# US 2020/0401784 A1 · Distributed intelligent traffic informatics using fiber sensing

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- Number: US 2020/0401784 A1
- Document ID: US20200401784A1
- Status: Published application
- Kind: A1
- Application number: 16/902,210
- Filed: 2020-06-15
- Published: 2020-12-24
- Earliest claimed: 2019-06-19
- Assignees: NEC Laboratories America, Inc.
- Applicants: NEC Laboratories America, Inc.
- Inventors: Milad SALEMI; Ming-Fang HUANG
- Web page: https://patentgrep.com/patent/US20200401784A1

## CPC

- G06N 3/08: Learning methods
- G06F 18/243: relating to the number of classes
- G06N 3/0464: Convolutional networks [CNN, ConvNet]
- G06N 3/09: Supervised learning
- G06V 10/30: Noise filtering
- G06V 10/454: {Integrating the filters into a hierarchical structure, e.g. convolutional neural networks [CNN]}
- G06V 10/82: using neural networks
- G06V 20/52: Surveillance or monitoring of activities, e.g. for recognising suspicious objects (recognising microscopic objects G06V20/69)
- G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N)
- G06V: IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING

## Abstract

Aspects of the present disclosure describe systems, methods and structures providing wide-area traffic monitoring based on distributed fiber-optic sensing (DFOS) that employs deep neural network(s) for denoising noisy waterfall traces measured by the DFOS. Such systems, methods, and structures according to aspects of the present disclosure may advantageously monitor multiple highways/roadways using a single interrogator and optical fiber switch(es) which provides traffic information along every sensing point of existing, deployed, in-service optical telecommunications facilities.

## Claims

### Claim 1

A distributed traffic informatics system comprising:
a length of optical fiber positioned proximal to a roadway supporting vehicular traffic;
an optical interrogator unit that generates optical pulses, introduces them into the optical fiber, and receives Rayleigh backscattered signals from the fiber; and
a data processor unit that is configured to:
determine from the backscattered signals, mechanical vibrations experienced by the optical fiber along its length resulting from a vehicle operating on the highway; and
determine characteristics of the vehicle that produced the determined mechanical vibrations.

### Claim 2 (depends on 1)

The system of claim 1 further comprising:
a vehicle classifier and weight including a deep neural network for weight-in-motion (WIM) applications.

### Claim 3 (depends on 2)

The system of claim 2 wherein the data processor is configured to generate from the backscattered signals time-distance (2-dimensional waterfall) graphs (images) representative of the vibrations experienced by the optical fiber along its length.

### Claim 4 (depends on 3)

The system of claim 3 wherein the data processor is configured to normalize the time-distance graphs through the effect of a column normalization technique where each column of the time-distance graph represents vibration data collected at a particular location along the length of the optical fiber.

### Claim 5 (depends on 4)

The system of claim 4 wherein the data processor is configured to perform the column normalization technique such that a sum of values in each column over each one minute duration is set to one.

### Claim 6 (depends on 5)

The system of claim 5 wherein the data processor is configured to denoise the time-distance graphs.

### Claim 7 (depends on 3)

The system of claim 3 wherein the classifier segments each pixel comprising the time-distance graphs into one of two classes selected from the group consisting of presence of a vehicle, and absence of a vehicle.

### Claim 8 (depends on 2)

The system of claim 2 wherein the neural network is trained on synthetic, noisy, two dimensional time-distance data.

### Claim 9 (depends on 2)

The system of claim 2 further comprising a plurality of optical fibers and an optical switch interposed between the interrogator and the plurality of optical fibers.

### Claim 10 (depends on 9)

The system of claim 9 wherein the data processor is configured to determine backscattered signals from each one of the plurality of optical fibers and to determine characteristics of vehicles operating on roadway(s) proximal to at least one of the plurality of optical fibers.