WiFi-based urban localisation using CNNs

IEEE Conference on Intelligent Transportation Systems - ITSC 2019, 27-30/10/2019, Auckland, Nueva Zelanda.The continuous expanding scale of WiFi deployments in metropolitan areas has made possible to find WiFi access points at almost any place in our cities. Although WiFi has been mainly used for indoor localisation, there is a growing number of research in outdoor WiFi-based localisation. This paper presents a WiFi-based localisation system that takes advantage of the huge deployment of WiFi networks in urban areas. The idea is to complement localisation in zones where the GPS coverage is low, such as urban canyons. The proposed method explores the CNNs ability to handle large amounts of data and their high accuracy with reasonable computational costs. The final objective is to develop a system able to handle the large number of access points present in urban areas while preserving high accuracy and real time requirements. The system was tested in a urban environment, improving the accuracy with respect to the state-of-the-art and being able to work in real time

Pedestrian and Passenger Interaction with Autonomous Vehicles: Field Study in a Crosswalk Scenario

This study presents the outcomes of empirical investigations pertaining to human-vehicle interactions involving an autonomous vehicle equipped with both internal and external Human Machine Interfaces (HMIs) within a crosswalk scenario. The internal and external HMIs were integrated with implicit communication techniques, incorporating a combination of gentle and aggressive braking maneuvers within the crosswalk. Data were collected through a combination of questionnaires and quantifiable metrics, including pedestrian decision to cross related to the vehicle distance and speed. The questionnaire responses reveal that pedestrians experience enhanced safety perceptions when the external HMI and gentle braking maneuvers are used in tandem. In contrast, the measured variables demonstrate that the external HMI proves effective when complemented by the gentle braking maneuver. Furthermore, the questionnaire results highlight that the internal HMI enhances passenger confidence only when paired with the aggressive braking maneuver.Comment: Submitted to the IEEE TIV; 13 pages, 13 figures, 7 tables. arXiv admin note: text overlap with arXiv:2307.1270

Diseño de un sistema de detección de intersecciones para la navegación autónoma de un vehículo inteligente

Este TFG se centra en el estudio, desarrollo y evaluación de un sistema inteligente, donde conocidos los diferentes elementos que rodean a un vehículo y su contextualización respecto a la vía, permite evaluar la presencia inminente de una intersección y clasificarla según su tipología. Para ello, se desarrolla un identificador-clasificador inteligente mediante la herramienta de computación conocida como MATLAB, junto a la toolbox de Fuzzy, a partir de la cual se analizan, diseñan y simulan escenarios con alta complejidad. Todo ello, a través de reglas que incluyen operadores lógicos, que plasman razonamientos cercanos a la heurística humana, llevando a cabo un sistema basado en lógica difusa. En el transcurso del trabajo, se estudiarán y utilizarán diferentes tipos de entradas para nuestro identificador-clasificador, obtenidas de algoritmos de deep learning, como puede ser la segmentación semántica de la vía a través de imágenes, que nos permite generar modelos donde se da relevancia a diferentes elementos del entorno.This TFG focuses on the study, development and evaluation of an intelligent system, where knowing the different elements that surround a vehicle and its contextualization with respect to the road, allows to evaluate the imminent presence of an intersection and classify it according to its typology. To do this, an intelligent identifier-classifier is developed using the computer tool known as MATLAB, together with the Fuzzy toolbox, from which scenarios with high complexity are analyzed, designed and simulated. All this, through rules that include logical operators, which reflect reasoning close to human heuristics, carrying out a system based on fuzzy logic. In the course of the work, different types of inputs for our identifier-classifier will be studied and used, obtained from deep learning algorithms, such as the semantic segmentation of the path through images, which allows us to generate models where relevance is given to different elements of the environment.Grado en Ingeniería en Electrónica y Automática Industria