Object presence detection system, object presence detection method, and program

Abstract

An object of the present disclosure is to provide an object presence detection system, an object presence detection method, and a non-transitory computer-readable medium that can detect the presence of an object more accurately. In one aspect, an object presence detection system (10) includes a data set processing unit (11) configured to acquire time-distance graph information of an oscillation signal for each distributed sensing portion, where the oscillation signal is acquired by a plurality of distributed sensing portions and is induced by a traffic of moving objects, an impulse detection unit (12) configured to detect an impulse response using the time-distance graph information measured by the plurality of distributed sensing portions, and a presence detection unit (13) configured to detect the presence of a moving object, including the moving direction information of the moving object, using the impulse response information in the time-distance graph information.

Description

[Technical Field]

[0001] The present disclosure relates to an object presence detection system, an object presence detection method, and a non-transitory computer-readable medium. [Background technology]

[0002] In recent years, monitoring systems for infrastructure such as roads have been developed.

[0003] For example, Patent Document 1 (PTL1) discloses a position detection device that reduces the detection accuracy of the position of a moving object. Specifically, this position detection device uses an optical fiber sensor, which is an optical transmission line laid along the movement path of a moving object, and includes a detector that detects backscattered light generated in the optical fiber sensor in response to an optical pulse, a maximum value extraction unit that extracts the generation position where the fluctuation in the intensity of the backscattered light within a search range is maximum, and an output unit that outputs the extraction result together with the position of the moving object. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] International Publication No. 2021 / 106025 Summary of the Invention [Problem to be solved by the invention]

[0005] Roads typically have lanes where vehicles travel in one direction and lanes where vehicles travel in the opposite direction. In the optical fiber measurement system described in Patent Document 1, optical fibers detect original vibration signals from both lanes, and the device detects the traffic information for both lanes together, rather than detecting each lane separately. This means that there is a possibility that the presence of a vehicle in a certain lane may not be detected accurately.

[0006] An object of the present disclosure is to provide an object presence detection system, an object presence detection method, and a non-transitory computer-readable medium that can more accurately detect the presence of an object. [Means for solving the problem]

[0007] According to a first aspect of the present disclosure, there is provided an object presence detection system, the system including: a dataset processing means for acquiring time-distance graph information of oscillation signals for each distributed sensing portion, the oscillation signals being acquired by a plurality of distributed sensing portions and induced by traffic of moving objects; an impulse detection means for detecting an impulse response using the time-distance graph information measured by the plurality of distributed sensing portions; and a presence detection means for detecting the presence of a moving object, including movement direction information of the moving object, using the impulse response information in the time-distance graph information.

[0008] According to a second aspect of the present disclosure, there is provided an object presence detection method, the method including: acquiring time-distance graph information of an oscillation signal for each distributed sensing portion, the oscillation signal being acquired by a plurality of distributed sensing portions and induced by traffic of moving objects; detecting an impulse response using the time-distance graph information measured by the plurality of distributed sensing portions; and detecting the presence of a moving object, including movement direction information of the moving object, using the impulse response information in the time-distance graph information.

[0009] According to a third aspect of the present disclosure, a non-transitory computer-readable medium is provided, the non-transitory computer-readable medium storing a program for causing a computer to perform the following: acquiring time-distance graph information of an oscillation signal for each distributed sensing portion; the oscillation signal being acquired by a plurality of distributed sensing portions and induced by traffic of moving objects; detecting an impulse response using the time-distance graph information measured by the plurality of distributed sensing portions; and detecting the presence of a moving object, including movement direction information of the moving object, using the impulse response information in the time-distance graph information. [Effects of the Invention]

[0010] According to the present disclosure, it is possible to provide an object presence detection system, an object presence detection method, and a non-transitory computer-readable medium that are capable of detecting the presence of an object more accurately. [Brief explanation of the drawings]

[0011] [Figure 1] 1 is a block diagram of an object presence detection system according to a first embodiment. [Figure 2] 3 is a flowchart showing a method of the object presence detection system according to the first embodiment. [Figure 3] 10 shows a schematic diagram of an object presence detection system and a road according to a second embodiment. [Figure 4] FIG. 10 is a block diagram of a detection server according to a second embodiment. [Figure 5] 10 is an example of a time-distance graph according to the second embodiment. [Figure 6A] FIG. 10 is a schematic diagram of a sensor for measuring the position of a vehicle and an oscillation signal from the vehicle according to a second embodiment. [Figure 6B] 10 shows a table illustrating the relationship between angle θ and source position (vehicle position) according to the second embodiment. [Figure 6C] FIG. 2 is a schematic diagram showing the relationship between τ (time difference of arrival) and angle θ. [Figure 7A]10 is a flowchart illustrating a method of a detection server according to a second embodiment. [Figure 7B] 10 is a flowchart illustrating a method of a detection server according to a second embodiment. [Figure 8] FIG. 1 is a block diagram of a computer device according to an embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0012] It should be noted that in describing this disclosure, elements described using the singular may also be plural elements unless explicitly stated otherwise.

[0013] (First embodiment) First, with reference to FIG. 1, an object presence detection system 10 according to a first embodiment of the present disclosure will be described.

[0014] 1, the object presence detection system 10 includes a data set processing unit 11, an impulse detection unit 12, and a presence detection unit 13. The object presence detection system 10 may be one or more computers and / or machines. As an example, at least one component in the object presence detection system 10 may be installed in a computer as a combination of one or more memories and one or more processors. The computer used as the object presence detection system 10 may be a server.

[0015] The dataset processing unit 11 obtains time-distance graph information of an oscillation signal for each distributed sensing portion. The oscillation signal is acquired by multiple distributed sensing portions and is caused by the traffic of moving objects (targets). The distributed sensing portions may be multiple spaced points on a long linear sensor (e.g., an optical fiber cable), multiple independent sensors, etc. The distributed sensing portions are laid along a path traversed by a moving object. The moving object may be various objects moving on land, such as a motor vehicle (including an automobile, motorcycle, bus, truck, etc.), a train, a streetcar, a bicycle, a non-mechanical vehicle, a pedestrian (a person walking), etc., and the path traversed by the moving object may be a road (including a highway and a general road), a railway, a bridge, a pedestrian or bicycle path, etc. In this disclosure, data consisting of a time-distance graph is also referred to as a waterfall dataset.

[0016] Known techniques can be applied to the processing of the data set processing unit 11. For example, the object presence detection system 10 can acquire raw data sets (oscillation signals) measured by multiple distributed sensing units and pre-process the raw data sets into time-distance. However, the data set processing unit 11 may also acquire time-distance graph information generated by another device.

[0017] The impulse detection unit 12 detects impulse responses using time-distance graph information measured by multiple distributed sensing units. The impulse responses are induced by the traffic of moving objects and are included in the time-distance graph information. The impulse responses can be described as an impulse matrix for subsequent processing. The impulse detection unit 12 can use various data analysis methods, such as linear / nonlinear transformation methods and / or detection models trained by artificial intelligence (AI). These methods will be described in detail later.

[0018] The presence detection unit 13 detects the presence of a moving object, including the moving direction information of the moving object, using the impulse response information in the time-distance graph information. For example, the presence detection unit 13 may obtain a direction matrix by performing an angle of arrival (AoA) and / or a time difference of arrival (TDoA) method using the impulse response information for the multiple distributed sensing parts.

[0019] Next, an example of the operation of this embodiment will be described with reference to the flowchart of Fig. 2. Details of each process in Fig. 2 have already been described.

[0020] First, the data set processing unit 11 acquires time-distance graph information of the oscillation signal for each distributed sensing portion (step S11). The data set processing unit 11 outputs the time-distance graph information to the impulse detection unit 12.

[0021] Next, the impulse detection unit 12 detects an impulse response using the time-distance graph information measured by the multiple distributed sensing parts (step S12). The data set processing unit 11 outputs the impulse response information to the presence detection unit 13.

[0022] Thereafter, the presence detection unit 13 detects the presence of a moving object including information about the moving object's direction of movement (step S13). Note that the object presence detection system 10 may process these steps not only for a single moving object but also for each of multiple moving objects. The object presence detection system 10 can use the result of the presence of the moving object to generate traffic information about the route the moving object is passing through, and the traffic information may include information about the position and movement status of the moving object.

[0023] The object presence detection system 10 can calculate not only the presence information of a moving object but also the moving direction information of the moving object by detecting the impulse response using the time-distance graph information. Therefore, the object presence detection system 10 can detect the presence of an object more accurately.

[0024] (Second embodiment) A second embodiment of the present disclosure will be described below with reference to the drawings. In this second embodiment, one specific example of the first embodiment will be described, but the specific example of the first embodiment is not limited to this.

[0025] FIG. 3 shows a traffic event detection system T (object presence detection system) including an optical fiber cable F (sensing optical fiber), a distributed acoustic sensor (DAS: functioning as a sensing device), and a detection server 20. FIG. 3 also shows a schematic view of a road R in which an optical fiber cable F is arranged along the road R, particularly along a lane whose presence is to be detected (hereinafter referred to as a detection lane). The optical fiber cable F is installed along the underside of the road R and is used to measure the response vibration of the road R caused by vehicles C1 to C3 shown in FIG. 3, which are moving objects passing along the optical fiber cable F. Furthermore, the optical fiber cable F is s a ~s c Each sensing portion in the optical fiber cable F is referred to as a sensor.

[0026] 3, vehicles C1 and C2 in lane 1 are passing through road R from right to left, and vehicle C3 in lane 2 is traveling in the opposite direction to vehicles C1 and C2. The detection server 20 monitors road R and can detect each traffic event of vehicles.

[0027] An oscillation signal (e.g., acoustic or vibration data) is induced in the optical fiber cable F by a vehicle (particularly, by the axle of the vehicle passing over the road R via the optical fiber cable). That is, the oscillation signal represents vibrations on the road R. For example, in FIG. 3, the sensor s b detects vibration signals from vehicles C2 and C3.

[0028] The DAS detects oscillation signals at each of a plurality of sensors on the optical fiber cable F. The DAS can detect oscillation signals on the road R induced by the axles of a vehicle when the vehicle is passing through any lane on the road R. The oscillation signals can be measured at any position on the optical fiber cable F. For example, if the detection range is 50 km and the spatial resolution is 4 m, oscillation signals at 12,500 points (detection channels) can be measured. The DAS transmits the oscillation signals as digital data to the detection server 20 via wired communication. However, communication between the DAS and the detection server 20 can be performed via wireless communication.

[0029] 4 is a block diagram of the detection server 20. Referring to FIG. 4, the detection server 20 includes a signal acquisition unit 21, a raw data set processing unit 22, an impulse detection unit 23, a direction estimation unit 24, a lane identification unit 25, a traffic information generation unit 26, a notification unit 27, a model storage device 28, and a model training unit 29. The detection server 20 is a specific example of the object presence detection system 10 and may include other units for calculation. Each unit of the detection server 20 will be described in detail.

[0030] The signal acquisition unit 21 functions as an interface with the detection server 20 and receives raw oscillation signal data (hereinafter referred to as raw data set: X ) from the DAS. raw The signal acquisition unit 21 acquires X raw to the raw data set processing unit 22. Furthermore, the signal acquisition unit 21 outputs X raw For example, the signal acquisition unit 21 may preprocess X raw Filter the filtered X raw may be output.

[0031] The raw data set processing unit 22 is an example of the data set processing unit 11 in the first embodiment, and X raw Specifically, the raw data set processor 22 uses a bandpass filter focused on the structural resonance frequency to preprocess X raw Standardize it to X rawThen, obtain the standard amplitude of each signal at the normalized X raw By applying the sum of absolute intensities to a window of a predetermined length of the oscillation signal using the above equation, a time-distance graph is calculated for each of the multiple sensors of the optical fiber cable F. The data comprising the time-distance graph is referred to as the waterfall dataset TD in this disclosure. waterfall Also called TD. waterfall is multi-channel (at least two-channel) data over the entire measurement time and data. The raw data set processing unit 22 processes X raw is output to the impulse detection unit 23 and the direction estimation unit 24, and TD waterfall is output to the lane identification unit 25.

[0032] Figure 5 shows the waterfall dataset TD waterfall The time-distance graph shown in FIG. 5 shows an example snapshot of A From time t B The waterfall data set from 1 to 5 is shown in Figure 5. Each line in Figure 5 represents a vehicle trajectory. The vibration intensity (oscillation signal) of the optical fiber cable F is visible and is proportional to the type of vehicle passing through the road R. In Figure 5, high vibration intensity of a vehicle is shown by a solid line, and low vibration intensity of a vehicle is shown by a dashed-dotted line. An example of the former vehicle is a truck or a bus, and an example of the latter vehicle is a passenger car.

[0033] The raw data set processing unit 22 processes the X measured within the target monitoring section of multiple channels (multiple sensors). raw The target monitoring section is a part of the sensing range to be analyzed in the detection server 20. For example, it is a section of 10 to 50 meters, but is not limited to this.

[0034] Returning to FIG. 4, the impulse detection unit 23 is an example of the impulse detection unit 12 in the first embodiment. Specifically, the impulse detection unit 23 detects the pre-processed X raw Receive the dataset and raw The following processing is performed using the dataset.

[0035] (1a) First, the impulse detector 23 detects X raw Feature reduction (dimensionality reduction) is performed on X raw The number of features in the input signal can be reduced by applying nonlinear transformation methods such as fast Fourier transform (FFT), principal component analysis (PCA) and / or independent component analysis (ICA), which can aggregate the amplitude of the data, to the feature reduction process. This process reduces the input signal from multiple channels to a single channel, thereby reducing the subsequent calculation process.

[0036] (1b) Next, the impulse detection unit 23 detects impulse responses using a detection method such as clustering or amplitude thresholding. Impulse responses are typically measured by multiple sensors, but the impulse detection unit 23 may extract an impulse response for a predetermined target monitoring section from all impulse responses. In this disclosure, the impulse response is also referred to as a peak in the vibration data.

[0037] In this example, the impulse detection unit 23 uses a detection model trained by artificial intelligence (AI). The AI ​​is included in the detection server 20 and performs unsupervised model training of the detection model. However, the AI ​​may also be included in another computer. The detection model can function as an impulse response function-based filter and is stored in the model storage device 28. The impulse detection unit 23 obtains impulse response information by inputting the processed data into the trained model.

[0038] (1c) Then, the impulse detection unit 23 converts the result of (1b), i.e., the peaks of the data, into a binary matrix format. This binary matrix indicates the presence of impulses in the data (impulse response information in the time-distance graph information) and is also called an impulse matrix. An example of an impulse matrix is ​​shown below.

number

[0039] Returning to FIG. 4, the direction estimation unit 24 calculates X raw and an impulse matrix. In this embodiment, the direction estimation unit 24 is an example of the presence detection unit 13 in the first embodiment. Using this information, the direction estimation unit 24 performs the following process.

[0040] (2a) First, the direction estimation unit 24 calculates the X raw to X for a given time range raw The direction estimation unit 24 analyzes the impulse matrix and determines the given time range as the time range when an impulse is detected in the impulse matrix.

[0041] (2b) Next, the direction estimation unit 24 calculates the extracted X raw A band-pass filter is applied to X in a given time range to generate intermediate data with clearer structural characteristics in a given frequency range (e.g., a low frequency band such as 1 Hz to 20 Hz). In other words, the intermediate data is transformed into a signal from the original X. raw Indicates the peak frequency of the data.

[0042] (2c) Furthermore, in this embodiment, the direction estimation unit 24 applies the angle of arrival (AoA) and time difference of arrival (TDoA) methods to the intermediate data to calculate the time difference (time difference of arrival) of the arrival of the propagated vibration between multiple distributed sensors, thereby estimating the angular direction (direction of arrival) of the vehicle crossing the optical fiber cable F.

[0043] Figures 6A to 6C show the principles of the AoA and TDoA methods used in (2c). In Figure 6A, vehicle C4 is passing from right to left. Figure 6A also shows sensors s0 and s1 of the optical fiber cable F connected to the DAS. Sensor s0 is the reference sensor, and sensor s1 is the target sensor. Let D be the distance between sensors s0 and s1, and θ be the angle (arrival angle) between the optical fiber cable F and the direction of the oscillation signal from vehicle C4 to sensor s0. In other words, θ is the angle of the source (vehicle C4) at sensor s1.

[0044] In FIG. 6A, θ can be calculated as follows:

number

[0045] Figure 6B shows a table showing the relationship between angle θ and source position (position of vehicle C4). When θ is greater than 0° (0) and less than 90° (π / 2), vehicle C4 is to the right of sensor s1 in Figure 6A. When θ is equal to 90° (π / 2), vehicle C4 is on an extension drawn perpendicular to the optical fiber cable F from sensor s1. Furthermore, when θ is greater than 90° (π / 2) and less than 180° (π), vehicle C4 is to the left of sensor s1 in Figure 6A.

[0046] Figure 6C shows the relationship between τ (time difference of arrival) and angle θ. (1) Figure 6C shows the relationship of the detected lanes. In Figure 6C, when θ1 is greater than 0° (0) and less than 90° (π / 2), vehicle C4 on the detected lane related to θ1 is located to the right of sensor s1. Also, when θ2 is greater than 0° (0) and less than 90° (π / 2), vehicle C4 on the detected lane related to θ2 is located to the right of sensor s1. Furthermore, (2) in Figure 6C shows the relationship of the oncoming lane (i.e., the non-detected lane). When θ3 is 90° (π / 2), vehicle C4 on the oncoming lane related to θ3 is located on an extension line drawn perpendicular to the optical fiber cable F from sensor s1.

[0047] The direction estimation unit 24 knows the value of D and also obtains intermediate data (X raw data) to τ 01 Since the values ​​of and c are detected, θ can be calculated using equation (m3). The value of c may be calibrated during system installation, and thus the direction estimator 24 can use a measured approximate value of c. However, the value of c may be a constant value, and the direction estimator 24 knows this value in advance. This allows the direction estimator 24 to calculate X raw Vehicle direction information (θ) is separated from the

[0048] (2d) Finally, the direction estimation unit 24 converts the result of (2c), i.e., the vehicle direction information (θ), into a binary matrix format. This binary matrix indicates the direction of the vehicle information and is also called a direction matrix (AoA feature). The direction matrix may be obtained by the gradient of the arrival time difference feature. For example, the arrival time difference changes from a leading time to a lag time as the vehicle passes from the detection position, and the arrival time difference may also change from a lag time to a leading time depending on the vehicle's direction. Thus, the gradient may be obtained from the arrival time difference and converted into a binary matrix format. The following is an example of a direction matrix:

number

[0049] The direction matrix includes vehicle presence information including vehicle movement direction information. The direction estimation unit 24 outputs the direction matrix to the lane identification unit 25.

[0050] Returning to FIG. 4, the lane identification unit 25 waterfall and the orientation matrix, and performs the following processing.

[0051] First, the lane identification unit 25 waterfall The lane identification unit 25 extracts the trajectory of the target vehicle using a deep neural network (TD) waterfall The mask matrix is ​​generated using a traffic net model, which is an example of a deep neural network that can generate the mask matrix shown in FIG. 2. In this way, the direction estimation unit 24 can estimate the vehicle trajectory in the form of a mask matrix.

[0052] Second, the lane identification unit 25 identifies the lane (e.g., inbound or outbound) on the road where the target vehicle is located by analyzing the vehicle's trajectory and direction matrix (vehicle presence information). For example, in the case of FIG. 3, if the target vehicle is vehicle C1, the lane identification unit 25 can identify that vehicle C1 is traveling in lane 1, not lane 2. The lane identification unit 25 outputs the lane identification result and direction matrix to the traffic information generation unit 26.

[0053] The signal acquisition unit 21, raw data set processing unit 22, impulse detection unit 23, direction estimation unit 24 and lane identification unit 25 can also perform the above processing for a plurality of vehicles, one for each vehicle.

[0054] The traffic information generation unit 26 receives the lane identification results and the direction matrix and uses them to generate traffic information for the road on which the target vehicle is located. Furthermore, the traffic information generation unit 26 can generate traffic information for the road on which the target vehicle is traveling by storing information on multiple vehicles and the direction matrix resulting from the lane identification results and analyzing the traffic information for the road on which these vehicles are traveling. For example, in the case of FIG. 3, the traffic information generation unit 26 can generate traffic information for road R. Specifically, the traffic information includes information on which lane each vehicle is traveling in and where it is located. The traffic information generation unit 26 generates this traffic information for each measurement time of the oscillation signal, thereby detecting the speed of each vehicle and updating the detected speed. The traffic information generation unit 26 outputs the traffic information, including the speed information of each vehicle, to the notification unit 27.

[0055] The notification unit 27 is an I / O interface that notifies other systems and devices of traffic information about each vehicle. For example, the notification unit 27 sends information to an automated driving system that controls autonomous (unmanned) vehicle driving on roads.

[0056] As a specific example, consider a situation on a highway where a branch road merges into the main road of the highway. When an autonomous vehicle attempts to move from the branch road to the main road, the vehicle needs to know traffic information on the main road (where other vehicles are located and at what speeds). Therefore, the vehicle is connected to a control server of an autonomous driving system commonly known as an Intelligent Transportation System (ITS). In this situation, if the detection server 20 shares traffic information with the control server in real time, the control server can analyze the traffic information and generate and output real-time instructions for controlling the vehicle's detailed driving. Therefore, by following the instructions, the vehicle can drive toward the junction without coming into contact with other vehicles on the main road driving toward the junction. In this example, the target monitoring section includes the area of ​​the main road surrounding the junction.

[0057] In the above example, the control server and the detection server 20 are described as being independent. However, the detection server 20 may further include an autonomous driving control unit and function as a control server in the autonomous driving system. Furthermore, the detection server 20 can alert the traffic flow monitoring system of traffic information.

[0058] The model storage device 28 stores the detection model used by the impulse detection unit 23. It may also store the methods and band-pass filters used in each unit of the detection server 20.

[0059] The model training unit 29 includes an AI and trains the detection model stored in the model storage device 28 to learn vehicle presence features (peaks) in the training data set corresponding to the vehicle impulse responses. The impulse responses may be data within the target monitoring section.

[0060] Next, an example of the operation of the detection server 20 will be described with reference to the flowcharts of Figures 7A and 7B. Details of each process in Figures 7A and 7B have already been described.

[0061] First, before measurement, the model training unit 29 trains a detection model and stores it in the model storage device 28 (step S21). The trained model is used in subsequent processes.

[0062] Next, the signal acquisition unit 21 acquires the raw oscillation signal (raw data set X raw ) (step S22). After that, the raw data set processing unit 22 obtains X raw (step S23). As a result, the raw dataset processing unit 22 preprocesses the waterfall dataset TD waterfall (time-distance graph) and preprocessed dataset X raw is acquired (step S24).

[0063] Then, the impulse detection unit 23 detects peaks (impulse matrix) using a feature reduction process and detection method (step S25), after which the direction estimation unit 24 estimates a direction matrix (AoA feature) (step S26).

[0064] The lane identification unit 25 identifies the lane on the road where the target vehicle is located (step S27). Then, the traffic information generation unit 26 generates traffic information using the result of step S27 (step S28). Finally, the notification unit 27 notifies the traffic information of each vehicle (step S29).

[0065] In related technology, waterfall datasets (time-distance graphs) are used in traffic monitoring applications to estimate traffic flow parameters. However, the vibration signal caused by the presence of a vehicle in the detection lane can be affected by the signals of other vehicles in the oncoming lane. Therefore, vehicles in the oncoming lane, but not in the detection lane, may be falsely detected as being in the detection lane.

[0066] In the present disclosure, the detection server 20 can detect the impulse matrix using the time-distance graph information and calculate the moving direction information of the vehicle, thereby enabling the detection server 20 to detect the presence of the vehicle.

[0067] Furthermore, the model training unit 29 can train the detection model used by the impulse detection unit 23 to detect impulse matrices to learn vehicle presence features (peaks) in the training dataset. In this way, the detection server 20 can more accurately detect the presence of vehicles by using the trained detection model.

[0068] Furthermore, the impulse detection unit 23 may obtain impulse response information by performing feature reduction processing on the time-distance graph information, thereby reducing the amount of calculation processing performed by the impulse detection unit 23.

[0069] Furthermore, the direction estimation unit 24 may detect the presence of a vehicle using the extracted time-distance graph information. The direction estimation unit 24 only needs to calculate the extracted time-distance graph information, and does not need to calculate all the information, which reduces the amount of calculation required.

[0070] Furthermore, the direction estimation unit 24 may obtain the time difference of arrival of impulses between multiple distributed sensors and calculate the moving direction information using the time difference of arrival. The direction estimation unit 24 uses a highly scalable and low-complexity method, so the direction estimation unit 24 can calculate the moving direction information in various situations.

[0071] Furthermore, the traffic event detection system T may include optical fiber cables as multiple sensors, so that the traffic event detection system T can detect a vehicular traffic event.

[0072] Furthermore, the lane identification unit 25 uses the trajectory and the presence of the vehicle to identify the lane on the road where the vehicle is located, thereby allowing the detection server 20 to know detailed traffic information.

[0073] Furthermore, the notification unit 27 notifies the presence of a vehicle and the traffic lane the vehicle is in. Therefore, other systems, such as an automated driving system or a traffic flow monitoring system, can use the traffic information for safe and efficient traffic.

[0074] Modifications and adjustments of each embodiment and example are possible within the scope of the overall disclosure of this disclosure (including the scope of claims) and based on the basic technical idea of ​​this disclosure. Therefore, the present embodiment should be considered to be illustrative in all respects and not restrictive.

[0075] For example, in (2a), the direction estimation unit 24 calculates TD waterfall In other words, the direction estimator 24 does not need to extract TD waterfallmay be used for all measured times. In such a case, after the processes of (2b), (2c), and (2d), the direction estimation unit 24 calculates a matrix V, which is the product set of the impulse matrix I and the direction matrix D, as follows, in order to calculate the same direction matrix as described in (2d) above.

number

[0076] The direction estimation unit 24 may also calibrate the matrix D and / or V using the trajectory extracted by the lane identification unit 25. Since the columns of the matrix D or V represent information on each lane (upbound or downbound) on which the vehicle is present, the direction estimation unit 24 can use the trajectory to verify whether the calculation of the matrix D or V is accurate.

[0077] The vehicle detection method described in the second embodiment can be applied to the detection of other moving objects, for example, it makes it possible to detect pedestrians walking on sidewalks and bicycles moving on bicycle paths / roads.

[0078] Next, with reference to FIG. 8, a configuration example of the traffic event detection device described in the above-mentioned embodiments will be described.

[0079] An object presence detection system, including examples of both the object presence detection system 10 and the traffic event detection system T, may be implemented on a computer system such as that shown in Figure 8. Referring to Figure 8, a computer system 90, such as a server, includes a communication interface 91, a memory 92, and a processor 93.

[0080] The communication interface 91 (e.g., a network interface controller (NIC)) can be configured to communicatively connect to sensors located in the infrastructure. For example, it may be located under a roadway lane, as shown in FIG. 3. Additionally, the communication interface 91 can communicate with other computers and / or machines to receive and / or transmit data related to the computations of the computer system 90.

[0081] The memory 92 stores a program 94 (program instructions) for causing the computer system 90 to function as the object presence detection system 10 or the detection server 20. The memory 92 may be, for example, a semiconductor memory (e.g., random access memory (RAM), read-only memory (ROM), electrically erasable programmable ROM (EEPROM)) ) , and / or a storage device including at least one of a hard disk drive (HDD), a solid-state drive (SSD), a compact disc (CD), a digital versatile disc (DVD), etc. From another perspective, the memory 92 is formed by a volatile memory and / or a non-volatile memory. The memory 92 may include a storage device located remotely from the processor 93. In this case, the processor 93 can access the memory 92 via an I / O interface (not shown).

[0082] The processor 93 is configured to read a program 94 (program instructions) from the memory 92 and execute the program 94 (program instructions) to implement the functions and processes of the above-described embodiments. The processor 93 may be, for example, a microprocessor, an MPU (microprocessing unit), or a CPU (central processing unit). Furthermore, the processor 93 may include multiple processors. In this case, each processor executes one or more programs including a set of instructions, thereby causing the computer to execute the algorithm described above with reference to the drawings.

[0083] The program 94 includes program instructions (program modules) for executing the processing of each part of the traffic event detection device in the above-described embodiments.

[0084] The program includes instructions (or software code) that, when loaded into a computer, cause the computer to perform one or more of the functions described in the embodiments. The program may be stored in a non-transitory computer-readable medium or tangible storage medium. By way of example and not limitation, the non-transitory computer-readable medium or tangible storage medium may include random access memory (RAM), read-only memory (ROM), flash memory, solid-state drive (SSD) or other memory technology, CD-ROM, digital versatile disc (DVD), Blu-ray Disc (®) or other optical disk storage device, magnetic cassette, magnetic tape, magnetic disk storage device, or other magnetic storage device. The program may be transmitted on a transitory computer-readable medium or communication medium. By way of example and not limitation, the transitory computer-readable medium or communication medium may include electrical, optical, acoustic, or other forms of propagated signals.

[0085] Within the scope of the claims of the present disclosure, various combinations and selections of various disclosed elements (including elements of each embodiment, elements of each drawing, etc.) are possible. In other words, the present disclosure naturally includes various modifications and changes that can be made by a person skilled in the art in accordance with the entire disclosure including the scope of the claims and the technical idea. [Explanation of symbols]

[0086] 10. Object Presence Detection System 11 Dataset Processing Section 12 Impulse detection unit 13 Presence detection unit 20 Discovery Server 21 Signal acquisition unit 22 Raw Dataset Processing Unit 23 Impulse detection unit 24 Direction estimation part 25 Lane Identification Unit 26 Traffic information generation section 27 Notification Department 28 Model Storage Device 29 Model Training Department F Optical Fiber Cable DAS Distributed Acoustic Sensor T Traffic Incident Detection System 90 Computer Systems 91 Communication Interface 92 memory 93 processors 94 Programs

Claims

1. a data set processing means for obtaining time-distance graph information of an oscillation signal for each distributed sensing portion, the oscillation signal being obtained by a plurality of the distributed sensing portions and being induced by traffic of moving objects; impulse detection means for detecting impulse response information using the time-distance graph information measured by the plurality of distributed sensing portions; a conversion means for converting the detected impulse response information into an impulse matrix, which is a binary matrix; a detection means for extracting the oscillation signal in a time range when an impulse is detected in the impulse matrix, generating intermediate data indicating a peak frequency of the extracted oscillation signal, and detecting moving direction information of the moving object relative to the distributed sensing portion using the intermediate data; An object presence detection system comprising:

2. A data set processing means for acquiring time-distance graph information of oscillation signals for each distributed sensing portion, the oscillation signals being acquired by a plurality of the distributed sensing portions and induced by traffic of moving objects; impulse detection means for detecting impulse response information using the time-distance graph information measured by the plurality of distributed sensing portions; a presence detection means for detecting the presence of the moving object, including movement direction information of the moving object, using the impulse response information in the time-distance graph information; model training means for training a model to learn presence features of moving objects in a training data set corresponding to the impulse response information of the moving objects; the impulse detection means performs a feature reduction process on the time-distance graph information, and inputs the feature-reduced data into the trained model to obtain the impulse response information. Object presence detection system.

3. A data set processing means for acquiring time-distance graph information of oscillation signals for each distributed sensing portion, wherein the oscillation signals are acquired by a plurality of the distributed sensing portions and are induced by traffic of moving objects; impulse detection means for detecting impulse response information using the time-distance graph information measured by the plurality of distributed sensing portions; presence detection means for detecting the presence of the moving object, including movement direction information of the moving object, using the impulse response information in the time-distance graph information; the presence detection means extracts the time-distance graph information for a time range in which the impulse indicated by the impulse response information is detected, determines an arrival time difference of the impulse between the plurality of distributed sensing portions using the extracted time-distance graph information, and calculates the movement direction information using the arrival time difference; Object presence detection system.

4. a fiber optic cable including the plurality of distributed sensing sections; The object presence detection system according to claim 1 , further comprising:

5. a lane identification means for extracting a trajectory of the moving object using the time-distance graph information, and identifying a lane on the road where the moving object is located using the trajectory and the presence of the moving object; The object presence detection system according to claim 2 or 3, further comprising:

6. notification means for notifying the presence of said moving object and the lane in which said moving object is located; The object presence detection system of claim 5 , further comprising:

7. Acquiring time-distance graph information of an oscillation signal for each distributed sensing portion, the oscillation signal being acquired by a plurality of the distributed sensing portions and induced by traffic of moving objects; Detecting impulse response information using the time-distance graph information measured by the plurality of distributed sensing portions; converting the detected impulse response information into an impulse matrix, which is a binary matrix; extracting the oscillation signal in a time range when an impulse is detected in the impulse matrix, generating intermediate data indicating a peak frequency of the extracted oscillation signal, and detecting movement direction information of the moving object relative to the distributed sensing portion using the intermediate data. A computer-implemented method for object presence detection.

8. Acquiring time-distance graph information of an oscillation signal for each distributed sensing portion, the oscillation signal being acquired by a plurality of the distributed sensing portions and induced by the traffic of moving objects; Detecting impulse response information using the time-distance graph information measured by the plurality of distributed sensing portions; Detecting the presence of the moving object using the impulse response information in the time-distance graph information, including movement direction information of the moving object; extracting the time-distance graph information for a time range in which an impulse indicated by the impulse response information is detected, determining a time difference of arrival of the impulse between the plurality of distributed sensing portions using the extracted time-distance graph information, and calculating the movement direction information using the time difference of arrival. A computer-implemented method for object presence detection.

9. Acquiring time-distance graph information of an oscillation signal for each distributed sensing portion, the oscillation signal being acquired by a plurality of the distributed sensing portions and induced by traffic of moving objects; Detecting impulse response information using the time-distance graph information measured by the plurality of distributed sensing portions; converting the detected impulse response information into an impulse matrix, which is a binary matrix; extracting the oscillation signal in a time range when an impulse is detected in the impulse matrix, generating intermediate data indicating a peak frequency of the extracted oscillation signal, and detecting movement direction information of the moving object relative to the distributed sensing portion using the intermediate data; A program that causes a computer to execute the following.

10. Acquiring time-distance graph information of an oscillation signal for each distributed sensing portion, the oscillation signal being acquired by a plurality of the distributed sensing portions and induced by the traffic of moving objects; Detecting impulse response information using the time-distance graph information measured by the plurality of distributed sensing portions; Detecting the presence of the moving object using the impulse response information in the time-distance graph information, including movement direction information of the moving object; extracting the time-distance graph information for a time range in which the impulse indicated by the impulse response information is detected, determining arrival time differences of the impulses between the plurality of distributed sensing portions using the extracted time-distance graph information, and calculating the movement direction information using the arrival time differences; A program that causes a computer to execute the following.

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