Vehicle tracking methods, vehicle tracking systems, and programs

Abstract

To provide a vehicle tracking method, a vehicle tracking system, and a program.SOLUTION: A vehicle tracking method includes: receiving dispersion type optical fiber sensing (DFOS) data; specifying a plurality of first hit points inside the DFOS data, each of which corresponds to a position of a corresponding first vehicle at a detection point; determining an initial seed point from among the plurality of specified first hit points; determining whether or not any of the plurality of specified first hit points is an outlier hit point; defining a first cluster by clustering the plurality of specified first hit points from which an arbitrary outlier hit point is excluded; and estimating first vehicle parameters of the first vehicle based on the first cluster.SELECTED DRAWING: Figure 2

Description

Technical Field

[0001] This application relates to distributed fiber optic sensing (DFOS) systems and methods of using the same.

Background Art

[0002] Optical fibers are present along a number of roadways. Distributed acoustic sensors (DAS) attached to these optical fibers can detect vibrations where the optical fibers are located. In some cases, these vibrations are the result of passing vehicles. DAS can collect data regarding the number of vehicles, the lane position of the vehicles, and the vehicle speed.

[0003] DAS generates waterfall data based on time and distance to determine traffic parameters. The ability of DAS to detect individual vehicles is related to the amount of noise in the signals detected by DAS.

Summary of the Invention

Problems to be Solved by the Invention

[0004] Improvement in the accuracy of vehicle tracking is desired.

Means for Solving the Problems

[0005] A vehicle tracking method according to a first embodiment of the present disclosure is A vehicle tracking method performed by a vehicle tracking system, comprising: receiving distributed optical fiber sensing (DFOS) data; identifying a plurality of first hit points in the DFOS data, each corresponding to the position of a first vehicle at the time of detection; determining an initial seed point from the identified plurality of first hit points; determining whether any of the identified plurality of first hit points is an outlier hit point; clustering the identified plurality of first hit points, excluding any outlier hit points, to define a first cluster; estimating first vehicle parameters of the first vehicle based on the first cluster; and each corresponding second vehicle This includes identifying a plurality of second hit points in the DFOS data corresponding to a location; estimating second vehicle parameters of the second vehicle based on the identified plurality of second hit points; plotting the identified plurality of first hit points and the identified plurality of second hit points; determining the plotted plurality of first hit points and the plotted plurality of second hit points on the first side of the intersection where the plotted plurality of first hit points intersect with the plotted plurality of second hit points; and identifying a seed point on the second side of the intersection opposite to the first side of the intersection. .

[0006] A vehicle tracking system according to a second embodiment of the present disclosure is The system comprises a non-temporary computer-readable medium configured to store instructions, and a processor connected to the non-temporary computer-readable medium, wherein the processor receives distributed fiber optic sensing (DFOS) data, identifies a plurality of first hit points in the DFOS data, each corresponding to the position of a first vehicle at the time of detection, determines an initial seed point from the identified plurality of first hit points, determines whether any of the identified plurality of first hit points is an outlier hit point, clusters the identified plurality of first hit points from which any outlier hit points have been excluded to define a first cluster, estimates first vehicle parameters of the first vehicle based on the first cluster, and The system is configured to execute instructions for: identifying a plurality of second hit points in the DFOS data, each corresponding to the position of a second vehicle; estimating second vehicle parameters of the second vehicle based on the identified plurality of second hit points; plotting the identified plurality of first hit points and the identified plurality of second hit points; determining the plotted plurality of first hit points and the plotted plurality of second hit points on the first side of the intersection if the plotted plurality of first hit points intersect with the plotted plurality of second hit points at an intersection; and identifying a seed point on the second side of the intersection opposite to the first side of the intersection. .

[0007] A program according to a third embodiment of the present disclosure is Receiving distributed fiber optic sensing (DFOS) data; identifying a plurality of first hit points in the DFOS data, each corresponding to the position of a first vehicle at the time of detection; determining an initial seed point from the identified plurality of first hit points; determining whether any of the identified plurality of first hit points is an outlier hit point; clustering the identified plurality of first hit points, excluding any outlier hit points, to define a first cluster; estimating first vehicle parameters of the first vehicle based on the first cluster; and the DFOS data, each corresponding to the position of a second vehicle. The processor is instructed to: identify a plurality of second hit points within the vehicle; estimate second vehicle parameters of the second vehicle based on the identified plurality of second hit points; plot the identified plurality of first hit points and the identified plurality of second hit points; and, if the plotted plurality of identified first hit points intersect with the plotted plurality of identified second hit points at an intersection, determine the plotted plurality of identified first hit points and the plotted plurality of identified second hit points on the first side of the intersection; and identify a seed point on the second side of the intersection opposite to the first side of the intersection. .

Brief Description of the Drawings

[0008] The aspects of this disclosure will be best understood from the following detailed description in conjunction with the attached drawings. Note that, in accordance with standard industry practice, various features are not depicted to scale. In fact, the dimensions of various features may be increased or decreased as appropriate for clarity in the description. [Figure 1A] This is a schematic diagram of a distributed acoustic sensor (DAS) system along a roadway, according to several embodiments. [Figure 1B] This is a schematic diagram of a distributed acoustic sensor (DAS) system along a roadway, based on one of the embodiments described above. [Figure 2] This is a flowchart illustrating a method for analyzing data from distributed fiber optic sensing (DFOS) data according to several embodiments. [Figure 3] This is a schematic diagram of a DAS system with waterfall data collected by the DAS system, according to several embodiments. [Figure 4] This figure shows filtered DFOS data according to several embodiments. [Figure 5A] This figure shows a plot of detected vehicle positions based on DFOS data according to several embodiments. [Figure 5B] This figure shows a plot of detected vehicle positions based on DFOS data according to several embodiments. [Figure 6] This figure shows a plot of clustered vehicle locations based on DFOS data, according to several embodiments. [Figure 7] This is a diagram of a plot for tracking the position of a vehicle along a roadway, according to several embodiments. [Figure 8] This is a diagram of a plot for tracking multiple vehicles along a roadway, according to several embodiments. [Figure 9] This is a flowchart illustrating a method for analyzing distributed fiber optic sensing (DFOS) data according to several embodiments. [Figure 10]This is a block diagram of a system for analyzing DFOS data according to several embodiments. [Modes for carrying out the invention]

[0009] The following disclosure provides many different embodiments or examples for implementing different features of the subject matter provided. To simplify this disclosure, specific examples of components, values, behaviors, materials, arrangements, etc., are described below. Naturally, these are merely examples and are not intended to be limiting. Other components, values, behaviors, materials, arrangements, etc., are possible. For example, the formation of a first feature over a second feature in the following description may include embodiments in which the first and second features are formed in direct contact, or it may include embodiments in which an additional feature may be formed between the first and second features so that the first and second features are not in direct contact. Furthermore, this disclosure may repeat reference numbers and / or letters in various examples. This repetition is for simplification and clarity and does not in itself presuppose any relationships between the various embodiments and / or configurations discussed.

[0010] Furthermore, spatially relative terms such as “beneath,” “below,” “lower,” “above,” and “upper” may be used herein to facilitate explanations of the relationship between one element or feature and another, as shown in the diagram. Spatially relative terms are intended to encompass different orientations of the device in use or operation, in addition to the orientation shown in the diagram. The device may be oriented in other directions (rotated 90 degrees or in other directions), and the spatially relative descriptors used herein may be interpreted accordingly.

[0011] Utilizing data from optical fibers along roadways is useful for determining traffic volume, speed, accidents, and other events along those roadways. To enhance the usefulness of traffic information obtained based on optical fiber data, determining vehicle parameters such as speed, acceleration, and lane position provides useful information for identifying traffic patterns and navigation information. The quality of collected distributed optical fiber sensing (DFOS) data is determined by many factors. Since DFOS data is at least partially based on vibration, vehicle size, traffic volume, and roadway type affect data quality. For example, a single vehicle traveling along a roadway directly on the ground is likely to provide higher quality signals than a large truck traveling on a busy bridge. Denoising DFOS data is helpful in analyzing DFOS data for use in applications such as traffic analysis, autonomous driving, and navigation instructions.

[0012] In addition, the accuracy of traffic information assists in the navigation of vehicles traveling along roadways. By providing drivers with more accurate traffic data, navigation systems and / or navigation applications become more useful to drivers. High-precision navigation is also useful for autonomous driving or driver assistance functions of vehicles. By accurately determining where traffic congestion or accidents have occurred, autonomous vehicles or driver assistance systems can guide vehicles along more efficient routes.

[0013] This application describes a method and system for improving vehicle tracking along a roadway using DFOS data. Using DFOS data, the detected locations, also known as hit points, of objects along the roadway make it possible to determine the position of a vehicle along the roadway at various points in time. Using thresholding techniques helps to remove erroneous data, also known as outlier hit points, from the DFOS data used to track vehicles. As a result, the accuracy and precision of vehicle tracking are improved compared to other methods. Hit point clustering also helps to provide vehicle data, such as speed, to further improve the usefulness of DFOS data for both vehicle tracking and traffic congestion analysis.

[0014] Furthermore, the improved accuracy and precision of vehicle tracking described in this application helps facilitate the tracking of multiple vehicles along the same roadway, even when DFOS data for multiple vehicles includes intersections. By utilizing thresholding and clustering, this application provides a method for accurately tracking multiple vehicles by utilizing vehicle data such as speed to accurately determine the path of each vehicle, even when multiple vehicles pass through intersections in the DFOS data. Intersections in DFOS data are where the hit points of multiple vehicles intersect each other on a distance-versus-time plot. Intersections in DFOS data do not represent intersections within a roadway.

[0015] Figure 1A is a schematic diagram of a distributed acoustic sensor (DAS) system 100A along a roadway 130A according to several embodiments. The DAS system 100A includes a traffic monitoring device 111 that communicates with a DAS 112. The DAS system 100A further includes an optical fiber 121 connected to the DAS 112. The optical fiber 121 runs along the roadway 130A. The roadway 130A includes three lanes. A large number of vehicles are on the roadway 130A. Some vehicles 140 on the roadway 130A are larger than other vehicles 150 on the roadway 130A. Although the description refers to the optical fiber 121, those skilled in the art will understand that in some embodiments the optical fiber 121 includes a multifiber bundle.

[0016] When vehicles 140 and 150 pass along lane 130A, the vehicles generate vibrations. Due to these vibrations, the way light propagates along optical fiber 121 changes. DAS 112 is connected to optical fiber 121, sends an optical signal to optical fiber 121, and detects the return light from optical fiber 121. The obtained data is called waterfall data. The waterfall data provides information regarding the number of vehicles, the direction of travel by the vehicles, the vehicle speeds and the lane positions of the vehicles on lane 130A.

[0017] Lane 130A in FIG. 1 is on a hard ground. The hard ground does not vibrate with an amplitude high enough to obscure the detection of vehicles 140 and 150 traveling along lane 130A. As a result, DAS 112 can detect vehicles 140, 150 traveling along lane 130A with high precision. In some embodiments, lane 130A includes at least one bridge such as lane 130B in FIG. 1B.

[0018] Unlike the hard ground, a bridge exhibits different vibration characteristics such as attenuation. The vibration characteristics of a bridge are affected by the length of the bridge, the building materials of the bridge, wind, and other factors. By utilizing these differences in the vibration characteristics of the bridge, the location where the bridge is located along optical fiber 121 can be determined.

[0019] FIG. 1A also includes exemplary measured DAS data. This exemplary measured DAS data is provided to assist in understanding the waterfall data collected by DAS 112.

[0020] FIG. 1B is a schematic diagram of a DAS system 100B along lane 130B according to some embodiments. Similar to DAS system 100A in FIG. 1A, DAS system 100B includes DAS 112 and optical fiber 121. In contrast to FIG. 1A, lane 130B in FIG. 1B includes a first bridge 160A and a second bridge 160B. Further, FIG. 1B includes an additional fiber portion 170.

[0021] Considering the first vehicle 150A and the second vehicle 150B helps in understanding the use of fixed reference points, such as the first bridge 160A and the second bridge 160B, when determining the location corresponding to traffic information with high accuracy. The distance between the first vehicle 150A and the second vehicle 150B along the roadway 130B is significantly different from the length of the optical fiber 121 between the location of the first vehicle 150A and the location of the second vehicle 150B. This difference is due to the presence of an additional fiber portion 170, as well as the fact that the optical fiber 121 is not exactly parallel to the roadway 130B. Determining the location of the first bridge 160A along the optical fiber 121 helps in determining the high-precision location of the first vehicle 150A along the roadway 130B. The location of the first bridge 160A along the roadway 130B is known based on publicly available geographical data. By determining the position of the first bridge 160A relative to the optical fiber 121, the length of the optical fiber 121 from DAS 112 to the end of the first bridge 160A closest to the first vehicle 150A is determined based on waterfall data. Next, based on the waterfall data, the length of the optical fiber 121 between the end of the first bridge 160A and the first vehicle 150A is determined. By limiting the distance of the optical fiber 121 from a fixed reference point on the first bridge 160A to the first vehicle 150A, errors in the length of the roadway 130 from the end of the first bridge 160A to DAS 112 are excluded from the position determination. As a result, the position of the first vehicle 150A along the roadway 130B can be determined with greater accuracy using a fixed reference point on the first bridge 160A.

[0022] Similarly, the position of the second vehicle 150B is determined more accurately by using a fixed reference point on the second bridge 160B. Waterfall data from DAS 112 can be used to determine the length of the optical fiber 121 between DAS 112 and the end of the second bridge 160B closest to the second vehicle 150B. Then, only the length of the optical fiber 121 between the second vehicle 150B and the second bridge 160B is used to determine the position of the second vehicle 150B along the roadway 130B. By using this short length of optical fiber 121, the length of the optical fiber 121 between the second vehicle 150B and DAS 112, including the additional fiber portion 170, is excluded from the position determination. As a result, the position of the second vehicle 150B is determined with greater accuracy by using a fixed reference point on the second bridge 160B.

[0023] Figure 2 is a flowchart of a method for analyzing data from distributed fiber optic sensing (DFOS) data according to several embodiments. Method 200 can be used with DAS system 100A (Figure 1A), DAS system 100B (Figure 1B), or another suitable system providing DFOS data.

[0024] In operation 205, raw DFOS data is received. In some embodiments, the raw DFOS data includes waterfall data, such as waterfall data detected by DAS112 (Figure 1). The waterfall data includes information about the time and location along the optical fiber where the vibration data was detected. Further details of the waterfall data are discussed with respect to Figure 3 below. In some embodiments, the waterfall data includes data along a roadway that includes both hard ground and at least one bridge.

[0025] In operation 210, the raw DFOS data is localized using structural information. Structural information includes information obtained from an external source regarding fixed reference points along the roadway. For example, in some embodiments, the structural information includes the location of a bridge, e.g., bridge 160A (Figure 1B), the location of an additional fiber, e.g., additional fiber 170 (Figure 1B), or other suitable reference points along the roadway. Localizing DFOS data using structural information helps improve the accuracy and precision of the detected vehicle position, as well as the accurate determination of vehicle parameters such as speed and acceleration. Localizing DFOS data also helps improve the accuracy and precision of navigation commands, autonomous driving functions, traffic monitoring, or other appropriate applications of DFOS data.

[0026] In operation 215, the raw DFOS data is preprocessed to enhance the received data. Preprocessing the data includes normalizing the vibration amplitude of the data at each location along the roadway, e.g., roadway 130B (Figure 1B), over a predetermined duration. Normalizing the vibration amplitude helps to account for variations in the sensitivity of the optical fiber. Variations in optical fiber sensitivity can be caused by several factors, including, but are not limited to, an uneven roadway surface, inconsistent installation of the optical fiber, and mismatch of the optical fiber. Normalizing the vibration amplitude also helps to account for variations in traffic volume. For example, as the number of vehicles on the roadway increases, the magnitude of vibrations detected by the DAS increases. By normalizing the vibration amplitude based on a predetermined duration, the impact of large vibrations detected during periods of high traffic volume on periods of low traffic volume is reduced, resulting in more accurate data for estimating traffic flow characteristics.

[0027] In some embodiments, preprocessing the data also limits the maximum vibration amplitude at each location along the optical fiber over a predetermined duration. Limiting the maximum vibration amplitude helps prevent vibrations from larger vehicles, such as trucks or construction vehicles, from obscuring vibrations generated by smaller vehicles, such as passenger cars.

[0028] Next, the pre-processed data is filtered by a bandpass filter based on the estimated frequency range from operation 210. This filtering removes portions of the roadway that did not exhibit the bridge's vibration damping characteristics. Figure 4 below provides an example of filtered DFOS data.

[0029] In operation 220, an initial seed is determined for each vehicle based on hit point data. The initial seed for each vehicle is determined based on the hit points identified at the first time the vehicle is detected, for example, at time (t) = 0. In some embodiments, the first time is the time when the vehicle first enters the detection area of ​​the DAS system, e.g., DAS system 100A (Figure 1A), DAS system 100B (Figure 1B), or another suitable DAS system. In some embodiments, the first time is a time selected by the user of the DAS system. Hit points are identified based on detected vibrations in the DFOS data. Detected vibrations are determined based on the width of the lines in the DFOS data. In some cases, vibrations occur more broadly, which indicates a higher vibration intensity. Wider lines carry a higher risk of resulting in the identification of multiple hit points for the same vehicle. Furthermore, in some embodiments, additional vibrations not caused by vehicles traveling along the roadway generate vibrations detected in the DFOS data. As a result of these additional vibrations, false hit points may be identified in locations where no vehicle is present.

[0030] In some embodiments, a trained neural network (NN) is used to identify hit points in the DFOS data. In some embodiments, hit points are identified or verified by a user of the DAS system. Identifying initial hit points assists in tracking a vehicle through at least a portion of the DAS system's detection area to determine vehicle parameters for use in autonomous driving, navigation commands, traffic monitoring, or other appropriate applications. Additional details regarding initial hit point detection are provided below with respect to Figure 5, according to some embodiments.

[0031] In operation 225, subsequent hit points are identified for each individual vehicle. A subsequent hit point is the vehicle position determined using DFOS data from a later time, e.g., t=1, t=2, etc. In some embodiments, the interval between the first time and the subsequent time is uniform across each time. In some embodiments, there is an irregular interval between the first time and the subsequent time. In some embodiments, the interval is predetermined. In some embodiments, the interval is set based on the speed limit along the roadway. In some embodiments, the interval is set based on the expected traffic volume along the roadway, e.g., shorter intervals for areas with heavier traffic congestion. In some embodiments, subsequent hit points are identified in the same way as the initial hit points. In some embodiments, in addition to detecting vibrations based on DFOS data, subsequent hit points are identified based on the number of initial hit points, which can be used to predict the number of vehicles being tracked through the detection area of ​​the DAS system. In some embodiments, in addition to detecting vibrations based on DFOS data, subsequent hit points are identified based on the expected position of the vehicle. In some embodiments, a trained NN can be used to identify subsequent hit points. Additional details regarding the detection of the initial hit point are provided with respect to Figure 5 below, according to several embodiments.

[0032] In operation 230, outlier hit points are removed. An outlier hit point is a hit point in the DFOS data that cannot reasonably correspond to the vehicle being tracked. For example, a hit point 100 meters (m) away from a hit point detected 0.5 seconds (s) earlier is considered an outlier hit point because the vehicle would have to be traveling at 200 meters (m / s) at 720 kilometers per hour (km / h) or 447 miles per hour (mph). Such a travel speed is not reasonable even assuming the vehicle can travel at such a speed. In some embodiments, determining whether a potential outlier hit point can reasonably correspond to the vehicle being tracked is based on the speed limit along the roadway monitored by the DAS. In some embodiments, determining whether a potential outlier hit point can reasonably correspond to the vehicle being tracked is based on the vehicle's previously determined speed. For example, in some embodiments, a hit point is determined to be an outlier hit point if the difference between the speed required for the vehicle to reach the potential outlier hit point and the vehicle's previously determined speed is greater than 50% of the vehicle's previously determined speed. In some embodiments, determining whether a potential outlier hit point can reasonably correspond to a tracked vehicle is based on the vehicle's direction of travel. For example, if a potential outlier hit point indicates that the vehicle made multiple rapid changes of direction, the potential outlier hit point is less likely to reasonably correspond to the tracked vehicle. In some embodiments, a trained neural network (NN) is used to identify outlier hit points in DFOS data. In some embodiments, the NN is trained using traffic congestion and / or vehicle tracking data along the same or similar roadways. By using traffic congestion and / or vehicle tracking data as training data, the NN can determine how often vehicles travel along the roadway in order to determine whether a potential outlier hit point can reasonably correspond to the tracked vehicle.

[0033] In some cases, outlier hitpoints are the result of other such occurrences, such as strong winds, construction, traffic accidents, or vibrations along the roadway that the DAS detects other than those caused by the movement of the vehicle being tracked.

[0034] Once a hit point is identified as an outlier hit point, it is no longer considered during vehicle tracking or traffic congestion analysis using DFOS data. In some embodiments, outlier hit points are removed from the DFOS data. In some embodiments, outlier hit points are used to train a neural network (NN) to better identify outlier hit points during future analysis of DFOS data.

[0035] In operation 235, a decision is made as to whether a sufficient number of hit points have been identified to perform the clustering operation. Clustering the hit points makes it possible to determine vehicle parameters such as speed along the roadway. As the number of hit points in a cluster increases, the accuracy of the determined vehicle parameters improves. However, the more hit points in a cluster, the longer it takes to collect more hit points. As a result, the determination of a sufficient number of hit points can be adjusted based on the use of DFOS data. In some embodiments, the number of hit points sufficient for clustering is a fixed value. In some embodiments, the number of hit points sufficient for clustering varies based on the vehicle or roadway conditions. For example, in situations where near real-time data is desired, such as in autonomous driving, in some embodiments, a small number of hit points, such as two or three hit points, is determined to be sufficient for clustering.

[0036] In some embodiments, determining the number of hit points sufficient for clustering is based on detected traffic congestion. As traffic congestion increases, vehicle speed decreases. Consequently, the number of hit points considered sufficient for clustering increases during heavy traffic congestion. For example, in some embodiments where DFOS data is used in vehicle navigation such as the Global Positioning System (GPS), increasing the number of hit points sufficient for clustering allows for the collection of more data to provide more accurate results without increasing the risk of the vehicle passing a location such as an exit ramp or turn due to its slower speed.

[0037] In some embodiments, the determination of a sufficient number of hit points for clustering is based on the vehicle's previously determined speed. As the vehicle's speed increases, the number of hit points considered sufficient for clustering decreases. For example, in some embodiments where DFOS data is used for vehicle navigation, reducing the number of hit points sufficient for clustering reduces the risk that the vehicle will pass a certain location due to increased vehicle speed.

[0038] In some embodiments, the determination of a sufficient number of hit points for clustering is based on a threshold determined by the DAS operator. In some embodiments, the threshold corresponds to hit points corresponding to 10 seconds (s). In some embodiments, the threshold is based on empirical data of driving along the monitored roadway.

[0039] In response to the decision that an insufficient number of hit points have been identified for clustering, method 200 returns to operation 225, where additional hit points are identified. In response to the decision that a sufficient number of hit points have been identified for clustering, method 200 proceeds to operation 240.

[0040] In operation 240, clustering is performed on the identified hit points. In some embodiments, the hit points are clustered using K-means clustering, and the number of identified hit points determined in operation 235 is used as the value of K. In some embodiments, clustering is performed using density-based spatial clustering of applications with noise (DBSCAN) clustering, mixture Gaussian model clustering, balanced iterative reducing and clustering using hierarchies (BIRCH) clustering, affinity propagation clustering, mean-shift clustering, ordering points to identify the clustering structure (OPTICS) clustering, aggregate hierarchical clustering, or another suitable type of clustering.

[0041] In operation 245, the next hit point is estimated based on the clustered hit points. The expected location of the next hit point is estimated based on vehicle parameters such as speed, which are determined based on the clustered hit points. For example, in some embodiments where DFOS data is captured at regular time intervals, the vehicle speed and the regular time intervals can be used to estimate the location where the hit point is expected to be found. In some embodiments, any detected hit point other than an outlier hit point is determined to be a match between the estimated hit point and the detected hit point. In response to the detection of a hit point at the estimated location, the hit point can be used to update the vehicle parameters determined based on the clustered hit points from operation 240. In response to the inability to detect a hit point at the estimated location, a subsequent hit point is estimated, and operation 245 is repeated to attempt to detect a hit point at the estimated subsequent hit point. The subsequent hit point is a hit point at least one detection period later than the next hit point initially determined in operation 245.

[0042] In some embodiments, operation 245 is repeated iteratively until an estimated hit point is detected at the expected location. In some embodiments, a maximum number of iterations is allowed before proceeding to operation 250. That is, if no match is found between the detected hit point and the estimated hit point within the maximum number of iterations, method 200 proceeds to operation 250. In some embodiments, the maximum number of iterations is set by the operator of the DAS. In some embodiments, the maximum number of iterations is based on the vehicle speed. As the vehicle speed increases, the maximum number of iterations decreases. In some embodiments, the maximum number of iterations is determined based on the user of the DFOS data. For example, in some embodiments, autonomous driving has a lower maximum number of iterations than traffic congestion analysis. In some embodiments, the maximum number of iterations ranges from 2 to 10. Estimating hit points helps improve the accuracy of vehicle tracking by reducing the risk of relying on hit point data that is incorrect but not significant enough to be identified as outlier hit points.

[0043] In operation 250, the initial seed is updated with a new hit point. In some embodiments, the initial seed corresponds to an estimated hit point determined in operation 245. In some embodiments, the initial seed corresponds to the most recent hit point in the clustered hit points from operation 240. In some embodiments, the initial seed corresponds to the first hit point detected after the clustered hit points from operation 240. Utilizing the most recent hit point in the clustered hit points improves the accuracy of method 200 when performing vehicle tracking or traffic congestion analysis. Utilizing hit points after the clustered hit points improves the processing speed for performing vehicle tracking or traffic congestion analysis.

[0044] Following operation 250, method 200 returns to operation 225, and further vehicle tracking is performed.

[0045] In some embodiments, method 200 includes additional operations. For example, in some embodiments, method 200 includes the step of generating instructions for controlling an autonomous vehicle based on estimated vehicle parameters. In some embodiments, at least one operation of method 200 is omitted. For example, in some embodiments, operation 245 is omitted and hit point estimation is not performed. In some embodiments, the order of operations of method 200 is adjusted. For example, in some embodiments, operation 215 is performed before operation 210.

[0046] Using Method 200, DFOS data can be used to improve the accuracy and precision of vehicle tracking along the roadway compared to other methods that cannot remove outlier and clustered hit points. Improved vehicle tracking decisions can help improve accuracy in traffic monitoring, navigation commands, autonomous driving commands, and other applications.

[0047] Figure 3 is a schematic diagram of DAS system 100B with waterfall data 300 collected by a DAS system according to several embodiments. DAS system 100B is the same as DAS system 100B in Figure 1B. Similar to Figure 1B, the roadway (not shown) in Figure 3 includes two bridges, as indicated by the waterfall data 300. The waterfall data 300 is pre-processed waterfall data.

[0048] Waterfall data 300 includes regions 302, 304, 306, 308, and 310. Regions 302, 306, and 310 include identifiable lines showing vibrations generated by vehicles crossing the roadway. Regions 304 and 308 represent a bridge. Compared to regions 302, 306, and 310, regions 304 and 308 do not include identifiable lines because the damped vibrations of the bridge obscure the detected vibrations of vehicles crossing the bridge.

[0049] Figure 4 shows filtered DFOS data 400 according to several embodiments. The filtered DFOS data 400 includes a region 410 that shows higher vibration intensity within the estimated frequency range. Region 410 is likely to be a bridge along the roadway. The filtered DFOS data 400 also includes a region 420 that shows lower vibration intensity within the estimated frequency range. Region 420 is likely to be a non-bridge portion of the roadway.

[0050] Figure 5A is a diagram of plot 500A of detected vehicle positions based on DFOS data according to several embodiments. Plot 500A includes hit points 505, missing data points 510, and seed points 515. Plot 500A includes this data in a distance-versus-time graph. The distance axis is a measure of how far the detected vibration is from a DAS such as DAS112 (Figure 1). Thus, the data in plot 500A indicates that the vehicle is traveling towards the DAS, as the distance decreases as time increases. Hit point 505 indicates that the detected vibration indicates that the vehicle is at a distance from the DAS at a particular time. Missing data point 510 indicates the absence of a predicted hit point based on analysis for tracking the vehicle. Seed point 515 indicates hit point 505 determined as the initial seed, for example using operation 220 (Figure 2). The following description includes non-limiting examples of some implementations of method 200 (Figure 2).

[0051] Hit point 505 is detected from the DFOS data, for example, using operations 205, 210, and 215 (Figure 2). Using hit point 505, seed point 515 is determined, for example, using operation 220 (Figure 2). Once seed point 515 is determined, subsequent hit points 520 and 525 are identified, for example, using operation 225 (Figure 2). Subsequent hit points 520 and 525 are hit point 505 detected at a later point in time than seed point 515. In plot 500A, only the subsequent hit points 520 and 525 are labeled. However, a person skilled in the art would recognize that plot 500A contains more subsequent hit points than the immediate hit points 520 and 525.

[0052] In region 530 of plot 500A, a missing data point 535a is located at the expected position of a subsequent hit point. Simultaneously with the missing data point 535a, a first hit point 535b and a second hit point 535c are detected. To determine whether either the first hit point 535b or the second hit point 535c is attributable to a vehicle associated with seed point 515, a determination is made, for example, using operation 230 (Figure 2), regarding whether either the first hit point 535b or the second hit point 535c is an outlier hit point.

[0053] To determine whether the first hit point 535b is an outlier hit point, the location of the first hit point 535b is compared to the location of the missing data point 535a. That is, the locations of the expected subsequent hit point corresponding to the missing data point 535a and the first hit point 535b are analyzed to determine whether a reasonable movement of the vehicle can explain the difference between these two locations. The first hit point 535b is located at a distance D1 from the missing data point 535a. In some embodiments, the distance D1 is compared to a threshold or to another option discussed above with respect to operation 230 (Figure 2). In the example of plot 500A, the first hit point 535b is detected by both the subsequent hit point 525 occurring before the first hit point 535b and the third hit point 535d occurring after the first hit point 535b, indicating that the vehicle must have made two rapid changes of direction. Given the extremely low probability of two rapid changes of direction in such a short time, the first hit point 535b is determined to be an outlier hit point. Consequently, even when distance D1 is within the threshold, the rapid change of direction indicates that the plausibility of the first hit point 535b is low enough to indicate that it is an outlier hit point. Therefore, the first hit point 535b is not considered for the analysis of plot 500A with respect to the tracked vehicle.

[0054] To determine whether the second hit point 535c is an outlier hit point, the location of the second hit point 535c is compared to the location of the missing data point 535a. That is, the expected subsequent hit point corresponding to the missing data point 535a and the location of the second hit point 535c are analyzed to determine whether a reasonable movement of the vehicle can explain the difference between these two locations. The second hit point 535c is located a distance D2 away from the missing data point 535a. In some embodiments, the distance D2 is compared to a threshold or to another option discussed above with respect to operation 230 (Figure 2). A large distance D2 indicates that the second hit point 535c is very unlikely to correspond to the tracked vehicle. For example, the change in vehicle speed to move from the subsequent hit point 525 to the second hit point 535c is so large that the second hit point 535c cannot reasonably correspond to the same vehicle tracked from seed point 515 through subsequent hit points 520 and 525. Furthermore, in the example of plot 500A, the second hit point 535c indicates that the vehicle would have to make two rapid turns of direction in order to be detected by both the subsequent hit point 525, which occurs before the second hit point 535c, and the third hit point 535d, which occurs after the second hit point 535c. Since the probability of two rapid turns of direction occurring in such a short time is extremely low, the second hit point 535c is further determined to be an outlier hit point. Therefore, the second hit point 535c is not considered for the analysis of plot 500A with respect to the tracked vehicle.

[0055] Those skilled in the art will understand that the analysis of plot 500A described above is merely illustrative to aid in understanding some embodiments of Method 200 (Figure 2) or other methods of analyzing DFOS data, and that the analysis of DFOS data in this description is not limited to the above-described examples.

[0056] Figure 5B shows plot 500B of detected vehicle locations based on DFOS data according to several embodiments. Plot 500B is similar to plot 500A. Some of the hit points and missing data points that were labeled in plot 500A are not labeled in plot 500B for clarity in the drawing. As described above, all hit points detected simultaneously with missing data points in region 530 were determined to be outlier hit points, so the detected hit points were not identified as reasonable candidates to replace missing data points in region 530.

[0057] In some embodiments, if the identified subsequent hit points, for example, subsequent hit points 520 and 525 (Figure 5A), are not sufficient to perform clustering, the missing data points in region 530 are skipped. For example, if operation 235 (Figure 2) indicates an insufficient number of hit points for clustering, method 200 returns to operation 225 (Figure 2). However, since the missing data in region 530 lies between subsequent hit point 540 and another subsequent hit point 545, in some embodiments, the missing data is skipped in the iterative execution of operation 225 (Figure 2). Thus, the determination of whether a sufficient number of hit points are available for clustering, for example, operation 235 (Figure 2), is performed without considering the missing data points in region 530.

[0058] Missing data points within region 530 include missing data points in a single time measurement. However, the same analysis described above for plots 500B and 500A (Figure 5A) is applicable to region 550, which contains multiple consecutive missing data points. Multiple consecutive missing data points are two or more consecutive periods in which no hit points were detected at the expected locations. In response to the determination that there are no suitable candidates to replace the missing data points within region 550, the identification of subsequent hit points skips from subsequent hit point 560 to subsequent hit point 565.

[0059] Those skilled in the art will understand that the analysis of plot 500B described above is merely illustrative to help understand some embodiments of Method 200 (Figure 2) or other methods of analyzing DFOS data, and that the analysis of DFOS data in this description is not limited to the examples described above.

[0060] Figure 6 shows a plot 600 of clustered vehicle positions based on DFOS data according to several embodiments. Plot 600 is similar to plots 500A (Figure 5A) and 500B (Figure 5B). Some of the hit points and missing data points that were labeled in plots 500A or 500B are not labeled in plot 600 for clarity in the drawing. Plot 600 includes multiple clustered hit points 605 to form a cluster 610. A vehicle parameter line 615 is determined based on the clustered hit points 605. The gradient of the vehicle parameter line 615 can be used to determine the vehicle speed over the duration of the clustered hit points 605.

[0061] In some embodiments, plot 600 shows DFOS data after operation 240 (Figure 2). In some embodiments, plot 600 shows DFOS data analyzed using a method other than method 200 (Figure 2). Cluster 610 does not contain cluster hit points 605 for each detection time along the y-axis of plot 600. For example, in some embodiments, cluster 610 does not contain cluster hit points 605 where the missing data 535a (Figure 5A) was located within plot 500A. Once a sufficient number of cluster hit points 605 were identified, cluster 610 was generated. For example, once operation 235 (Figure 2) was satisfied, cluster 610 was formed in some embodiments.

[0062] The vehicle parameter line 615 is determined based on linear regression of the clustered hit points 605. In some embodiments, the vehicle parameter line 615 is determined based on an algorithm other than linear regression. In some embodiments, the vehicle parameter line 615 is determined using a trained neural network (NN). The vehicle parameter line 615 represents the vehicle's speed during the period of the clustered hit points 605.

[0063] Those skilled in the art will understand that the analysis of plot 600 described above is merely illustrative to help understand some embodiments of Method 200 (Figure 2) or other methods of analyzing DFOS data, and that the analysis of DFOS data in this description is not limited to the examples described above.

[0064] Figure 7 shows plots 700A and 700B for tracking vehicle positions along a roadway, according to several embodiments. Plot 700A is similar to plot 500A (Figure 5A), but differs in the DFOS data input to plot 700A compared to plot 500A. Plot 700B is similar to plot 600 (Figure 6), but has a larger cluster 720 compared to plot 600.

[0065] Plot 700A includes multiple hit points 705 and multiple missing data points 710. Hit point 705 indicates that the detected vibration indicates that the vehicle was at a distance from the DAS at a particular time. Missing data point 710 indicates the absence of an expected hit point based on analysis for tracking the vehicle. Hit point 705 is detected from DFOS data using, for example, operations 205, 210, and 215 (Figure 2).

[0066] Plot 700B includes multiple clustered hit points 715 to form cluster 720. A vehicle parameter line 730 is determined based on the clustered hit points 715. The gradient of the vehicle parameter line 730 can be used to determine the vehicle's speed over the duration of the clustered hit points 715.

[0067] Plot 700B is generated by analyzing the DFOS data in plot 700A for tracking the vehicle. Hit points 705 are analyzed to determine which hit points accurately reflect the vehicle's movement and which hit points are outlier hit points. Once a sufficient number of hit points 715 are identified, the hit points 705 are clustered into clustered hit points 705. Analysis of cluster 720 can be used to determine the speed of the tracked vehicle using the gradient of the vehicle parameter line 730. In some embodiments, plot 700B is generated by performing method 200 (Figure 2) on the DFOS data of plot 700A. In some embodiments, a tracking method other than method 200 (Figure 2) is used to generate plot 700B based on the DFOS data in plot 700A.

[0068] Those skilled in the art will understand that the analyses of plots 700A and 700B described above are merely illustrative to aid in understanding some embodiments of Method 200 (Figure 2) or other methods of analyzing DFOS data, and that the analysis of DFOS data in this description is not limited to the examples described above.

[0069] Figure 8 shows plots 800A and 800B for tracking multiple vehicles along a roadway, according to several embodiments. Similar to plots 700A and 700B (Figure 7), plots 800A and 800B include hit points. In contrast to plots 700A and 700B (Figure 7), plots 800A and 800B include hit points for multiple vehicles. Plots 800A and 800B do not include missing data points or outlier hit points for clarity in the drawing. However, those skilled in the art will recognize that including missing data points and outlier hit points in plots of multiple vehicles is within the scope of this description.

[0070] Plot 800A includes a first cluster 810 of hit points and a second cluster 820 of hit points. The first cluster 810 corresponds to the hit points of a first vehicle. The second cluster 820 corresponds to the hit points of a second vehicle. Those skilled in the art will understand that tracking three or more vehicles using DFOS data is within the scope of this description. In some embodiments, the first cluster 810 and the second cluster 820 are determined using method 200 (Figure 2). In some embodiments, at least one of the first cluster 810 or the second cluster 820 is determined using a method other than method 200 (Figure 2). The first cluster 810 intersects with the second cluster 820 at intersection 830. Intersection 830 is the location in plot 800A where the hit points of multiple vehicles intersect and does not correspond to a roadway intersection. In some embodiments, intersection 830 is generated by one vehicle overtaking another, by a vehicle changing lanes, or by other suitable circumstances. When the hit points of each vehicle are close together, the difficulty of tracking the first vehicle and the second vehicle separately after intersection 830 increases.

[0071] Using a vehicle tracking method such as Method 200 (Figure 2) helps improve the tracking of multiple vehicles passing through intersection 830. Determining a first cluster 810 and the vehicle parameter line 815 of the first cluster 810 provides the speed of the first vehicle over the duration of the first cluster 810. Similarly, determining a second cluster 820 and the vehicle parameter line 825 of the second cluster 820 provides the speed of the second vehicle over the duration of the second cluster 820.

[0072] Once the first cluster 810 and the second cluster 820 are determined on the first side of intersection 830, the seed point is identified on the second side of intersection 830, opposite to the first side. One seed point is determined for each of the vehicles being tracked in plots 800A and 800B. Using a vehicle tracking method such as Method 200 (Figure 2), clusters are determined using the newly identified seed points on the second side of intersection 830. Plot 800B includes a third cluster 810' determined using one seed point on the second side of intersection 830. Plot 800B further includes a fourth cluster 820' determined using another seed point on the second side of intersection 830. The vehicle parameter line 815' is determined using the third cluster 810'. The vehicle parameter line 825' is determined using the fourth cluster 820'.

[0073] To track each of the multiple vehicles passing through intersection 830, vehicle parameter line 815' is compared to vehicle parameter line 815 and vehicle parameter line 825, respectively. Vehicle parameter line 815' is determined to correspond to the vehicle associated with the vehicle parameter line having the gradient most similar to vehicle parameter line 815'. Similarly, vehicle parameter line 825' is compared to each vehicle parameter line 815 and vehicle parameter line 825 to determine which vehicle corresponds to vehicle parameter line 825'. For plot 800B, vehicle parameter line 815' has the gradient most similar to vehicle parameter line 815. Therefore, vehicle parameter line 815' is determined to correspond to the first vehicle. Similarly, vehicle parameter line 825' has the gradient most similar to vehicle parameter line 825. Vehicle parameter line 825' is determined to correspond to the second vehicle. In some embodiments, once it is determined that a vehicle corresponds to a vehicle parameter line on each side of intersection 830, all vehicle parameter lines corresponding to that vehicle are excluded from further comparisons related to intersection 830 in order to reduce processing load.

[0074] By applying tracking methods such as Method 200 (Figure 2) to hit points on both sides of an intersection, multiple vehicles can be tracked with high accuracy and precision using DFOS data.

[0075] Figure 9 is a flowchart of Method 900 for analyzing distributed fiber optic sensing (DFOS) data in several embodiments. Method 900 can be used in combination with Method 200 (Figure 2). Method 900 can also be used independently of Method 200 (Figure 2). Method 900 can be used to perform the functions discussed above with respect to plots 500A (Figure 5A), plot 500B (Figure 5B), plot 600 (Figure 6), plots 700A and 700B (Figure 7), and plots 800A and 800B (Figure 8). Method 900 can also be used to perform functions other than those discussed above with respect to various plots.

[0076] Method 900 is similar to Method 200 (Figure 2), and similar operations have the same reference number as Method 200. Compared to Method 200 (Figure 2), Method 900 includes operation 910 in which the tracked vehicle speed and vehicle position are updated. The vehicle position update is based on the most recently detected hit point, which is not an outlier hit point. In some embodiments, the most recently detected hit point corresponds to a detected hit point that matches the next hit point estimated from operation 245. The vehicle speed update is based on an analysis of clustered hit points, as described above with respect to plot 600 (Figure 6).

[0077] In some embodiments, method 900 includes additional operations. For example, in some embodiments, method 900 includes generating instructions for controlling an autonomous vehicle based on estimated vehicle parameters. In some embodiments, at least one operation of method 900 is omitted. For example, in some embodiments, operation 245 is omitted and hit point estimation is not performed. In some embodiments, the order of operations of method 900 is adjusted. For example, in some embodiments, operation 215 is performed before operation 210.

[0078] Using Method 900, DFOS data can be used to improve the accuracy and precision of tracking vehicles along the roadway compared to other methods that cannot remove outlier and clustered hit points. Improved vehicle tracking decisions can help improve accuracy in traffic monitoring, navigation commands, autonomous driving commands, and other applications.

[0079] Figure 10 is a block diagram of a system 1000 for analyzing DFOS data according to several embodiments. The system 1000 includes a hardware processor 1002 and a non-temporary computer-readable storage medium 1004 that stores computer program code 1006, i.e., a set of executable instructions. The computer-readable storage medium 1004 is also encoded with instructions 1007 for interfacing with external devices. The processor 1002 is electrically coupled to the computer-readable storage medium 1004 via a bus 1008. The processor 1002 is also electrically coupled to an I / O interface 1010 via the bus 1008. The network interface 1012 is also electrically connected to the processor 1002 via the bus 1008. The network interface 1012 is connected to a network 1014 so that the processor 1002 and the computer-readable storage medium 1004 can connect to external elements via the network 1014. The processor 1002 is configured to execute encoded computer program code 1006 in a computer-readable storage medium 1004 in order to make system 100 available to perform some or all of the operations described with respect to DAS system 100A (Figure 1A), DAS system 100B (Figure 1B), method 200 (Figure 2), method 900 (Figure 9), or another suitable system for analyzing DFOS data.

[0080] In some embodiments, the processor 1002 is a central processing unit (CPU), a multiprocessor, a distributed processing system, an application-specific integrated circuit (ASIC), and / or a suitable processing unit.

[0081] In some embodiments, the computer-readable storage medium 1004 is an electronic, magnetic, optical, electromagnetic, infrared, and / or semiconductor system (or apparatus or device). For example, the computer-readable storage medium 1004 includes semiconductor or solid-state memory, magnetic tape, removable computer diskette, random access memory (RAM), read-only memory (ROM), rigid magnetic disk, and / or optical disk. In some embodiments using optical disks, the computer-readable storage medium 1004 includes compact disc read-only memory (CD-ROM), compact disc read / write (CD-R / W), and / or digital video disc (DVD).

[0082] In some embodiments, the storage medium 1004 stores computer program code 1006 configured to cause system 1000 to perform some or all of the operations described with respect to DAS system 100A (Figure 1A), DAS system 100B (Figure 1B), method 200 (Figure 2), method 900 (Figure 9), or another suitable system for analyzing DFOS data. In some embodiments, the storage medium 1004 also stores information necessary to perform some or all of the operations described with respect to DAS system 100A (Figure 1A), DAS system 100B (Figure 1B), method 200 (Figure 2), method 900 (Figure 9), or another suitable system for analyzing DFOS data, as well as information generated while performing some or all of the operations described with respect to DAS system 100A (Figure 1A), DAS system 100B (Figure 1B), method 200 (Figure 2), method 900 (Figure 9), or another suitable system for analyzing DFOS data, such as sensor data parameter 1016, threshold parameter 1018, vehicle parameter 1020, hit point parameter 1022, and / or a set of executable instructions to perform some or all of the operations described with respect to DAS system 100A (Figure 1A), DAS system 100B (Figure 1B), method 200 (Figure 2), method 900 (Figure 9), or another suitable system for analyzing DFOS data.

[0083] In some embodiments, the storage medium 1004 stores instructions 1007 for interfacing with an external device. The instructions 1007 enable the processor 1002 to generate instructions readable by an external device in order to effectively perform some or all of the operations described with respect to DAS system 100A (Figure 1A), DAS system 100B (Figure 1B), method 200 (Figure 2), method 900 (Figure 9), or another suitable system for analyzing DFOS data.

[0084] System 1000 includes an I / O interface 1010. The I / O interface 1010 is coupled to external circuitry. In some embodiments, the I / O interface 1010 includes a keyboard, keypad, mouse, trackball, trackpad, and / or cursor directional keys for communicating information and commands to the processor 1002.

[0085] System 1000 also includes a network interface 1012 coupled to processor 1002. The network interface 1012 enables System 1000 to communicate with a network 1014 to which one or more other computer systems are connected. The network interface 1012 includes wireless network interfaces such as BLUETOOTH®, WIFI, WiMAX, GPRS, or WCDMA®, or wired network interfaces such as ETHERNET, USB, or IEEE-1394. In some embodiments, some or all of the operations described with respect to DAS system 100A (Figure 1A), DAS system 100B (Figure 1B), method 200 (Figure 2), method 900 (Figure 9), or another suitable system for analyzing DFOS data are performed in two or more systems 1000, and information such as sensor data, bridge position, frequency range, and extra fiber portions is exchanged between different systems 1000 via network 1014.

[0086] System 1000 is configured to receive information related to DFOS data via I / O interface 1010 or network interface 1012. The DFOS data is transferred to processor 1002 via bus 1008 for frequency range estimation and preprocessing and / or filtering. The frequency range is stored in computer-readable medium 1004 as frequency range parameter 1020. In some embodiments, the estimated frequency range parameter 1020 is received via I / O 1010 or network interface 1012. The preprocessed DFOS data is then stored in computer-readable medium 1004 as sensor data parameter 1016. Processor 1002 retrieves sensor data parameter 1016 from computer-readable medium 1004 and retrieves hit points for hit point parameter 1022. Processor 1002 performs iterative denoising of hit point parameter 1022, for example, by using threshold parameter 1018. The processor 1002 performs an analysis of the good clusters remaining after iterative denoising to determine the vehicle parameters 1020. In some embodiments, the system 1000 can be used to implement a trained NN that can be used to effectively perform some or all of the operations described with respect to the DAS system 100A (Figure 1A), the DAS system 100B (Figure 1B), method 200 (Figure 2), method 900 (Figure 9), or another suitable process for analyzing DFOS data.

[0087] Some or all of the above embodiments may also be described as follows, but are not limited to the following.

[0088] (Note 1) A vehicle tracking method includes receiving distributed fiber optic sensing (DFOS) data. The method further includes identifying a plurality of first hit points in the DFOS data, each corresponding to the position of a first vehicle at a time of detection. The method further includes determining an initial seed point from the identified plurality of first hit points. The method further includes determining whether any of the identified plurality of first hit points is an outlier hit point. The method further includes clustering the identified plurality of first hit points, excluding any outlier hit points, to define a first cluster. The method further includes estimating first vehicle parameters of the first vehicle based on the first cluster.

[0089] (Note 2) The vehicle tracking method according to Appendix 1, wherein determining whether any of the identified first hit points is an outlier hit point includes analyzing the first hit point in question to determine whether the first vehicle could reasonably travel from the identified first hit point to the first hit point in question, and determining that the first hit point in question is the outlier hit point in response to the determination that the first vehicle could not reasonably travel from the identified first hit point to the first hit point in question.

[0090] (Note 3) The vehicle tracking method according to Appendix 2, wherein analyzing the first hit point in question includes analyzing the first hit point in question based on a determined speed of the first vehicle.

[0091] (Note 4) The vehicle tracking method according to Appendix 2, wherein analyzing the first hit point in question includes analyzing the first hit point in question based on parameters of the roadway on which the first vehicle is traveling.

[0092] (Note 5) The vehicle tracking method according to Appendix 1, further comprising determining whether a sufficient number of the plurality of first hit points have been identified for clustering, wherein clustering the identified plurality of first hit points is performed in response to the determination that a sufficient number of the plurality of first hit points have been identified.

[0093] (Note 6) The vehicle tracking method according to Appendix 5, further comprising identifying at least one subsequent first hit point from the DFOS data in response to a determination that an insufficient number of first hit points have been identified.

[0094] (Note 7) The vehicle tracking method according to Appendix 1, further comprising: identifying a plurality of second hit points in the DFOS data, each corresponding to the position of a corresponding second vehicle; estimating second vehicle parameters of the second vehicle based on the identified plurality of second hit points; and plotting the identified plurality of first hit points and the identified plurality of second hit points, wherein the plotted identified plurality of first hit points intersect with the plotted identified plurality of second hit points at an intersection.

[0095] (Note 8) The vehicle tracking method according to Appendix 7, further comprising tracking each of the first vehicle and the second vehicle crossing the intersection based on the first vehicle parameters and the second vehicle parameters.

[0096] (Note 9) A vehicle tracking system comprises a non-temporary computer-readable medium configured to store instructions, and a processor connected to the non-temporary computer-readable medium. The processor is configured to execute instructions for receiving distributed fiber optic sensing (DFOS) data, identifying a plurality of first hit points in the DFOS data, each corresponding to the position of a first vehicle at a time of detection, determining an initial seed point from the identified plurality of first hit points, determining whether any of the identified plurality of first hit points is an outlier hit point, clustering the identified plurality of first hit points, excluding any outlier hit points, to define a first cluster, and estimating first vehicle parameters of the first vehicle based on the first cluster.

[0097] (Note 10) The vehicle tracking system according to Appendix 9, further configured to execute the instruction for determining whether any of the identified first hit points is an outlier hit point, by having the processor analyze the first hit point in question to determine whether the first vehicle could reasonably travel from the identified first hit point to the first hit point in question, and in response to the determination that the first vehicle could not reasonably travel from the identified first hit point to the first hit point in question, by determining that the first hit point in question is the outlier hit point.

[0098] (Note 11) The vehicle tracking system according to Appendix 10, further configured to execute the instructions for analyzing the first hit point in question based on a determined speed of the first vehicle.

[0099] (Note 12) The vehicle tracking system according to Appendix 10, wherein the processor is configured to execute the instructions for analyzing the first hit point in question based on the parameters of the roadway on which the first vehicle is traveling.

[0100] (Note 13) The vehicle tracking system according to Appendix 9, wherein the processor is configured to execute the instruction for determining whether a sufficient number of the plurality of first hit points have been identified for clustering, and clustering the identified plurality of first hit points is performed in response to the determination that a sufficient number of the plurality of first hit points have been identified.

[0101] (Note 14) The vehicle tracking system according to Appendix 13, wherein the processor is configured to execute the instruction to identify at least one subsequent first hit point from the DFOS data in response to a determination that an insufficient number of first hit points have been identified.

[0102] (Note 15) The vehicle tracking system according to Appendix 9, wherein the processor is further configured to execute instructions for identifying a plurality of second hit points in the DFOS data, each corresponding to the position of a corresponding second vehicle; estimating second vehicle parameters of the second vehicle based on the identified plurality of second hit points; and plotting the identified plurality of first hit points and the identified plurality of second hit points, wherein the plotted identified plurality of first hit points intersect with the plotted identified plurality of second hit points at intersections.

[0103] (Note 16) The vehicle tracking system according to Appendix 15, wherein the processor is further configured to execute the instructions for tracking each of the first and second vehicles crossing the intersection based on the first and second vehicle parameters.

[0104] (Note 17) The program causes the processor to receive distributed fiber optic sensing (DFOS) data, identify a plurality of first hit points in the DFOS data, each corresponding to the position of a first vehicle at the time of detection, determine an initial seed point from the identified plurality of first hit points, determine whether any of the identified plurality of first hit points is an outlier hit point, cluster the identified plurality of first hit points, excluding any outlier hit points, to define a first cluster, and estimate the first vehicle parameters of the first vehicle based on the first cluster.

[0105] (Note 18) The program described in Appendix 17 causes the processor to determine whether any of the identified first hit points is an outlier hit point by analyzing the first hit point in question to determine whether the first vehicle could reasonably travel from the identified first hit point to the first hit point in question, and by determining that the first hit point in question is an outlier hit point in response to the determination that the first vehicle could not reasonably travel from the identified first hit point to the first hit point in question.

[0106] (Note 19) The program according to Appendix 17, wherein the processor determines whether a sufficient number of the plurality of first hit points have been identified for clustering, and clusters the identified plurality of first hit points, which is performed in response to the determination that a sufficient number of the plurality of first hit points have been identified.

[0107] (Note 20) The program according to Appendix 17, which causes the processor to: identify a plurality of second hit points in the DFOS data, each corresponding to the position of a corresponding second vehicle; estimate second vehicle parameters of the second vehicle based on the identified plurality of second hit points; plot the identified plurality of first hit points and the identified plurality of second hit points, wherein the plotted identified first hit points intersect with the plotted identified second hit points at an intersection; and track each of the first vehicle and the second vehicle crossing the intersection based on the first vehicle parameters and the second vehicle parameters.

[0108] The foregoing outlines some features of embodiments so that those skilled in the art may better understand aspects of the present disclosure. Those skilled in the art will understand that the present disclosure can be readily used as a basis for designing or modifying other processes and structures to perform the same purposes and / or achieve the same advantages as the embodiments introduced herein. Those skilled in the art will also understand that such equivalent configurations do not deviate from the spirit and scope of the present disclosure, and that various modifications, substitutions, and changes may be made herein without departing from the spirit and scope of the present disclosure.

[0109] This application claims priority based on U.S. Patent Application No. 18 / 537,669, filed on 12 December 2023, and incorporates all of its disclosures herein. [Explanation of symbols]

[0110] 100A, 100B Distributed Acoustic Sensor (DAS) System, DAS System 111 Traffic monitoring device 121 Optical Fiber 130, 130A, 130B Road Vehicles 140, 150, 150A, 150B 160A, 160B Bridge 170 Fiber 300 Waterfall Data 302, 304, 306, 308, 310 areas 400 DFOS data 410, 420 area 500A, 500B plot 505 Hit Points 510 missing data points 515 seed points 520, 525 subsequent hit points 530 areas 535a Missing data point 535 hit points 535c Hit Points 535d Hit Points 540 subsequent hit points 545 Another subsequent hit point 550 areas 560 subsequent hit points 565 subsequent hit points 600 plots 605 Cluster Hit Points 610 clusters 615 Vehicle Parameter Line 700A plot 700B plot 705 Hit Points 710 missing data points 715 clustered hit points 720 clusters 730 Vehicle Parameter Line 800A plot 800B plot 810 clusters 810' Cluster 815 Vehicle Parameter Line 815' Vehicle Parameter Line 820 clusters 820' cluster 825 Vehicle Parameter Line 825' Vehicle Parameter Line 830 Intersection 1000 systems 1002 Hardware Processor 1004 Non-temporary computer-readable storage medium 1006 Computer program code 1007 Instructions 1008 Bus 1010 I / O Interface 1012 Network Interface 1014 Network 1016 Sensor data parameters 1018 Threshold parameters 1020 Vehicle parameters, frequency range parameters, estimated frequency range parameters 1022 Hit Point Parameters D1 Distance D2 distance

Claims

1. A vehicle tracking method performed by a vehicle tracking system, Receiving distributed fiber optic sensing (DFOS) data, Identifying a plurality of first hit points in the DFOS data, each corresponding to the position of the first vehicle at the time of detection, The process involves determining an initial seed point from among the aforementioned identified first hit points, Determining whether any of the aforementioned identified first hit points is an outlier hit point, The process involves clustering the identified first hit points, from which any outlier hit points have been excluded, to define a first cluster, Estimating the first vehicle parameters of the first vehicle based on the first cluster, Identifying multiple second hit points in the DFOS data, each corresponding to the position of the second vehicle, Estimating the second vehicle parameters of the second vehicle based on the identified plurality of second hit points, Plotting the identified plurality of first hit points and the identified plurality of second hit points, When the plotted and identified first hit points intersect with the plotted and identified second hit points at an intersection, The plotted and identified plurality of first hit points and the plotted and identified plurality of second hit points are determined on the first side of the intersection, A vehicle tracking method comprising identifying a seed point on a second side of the intersection opposite to the first side of the intersection.

2. Determining whether any of the aforementioned identified first hit points is an outlier hit point is In order to determine whether the first vehicle could reasonably travel from the confirmed first hit point to the first hit point in question, the first hit point in question is analyzed, The vehicle tracking method according to claim 1, comprising determining that the first hit point in question is an outlier hit point in response to a determination that the first vehicle could not reasonably have traveled from the confirmed first hit point to the first hit point in question.

3. The vehicle tracking method according to claim 2, wherein analyzing the first hit point in question includes analyzing the first hit point in question based on a determined speed of the first vehicle.

4. The vehicle tracking method according to claim 2, wherein analyzing the first hit point in question includes analyzing the first hit point in question based on parameters of the roadway on which the first vehicle is traveling.

5. The vehicle tracking method according to claim 1, further comprising determining whether a sufficient number of the plurality of first hit points have been identified for clustering, wherein clustering the identified plurality of first hit points is performed in response to the determination that a sufficient number of the plurality of first hit points have been identified.

6. The vehicle tracking method according to claim 5, further comprising identifying at least one subsequent first hit point from the DFOS data in response to a determination that an insufficient number of first hit points have been identified.

7. The vehicle tracking method according to claim 1, wherein the plotted specified plurality of first hit points intersect with the plotted specified plurality of second hit points at the intersection.

8. The vehicle tracking method according to claim 7, further comprising tracking each of the first vehicle and the second vehicle crossing the intersection based on the first vehicle parameters and the second vehicle parameters.

9. A non-temporary computer-readable medium configured to store instructions, The system comprises a processor connected to the aforementioned non-temporary computer-readable medium, The aforementioned processor, Receiving distributed fiber optic sensing (DFOS) data, Identifying a plurality of first hit points in the DFOS data, each corresponding to the position of the first vehicle at the time of detection, The process involves determining an initial seed point from among the aforementioned identified first hit points, Determining whether any of the aforementioned identified first hit points is an outlier hit point, The process involves clustering the identified first hit points, from which any outlier hit points have been excluded, to define a first cluster, Estimating the first vehicle parameters of the first vehicle based on the first cluster, Identifying multiple second hit points in the DFOS data, each corresponding to the position of the second vehicle, Estimating the second vehicle parameters of the second vehicle based on the identified plurality of second hit points, Plotting the identified plurality of first hit points and the identified plurality of second hit points, When the plotted and identified first hit points intersect with the plotted and identified second hit points at an intersection, The plotted and identified plurality of first hit points and the plotted and identified plurality of second hit points are determined on the first side of the intersection, A vehicle tracking system configured to execute the command for identifying a seed point on a second side of the intersection opposite to the first side of the intersection.

10. Receiving distributed fiber optic sensing (DFOS) data, Identifying a plurality of first hit points in the DFOS data, each corresponding to the position of the first vehicle at the time of detection, The process involves determining an initial seed point from among the aforementioned identified first hit points, Determining whether any of the aforementioned identified first hit points is an outlier hit point, The process involves clustering the identified first hit points, from which any outlier hit points have been excluded, to define a first cluster, Estimating the first vehicle parameters of the first vehicle based on the first cluster, Identifying multiple second hit points in the DFOS data, each corresponding to the position of the second vehicle, Estimating the second vehicle parameters of the second vehicle based on the identified plurality of second hit points, Plotting the identified plurality of first hit points and the identified plurality of second hit points, When the plotted and identified first hit points intersect with the plotted and identified second hit points at an intersection, The plotted and identified plurality of first hit points and the plotted and identified plurality of second hit points are determined on the first side of the intersection, Identifying the seed point on the second side of the intersection opposite to the first side of the intersection. A program that instructs the processor to perform this task.

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