Distributed Fiber Optic Sensing for Vehicle Classification
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Solution Overview
Problem
Current vehicle-to-infrastructure interaction monitoring systems face limitations in providing real-time, precise data on vehicle parameters like speed, type, and weight-in-motion, especially in dense traffic scenarios, and require expensive hardware and sophisticated algorithms, while existing sensors are sparse, costly, and lack capability to discriminate closely following vehicles.
Innovation Solution
A distributed fiber optic sensing system utilizing pre-deployed fiber-optic cables buried alongside highways to continuously monitor vehicle traffic, employing a single optical sensor cable that measures multiple parameters, including driving speed, wheelbase, and tire pressure, with a peak-finding algorithm for accurate data extraction and machine learning for enhanced data processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If special sensors (cameras, pneumatic road tubes, piezoelectric sensors, magnetic sensors) are deployed to measure vehicle parameters, then measurement precision is improved, but device complexity and deployment cost increase
Solution Approach 1:
The patent applies universality by using a single distributed fiber optic sensing system to perform multiple measurement functions simultaneously. The system measures vehicle speed, weight, tire pressure, and other parameters through one integrated infrastructure rather than deploying separate specialized sensors for each parameter, thereby reducing device complexity while maintaining measurement precision.
Solution Approach 2:
The patent replaces mechanical sensor systems (pneumatic road tubes, piezoelectric sensors) with an optical sensing system based on distributed fiber optic sensing. This substitution eliminates the need for complex mechanical deployment while achieving comparable or superior measurement precision for vehicle parameters.
2Device complexity
If point sensors are deployed sparsely along the highway, then device complexity is reduced, but measurement precision and ability to discriminate closely following vehicles deteriorates
Solution Approach 1:
The patent applies segmentation by dividing the sensing function into distributed segments along the fiber optic cable. Instead of using a few point sensors, the system creates numerous virtual sensing points distributed continuously along the highway, enabling precise discrimination of closely following vehicles while maintaining simple infrastructure deployment.
Solution Approach 2:
The patent transitions from spatial distribution of discrete point sensors to a continuous linear distribution along the fiber optic cable. This dimensional change allows the system to achieve high measurement precision for closely following vehicles by utilizing the continuous nature of the distributed sensing along the cable length.
3Measurement precision
If dedicated fiber cables are deployed for sensing, then measurement precision is improved, but device complexity and deployment cost increase
Solution Approach 1:
The patent applies universality by making the existing telecommunication cable serve dual purposes: both its original communication function and as a distributed sensing element. This eliminates the need for separate dedicated fiber cables, reducing deployment complexity while maintaining the measurement precision of distributed fiber optic sensing.
Solution Approach 2:
The patent applies self-service by utilizing the existing telecommunication cable infrastructure to provide sensing capabilities. The cable serves itself by detecting vehicle-induced vibrations and acoustic signals without requiring additional dedicated sensing infrastructure, thereby reducing deployment complexity while achieving precise distributed measurements.
4Measurement precision
If sophisticated computer vision or machine learning algorithms are used, then vehicle parameter detection accuracy is improved, but device complexity and processing requirements increase
Solution Approach 1:
The patent replaces complex computer vision and machine learning algorithms with a simplified signal processing approach based on acoustic and vibration detection. By using the distributed fiber optic sensing system to directly measure physical parameters through acoustic waves and vibrations, the system achieves high detection accuracy without requiring sophisticated algorithms or expensive hardware.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables continuous, precise monitoring of vehicle traffic with high temporal sampling rates, providing accurate data on vehicle types and cargo weights, improving traffic management and reducing deployment costs by using existing infrastructure and low-cost digital signal processing.
Implementation Method 1
a distributed fiber optic sensing system operating with pre-deployed fiber-optic telecommunication cables that are buried alongside/proximate to highways/roadways
Implementation Method 2
high temporal sampling rates of the distributed acoustic sensing
Data Source
AI summary
Disclosed are vehicle-infrastructure interaction systems and methods employing a distributed fiber optic sensing (DFOS) system operating with pre-deployed fiber-optic telecommunication cables buried alongside/proximate to highways/roadways which provide 24/7 continuous information stream of vehicle traffic at multiple sites; only require a single optical sensor cable that senses/monitors multiple locations of interest and multiple lanes of traffic; the single optical sensor cable measures multiple related information (multi-parameters) about a vehicle, including driving speed, wheelbase, number of axles, tire pressure, and others, that can be used to derive secondary information such as weight-in-motion; and overall information about a fleet of vehicles, such as traffic congestion or traffic-cargo volume. Different from merely traffic counts, our approach can provide the count grouped by vehicle-types and cargo weights. Precise measurements are facilitated by high temporal sampling rates of the distributed acoustic sensing and a dedicated peak finding algorithm for extracting the timing information reliably.