Traffic monitoring using optical sensor

Abstract

To provide a component, a device, a system, and a method regarding technology for traffic monitoring.SOLUTION: A system comprises a sensor network with at least two optical fibers coupled with a pavement. Each optical fiber comprises one or more optical sensors installed at predetermined distance from one or more adjacent optical fibers. The one or more optical sensors are constituted to generate a wavelength shift signal. A processor is constituted to determine one or both of one or more attributes of one or more objects moving on the pavement and a traffic state of the pavement on the basis of the wavelength shift signal. A transmitter is constituted to transmit the one or more attributes to a predetermined place.SELECTED DRAWING: Figure 1A

Description

Technical Field

[0001] This application generally relates to techniques for traffic monitoring. This application also relates to components, devices, systems, and methods related to such techniques.

Background Art

[0002] Fiber optic (FO) sensors can be used to detect parameters such as strain, temperature, pressure, current, voltage, chemical composition, and vibration. FO sensors are attractive components because they are thin, lightweight, highly sensitive, robust against harsh environments, and not affected by electromagnetic interference (EMI) and electrostatic discharge. FO sensors can be arranged to simultaneously measure multiple parameters that are highly sensitive and distributed in space in a multiplexed configuration over a long fiber optic cable. An example of a method by which this can be achieved is a fiber Bragg grating (FBG) sensor. An FBG sensor is formed by a periodic modulation of the refractive index along a finite length (typically a few millimeters) of the core of an optical fiber. This pattern reflects a wavelength called the Bragg wavelength, which is determined by the periodicity of the refractive index profile. The Bragg wavelength is sensitive to external stimuli (such as strain and / or temperature) that change the periodicity of the grating and / or the refractive index of the fiber. Thus, FBG sensors rely on the detection of small wavelength changes in response to the stimulus of interest. In some implementations, FO sensors can be installed, for example, on and / or under a road surface and operate to detect parameters related to vehicles moving on the road, such as strain, temperature, and vibration.

Summary of the Invention

[0003] Embodiments described herein involve a system comprising a sensor network having at least two optical fibers coupled to a pavement. Each optical fiber comprises one or more optical sensors positioned at a predetermined distance from one or more adjacent optical fibers. One or more optical sensors are configured to generate wavelength-shifted signals. A processor is configured to determine, based on the wavelength-shifted signals, one or more attributes of one or more objects moving on the pavement and or both of the traffic conditions on the pavement. A transmitter is configured to transmit one or more attributes to a predetermined location.

[0004] The method involves receiving wavelength shift signals from multiple optical sensors coupled to the pavement. The multiple optical sensors are arranged on at least two optical fibers. Each optical fiber is located at a predetermined distance from at least one other optical fiber. One or both of one or more attributes and traffic conditions of one or more objects moving on the pavement are determined based on the wavelength shift signals. One or both of the one or more attributes and traffic conditions are transmitted to a predetermined location. [Brief explanation of the drawing]

[0005] Throughout this specification, refer to the attached drawings. [Figure 1A] A diagram illustrating an FO traffic monitoring system according to an embodiment described herein illustrates this point. [Figure 1B] This demonstrates that a wavelength multiplexing sensor system can be used with a compensated sensor array comprising multiple FBG sensors arranged on a single optical fiber, as described in the embodiments herein. [Figure 2A] A more detailed diagram of the analysis module according to the embodiments described herein is shown. [Figure 2B] A more detailed diagram of the analysis module according to the embodiments described herein is shown. [Figure 3A]An example of a sensing system for monitoring traffic and / or vehicle parameters, according to embodiments described herein, is illustrated. [Figure 3B] An example of a sensing system for monitoring traffic and / or vehicle parameters, according to embodiments described herein, is illustrated. [Figure 3C] This shows stimulated strain of a pavement that can be captured by a sensor according to embodiments described herein. [Figure 4] This specification describes a system using an ensemble method according to embodiments described herein. [Figure 5] This specification describes a system for determining various axle attributes according to embodiments described herein. [Figure 6A] The vehicle classification of small vehicles according to the embodiments described herein will be illustrated. [Figure 6B] The vehicle classification of small vehicles according to the embodiments described herein will be illustrated. [Figure 6C] The vehicle classification of small vehicles according to the embodiments described herein will be illustrated. [Figure 7A] The vehicle classification of a six-axle vehicle according to the embodiments described herein will be illustrated. [Figure 7B] The vehicle classification of a six-axle vehicle according to the embodiments described herein will be illustrated. [Figure 7C] The vehicle classification of a six-axle vehicle according to the embodiments described herein will be illustrated. [Figure 8A] The vehicle classification of larger vehicles according to the embodiments described herein is illustrated. [Figure 8B] The vehicle classification of larger vehicles according to the embodiments described herein is illustrated. [Figure 8C] The vehicle classification of larger vehicles according to the embodiments described herein is illustrated. [Figure 9]The following illustrates a process for monitoring traffic and / or vehicle parameters according to embodiments described herein.

[0006] Drawings are not necessarily to scale. Similar numbers used in drawings refer to similar components. However, it should be understood that the use of numbers to refer to components in a given drawing is not intended to limit components in another drawing labeled with the same number. [Modes for carrying out the invention]

[0007] Embodiments described herein may include a traffic monitoring system capable of extracting traffic parameters, including vehicle characteristics and their movement on the road. Extracting these traffic parameters can enable better traffic management and road surface maintenance / design, which helps to mitigate traffic congestion problems, prevent catastrophic breakdowns due to poor road conditions, and / or improve the quality of life for the general public.

[0008] Embodiments described herein are systems for accurately monitoring traffic and / or identifying vehicles, which may be used in intelligent traffic management and planning systems. Embodiments herein describe systems and methods for integrated traffic monitoring (e.g., traffic volume, speed, and / or road occupancy) and vehicle attribute extraction (e.g., number of axles, axle groups, vehicle type, and / or axle weight) using distributed optical fiber (FO) sensors embedded in the pavement.

[0009] Embodiments described herein may include one or more of the following: 1) the ability to monitor multiple parameters; 2) high accuracy; 3) robustness under various field and / or weather conditions; 4) low installation and / or maintenance costs; and 5) short downtime. Embodiments herein may involve hardware for a traffic monitoring system based on optical sensors. Depending on the configuration, the sensors may be fiber Bragg grating (FBG) strain sensors, Fabry-Perot sensors, and / or other interferometric optical sensors. In some cases, the sensors may include one or more of electrical and / or resistance sensors, mechanical sensors, and / or other types of strain gauges. In some cases, a combination of different types of sensors may be used.

[0010] The sensors described herein are generally described as fibers engraved with FBG arrays as sensing elements for traffic monitoring. FBGs are wavelength-specific narrowband reflectors formed within the core of a standard fiber by introducing periodic fluctuations in the refractive index (RI) of the fiber core. Several factors, including temperature and strain, that alter the RI fluctuations will shift the reflected wavelength of the FBG and are therefore sensed by the FBG. While many embodiments described herein use FBGs as an example, it should be understood that any suitable type of sensor can be used. Detailed considerations for FBG array design in specific use cases are discussed. The proposed optical fiber (FO)-based sensing system has several inherent characteristics. For example, the sensing system may be substantially unaffected by electromagnetic interference. This allows for less frequent system maintenance and / or calibration, which may be useful for reliable long-term deployment in the field. The proposed system may be independent of field visibility conditions. The proposed system may be capable of temperature self-calibration.

[0011] The proposed scheme may be capable of monitoring multiple parameters, including moving weight, speed, axle count, and one or more vehicle classes having high accuracy and high dynamic range. The proposed scheme can provide improved spatial resolution for vehicles in lanes and can detect lane change events and / or lane crossing events.

[0012] Various embodiments demonstrate installation strategies for substantially permanently integrating fibers into the pavement. While this involves invasive installations that introduce a certain amount of material into the pavement, the proposed FBG-based FO sensing system facilitates standardized installation procedures, offers a high level of redundancy potential, has a longer lifespan, and is considered suitable for the mass production of completed FBG FO sensors. For this reason, the present invention is more capable and cost-effective for large-scale deployments of multi-parameter traffic monitoring.

[0013] The embodiments described herein involve a fiber embedded in a pavement with an FBG array inscribed therein for sensing an object (e.g., a vehicle and / or a pedestrian) moving on the pavement. FIG. 1A illustrates a diagram of a FO traffic monitoring system according to an embodiment described herein. A vehicle moving in the sensing area 105 can induce pavement deformation, which can cause strain in the pavement-embedded sensor 120 and generate an FBG wavelength shift signal. The fiber is connected to an FBG interrogator at one end, and the center wavelength of each FBG on the fiber is tracked at a desired frequency. The center wavelength of the FBG can be streamed to a processor 130 having a data collection module 132 and an analysis module 134, and this information is converted into traffic parameters. The traffic parameters can include, for example, vehicle speed, traffic volume, the number of axles of at least one vehicle on the road, the vehicle classification of at least one vehicle on the road, the location of the vehicle on the lane, the weight of the vehicle, and the weight per axle of at least one vehicle on the road, among one or more of them. Then, the extracted information can be transferred to a predetermined location via a transmitter 140. For example, the extracted information can be transferred to the cloud, enabling a remote control center to use the information for traffic and / or road condition management. In some embodiments, the information conversion can occur after the raw sensing data is transferred to the cloud.

[0014] Typically, multiple FBG sensors reside on a single fiber. The center wavelength of the reflection band of each FBG is distributed within a specific wavelength range. For example, the wavelength range may be 1510 nm to 1590 nm. In one embodiment, the reflection wavelengths of each FBG on the same fiber have a certain spacing in the spectrum. For example, the spectral spacing of FBGs on the same fiber may be about 2 to 3 nm. In the wavelength range of 1510 to 1590 nm, a 3 nm spacing allows approximately 26 FBGs on a single fiber to be queried simultaneously. In another embodiment, FBGs on the same fiber may have overlapping reflection bands, and signals from different FBGs are distinguished by additional time-domain features (e.g., reflection time). In general, the sensing fiber design of this application must consider the trade-offs between the required level of multiplexing, system performance (sampling rate, wavelength accuracy, etc.) and overall cost (hardware, installation, maintenance, etc.).

[0015] FO sensors can simultaneously measure multiple parameters distributed in space with high sensitivity in a multiplexed configuration over long FO cables. One example of a method that can achieve this is a fiber Bragg grating (FBG) sensor. Figure 1B shows that the wavelength multiplexing system 100 can use a compensated sensor array comprising multiple FBG sensors 121, 122, 123 arranged on a single optical fiber 111. Sensors 121-123 may be configured to sense parameters including, for example, one or more of temperature, strain, and / or vibration. As shown in Figure 1B, the input light is provided by a light source 110 comprising, or potentially comprising, a light-emitting diode (LED) or a superluminescent laser diode (SLD). The spectral characteristics (intensity vs. wavelength) of the broadband light are shown by inset 191. Intensity is highest near the center of the spectrum and decreases at the edges of the spectrum. Sensors 121, 122, and 123 include compensation, e.g., one or more of different reflectances and different attenuations, to reduce the difference in intensity of the output signal light reflected by the sensors, thereby compensating for input light whose intensity is non-uniform due to, for example, spectral non-uniformity of the light source and / or scattering loss in the optical fiber. Input light is transmitted to the first FBG sensor 121 via an optical fiber (FO) cable 111. The first FBG sensor 121 reflects a portion of the light in a first wavelength band having a center wavelength λ1. Light having wavelengths outside the first wavelength band is transmitted through the first FBG sensor 121 to the second FBG sensor 122. The spectral characteristics of the light transmitted to the second FBG sensor 122 are shown in inset graph 192, exhibiting a notch 181 in the first wavelength band centered at λ1, indicating that light in this wavelength band is reflected by the first sensor 121.

[0016] The second FBG sensor 122 reflects a portion of the light in the second wavelength band having a central wavelength λ2. The light not reflected by the second FBG sensor 122 passes through the second FBG sensor 122 and is transmitted to the third FBG sensor 123. The spectral characteristics of the light transmitted to the third FBG sensor 123 are shown in the inserted graph 193 and include notches 181, 182 centered on λ1 and λ2.

[0017] The third FBG sensor 123 reflects a portion of the light in the third wavelength band having a central wavelength or peak wavelength λ3. The light not reflected by the third FBG sensor 123 passes through the third FBG sensor 123. The spectral characteristics of the light transmitted through the third FBG sensor 123 are shown in the inserted graph 194 and include notches 181, 182, 183 centered on λ1, λ2, and λ3.

[0018] (Illustrated in the inserted graph 195) The light in the wavelength bands 161, 162, 163 having central wavelengths λ1, λ2, and λ3 is reflected by the first, second, or third FBG sensors 121, 122, 123 to the optical wavelength demultiplexer 150 along the FO cables 111 and 111'. The compensation input characteristics of the sensors 121, 122, 123 reduce the difference in the intensity peaks of the lights 161, 162, 163 when compared to the intensity peaks from the uncompensated sensor array.

[0019] From the wavelength demultiplexer 150, the sensor light 161, 162, and 163 can be sent to the wavelength shift detector 155, which generates an electrical signal in response to the shift in the central wavelengths λ1, λ2, and λ3 and / or wavelength band of the sensor light. The wavelength shift detector 155 receives the reflected light from each of the sensors and generates a corresponding electrical signal in response to the shift in the central wavelengths λ1, λ2, and λ3 or wavelength band of the light reflected by the sensors 121-123. The analyzer 156 can compare the shift to a characteristic fundamental wavelength (a known wavelength) to determine whether a change has occurred in the value of the parameter sensed by the sensors 121-123. The analyzer 156 can determine that the value of one or more of the sensed parameters has changed based on the wavelength shift analysis and can calculate a relative or absolute measurement of the change.

[0020] In some cases, instead of emitting broadband light, a light source may scan a wavelength range, and various sensors placed on the FO cable may emit light in a narrow wavelength band to which they are sensitive. The reflected light is sensed during several sensing periods timed to the emission of narrowband light. For example, consider a scenario in which sensors 1, 2, and 3 are placed on the FO cable. Sensor 1 is sensitive to a wavelength band (WB1), sensor 2 is sensitive to a wavelength band WB2, and sensor 3 is sensitive to WB3. The light source may be controlled to emit light with WB1 during period 1 and sense the reflected light during period 1a, which overlaps with period 1. Following period 1a, the light source may emit light with WB2 during period 2 and sense the reflected light during period 2a, which overlaps with period 2. Following period 2a, the light source may emit light with WB3 during period 3 and sense the reflected light during period 3a, which overlaps with period 3. Using this version of time-domain multiplexing, each sensor can be queried during separate periods. When the intensity of a narrowband light source changes, a compensated sensor array, such as those discussed herein, may be useful for compensating for fluctuations in the light source intensity.

[0021] FO cables can consist of single-mode (SM) FO cables or multi-mode (MM) FO cables. Single-mode fiber optic cables provide signals that are easier to interpret, but multi-mode fibers may be used to achieve broader applicability and lower manufacturing costs. MM fibers may be made of plastic instead of silica, which is typically used in SM fibers. Plastic fibers may have a smaller radius of gyration compared to silica fibers. This can, for example, offer the possibility of curved or flexible configurations. Furthermore, MM fibers can operate with less expensive light sources (e.g., LEDs) in contrast to SM fibers, which may require more precise alignment with superluminescent diodes (SLDs). Therefore, sensing systems based on optical sensors within MM fibers can produce lower-cost systems.

[0022] Figure 2 shows a more detailed diagram of the analysis module 130 according to an embodiment described herein. The vehicle detection module 210 may be configured to detect vehicle entry 212. Detecting vehicle entry may include determining the time when a vehicle enters a sensing zone (e.g., located between two adjacent optical fibers). For example, detecting vehicle entry may involve detecting when a vehicle first crosses an embedded FO sensor. Similarly, the vehicle detection module 210 may be configured to detect vehicle exit 214. Detecting vehicle exit may include determining the time when a vehicle leaves a sensing zone. For example, detecting vehicle exit may involve detecting the time when the last axle of a vehicle crosses an embedded FO sensor and / or leaves a sensing zone containing one or more embedded FO sensors. Vehicle event data may be retrieved in 216 based on sensor data.

[0023] The attribute extraction module 220 may be configured to extract various traffic and / or vehicle attributes according to embodiments described herein. Attributes may include one or more of the following: speed 222, number of axles 224, distance between axles 225, axle group 226, lane the vehicle is traveling in 228, weight per axle 229, and / or vehicle classification 227 for a given jurisdiction. Other types of attributes may also be extracted. For example, the direction of travel of the vehicle may be extracted.

[0024] Attributes can be aggregated to determine other characteristics of a vehicle and / or traffic moving on the road.230 The aggregated attributes may include information about multiple vehicles within a given period (e.g., 20 seconds). According to various embodiments, attributes of two or more vehicles can be aggregated to determine one or more of aggregated speeds232, classifications234, and axle weights239. In some cases, attributes can be aggregated to determine one or more of the occupancy rate236 and / or amount of vehicles moving on the road238. Aggregated traffic speeds can be used, for example, to understand traffic origins. Vehicle classifications and / or axle weight data can be used, for example, to understand road wear and / or usage patterns from aggregated data. One or more of the raw data, attribute data, and / or aggregated data can be stored in a database244 and / or in a preferred data file242 (e.g., CSV).240

[0025] Figures 3A and 3B illustrate an example of a sensing system for monitoring traffic and / or vehicle parameters according to embodiments described herein. Two optical fibers 360, 370 are installed substantially parallel to each other. In some cases, the optical fibers are installed in a configuration in which at least two of the optical fibers are not installed substantially parallel to each other. Each optical fiber 360, 370 has a plurality of FO sensors 320 installed substantially perpendicular to the direction of traffic. For example, the second optical fiber 370 may be installed at a predetermined distance D from the first optical fiber 360. D may be in the range of about 0.5 m to about 5 m. In some cases, D is in the range of about 1 m to about 3 m.

[0026] In some cases, the optical fibers 360, 370 may be supported within the pavement by support bars and / or support structures 330 within the road pavement 340. According to various embodiments, the optical fibers may be installed within the pavement or in trenches beneath the pavement. Several embodiments for installing optical fibers are described in further detail in U.S. Patent Application No. 17 / 393,927, which is incorporated in whole by reference. According to various embodiments, there may be three or more optical fibers, and / or the optical fibers may be installed in configurations other than perpendicular to the direction of traffic. Figures 3A and 3B show optical fibers installed on and / or beneath two lanes 350, 355, but it should be understood that the optical fibers may be installed on and / or beneath any number of lanes.

[0027] As the vehicle axles 380 and 385 pass the sensors, stimulated strain in the pavement can be captured by the sensors, as shown in Figure 3C. The first curve 365 represents the vehicle axles 380 and 385 passing through the first optical fiber 360. Peaks 367 and 369 represent the first axle 380 and the second axle 385 passing through the first optical fiber 360, respectively. Similarly, the second curve 375 represents the vehicle axles 380 and 385 passing through the second optical fiber 370. Peaks 377 and 379 represent the first axle 380 and the second axle 385 passing through the second optical fiber 370, respectively.

[0028] Next, vehicle and traffic attributes can be inferred from spatiotemporal sensor data. For example, a simple vehicle speed estimate can be determined by calculating the time (Δt) it takes for the first axle 380 to move from the first optical fiber 360 to the second optical fiber. Since the distance between the two fiber lines is known (D), the vehicle speed can be simply calculated as shown in (1).

[0029]

number

[0030] Another method for estimating velocity is to use the correlation between time-series data from two fiber lines. Ensemble methods are used to increase the robustness of the method against sensor errors or inconsistencies in the sensor data. Figure 4 depicts a system 400 using an ensemble method according to an embodiment described herein. Sensor data 410 is used to estimate velocity using one or more of the velocity estimation modules 422, 424, 426, and 428 of the velocity estimation method pool 420. The information injection module 430 may use an averaging mechanism (e.g., median and / or mean aggregation). In some implementations, more sophisticated information injection methods, such as Dempster-Schafer rule theory, can be used to integrate estimations from multiple approaches. The result of the integrated estimations yields a final velocity estimation 440.

[0031] Figure 5 shows a system 500 for determining various axle attributes according to embodiments described herein. Single vehicle data is extracted 510. Multiple axles are extracted 520 by detecting peaks from sensor data associated with one vehicle. The extracted axles can then be grouped based on proximity rules. For example, if the distance between two axles is less than a predetermined distance (e.g., 2 m), they can be counted as one axle group. Various signal features can be extracted from single vehicle data 530. For example, signal features may include one or more of the area under a curve, full width at half maximum, magnitude of one or more times, and / or one or more gradients. Axle weight can be extracted from a regression model that takes a set of axle signal features as input, as depicted 542. According to various embodiments, the regression model may additionally or alternatively use calibration data 540 from controlled road tests as input. The system 500 can then output an axle weight 544 based on the estimated axle weight 542.

[0032] According to various embodiments, the vehicle type can be inferred from the estimated vehicle speed and / or axle attributes. In some implementations, a strict rule-based system is used to classify vehicles based on their length, number of axles, and / or number of axle groups. In some cases, fuzzy logic may be used to classify vehicles based on their length, number of axles, and number of axle groups, taking into account the uncertainty of the estimated axle attributes.

[0033] Figures 6A to 6C illustrate vehicle classification of small vehicles according to embodiments described herein. An example of a small vehicle is shown in Figure 6A. Figure 6B shows a strain heatmap of a small vehicle. Figure 6C illustrates strain versus time for a first fiber 610 and a second fiber 620. As an example, using the first fiber 610, it can be observed that there are two strain peaks 612 and 614 corresponding to the first axle 605 and the second axle 607, respectively. According to various embodiments described herein, the heatmap illustrates an example of a 2D representation of a vehicle. Other types of representations may be used. In some cases, a 3D representation of a vehicle may be created based on sensor data.

[0034] Figures 7A to 7C illustrate the vehicle classification of a six-axle vehicle according to embodiments described herein. An example of a six-axle vehicle is shown in Figure 7A. Figure 7B shows a strain heatmap of an exemplary six-axle vehicle. Figure 7C illustrates strain versus time for a first fiber 710 and a second fiber 720. Using the first fiber 710 as an example, it can be observed that there are three strain peak groups 712, 714, and 716. The first strain peak group 712 corresponds to the first axle group 705. The second strain peak group 714 corresponds to the second axle group 707. In this example, the second axle group 707 has two axles and two corresponding peaks in the second strain peak group 714. The third strain peak group 716 corresponds to the third axle group 709. In this example, the third axle group 709 has three axles and three corresponding peaks in the third strain peak group 716.

[0035] Figures 8A to 8C illustrate vehicle classification of larger vehicles according to embodiments described herein. These types of larger vehicles, as well as other types of vehicles, can be detected using velocity estimation, time of flight between fiber lines, and / or other signal features. An example of an 8-axle vehicle is shown in Figure 8A. Figure 8B shows a strain heatmap of an exemplary 6-axle vehicle. Figure 8C illustrates strain versus time for a first fiber 810 and a second fiber 820. Using the first fiber 810 as an example, it can be observed that there are four strain peak groups 812, 814, 816, and 818. The first strain peak group 812 corresponds to the first axle group 805, which has a single peak. The second strain peak group 814 corresponds to the second axle group 806. In this example, the second axle group 806 has two axles and two corresponding peaks in the second strain peak group 814. The third strain peak group 816 corresponds to the third axle group 807. In this example, the third axle group 807 has three axles and three corresponding peaks in the third strain peak group 816. The fourth strain peak group 818 corresponds to the fourth axle group 809. In this example, the fourth axle group 809 has two axles and two corresponding peaks in the fourth strain peak group 818.

[0036] Figure 9 illustrates a process for monitoring traffic and / or vehicle parameters according to embodiments described herein. A wavelength shift signal is received from a plurality of optical sensors coupled to the pavement. The optical sensors may be arranged on at least two optical fibers. Each optical fiber is located at a predetermined distance from at least one other optical fiber. The wavelength shift signal may include a distortion signal. The pavement may include one or more of a sidewalk, road, and bridge.

[0037] One or more attributes of one or more objects moving on the sidewalk, and one or both of the traffic conditions, are determined based on one or more wavelength shift values. Objects may include one or more vehicles and pedestrians. Attributes may include one or more of the following: speed of one or more objects, direction of movement, number of axles of one or more objects, distance between axles of one or more objects, group of axles of one or more objects, traffic lane in which one or more objects are moving, lane-crossing status of one or more objects, and / or weight per axle for one or more objects. One or more attributes may be aggregated to determine one or more of the object classification, road occupancy rate, and road traffic volume. Warnings may be issued based on the wavelength shift signal. For example, a warning may be issued if one or more of the determined vehicle classification, weight, and / or speed of a vehicle exceeds the specifications of the type of sidewalk it is moving on.

[0038] According to various embodiments, velocity may be determined by aggregating two or more sensors. In some cases, the velocity of one or more objects is determined using a single pair of sensors. The velocity of one or more objects may be determined using a correlation between a first wavelength shift signal received from a sensor arranged on a first optical fiber and a second wavelength shift signal received from a sensor arranged on a second optical fiber. In some cases, the velocity of one or more objects is determined using the time shift of the wavelength shift peak of the wavelength shift signal.

[0039] One or both of one or more attributes and traffic conditions can be transferred to a predetermined location.930 For example, attributes and / or traffic conditions can be transferred to a database and / or operator terminal.

[0040] Other types of vehicles and / or traffic attributes may be detected using the systems and methods described herein. For example, lane crossing may be monitored by creating a virtual lane centered on a divider. For example, on a two-lane road, a virtual lane is created that includes approximately half of the sensors from both lanes.

[0041] Unless otherwise indicated, all numbers used in this specification and the claims to represent shape, size, quantity, and physical properties should be understood in all cases to be modified by the term "approximately." Therefore, unless otherwise indicated, the numerical parameters described in the foregoing specification and the appended claims are approximations that may vary depending on the desired properties sought by those skilled in the art using the teachings disclosed herein. The use of numerical ranges by endpoints includes all numbers within that range (for example, 1 to 5 includes 1, 1.5, 2, 2.75, 3, 3.80, 4, and 5), as well as any range within that range.

[0042] The various embodiments described above may be implemented using circuits and / or software modules that interact to provide specific results. Those skilled in the field of computing can readily implement such described functions, either at the module level or as a whole, using knowledge generally known in the art. For example, flowcharts illustrated herein may be used to generate computer-readable instructions / code for execution by a processor. Such instructions may be stored in a computer-readable medium and transferred to a processor for execution, as is known in the art.

[0043] The above description of exemplary embodiments is provided for illustrative and explanatory purposes only. It is not intended to be exhaustive of the concepts of the invention, or to limit the concepts of the invention to the exact forms disclosed. Many modifications and variations are possible in light of the above teachings. Any or all features of the disclosed embodiments can be applied individually or in any combination, and are not intended to be limiting, but are purely illustrative. The scope of the invention is intended to be limited not by the detailed description, but by the claims appended herein.

Claims

Claim 1 A system comprising a sensor network comprising at least two optical fibers coupled to a paved road, each optical fiber comprising one or more optical sensors disposed at a predetermined distance from one or more adjacent optical fibers, the one or more optical sensors being configured to generate a wavelength shift signal; a processor configured to determine one or both of one or more attributes of one or more objects moving on the paved road and the traffic state of the paved road based on the wavelength shift signal, the one or more attributes including the speed of the one or more objects, the speed of the one or more objects being aggregating data from two or more of the one or more optical sensors; determined by determining one or both of the median and the mean of the aggregated data; a transmitter configured to transmit one or both of the one or more attributes and the traffic state to a predetermined location. Claim 2 The system of claim 1, wherein the processor is further configured to detect one or more of entry and exit of an object from a sensing zone. Claim 3 The system of claim 1, wherein the one or more attributes include one or more of a direction of movement, a number of axles of the one or more objects, a distance between axles of the one or more objects, a group of axles of the one or more objects, a traffic lane in which the one or more objects are moving, and a weight per axle for the one or more objects. Claim 4 The system of claim 3, wherein the processor is configured to aggregate the one or more attributes to determine one or more of object classification, road occupancy, and traffic volume of the paved road. Claim 5 The system of claim 1, wherein the speed of the one or more objects is determined using a single sensor pair. Claim 6 The system of claim 1, wherein the speed of the one or more objects is determined using a correlation between a first wavelength shift signal received from a sensor disposed on a first optical fiber and a second wavelength shift signal received from a sensor disposed on a second optical fiber. Claim 7 The system of claim 1, wherein the speed of the one or more objects is determined using a time shift of a wavelength shift peak of the wavelength shift signal.

8. The system according to claim 1, wherein the at least two optical fibers are installed parallel to each other.

9. The system according to claim 1, wherein the processor is further configured to detect lane crossing of the one or more objects based on the wavelength shift signal.

10. The system according to claim 1, wherein the processor is further configured to issue a warning based on the wavelength shift signal.

11. The system according to claim 1, wherein the one or more objects include one or more of vehicles and pedestrians.

12. The system according to claim 1, wherein the paved road includes one or more of a sidewalk and a road.

13. The system according to claim 1, wherein the wavelength shift signal includes a strain signal.

14. The processor is configured to determine one or both of the one or more attributes of the one or more objects moving on the paved road based on at least two different methods, integrate the results from the at least two different methods, as described in claim 1.

15. A method comprising: receiving a wavelength shift signal from a plurality of optical sensors coupled to a paved road, wherein the plurality of optical sensors are disposed on at least two optical fibers, and each optical fiber is disposed at a predetermined distance from at least one other optical fiber; determining one or both of one or more attributes of one or more objects moving on the paved road and a traffic state based on the wavelength shift signal, wherein the one or more attributes include a speed of the one or more objects, and the speed of the one or more objects is aggregating data from two or more of the one or more optical sensors, determined by determining one or both of a median and an average of the aggregated data; transferring one or both of the one or more attributes and the traffic state to a predetermined location.

16. The method according to claim 15, further comprising detecting one or more of entry and exit of an object from a sensing zone.

17. The method according to claim 15, wherein the one or more attributes include one or more of a moving direction, a number of axles of the one or more objects, a distance between axles of the one or more objects, a group of axles of the one or more objects, a traffic lane in which the one or more objects are moving, and a weight per axle for the one or more objects.

18. The method according to claim 17, further comprising aggregating the one or more attributes to determine one or more of an object classification, a road occupancy rate, and a traffic volume of the paved road.

19. The method according to claim 17, further comprising determining the speed of the one or more objects using a correlation between a first wavelength shift signal received from a sensor disposed on a first optical fiber and a second wavelength shift signal received from a sensor disposed on a second optical fiber.

20. The method according to claim 17, further comprising determining the speed of the one or more objects using a time shift of a wavelength shift peak of the wavelength shift signal.

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