a location along the driving section 3 in accordance with the time delay of the returning light.
  • the strength and/or phase and/or energy of the returning light can be used as a measured value for characterizing vibrations or pressure changes in the location in question.
  • a measured value m(x, t) in particular a measurement signal, is available for characterizing the vibration or a pressure change for a number of location points along the driving section 3 and for a number of points in time t.
  • Image and signal processing algorithms are used according to the invention in order to derive the traffic density and speed of vehicles moving on a driving section from the determined DAS measured values.
  • the traffic volume i.e. vehicles per unit of time
  • the traffic density i.e. vehicles per unit of length
  • the average speeds in a waterfall diagram based on DAS measurement values, in which individual tracking of each individual vehicle is not possible due to the high density of vehicle trajectories more is possible.
  • a method according to the invention comprises the following steps: In a detection step, a probability Wi . . . W x is first determined as to whether a respective measured value m(x, t) corresponds to the presence of a vehicle. Then these probabilities are displayed in a diagram.
  • the pixel values of the image matrix correspond to the signal energy of segments with a specific time duration and spatial length.
  • the lanes contained therein correspond to trajectories of vehicles that pass driving section 2 .
  • a trajectory describes the time-path profile of a vehicle (see FIG. 7), for example the movement of a vehicle in relation to the location points Mi . . . M x for a number of times t.
  • a trajectory x(t) can be represented both as the location point Mi ... M x , where the front of the vehicle is located, for example, as a function of time t and as the inverse t(x) of this function, i.e. as a function which indicates the time t at which the vehicle is located at the location Mi . . . M x .
  • Vehicles with a finite length have trajectories with a width that represent the time-path profile of the leading edge of the vehicle (to in FIG. 7) as well as the time profile of the trailing edge of the vehicle (t p in FIG. 7). In FIG. 2, these tracks of the vehicles correspond to straight lines with a width which corresponds to the respective vehicle length.
  • the trajectories of the vehicles i.e. the path-time course of a respective vehicle in world coordinates, are shown as traces in the waterfall diagram in the form of an x-y course in image coordinates. These lanes are generally in the form of straight lines with a width equal to the length of the vehicle.
  • individual vehicle trajectories can be seen as straight lines, with the gradient of the lines corresponding to the speed of the vehicle.
  • the sign of the slope that is, whether the line slopes to the right or left on the graph, corresponds to the direction of vehicle motion.
  • a positive slope corresponds to a movement of the vehicle away from the interrogator device 2 assumed in the diagram at the origin, while a negative slope corresponds to a movement of the vehicle towards the interrogator device 2 .
  • the number of lanes and/or their incline is then determined from the values of the diagram, for example the waterfall diagram, and used to determine the speed and/or the number of vehicles.
  • a frequency analysis of the DAS raw signal is carried out in the first exemplary embodiment in the detection step.
  • location points Mi . . . M o each separately in the area of vehicle 3 and/or its trajectory xo(t); to(x) in the respective location Mi ... M x a number of time windows U; Wow...LI? predetermined.
  • each of the time windows U; Ui . . . U 7 of a location Mi This signal energy is assigned to the respective time window U; Ui ... U7 associated time t assigned, so that a discrete signal d (x, t, f), which each location Mi ... M x assigns the associated signal energies for individual times, is available.
  • a Fast Fourier Transform was carried out in 1 s time intervals for the original DAS raw signal, which is recorded with a sampling rate of 1 kHz, and data reduction by calculating the signal energy in a selected frequency band of 5-50 Hz, in the respective 1 s period.
  • the signal energies obtained are shown in FIG. 2 as gray-coded pixel values, with high signal energies being shown light and low signal energies being shown dark.
  • the pre-processed DAS signal then corresponds to the total energy of the DAS raw signal within the selected frequency band.
  • Equation (1) shows the calculation of the pixel value b from the summed energies in the selected frequency band f, where B corresponds to the frequency range and a to the frequency response of the DAS signal.
  • the probability of the presence of a vehicle is derived by calculating and combining the signal energies for different frequency bands f.
  • this combination can be done in different ways: If only one frequency band f is present or specified, this frequency band f can be viewed as a measure of the probability of the presence of a vehicle.
  • a normalization step can first be carried out here in order to obtain values between 0 and 1 as a measure of the probability.
  • the signal energies can also be combined by using a machine learning method.
  • a machine learning method is used that learns in a training step or a training phase how the individual frequency bands f are combined in order to calculate a probability of the presence of a vehicle.
  • a pattern analysis of the DAS raw signal can optionally also be carried out in the detection step. For this purpose, all or only selected measured values m(x, t) are subjected to a pattern analysis, with the similarity to a predetermined pattern being determined in the pattern analysis. The similarity to the specified pattern is used as the probability of the presence of a vehicle.
  • a pattern matching method can be used for the pattern analysis.
  • the simplest form of this method is the convolution of a template for vehicles with the measured values m(x, t), with local maxima in the convolution signal occurring at the points at which the measured values contain vehicle-like patterns.
  • the template for this can be determined empirically from sample data.
  • the DAS raw signal can optionally also be classified in the detection step using a machine learning method. All or selected measured values m(x, t) are classified using a classifier and the probability of the presence of a vehicle is determined in this way.
  • This classifier can be learned in advance in a training phase. For example, vehicles can be marked manually and the associated signals can then be extracted and fed to the learning process as such. Furthermore, signals that do not correspond to any vehicle are also marked and also fed into the learning process as "negative examples”.
  • "Deep Learning”, such as LSTM networks for the analysis of time series, can be used as a learning method.
  • the waterfall diagram containing the probabilities Wi...W x for the presence of a vehicle is converted into a binary image by applying a threshold value. This binary image is then subdivided into smaller binary images in such a way that these contain local sections of a few 100 meters and time sections of a few seconds. For the selection of the size of these location and time sections, it is crucial on the one hand how many result points are required for the traffic density and speed along the road, and on the other hand that sufficiently long vehicle lanes are contained in the time section.
  • the number of tracks and their respective slope are then determined using Hough transformation in the smaller binary images generated in this way.
  • the Hough transformation is an image processing method for recognizing any parameterizable geometric figures in a binary gradient image.
  • binary images can also be examined for the presence of specified lines (see Hough transform for line detection” in Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (see Cat. No PR00149), Jun. 1999 1, pp. 554-560 Vol 1, doi: 10.1109/CVPR.1999.786993 or RO Duda and PE Hart, "Use of the Hough transformation to detect lines and curves in pictures," Com mu n ACM, vol 15, no. 1, pp. 11-15, Jan. 1972, doi: 10.1145/361237.361242).
  • the value provided by the Hough transform for each line indicates the number of positive pixels on each line. All possible lines are parameterized by polar coordinates, i.e. radius r and angle 0 (see Fig. 3).
  • the Hough transform supplies a matrix whose entries are assigned possible values of radius r and angle 0. For each entry (0, r), the Hough transform returns the number of pixels that correspond to a line in the original image. Those entries for which the Hough transform yields the highest values are most likely to correspond to a line in the original image.
  • FIG. 4 An embodiment of a Hough transform of a binary image can be found in Figures 4-6.
  • a binary image of a pre-processed DAS signal is shown schematically in FIG. 4, in which two lines corresponding to vehicles are shown.
  • the Hough transformation transforms the coordinates (x, t) in the binary image into the coordinates (0, r) in Hough space.
  • FIG. 5 two points of intersection can be seen whose coordinates (0, r) correspond to the original two lines in FIG.
  • the position of the points (0, r) in Hough space in Fig. 5 thus represents the lines in the binary image in Fig. 4.
  • Fig. 6 the sum of the logarithmic Hough transform is shown, with the rectangle representing the possible position of the Marked extreme value point for estimating the angle 0.
  • the two points of intersection in Fig. 5 also form the minima of the transformation matrix in Fig. 6.
  • Equation (3) The speed in image coordinates can be found by Equation (3), while the speed in km/h can be found by Equation (4).
  • Equation (4) d corresponds to the distance between two measurement points, sp to the normalized speed in the image coordinates of the binary image and Skm/h to the actual speed in km/h. 3.6
  • the columns that correspond to the angles 0 previously extracted are now extracted from the Hough transformation matrix.
  • the number of extreme points can be determined from these columns, which corresponds to the number of vehicles (see border in FIG. 6).
  • the quality of the vehicle trajectories or the tracks can first be improved in the waterfall diagram by detection using contrast enhancement and/or edge detection. Common methods from image processing can be used for this. However, this step is optional and analysis can also be done without such an improvement.
  • the local orientations in the thus improved binary bitmap image are then “coded” using standard image processing methods. This coding is done in such a way that each pixel can be assigned a local orientation value, i.e. an angle.
  • a binary bitmap image is typically obtained in such a step, in which each pixel is assigned a color value or a gray value corresponding to the local orientation in the image.
  • the angles of the vehicle trajectories in the binary bitmap image represent a measure of the speed.
  • FIG. 8a shows, by way of example, such vehicle trajectories for three vehicles in a waterfall diagram as three lines, with the x-axis representing the location along the road and the y-axis representing time.
  • Figure 8b shows the binary bitmap image generated from Figure 8a by thresholding.
  • the local orientations in the waterfall diagram are used to extract the vehicle speeds.
  • the local gradients in the binary bitmap image are calculated from the image using common image processing methods such as Sobel, Prewitt or Roberts operators. These operators are common methods that are already available in various program libraries and can be found, for example, in and https://de.wikipedia.orq/wiki/Fsoberts- Q erator, each last accessed on July 14, 2022.
  • FIG. 9 shows an orientation image with the evaluated local orientations of the gradients of the lines in FIG. 8, the x-axis representing the location along the road and the y-axis representing time.
  • the gray value scale on the right-hand edge of FIG. 9 shows the coding of the orientation in angular degrees.
  • the black arrows show the orientation of the gradients, the gray arrow shows the orientation of a lane as an example.
  • histograms (see Figure 10) of the pixel values are formed for sections along the x-axis in the orientation image which correspond to periodic local sections (e.g. several meters to 100 m). A histogram of the frequencies of the orientations contained therein is thus obtained for each local section.
  • the maximum values in the histogram are isolated according to their position and their height. Common methods of determining the maximum value are used for this purpose, as they are available in various program libraries. Dominant velocities in the waterfall diagram each generate two corresponding maxima, with a distance of 180°, in the histogram. The corresponding velocities are offset 90° from the local orientations, as illustrated by arrows in FIG. This angle value can be determined by finding two associated maxima with a distance of 180° in the histogram. This angular value corresponds to a driving speed v, according to formulas (3) and (4). Local maxima at 0°, 90° are artifacts corresponding to 0 and °° velocities, respectively, and can easily be discarded.
  • the histogram is then standardized in such a way that a specific frequency in the histogram corresponds to a specific number of vehicles.
  • the normalization depends on the selected size of the local sections. In the exemplary embodiment shown, a frequency of 2000 pixels corresponds to one vehicle if the binary bitmap image has a width of 500 pixels and the width of the gradient is approximately 4 pixels.
  • the number of pixels that corresponds to a vehicle for a selected width of the binary bitmap image depends on the specific embodiment of the method according to the invention and can be determined empirically.
  • FIG. 11 shows a schematic representation of an example for the evaluation of the local orientations over an entire driving section. Histograms (as in FIG. 10) assigned to the individual locations in the driving section are created from the local orientations (as in FIG. 9).
  • a method according to the invention it is optionally possible to classify vehicles into different length ranges, e.g. into large and small vehicles.
  • the width of a track corresponds to a section in the waterfall diagram at a particular point in time t along the location.
  • smaller vehicles can then be analyzed separately from larger vehicles in another image.
  • the separate images can each be further examined separately.
  • a filter can be used for this, which isolates the widths of the trajectories in the waterfall diagram, so that the waterfall diagram can be split into several separate images.

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Abstract

La présente invention concerne un procédé de mesure de la densité de trafic et/ou de la vitesse d'un certain nombre de véhicules se déplaçant le long d'une section d'itinéraire, dans lequel, le long de la section d'itinéraire, un guide d'ondes est utilisé pour déterminer des valeurs mesurées pour caractériser des vibrations ou des changements de pression au niveau d'une multiplicité de points locaux disposés le long de la section d'itinéraire, des impulsions électromagnétiques étant émises dans le guide d'ondes et l'onde électromagnétique revenant du guide d'ondes étant mesurée, le signal étant attribué à un point local le long de la section d'itinéraire conformément au retard temporel, une valeur de mesure pour caractériser la vibration ou un changement de pression étant fournie dans chaque cas pour un certain nombre de points locaux le long de la section d'itinéraire et pour un certain nombre de points dans le temps, une probabilité de savoir si une valeur mesurée particulière correspond à une présence d'un véhicule étant déterminée, et les probabilités sont représentées dans un graphique et sont utilisées pour déterminer la vitesse et/ou le nombre de véhicules.
PCT/AT2022/060245 2021-09-17 2022-07-07 Procédé de mesure du volume de trafic à l'aide de das WO2023039620A1 (fr)

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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN118038691A (zh) * 2024-04-12 2024-05-14 广东振声科技集团有限公司 一种基于物联网的智能监控指挥系统

Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20180342156A1 (en) * 2015-10-30 2018-11-29 Optasense Holdings Limited Monitoring Traffic Flow
WO2021152648A1 (fr) * 2020-01-27 2021-08-05 Nec Corporation Appareil de surveillance de trafic, système, procédé de surveillance de trafic et support non transitoire lisible par ordinateur
US20210241615A1 (en) * 2020-01-30 2021-08-05 Nec Corporation Traffic monitoring apparatus and method of using the same
WO2021207096A1 (fr) * 2020-04-07 2021-10-14 Nec Laboratories America, Inc. Surveillance de trafic à l'aide d'une détection à fibres optiques distribuée

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20180342156A1 (en) * 2015-10-30 2018-11-29 Optasense Holdings Limited Monitoring Traffic Flow
WO2021152648A1 (fr) * 2020-01-27 2021-08-05 Nec Corporation Appareil de surveillance de trafic, système, procédé de surveillance de trafic et support non transitoire lisible par ordinateur
US20210241615A1 (en) * 2020-01-30 2021-08-05 Nec Corporation Traffic monitoring apparatus and method of using the same
WO2021207096A1 (fr) * 2020-04-07 2021-10-14 Nec Laboratories America, Inc. Surveillance de trafic à l'aide d'une détection à fibres optiques distribuée

Non-Patent Citations (11)

* Cited by examiner, † Cited by third party
Title
"Distributed fiber-optic intrusion sensor system", J. LIGHTWAVE TECHNOL., vol. 23, no. 6, June 2005 (2005-06-01), pages 2081 - 2087
"Hough transform for line detection'' in Proceedings", 1999 IEEE COMPUTER SOCIETY CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION, vol. 1, June 1999 (1999-06-01), pages 554 - 560
C. WANGM. OLSONB. SHERMANN. DORJKHANDJ. MEHRS. SINGH: "Reliable Leak Detection in Pipelines Using Integrated DdTS Temperature and DAS Acoustic Fiber-Optic Sensor", 2018 INTERNATIONAL CARNAHAN CONFERENCE ON SECURITY TECHNOLOGY (ICCST), MONTREAL, QC, October 2018 (2018-10-01), pages 1 - 5, XP033483566, DOI: 10.1109/CCST.2018.8585687
C. WIESMEYR ET AL.: "Real-Time Train Tracking from Distributed Acoustic Sensing Data", APPLIED SCIENCES, vol. 10, no. 2, January 2020 (2020-01-01), pages 448, XP055781144, DOI: 10.3390/app10020448
F. PENGH. WUX.-H. JIAY.-J. RAOZ.-N. WANGZ.-P. PENG: "Ultra-Iong high-sensitivity Φ-070/? for high spatial resolution intrusion detection of pipelines", OPT. EXPRESS, vol. 22, no. 11, June 2014 (2014-06-01), pages 13804
F. WALTER ET AL.: "Distributed acoustic sensing of microseismic sources and wave propagation in glaciated terrain", NAT COMMUN, vol. 11, no. 1, December 2020 (2020-12-01), pages 2436
R. O. DUDAP. E. HART: "Use of the Hough transformation to detect lines and curves in pictures", COMMUN. ACM, vol. 15, no. 1, January 1972 (1972-01-01), pages 11 - 15
S. DOU ET AL.: "Distributed Acoustic Sensing for Seismic Monitoring of The Near Surface: A Traffic-Noise Interferometry Case Study,", SEI REP, vol. 7, no. 1, December 2017 (2017-12-01), pages 11620
S. KOWARIK ET AL.: "Fiber Optic Train Monitoring with Distributed Acoustic Sensing: Conventional and Neural Network Data Analysis", SENSORS, vol. 20, no. 2, January 2020 (2020-01-01), pages 450
WIESMEYR CHRISTOPH ET AL: "Distributed Acoustic Sensing for Vehicle Speed and Traffic Flow Estimation", 2021 IEEE INTERNATIONAL INTELLIGENT TRANSPORTATION SYSTEMS CONFERENCE (ITSC), IEEE, 19 September 2021 (2021-09-19), pages 2596 - 2601, XP033994085, DOI: 10.1109/ITSC48978.2021.9564517 *
WIESMEYR CHRISTOPH ET AL: "Real-Time Train Tracking from Distributed Acoustic Sensing Data", APPLIED SCIENCES, vol. 10, no. 2, 8 January 2020 (2020-01-08), pages 448, XP055781144, DOI: 10.3390/app10020448 *

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN118038691A (zh) * 2024-04-12 2024-05-14 广东振声科技集团有限公司 一种基于物联网的智能监控指挥系统

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