Towards Social Autonomous Vehicles: Efficient Collision Avoidance Scheme Using Richardson's Arms Race Model

Background Road collisions and casualties pose a serious threat to commuters around the globe. Autonomous Vehicles (AVs) aim to make the use of technology to reduce the road accidents. However, the most of research work in the context of collision avoidance has been performed to address, separately, the rear end, front end and lateral collisions in less congested and with high inter-vehicular distances. Purpose The goal of this paper is to introduce the concept of a social agent, which interact with other AVs in social manners like humans are social having the capability of predicting intentions, i.e. mentalizing and copying the actions of each other, i.e. mirroring. The proposed social agent is based on a human-brain inspired mentalizing and mirroring capabilities and has been modelled for collision detection and avoidance under congested urban road traffic. Method We designed our social agent having the capabilities of mentalizing and mirroring and for this purpose we utilized Exploratory Agent Based Modeling (EABM) level of Cognitive Agent Based Computing (CABC) framework proposed by Niazi and Hussain. Results Our simulation and practical experiments reveal that by embedding Richardson's arms race model within AVs, collisions can be avoided while travelling on congested urban roads in a flock like topologies. The performance of the proposed social agent has been compared at two different levels.Comment: 48 pages, 21 figure

Working With Incremental Spatial Data During Parallel (GPU) Computation

Central to many complex systems, spatial actors require an awareness of their local environment to enable behaviours such as communication and navigation. Complex system simulations represent this behaviour with Fixed Radius Near Neighbours (FRNN) search. This algorithm allows actors to store data at spatial locations and then query the data structure to find all data stored within a fixed radius of the search origin. The work within this thesis answers the question: What techniques can be used for improving the performance of FRNN searches during complex system simulations on Graphics Processing Units (GPUs)? It is generally agreed that Uniform Spatial Partitioning (USP) is the most suitable data structure for providing FRNN search on GPUs. However, due to the architectural complexities of GPUs, the performance is constrained such that FRNN search remains one of the most expensive common stages between complex systems models. Existing innovations to USP highlight a need to take advantage of recent GPU advances, reducing the levels of divergence and limiting redundant memory accesses as viable routes to improve the performance of FRNN search. This thesis addresses these with three separate optimisations that can be used simultaneously. Experiments have assessed the impact of optimisations to the general case of FRNN search found within complex system simulations and demonstrated their impact in practice when applied to full complex system models. Results presented show the performance of the construction and query stages of FRNN search can be improved by over 2x and 1.3x respectively. These improvements allow complex system simulations to be executed faster, enabling increases in scale and model complexity

Implementing sub steps in a parallel automata cellular model

Computer simulations using Cellular Automata (CA) have been applied with considerable success in different scientific areas. In this work we use CA in order to specify and implement a simulation model that allows to investigate behavioural dynamics for pedestrians in an emergency evacuation. A CA model is discrete and handled by rules. However several aspects of the crown behaviour should appear as a continuous phenomenon. In this paper we implement the sub steps technique in order to solve an unexpected phenomenon: the formation of holes (empty cells) both around the exit and mixed in the crowd evacuation. The holes occur when individuals of consecutive cells want move towards an exit. Additionally, the methodology allowed us to include, as a new parameter of the model, speeds associated to the pedestrians. Due to the incorporation of new features, the model complexity increases. We apply a parallel technique in order to accelerate the simulation and take advantage of modern computer architectures. We test our approaches to several environment configurations achieving important reduction of simulation time and the total evacuation time.Presentado en el XI Workshop Procesamiento Distribuido y Paralelo (WPDP)Red de Universidades con Carreras en Informática (RedUNCI

A novel application of deep learning with image cropping: a smart city use case for flood monitoring

© 2020, The Author(s). Event monitoring is an essential application of Smart City platforms. Real-time monitoring of gully and drainage blockage is an important part of flood monitoring applications. Building viable IoT sensors for detecting blockage is a complex task due to the limitations of deploying such sensors in situ. Image classification with deep learning is a potential alternative solution. However, there are no image datasets of gullies and drainages. We were faced with such challenges as part of developing a flood monitoring application in a European Union-funded project. To address these issues, we propose a novel image classification approach based on deep learning with an IoT-enabled camera to monitor gullies and drainages. This approach utilises deep learning to develop an effective image classification model to classify blockage images into different class labels based on the severity. In order to handle the complexity of video-based images, and subsequent poor classification accuracy of the model, we have carried out experiments with the removal of image edges by applying image cropping. The process of cropping in our proposed experimentation is aimed to concentrate only on the regions of interest within images, hence leaving out some proportion of image edges. An image dataset from crowd-sourced publicly accessible images has been curated to train and test the proposed model. For validation, model accuracies were compared considering model with and without image cropping. The cropping-based image classification showed improvement in the classification accuracy. This paper outlines the lessons from our experimentation that have a wider impact on many similar use cases involving IoT-based cameras as part of smart city event monitoring platforms

Implementing sub steps in a parallel automata cellular model

Computer simulations using Cellular Automata (CA) have been applied with considerable success in different scientific areas. In this work we use CA in order to specify and implement a simulation model that allows to investigate behavioural dynamics for pedestrians in an emergency evacuation. A CA model is discrete and handled by rules. However several aspects of the crown behaviour should appear as a continuous phenomenon. In this paper we implement the sub steps technique in order to solve an unexpected phenomenon: the formation of holes (empty cells) both around the exit and mixed in the crowd evacuation. The holes occur when individuals of consecutive cells want move towards an exit. Additionally, the methodology allowed us to include, as a new parameter of the model, speeds associated to the pedestrians. Due to the incorporation of new features, the model complexity increases. We apply a parallel technique in order to accelerate the simulation and take advantage of modern computer architectures. We test our approaches to several environment configurations achieving important reduction of simulation time and the total evacuation time.Presentado en el XI Workshop Procesamiento Distribuido y Paralelo (WPDP)Red de Universidades con Carreras en Informática (RedUNCI

Contribution to design a communication framework for vehicular ad hoc networks in urban scenarios

The constant mobility of people, the growing need to be always connected, the large number of vehicles that nowadays can be found in the roads and the advances in technology make Vehicular Ad hoc Networks (VANETs) be a major area of research. Vehicular Ad hoc Networks are a special type of wireless Mobile Ad hoc Networks (MANETs), which allow a group of mobile nodes configure a temporary network and maintain it without the need of a fixed infrastructure. A vehicular network presents some specific characteristics, as the very high speed of nodes. Due to this high speed the topology changes are frequent and the communication links may last only a few seconds. Smart cities are now a reality and have a direct relationship with vehicular networks. With the help of existing infrastructure such as traffic lights, we propose a scheme to update and analyse traffic density and a warning system to spread alert messages. With this, traffic lights assist vehicular networks to take proper decisions. This would ensure less congested streets. It would also be possible that the routing protocol forwards data packets to vehicles on streets with enough neighbours to increase the possibility of delivering the packets to destination. Sharing updated, reliable and real-time information, about traffic conditions, weather or security alerts, increases the need of algorithms for the dissemination of information that take into account the main beneffits and constraints of these networks. For all this, routing protocols for vehicular networks have the difficult task to select and establish transmission links to send the data packets from source to destination through multiple nodes using intermediate vehicles efficiently. The main objective of this thesis is to provide improvements in the communication framework for vehicular networks to improve decisions to select next hops in the moment to send information, in this way improving the exchange of information to provide suitable communication to minimize accidents, reduce congestion, optimize resources for emergencies, etc. Also, we include intelligence to vehicles at the moment to take routing decisions. Making them map-aware, being conscious of the presence of buildings and other obstacles in urban environments. Furthermore, our proposal considers the decision to store packets for a maximum time until finding other neighbouring nodes to forward the packets before discarding them. For this, we propose a protocol that considers multiple metrics that we call MMMR (A Multimetric, Map-Aware Routing Protocol ). MMMR is a protocol based on geographical knowledge of the environment and vehicle location. The metrics considered are the distance, the density of vehicles in transmission range, the available bandwidth and the future trajectory of the neighbouring nodes. This allows us to have a complete view of the vehicular scenario to anticipate the driver about possible changes that may occur. Thus, a node can select a node among all its neighbours, which is the best option to increase the likelihood of successful packet delivery, minimizing time and offering a level of quality and service. In the same way, being aware of the increase of information in wireless environments, we analyse the possibility of offering anonymity services. We include a mechanism of anonymity in routing protocols based on the Crowd algorithm, which uses the idea of hiding the original source of a packet. This allowed us to add some level of anonymity on VANET routing protocols. The analytical modeling of the available bandwidth between nodes in a VANET, the use of city infrastructure in a smart way, the forwarding selection in data routing byvehicles and the provision of anonymity in communications, are issues that have been addressed in this PhD thesis. In our research work we provide contributions to improve the communication framework for Vehicular Ad hoc Networks obtaining benefits toenhance the everyday of the population.La movilidad constante de las personas y la creciente necesidad de estar conectados en todo momento ha hecho de las redes vehiculares un área cuyo interés ha ido en aumento. La gran cantidad de vehículos que hay en la actualidad, y los avances tecnológicos han hecho de las redes vehiculares (VANETS, Vehicular Ad hoc Networks) un gran campo de investigación. Las redes vehiculares son un tipo especial de redes móviles ad hoc inalámbricas, las cuales, al igual que las redes MANET (Mobile Ad hoc Networks), permiten a un grupo de nodos móviles tanto configurar como mantener una red temporal por si mismos sin la necesidad de una infraestructura fija. Las redes vehiculares presentan algunas características muy representativas, por ejemplo, la alta velocidad que pueden alcanzar los nodos, en este caso vehículos. Debido a esta alta velocidad la topología cambia frecuentemente y la duración de los enlaces de comunicación puede ser de unos pocos segundos. Estas redes tienen una amplia área de aplicación, pudiendo tener comunicación entre los mismos nodos (V2V) o entre los vehículos y una infraestructura fija (V2I). Uno de los principales desafíos existentes en las VANET es la seguridad vial donde el gobierno y fabricantes de automóviles han centrado principalmente sus esfuerzos. Gracias a la rápida evolución de las tecnologías de comunicación inalámbrica los investigadores han logrado introducir las redes vehiculares dentro de las comunicaciones diarias permitiendo una amplia variedad de servicios para ofrecer. Las ciudades inteligentes son ahora una realidad y tienen una relación directa con las redes vehiculares. Con la ayuda de la infraestructura existente, como semáforos, se propone un sistema de análisis de densidad de tráfico y mensajes de alerta. Con esto, los semáforos ayudan a la red vehicular en la toma de decisiones. Así se logrará disponer de calles menos congestionadas para hacer una circulación más fluida (lo cual disminuye la contaminación). Además, sería posible que el protocolo de encaminamiento de datos elija vehículos en calles con suficientes vecinos para incrementar la posibilidad de entregar los paquetes al destino (minimizando pérdidas de información). El compartir información actualizada, confiable y en tiempo real sobre el estado del tráfico, clima o alertas de seguridad, aumenta la necesidad de algoritmos de difusión de la información que consideren los principales beneficios y restricciones de estas redes. Así mismo, considerar servicios críticos que necesiten un nivel de calidad y servicio es otro desafío importante. Por todo esto, un protocolo de encaminamiento para este tipo de redes tiene la difícil tarea de seleccionar y establecer enlaces de transmisión para enviar los datos desde el origen hacia el destino vía múltiples nodos utilizando vehículos intermedios de una manera eficiente. El principal objetivo de esta tesis es ofrecer mejoras en los sistemas de comunicación vehicular que mejoren la toma de decisiones en el momento de realizar el envío de la información, con lo cual se mejora el intercambio de información para poder ofrecer comunicación oportuna que minimice accidentes, reduzca atascos, optimice los recursos destinados a emergencias, etc. Así mismo, incluimos más inteligencia a los coches en el momento de tomar decisiones de encaminamiento de paquetes. Haciéndolos conscientes de la presencia de edificios y otros obstáculos en los entornos urbanos. Así como tomar la decisión de guardar paquetes durante un tiempo máximo de modo que se encuentre otros nodos vecinos para encaminar paquetes de información antes de descartarlo. Para esto, proponemos un protocolo basado en múltiples métricas (MMMR, A Multimetric, Map-aware Routing Protocol ) que es un protocolo geográfio basado en el conocimiento del entorno y localización de los vehículos. Las métricas consideradas son la distancia, la densidad de vehículos en el área de transmisión, el ancho de banda disponible y la trayectoria futura de los nodos vecinos. Esto nos permite tener una visión completa del escenario vehicular y anticiparnos a los posibles cambios que puedan suceder. Así, un nodo podrá seleccionar aquel nodo entre todos sus vecinos posibles que sea la mejor opción para incrementar la posibilidad de entrega exitosa de paquetes, minimizando tiempos y ofreciendo un cierto nivel de calidad y servicio. De la misma manera, conscientes del incremento de información que circula por medios inalámbricos, se analizó la posibilidad de servicios de anonimato. Incluimos pues un mecanismo de anonimato en protocolos de encaminamiento basado en el algoritmo Crowd, que se basa en la idea de ocultar la fuente original de un paquete. Esto nos permitió añadir cierto nivel de anonimato que pueden ofrecer los protocolos de encaminamiento. El modelado analítico del ancho de banda disponible entre nodos de una VANET, el uso de la infraestructura de la ciudad de una manera inteligente, la adecuada toma de decisiones de encaminamiento de datos por parte de los vehículos y la disposición de anonimato en las comunicaciones, son problemas que han sido abordados en este trabajo de tesis doctoral que ofrece contribuciones a la mejora de las comunicaciones en redes vehiculares en entornos urbanos aportando beneficios en el desarrollo de la vida diaria de la población

Multiple classifiers fusion and CNN feature extraction for handwritten digits recognition

Handwritten digits recognition has been treated as a multi-class classification problem in the machine learning context, where each of the ten digits (0-9) is viewed as a class and the machine learning task is essentially to train a classifier that can effectively discriminate the ten classes. In practice, it is very usual that the performance of a single classifier trained by using a standard learning algorithm is varied on different data sets, which indicates that the same learning algorithm may train strong classifiers on some data sets but weak classifiers may be trained on other data sets. It is also possible that the same classifier shows different performance on different test sets, especially when considering the case that image instances can be highly diverse due to the different handwriting styles of different people on the same digits. In order to address the above issue, development of ensemble learning approaches have been very necessary to improve the overall performance and make the performance more stable on different data sets. In this paper, we propose a framework that involves CNN based feature extraction from the MINST data set and algebraic fusion of multiple classifiers trained on different feature sets, which are prepared through feature selection applied to the original feature set extracted using CNN. The experimental results show that the classifiers fusion can achieve the classification accuracy of ≥ 98%

Modeling flocks with perceptual agents from a dynamicist perspective

Computational simulations of flocks and crowds have typically been processed by a set of logic or syntactic rules. In recent decades, a new generation of systems has emerged from dynamicist approaches in which the agents and the environment are treated as a pair of dynamical systems coupled informationally and mechanically. Their spontaneous interactions allow them to achieve the desired behavior. The main proposition assumes that the agent does not need a full model or to make inferences before taking actions; rather, the information necessary for any action can be derived from the environment with simple computations and very little internal state. In this paper, we present a simulation framework in which the agents are endowed with a sensing device, an oscillator network as controller and actuators to interact with the environment. The perception device is designed as an optic array emulating the principles of the animal retina, which assimilates stimuli resembling optic flow to be captured from the environment. The controller modulates informational variables to action variables in a sensory-motor flow. Our approach is based on the Kuramoto model that describes mathematically a network of coupled phase oscillators and the use of evolutionary algorithms, which is proved to be capable of synthesizing minimal synchronization strategies based on the dynamical coupling between agents and environment. We carry out a comparative analysis with classical implementations taking into account several criteria. It is concluded that we should consider replacing the metaphor of symbolic information processing by that of sensory-motor coordination in problems of multi-agent organizations