Implementation of a Mobile Platform Based on Fiber Bragg Grating Sensors for Automotive Traffic Monitoring

Vehicle identification using deep learning for expressway monitoring based on ultra-weak FBG arrays

A deep learning with knowledge distillation scheme for lateral lane-level vehicle identification based on ultra-weak fiber Bragg grating (UWFBG) arrays is proposed. Firstly, the UWFBG arrays are laid underground in each expressway lane to obtain the vibration signals of vehicles. Then, three types of vehicle vibration signals (the vibration signal of a single vehicle, the accompanying vibration signal, and the vibration signal of laterally adjacent vehicles) are separately extracted by density-based spatial clustering of applications with noise (DBSCAN) to produce a sample library. Finally, a teacher model is designed with a residual neural network (ResNet) connected to a long short-term memory (LSTM), and a student model consisting of only one LSTM layer is trained by knowledge distillation (KD) to satisfy the real-time monitoring with high accuracy. Experimental demonstration verifies that the average identification rate of the student model with KD is 95% with good real-time capability. By comparison tests with other models, the proposed scheme shows a solid performance in the integrated evaluation for vehicle identification.

Review of IoT Sensor Systems Used for Monitoring the Road Infrastructure

An intelligent transportation system is one of the fundamental goals of the smart city concept. The Internet of Things (IoT) concept is a basic instrument to digitalize and automatize the process in the intelligent transportation system. Digitalization via the IoT concept enables the automatic collection of data usable for management in the transportation system. The IoT concept includes a system of sensors, actuators, control units and computational distribution among the edge, fog and cloud layers. The study proposes a taxonomy of sensors used for monitoring tasks based on motion detection and object tracking in intelligent transportation system tasks. The sensor's taxonomy helps to categorize the sensors based on working principles, installation or maintenance methods and other categories. The sensor's categorization enables us to compare the effectiveness of each sensor's system. Monitoring tasks are analyzed, categorized, and solved in intelligent transportation systems based on a literature review and focusing on motion detection and object tracking methods. A literature survey of sensor systems used for monitoring tasks in the intelligent transportation system was performed according to sensor and monitoring task categorization. In this review, we analyzed the achieved results to measure, sense, or classify events in intelligent transportation system monitoring tasks. The review conclusions were used to propose an architecture of the universal sensor system for common monitoring tasks based on motion detection and object tracking methods in intelligent transportation tasks. The proposed architecture was built and tested for the first experimental results in the case study scenario. Finally, we propose methods that could significantly improve the results in the following research.

Identification of Moving Load Characteristic on Pavement Using F-P Cavity Fiber Optical Technology

The weigh-in-motion (WIM) system is a necessary piece of equipment for an intelligent road. It can provide real-time vehicle weight and lateral distribution data on wheel load to effectively support pavement structure design and service life analysis for autonomous driving. This paper proposed an enhanced weigh-in-motion sensors system using Fabry–Pérot (F-P) cavity fiber optical technology. Laboratory testing was performed to evaluate the feasibility of the proposed system and field application was conducted as well. The laboratory results show that the traffic loads could be obtained by measuring the center wavelength changes in the embedded F-P Cavity tunable filter. The laboratory results also show that the vehicle load and the number of vehicle axles can be estimated based on the system transfer function between the dynamic loading and the wavelength variation. The field application indicates that the weighting accuracy of the proposed system could reach 94.46% for moving vehicles, and the vehicle passing speed is the potentially relevant factor. The proposed system also has the ability to estimate the number of vehicle axles and the loading position, and the precision could reach 97.1% and 300 mm, respectively.

IoT-Enabled Smart Cities: Evolution and Outlook

For the last decade the Smart City concept has been under development, fostered by the growing urbanization of the world’s population and the need to handle the challenges that such a scenario raises. During this time many Smart City projects have been executed–some as proof-of-concept, but a growing number resulting in permanent, production-level deployments, improving the operation of the city and the quality of life of its citizens. Thus, Smart Cities are still a highly relevant paradigm which needs further development before it reaches its full potential and provides robust and resilient solutions. In this paper, the focus is set on the Internet of Things (IoT) as an enabling technology for the Smart City. In this sense, the paper reviews the current landscape of IoT-enabled Smart Cities, surveying relevant experiences and city initiatives that have embedded IoT within their city services and how they have generated an impact. The paper discusses the key technologies that have been developed and how they are contributing to the realization of the Smart City. Moreover, it presents some challenges that remain open ahead of us and which are the initiatives and technologies that are under development to tackle them.

Multiple Dimensions of Smart Cities’ Infrastructure: A Review

In recent years, there has been significant focus on smart cities, on how they operate and develop, and on their technical and social challenges. The importance of infrastructure as a major pillar of support in cities, in addition to the rapid developments in smart city research, necessitate an up-to-date review of smart cities’ infrastructure issues and challenges. Traditionally, a majority of studies have focused on traffic control and management, transport network design, smart grid initiatives, IoT (Internet of Things) integration, big data, land use development, and how urbanization processes impact land use in the long run. The work presented herein proposes a novel review framework that analyzes how smart city infrastructure is related to the urbanization process while presenting developments in IoT sensor networks, big data analysis of the generated information, and green construction. A classification framework was proposed to give insights on new initiatives regarding smart city infrastructure through answering the following questions: (i) What are the various dimensions on which smart city infrastructure research focuses? (ii) What are the themes and classes associated with these dimensions? (iii) What are the main shortcomings in current approaches, and what would be a good research agenda for the future? A bibliometric analysis was conducted, presenting cluster maps that can be used to understand different research trends and refine further searches. A bibliographic analysis was then followed, presenting a review of the most relevant studies over the last five years. The method proposed serves to stress where future research into understanding smart systems, their implementation and functionality would be best directed. This research concluded that future research on the topic should conceptualize smart cities as an emergent socio-techno phenomenon.

High Speed, Localized Multi-Point Strain Measurements on a Containment Vessel at 1.7 MHz Using Swept-Wavelength Laser-Interrogated Fiber Bragg Gratings

Dynamic elastic strain in ~1.8 and 1.0 m diameter containment vessels containing a high explosive detonation was measured using an array of fiber Bragg gratings. The all-optical method, called real-time localized strain measurement, recorded the strain for 10 ms after detonation with additional measurements being sequentially made at a rate of 1.7 MHz. A swept wavelength laser source provided the repetition rate necessary for such high-speed measurements while also providing enough signal strength and bandwidth to simultaneously measure 8 or more unique points on the vessel’s surface. The data presented here arethen compared with additional diagnostics consisting of a fast spectral interferometer and an optical backscatter reflectometer to show a comparison between the local and global changes in the vessel strain, both dynamically and statically to further characterize the performance of the localized strain measurement. The results are also compared with electrical resistive strain gauges and finite element analysis simulations.

Numerical and Experimental Analysis of Matched Filter Interrogation of FBG Sensors with Large Side Lobes

This article presents the effect of fiber Bragg gratings side lobes on interrogation systems consisting of sensor and matched filters. The conducted research shows that high-value side lobe structures applied as sensors and/or filters are characterized by some interesting properties. The paper presents both numerical analysis and experimental verification of the fiber Bragg gratings (FBG) interrogation systems with matched filters for gratings containing high side lobes. Numerical modeling of Bragg structures was performed for two different cases: uniform and inverse apodization. Modification of apodization can change the side lobe reflectance level even above levels found in uniform structures. This is a case not described in the literature, especially in terms of possible applications. Transfer characteristics, i.e., the relationship between power intensity as a function of wavelength shift, were determined. A collection of gratings with spectra corresponding to those analyzed in numerical experiments were fabricated. Next, the transfer characteristics of the interrogation systems containing real FBG were determined. The properties of the proposed systems are described. It has been shown that a significant level of sidebands, which is often the subject of many drawbacks in filtering or telecommunications systems, can be an advantage. It has been demonstrated that a high level of side lobes can be used to increase the measurement range of the FBG sensor interrogation systems. It has been determined numerically and confirmed experimentally that from the point of view of the design of sensor interrogation systems, it is beneficial to combine specific pairs of gratings: one with a spectrum characterized by a low side lobe level and a second one in which the spectrum has very high side lobes.

Sensor Data Fusion in Multi-Sensor Weigh-In-Motion Systems

In this paper, we present the results of a comparison of two estimators of the gross vehicle weight (GVW) and the static load of individual axles of vehicles. The estimators were used to process measurement data derived from Multi-Sensor Weigh-In-Motion systems (MS-WIM). The term estimator is understood as an algorithm according to which the dynamic axle load measurement results are processed in order to determine the static load. The result obtained is called static load estimate. As a measure of measurement uncertainty, we adopted the standard deviation of the static load estimate. The mean value and the maximum likelihood estimators were compared. Studies were conducted using simulation methods based on synthetic data and experimental data obtained from a WIM system equipped with 16 lines of polymer axle load sensors. We have shown a substantially lower uncertainty of estimates determined using the maximum likelihood estimator. The results obtained have considerable practical significance, particularly during long-term usage of multi-sensor WIM systems.

Optimised Autocalibration Algorithm of Weigh-In-Motion Systems for Direct Mass Enforcement

Dynamic vehicle weighing systems, also known as Weigh-In-Motion (WIM), are sensitive to factors which interfere with the measurement, including weather and climate conditions. This is a result of the sensitivity of the axle load sensors used in the systems. As a result, a significant change in the precision of weighing can be observed over short periods of time (even less than 1 h). This fact is a deterrent to the use of such systems for direct mass enforcement. In this article, we present a solution for this problem using an optimised autocalibration algorithm. We show the results of simulation studies which we conducted on the proposed algorithm. These were then verified experimentally at an in-road site. We demonstrated that autocalibration of the WIM system allows for effective limitation of the sensitivity of weighing results to interfering factors. This is, however, conditioned on a sufficiently high frequency of reference vehicles crossing the WIM site. The required frequency depends on the speed of change in the concentration of influencing factors.