Road anomalies detection system evaluation

Anomalies on road pavement cause discomfort to drivers and passengers, and may cause mechanical failure or even accidents. Governments spend millions of Euros every year on road maintenance, often causing traffic jams and congestion on urban roads on a daily basis. This paper analyses the difference between the deployment of a road anomalies detection and identification system in a “conditioned” and a real world setup, where the system performed worse compared to the “conditioned” setup. It also presents a system performance analysis based on the analysis of the training data sets; on the analysis of the attributes complexity, through the application of PCA techniques; and on the analysis of the attributes in the context of each anomaly type, using acceleration standard deviation attributes to observe how different anomalies classes are distributed in the Cartesian coordinates system. Overall, in this paper, we describe the main insights on road anomalies detection challenges to support the design and deployment of a new iteration of our system towards the deployment of a road anomaly detection service to provide information about roads condition to drivers and government entities.This work was supported by European Structural and Investment Funds in the FEDER component, through the Operational Competitiveness and Internationalization Programme (COMPETE 2020) [Project No. 002797; Funding Reference: POCI-01-0247-FEDER-002797].info:eu-repo/semantics/publishedVersio

Distress detection in road pavements using neural networks

Combining Computer Vision (CV) and Anomaly Detection (AD), there is a convergence of methodologies using convolutional layers in AD architectures, which we consider an innovation in the field. The main goal of this work is to present different Artificial Neural Networks (ANN) architectures, applying them to distress detection in road pavements and comparing the results obtained in each approach. The experimented methods for AD in images include a binary classifier as a baseline, an Autoencoder (AE) and a Variational Autoencoder (VAE). Supervised and unsupervised practises are also compared, proving their utility in scenarios where there is no labelled data available. Using the VAE model in a supervised setting, it presents an excellent distinction between good and bad pavement. When labelled data is not available, using the AE model and the distribution of similarities of good pavement reconstructions to calculate the threshold is the best option with accuracy and precision above 94%. The development of these models shows that it is possible to develop an alternative solution to reduce operating costs compared to expensive commercial systems and to improve the usability compared to conventional methods of classifying road surfaces.This work has been supported by FCT – Fundação para a Ciência e Tecnologia within the R&D Units Project Scope: UIDB/00319/2020

Fuzzy System to Assess Dangerous Driving: A Multidisciplinary Approach

Dangerous driving can cause accidents, injuries and loss of life. An efficient assessment helps to identify the absence or degree of dangerous driving to take the appropriate decisions while driving. Previous studies assess dangerous driving through two approaches: (i) using electronic devices or sensors that provide objective variables (acceleration, turns and speed), and (ii) analyzing responses to questionnaires from behavioral science that provide subjective variables (driving thoughts, opinions and perceptions from the driver). However, we believe that a holistic and more realistic assessment requires a combination of both types of variables. Therefore, we propose a three-phase fuzzy system with a multidisciplinary (computer science and behavioral sciences) approach that draws on the strengths of sensors embedded in smartphones and questionnaires to evaluate driver behavior and social desirability. Our proposal combines objective and subjective variables while mitigating the weaknesses of the disciplines used (sensor reading errors and lack of honesty from respondents, respectively). The methods used are of proven reliability in each discipline, and their outputs feed a combined fuzzy system used to handle the vagueness of the input variables, obtaining a personalized result for each driver. The results obtained using the proposed system in a real scenario were efficient at 84.21%, and were validated with mobility experts’ opinions. The presented fuzzy system can support intelligent transportation systems, driving safety, or personnel selection

Diseño de un prototipo electrónico utilizando sensores acelerómetro y giroscopio para optimizar el control de velocidad y estabilidad dinámica de un vehículo

El presente trabajo tiene interés en el área del automovilismo donde se plantea el diseño de un prototipo electrónico para optimizar el control de velocidad y estabilidad dinámica de un vehículo, utilizando la información de la trayectoria vehicular. Asimismo, se analiza como objetivo principal el uso de Sensores Microelectromecánicos (MEMS), acelerómetro y giroscópico e incorporando el geoposicionamiento en tiempo real. Además, se realiza la búsqueda de aplicaciones con tecnología (MEMS), las cuales fueron incorporados en diferentes países y que contribuyen al desarrollo del prototipo electrónico. En ese sentido, se plantea metodologías mediante los ángulos de navegación, Yaw, Pitch, Roll, Sistemas de Control coordinado, Filtro de Kalman, Sistema de Posicionamiento global (GPS). Sensores Microelectromecánicos (MEMS). Así también, Captar las variables de velocidad, dirección y referencia entre el sistema de trasmisión y Frenado, Tracción a las 4 Ruedas (4WD), subviraje, sobreviraje, equilibrado, velocidad, torque, aceleración, Modulación por ancho de pulso (PWM), codificadores rotatorios y el Regulador Lineal Cuadrático (LQR). Los principales resultados obtenidos para el diseño del prototipo se logran mediante el procesamiento de las señales de inclinación y orientación a la entrada de dirección del conductor, visualizando en un monitor local y otro remoto los valores de referencia, orientación y geoposicionamiento. La velocidad de todo el sistema integrado en el vehículo será coordinada y con un frenado dinámico en las 4 ruedas. Finalmente, se Logra como resultado un sistema de navegación portátil con GPS y estabilidad de manejo al volante empleando sensores Microelectromecánicos (MEMS), a fin de evitar un siniestro vial ocacionado por la impericia de conductor.Trabajo de investigaciónCampus Lima Centr

Tire/road noise, texture, and vertical accelerations: Surface assessment of an urban road

Pavements are made up of several layers with different mechanical and functional characteristics. The correct design of the surface layerof a roadmay leadto pavements with bettercharacteristics regarding ride qualityand safety, but also pavements that may be used as a measure against noise.The use oflow-noise pavements maybe aneffective measure to reduce the generation of acoustic pollutionby roadtraffic.This work aims to assesssome functionalcharacteristicsof a rehabilitated urban street,after two months in service conditions.The pavement was fabricated with a gap-graded bituminous mixture type Stone Mastic Asphalt(SMA) with crumb rubber(CR)from end-of-life (EOL) tires.This work studies the acoustic performance of the pavement, as well as other surface characteristics such as the macrotexturedepth(MPD)and the unevenness(IRI), establishing the relationship between them and the tire/road noise at different frequencies.Finally, the mainvertical acceleration frequencies of the pavement/vehiclesystemat 50 km/h were also assessedand related to the pavement unevennessand conservation.According to the results, this mixture might be used as a noise mitigation measure within the Action Plans of some urban areas with problems related to noise. The macrotexturedepthofthe mixture contributes to its acoustic performance at low frequencies;however, its acoustic performance cannot be totally explained from a macrotexture point of vie

Training and Applying Artificial Neural Networks in Traffic Light Control: Improving the Management and Safety of Road Traffic in Tyumen (Russia)

The article presents the initial experience (spring-summer 2023) of using artificial neural networks (ANN) to improve traffic management in the large Russian city of Tyumen. Using the example of one of the intersections of the city's road network, it is shown how much transport delays are reduced when the duration of the traffic light cycle phases is quickly adjusted to the actual traffic intensity when compared with the usual previously used traffic light predictive mode. For the specific intersection of Odesskaya and Kotovskogo streets in Tyumen, considered in this article, the traffic light control mode using an ANN can significantly (by 20.6 ... 22.4%) reduce the average delay time of vehicles. It is also important that the reduction in traffic delays, which is possible with the regulation of traffic using ANN, helps to reduce stress for road users and improve road safety. The article presents historical data illustrating the dynamics of changes in the field of traffic management and road safety in Tyumen. This information confirms the thesis about the dialectic of systemic development and the need for a gradual increase in the intellectual component of traffic management in large cities. The Applications (Appendix A and Appendix B) present the code of the auxiliary procedures and functions module and the code of the main data collection module used to optimize the traffic light control mode at the experimental intersection of the Tyumen road network. The main conclusion of the study is that the use of an ANN allows for taking into account a much larger number of factors and optimizing the control of the entire object, consisting of several intersections, which is not achievable using predictive modes and local adaptive control

Connectionist systems for image processing and anomaly detection

Dissertação de mestrado integrado em Engenharia InformáticaA Inteligência Artificial (IA) e a Ciência de Dados estão cada vez mais presentes no nosso quotidiano e os benefícios que trouxeram para a sociedade nos últimos anos são notáveis. O sucesso da IA foi impulsionado pela capacidade adaptativa que as máquinas adquiriram e está estreitamente relacionada com a sua habilidade para aprender. Os sistemas conexionistas, apresentados na forma de Redes Neurais Artificiais (RNAs), que se inspiram no sistema nervoso humano, são um dos mais importantes modelos que permitem a aprendizagem. Estes são utilizados em diversas áreas, como em problemas de previsão ou classificação, apresentando resultados cada vez mais satisfatórios. Uma das áreas em que esta tecnologia se tem destacado é a Visão Computacional (Computer Vision (CV)), permitindo, por exemplo, a localização de objetos em imagens e a sua correta identificação. A Deteção de Anomalias (Anomaly Detection (AD)) é outro campo onde as RNAs vêm surgindo como uma das tecnologias para a resolução de problemas. Em cada área são utilizadas diferentes arquiteturas de acordo com o tipo de dados e o problema a resolver. Combinando o processamento de imagens e a deteção de anomalias, verifica-se uma convergência de metodologias que utilizam módulos convolucionais em arquiteturas dedicadas a AD. O objetivo principal desta dissertação é estudar as técnicas existentes nestes domínios, desenvolvendo diferentes arquiteturas e modelos, aplicando-as a casos práticos de forma a comparar os resultados obtidos em cada abordagem. O caso prático principal consiste na monitorização de pavimentos rodoviários por meio de imagens para a identificação automática de áreas degradadas. Para isso, dois protótipos de software são propostos para recolher e visualizar os dados adquiridos. O estudo de arquiteturas de RNAs para o diagnóstico da condição do asfalto por meio de imagens é o foco central no processo científico apresentado. Os métodos de Machine Learning (ML) utilizados incluem classificadores binários, Autoencoders (AEs) e Variational Autoencoders (VAEs). Para os dois últimos modelos, práticas supervisionadas e não supervisionadas são também comparadas, comprovando a sua utilidade em cenários onde não há dados rotulados disponíveis. Usando o modelo VAE num ambiente supervisionado, este apresenta uma excelente distinção entre áreas de pavimentação em boas condições e degradadas. Quando não existem dados rotulados disponíveis, a melhor opção é utilizar o modelo AE, utilizando a distribuição de semelhanças das reconstruções para calcular o threshold de separação, atingindo accuracy e precision superiores a 94%). O processo completo de desenvolvimento mostra que é possível construir uma solução alternativa para diminuir os custos de operação em relação aos sistemas comerciais existentes e melhorar a usabilidade quando comparada às soluções tradicionais. Adicionalmente, dois estudos demonstram a versatilidade dos sistemas conexionistas na resolução de problemas, nomeadamente no projeto de estruturas mecânicas, possibilitando a modelação de campos de deslocamento e pressão em placas reforçadas; e na utilização de AD para identificar locais de aglomeração de pessoas através de técnicas de crowdsensing.Artificial Intelligence (AI) and Data Science (DS) have become increasingly present in our daily lives, and the benefits it has brought to society in recent years are remarkable. The success of AI was driven by the adaptive capacity that machines gained, and it is closely related to their ability to learn. Connectionist systems, presented in the form of Artificial Neural Networks (ANNs), which are inspired by the human nervous system, are one of the principal models that allows learning. These models are used in several areas, like forecasting or classification problems, presenting increasingly satisfactory results. One area in which this technology has excelled is Com puter Vision (CV), allowing, for example, the location of objects in images and their correct identification. Anomaly Detection (AD) is another field where ANNs have been emerging as one technology for problem solving. In each area, different architectures are used according to the type of data and the problem to be solved. Combining im age processing and the finding of anomalies in this type of data, there is a convergence of methodologies using convolutional modules in architectures dedicated to AD. The main objective of this dissertation is to study the existent techniques in these domains, developing different model architectures, and applying them to practical case studies in order to compare the results obtained in each approach. The major practical use case consists of monitoring road pavements using images to automatically identify degraded areas. For that, two software prototypes are proposed to gather and visualise the acquired data. Moreover, the study of ANN architectures to diagnose the asphalt condition through images is the central focus of this work. The experimented methods for AD in images include a binary classifier network as a baseline, Autoencoders (AEs) and Variational Autoen coders (VAEs). Supervised and unsupervised practises are also compared, proving their utility also in scenarios where there is no labelled data available. Using the VAE model in a supervised setting, it presents a excellent distinction between good and bad pavement areas. When labelled data is not available, using the AE and the distribution of similarities of good pavement reconstructions to calculate the threshold is the best option with both accuracy and precision above 94%. The full development process shows it is possible to build an alternative solution to decrease the operation costs relatively to expensive commercial systems and improve usability when compared with traditional solutions. Additionally, two case studies demonstrate the versatility of connectionist systems to solve problems, namely in Mechanical Structural Design enabling the modelling of displacement and pressure fields in reinforced plates; and using AD to identify crowded places through crowd-sensing techniques

Machine Learning in Sensors and Imaging

Machine learning is extending its applications in various fields, such as image processing, the Internet of Things, user interface, big data, manufacturing, management, etc. As data are required to build machine learning networks, sensors are one of the most important technologies. In addition, machine learning networks can contribute to the improvement in sensor performance and the creation of new sensor applications. This Special Issue addresses all types of machine learning applications related to sensors and imaging. It covers computer vision-based control, activity recognition, fuzzy label classification, failure classification, motor temperature estimation, the camera calibration of intelligent vehicles, error detection, color prior model, compressive sensing, wildfire risk assessment, shelf auditing, forest-growing stem volume estimation, road management, image denoising, and touchscreens