The Application of Sensors on Guardrails for the Purpose of Real Time Impact Detection

The United States roadway system has deteriorated over time due to its age, increasing delays in completing preventative maintenance, and the lack of timely repairs following damage to the infrastructure. Proper asset management drives the need for generalized methods to integrate new sensing capabilities into existing Intelligent Transportation Systems in a time efficient and cost effective manner. In this thesis, we present a methodology for the deployment of new sensors into an existing ITS system. The proposed methodology employs a three phase approach that incorporates data modeling, spatial analysis in Geographic Information Systems, and cost optimization to provide enhanced decision support when deploying new sensing capabilities within an existing ITS. Additionally, we also demonstrate the usefulness of computing while integrating these new sensors using a guardrail sensor case study and focusing on data modeling. The results of the three phase methodology demonstrate an effective means for planning new sensor deployments by analyzing tradeoffs in equipment selection yielding the minimum cost solution for a given set of requirements. Furthermore, the results of the data models demonstrate necessary considerations that must be made with a systems engineering method. The data models accomplish this while accounting for asset management principles taking a systematic approach and incorporating engineering principles

Improving Safety on Construction Sites Using BIM-Based Dynamic Virtual Fences and Ultra-Wideband Technology

The identification of potential accidents on construction sites has been a major concern in the construction industry and it needs a proactive safety plan to reduce the risk of accidents. There are no efficient methods for checking if safety measures are taken properly on construction sites. Consequently, workers on site are not given enough awareness about dangerous areas. In addition, construction sites are dynamic and on-site situations are changing in terms of permanent and temporary structures and facilities. This information can be represented using Building Information Modeling (BIM). The present research aims to investigate a new method for the automatic generation of Dynamic Virtual Fences (DVFs) as part of a BIM-based prevention program for construction safety following the Safety Code of Quebec Provence in Canada. First, the Safety Code is reviewed to identify the information that has spatial aspects and can be represented in BIM. Then, a method is proposed for automatic identification of falling and collision risks to generate DVFs for them. In this method, workspaces are generated in BIM based on Work Breakdown Structure (WBS) deliverables, the project schedule, the dimensions of equipment, and the geometry of the building. One set of DVFs for collision prevention is generated based on the defined workspaces. Another set of DVFs is generated where physical barriers are needed for fall prevention. The generated DVFs are used coupled with Real-time Location System (RTLS) tracking of workers and physical fences to check safety requirements and to provide safety warnings

FMCW Radar with Enhanced Resolution and Processing Time by Beam Switching

We present the design of a novel K-band radar architecture for short-range target detection. Applications include direction finding systems and automotive radar. The developed system is compact and low cost and employs substrate-integrated-waveguide (SIW) antenna arrays and a $4\times 4$ Butler matrix (BM) beamformer. In particular, the proposed radar transmits a frequency modulated continuous-wave (FMCW) signal at 24 GHz, scanning the horizontal plane by switching the four input ports of the BM in time. Also, in conjunction with a new processing method for the received radar signals, the architecture is able to provide enhanced resolution at reduced computational burden and when compared to more standard single-input multiple-output (SIMO) and multiple-input multiple-output (MIMO) systems. The radar performance has also been measured in an anechoic chamber and results have been analyzed by illuminating and identifying test targets which are 2° apart, while also making comparisons to SIMO and MIMO FMCW radars. Moreover, the proposed radar architecture, by appropriate design, can also be scaled to operate at other microwave and millimeter-wave frequencies, while also providing a computationally efficient multi-channel radar signal processing platform

A Highly Accurate Deep Learning Based Approach For Developing Wireless Sensor Network Middleware

Despite the popularity of wireless sensor networks (WSNs) in a wide range of applications, the security problems associated with WSNs have not been completely resolved. Since these applications deal with the transfer of sensitive data, protection from various attacks and intrusions is essential. From the current literature, we observed that existing security algorithms are not suitable for large-scale WSNs due to limitations in energy consumption, throughput, and overhead. Middleware is generally introduced as an intermediate layer between WSNs and the end user to address security challenges. However, literature suggests that most existing middleware only cater to intrusions and malicious attacks at the application level rather than during data transmission. This results in loss of nodes during data transmission, increased energy consumption, and increased overhead. In this research, we introduce an intelligent middleware based on an unsupervised learning technique called the Generative Adversarial Networks (GANs) algorithm. GANs contain two networks: a generator (G) network and a discriminator (D) network. The G network generates fake data that is identical to the data from the sensor nodes; it combines fake and real data to confuse the adversary and stop them from differentiating between the two. This technique completely eliminates the need for fake sensor nodes, which consume more power and reduce both throughput and the lifetime of the network. The D network contains multiple layers that have the ability to differentiate between real and fake data. The output intended for this algorithm shows an actual interpretation of the data that is securely communicated through the WSN. The framework is implemented in Python with experiments performed using Keras. The results illustrate that the suggested algorithm not only improves the accuracy of the data but also enhances its security by protecting it from attacks. Data transmission from the WSN to the end user then becomes much more secure and accurate compared to conventional techniques. Simulation results show that the proposed technique provides higher throughput and increases successful data rates while keeping the energy consumption low

System Design of Advanced Multi-Beam and Multi-Range Automotive Radar

학위논문 (박사)-- 서울대학교 대학원 : 전기·컴퓨터공학부, 2017. 2. 김성철.As the number of vehicles on the road is increased, the incidence of traffic accident is gradually increased and the number of death on roads is also increased. Most accidents are due to carelessness of the driver. If the vehicle can actively recognize the dangerous situation and alert the driver to avoid accident, it will be a great help to the driver. As concern for safety and driver assistance increases, needs for driver assistance system (DAS) are consistently increasing. Moreover, with the grooming demand for autonomous driving, there has been paid a great attention to the incorporation of multiple sensors. Various sensors for safety and convenience are being introduced for automobiles. The detection performance of the automotive radar looks outstanding compared to other sensors such as Lidar, camera, and ultrasonic sensors, in poor weather conditions or environmental conditions of the roads. Among many applications using automotive radars, the adaptive cruise control (ACC) and the autonomous emergency braking (AEB) using forward looking radars are the most basic functions for safety and convenience. Using ACC and AEB functions, drivers can be guaranteed safety as well as convenience when visibility is poor under bad weather conditions. Generally, the radar system for ACC and AEB had been composed of singe longrange radar (LRR) and two of short-range radar (SRR) and the system cost was very expensive. However, the cost can be lowered by the concept of multi-beam, multirange (MBMR) radar which consist of integrated narrow long range beam and wide short range beam in a single radar sensor. In this dissertation, we propose an advanced MBMR radar for ACC and AEB using 77 GHz band and highly integrated RF ICs. The detection specifications are investii gated base on theoretical radar principles and effective design concepts are suggested to satisfy the specifications. We implemented an actually working forward looking MBMR radar and performed experiments to verify the detection performance. To overcome the limitation of radar hardware resources for cost-effective design, we propose novel signal processing schemes to recognize environment on roads which are regarded as impossible with automotive radar. Characteristics of an iron tunnel which deteriorate the detection performance of the radar are analyzed and a measure for the recognition is proposed. Moreover, the recognition method is expanded to harmonic clutters which are caused by man-made structures on roads containing periodic structures such as iron tunnels, guardrails, and sound-proof wall. The harmonic clutter suppression method is also proposed to enhance the quality of the received signal and improve the detection performance of the radar. All experiments are performed using the proposed MBMR radar to verify the detection performance and the usefulness of proposed signal processing methods for recognition and suppression of clutters on roads.1 Introduction 1 2 A Multi-Beam and Multi-Range FMCW Radar using 77 GHz Frequency Band for ACC and AEB 6 2.1 Introduction 6 2.2 System Design of Advanced MBMR Radar 7 2.3 Waveform and Signal Processing Structure Design 14 2.4 Advanced Singal Processing Technique for AEB 19 2.5 Design Results 20 2.6 Experimental Results 22 2.6.1 Anechoic Chamber 22 2.6.2 Field Test 27 2.7 Summary 29 3 Iron-tunnel Recognition 30 3.1 Introduction 30 3.2 Iron-Tunnel Recognition 32 3.2.1 Radar Model 32 3.2.2 Spectral Characteristics of an Iron-Tunnel 34 3.2.3 Measuring Spectrum Spreading 40 3.3 Experimental Result 45 3.3.1 Iron-Tunnel Recognition 45 3.3.2 Early Target Detection and Prevention of Target Drop 49 3.4 Summary 53 4 Clutter Suppression 55 4.1 Introduction 55 4.2 Clutter Recognition 57 4.2.1 Radar Model 57 4.2.2 Spectral Analysis of Road Environment 62 4.2.3 Proposed Clutter-recognition Method (Measuring Harmonics of Clutter) 64 4.3 Clutter Suppression 69 4.3.1 Proposed clutter suppression method 69 4.3.2 Verification using real data 71 4.4 Experimental results 74 4.5 Summary 81 5 Conclusion and Future Works 82 Bilbliography 85 Abstract (In Korean) 89Docto

INSPIRE Newsletter Fall 2020

https://scholarsmine.mst.edu/inspire-newsletters/1007/thumbnail.jp

Deteção de colisões em rails de estradas

Os dispositivos de retenção (rails) são estruturas básicas de segurança rodoviária, sendo importante manter a sua integridade através de uma manutenção adequada. A verificação destas estruturas, por parte das empresas de manutenção, é efetuada manualmente. A monotorização remota destes dispositivos permite uma melhor deteção de qualquer impacto, de modo a realizar uma reparação/substituição mais eficaz. Uma rede de sensores sem fios, de baixa potência e de baixo custo, pode ser implementada para a deteção remota de qualquer colisão. Nesta dissertação, está presente o desenvolvimento de uma ligação ponto-a-ponto desta rede e a sua possível implementação global. Esta ligação possui uma comunicação baseada em LoRa e uma deteção de impacto através de um acelerómetro MEMS.The guardrails are basic road safety structures and it’s important to maintain their integrity through proper maintenance. The verification of these structures is carried out manually, by the maintenance companies. A remote monitorization of these devices allows for a better detection, of any impact, and a more effective repair/replacement. A low-cost, low-power wireless sensor network can be implemented for remote collision detection. This dissertation presents the development of a point-to-point connection of this network and its possible global implementation. This connection features LoRa communication and impact detection via a MEMS accelerometer.Mestrado em Engenharia Eletrónica e Telecomunicaçõe

Performance and Challenges of Service-Oriented Architecture for Wireless Sensor Networks

Wireless Sensor Networks (WSNs) have become essential components for a variety of environmental, surveillance, military, traffic control, and healthcare applications. These applications face critical challenges such as communication, security, power consumption, data aggregation, heterogeneities of sensor hardware, and Quality of Service (QoS) issues. Service-Oriented Architecture (SOA) is a software architecture that can be integrated with WSN applications to address those challenges. The SOA middleware bridges the gap between the high-level requirements of different applications and the hardware constraints of WSNs. This survey explores state-of-the-art approaches based on SOA and Service-Oriented Middleware (SOM) architecture that provide solutions for WSN challenges. The categories of this paper are based on approaches of SOA with and without middleware for WSNs. Additionally, features of SOA and middleware architectures for WSNs are compared to achieve more robust and efficient network performance. Design issues of SOA middleware for WSNs and its characteristics are also highlighted. The paper concludes with future research directions in SOM architecture to meet all requirements of emerging application of WSNs.https://doi.org/10.3390/s1703053

Reliable localization methods for intelligent vehicles based on environment perception

Mención Internacional en el título de doctorIn the near past, we would see autonomous vehicles and Intelligent Transport Systems (ITS) as a potential future of transportation. Today, thanks to all the technological advances in recent years, the feasibility of such systems is no longer a question. Some of these autonomous driving technologies are already sharing our roads, and even commercial vehicles are including more Advanced Driver-Assistance Systems (ADAS) over the years. As a result, transportation is becoming more efficient and the roads are considerably safer. One of the fundamental pillars of an autonomous system is self-localization. An accurate and reliable estimation of the vehicle’s pose in the world is essential to navigation. Within the context of outdoor vehicles, the Global Navigation Satellite System (GNSS) is the predominant localization system. However, these systems are far from perfect, and their performance is degraded in environments with limited satellite visibility. Additionally, their dependence on the environment can make them unreliable if it were to change. Accordingly, the goal of this thesis is to exploit the perception of the environment to enhance localization systems in intelligent vehicles, with special attention to their reliability. To this end, this thesis presents several contributions: First, a study on exploiting 3D semantic information in LiDAR odometry is presented, providing interesting insights regarding the contribution to the odometry output of each type of element in the scene. The experimental results have been obtained using a public dataset and validated on a real-world platform. Second, a method to estimate the localization error using landmark detections is proposed, which is later on exploited by a landmark placement optimization algorithm. This method, which has been validated in a simulation environment, is able to determine a set of landmarks so the localization error never exceeds a predefined limit. Finally, a cooperative localization algorithm based on a Genetic Particle Filter is proposed to utilize vehicle detections in order to enhance the estimation provided by GNSS systems. Multiple experiments are carried out in different simulation environments to validate the proposed method.En un pasado no muy lejano, los vehículos autónomos y los Sistemas Inteligentes del Transporte (ITS) se veían como un futuro para el transporte con gran potencial. Hoy, gracias a todos los avances tecnológicos de los últimos años, la viabilidad de estos sistemas ha dejado de ser una incógnita. Algunas de estas tecnologías de conducción autónoma ya están compartiendo nuestras carreteras, e incluso los vehículos comerciales cada vez incluyen más Sistemas Avanzados de Asistencia a la Conducción (ADAS) con el paso de los años. Como resultado, el transporte es cada vez más eficiente y las carreteras son considerablemente más seguras. Uno de los pilares fundamentales de un sistema autónomo es la autolocalización. Una estimación precisa y fiable de la posición del vehículo en el mundo es esencial para la navegación. En el contexto de los vehículos circulando en exteriores, el Sistema Global de Navegación por Satélite (GNSS) es el sistema de localización predominante. Sin embargo, estos sistemas están lejos de ser perfectos, y su rendimiento se degrada en entornos donde la visibilidad de los satélites es limitada. Además, los cambios en el entorno pueden provocar cambios en la estimación, lo que los hace poco fiables en ciertas situaciones. Por ello, el objetivo de esta tesis es utilizar la percepción del entorno para mejorar los sistemas de localización en vehículos inteligentes, con una especial atención a la fiabilidad de estos sistemas. Para ello, esta tesis presenta varias aportaciones: En primer lugar, se presenta un estudio sobre cómo aprovechar la información semántica 3D en la odometría LiDAR, generando una base de conocimiento sobre la contribución de cada tipo de elemento del entorno a la salida de la odometría. Los resultados experimentales se han obtenido utilizando una base de datos pública y se han validado en una plataforma de conducción del mundo real. En segundo lugar, se propone un método para estimar el error de localización utilizando detecciones de puntos de referencia, que posteriormente es explotado por un algoritmo de optimización de posicionamiento de puntos de referencia. Este método, que ha sido validado en un entorno de simulación, es capaz de determinar un conjunto de puntos de referencia para el cual el error de localización nunca supere un límite previamente fijado. Por último, se propone un algoritmo de localización cooperativa basado en un Filtro Genético de Partículas para utilizar las detecciones de vehículos con el fin de mejorar la estimación proporcionada por los sistemas GNSS. El método propuesto ha sido validado mediante múltiples experimentos en diferentes entornos de simulación.Programa de Doctorado en Ingeniería Eléctrica, Electrónica y Automática por la Universidad Carlos III de MadridSecretario: Joshué Manuel Pérez Rastelli.- Secretario: Jorge Villagrá Serrano.- Vocal: Enrique David Martí Muño

Static Background Removal in Vehicular Radar: Filtering in Azimuth-Elevation-Doppler Domain

A significant challenge in autonomous driving systems lies in image understanding within complex environments, particularly dense traffic scenarios. An effective solution to this challenge involves removing the background or static objects from the scene, so as to enhance the detection of moving targets as key component of improving overall system performance. In this paper, we present an efficient algorithm for background removal in automotive radar applications, specifically utilizing a frequency-modulated continuous wave (FMCW) radar. Our proposed algorithm follows a three-step approach, encompassing radar signal preprocessing, three-dimensional (3D) ego-motion estimation, and notch filter-based background removal in the azimuth-elevation-Doppler domain. To begin, we model the received signal of the FMCW multiple-input multiple-output (MIMO) radar and develop a signal processing framework for extracting four-dimensional (4D) point clouds. Subsequently, we introduce a robust 3D ego-motion estimation algorithm that accurately estimates radar ego-motion speed, accounting for Doppler ambiguity, by processing the point clouds. Additionally, our algorithm leverages the relationship between Doppler velocity, azimuth angle, elevation angle, and radar ego-motion speed to identify the spectrum belonging to background clutter. Subsequently, we employ notch filters to effectively filter out the background clutter. The performance of our algorithm is evaluated using both simulated data and extensive experiments with real-world data. The results demonstrate its effectiveness in efficiently removing background clutter and enhacing perception within complex environments. By offering a fast and computationally efficient solution, our approach effectively addresses challenges posed by non-homogeneous environments and real-time processing requirements