Persona:
González Boticario, Jesús

Foto de perfil
Dirección de correo electrónico
ORCID
0000-0003-4949-9220
Fecha de nacimiento
Proyectos de investigación
Unidades organizativas
Puesto de trabajo
Apellidos
González Boticario
Nombre de pila
Jesús
Nombre

Filtros

2024 - 20252
Restablecer filtros

Ajustes

Autor: Fernandez Matellan, Raul ×

Resultados de la búsqueda

Mostrando 1 - 3 de 3
  • Miniatura
    Publicación
    Improving autonomous vehicle automation through human-system interaction
    (EUROSIS) Fernandez Matellan, Raul; Martin Gomez, David; Tena Gago, David; Puertas Ramírez, David; González Boticario, Jesús
    Self-driving cars (a.k.a. Autonomous Vehicles) have many challenges to tackle before having them fully deployed in our roads and cities. A critical one, which has been somehow neglected till recently, is to consider the driver in the system-user loop of vehicle performance. The purpose here is to tackle some of the current pending challenges involved in scaling up the level of autonomy of these systems. We have designed two user-vehicle experiences in two different sites with a common methodology that serves as an umbrella to collect all features required to model the driver-user. These two sites allow us to contrast and fine-tune this modelling issue. The approach consists in following a Learning Apprentice approach, where both the user behaviour and the system behaviour are learned and improved in a symbiotic ecosystem. This paper focuses on discussing the advantages of this approach and the main issues that require further research.
  • Miniatura
    Publicación
    Fusion of physiological signals for modeling driver awareness levels in conditional autonomous vehicles using semi-supervised learning
    (IEEE, 2024-10-11) Fernandez Matellan, Raul; Puertas Ramírez, David; Martín Gómez, David; González Boticario, Jesús
    The evolution of autonomous vehicles (AVs) requires a paradigm shift towards the integration of human factors to improve safety and efficiency at levels 2,3 and 4 of automation. This paper presents a comparison of three different fusion technologies (Low-Level fusion, Medium-Level fusion, and a hybrid fusion), highlighting the critical role of multimodal data integration and semi-supervised learning in predicting and adapting to levels of driver awareness. Our approach uses semi-supervised learning to deal with the data labelling problem, using unlabelled data to train an autoencoder and sparsely labelled data to train a 4-state classifier. Our model facilitates the fusion of data from different physiological signals, including skin electrodermal activity, heart rate, body temperature and acceleration. Using real driving data, the Medium-Level fusion approach gives the best performance, achieving 84% accuracy in predicting situations where the user may not be aware enough to take control of the vehicle. This research highlights the essential nature of fusion technologies to create adaptive and user-centred AV systems.
  • Miniatura
    Publicación
    Comparison of physiological data acquisition for modeling of drivers in autonomous vehicles
    (Springer Nature, 2025-04-24) Fernandez Matellan, Raul; Puertas Ramírez, David; Martín Gómez, David; González Boticario, Jesús
    Humans can undergo rapid emotional changes and these changes can significantly affect their ability to perform tasks. Consequently, when we develop Human-Centred Symbiotic Artificial Intelligence (HCSAI) systems to support the interaction between autonomous systems and drivers, the intelligent system controlling the vehicle must adapt to the state of the user. This symbiotic relationship highlights the importance of collaboration and cooperation between humans and agents of artificial intelligence (AI). In the field of Autonomous Vehicles (AV), measurements must be made using non-invasive devices that do not interfere with the driving task. We have therefore used wristbands to measure physiological signals. This comparison is used to select the right equipment for setting up user modelling in different levels of autonomous vehicles. We compared the accuracy, precision and ease of use of three different wristbands: Fitbit Sense2, Empatica E4 and Emotibit. We tested the performance of the bands in two different driving scenarios: SAE Level 4 environment using autonomous golf carts (iCab), and a real-world SAE Level 2 driving environment in Scotland using a Toyota Prius equipped with Comma OpenPilot technology. The Fitbit Sense2 does not allow researchers to access raw data. The Emotibit and the Empatica E4 are designed for research, so they provide access to raw data, while the Empatica E4 is easier to use than the Emotibit. The comparison calls for the development of open source codes that will facilitate integration with different operating systems and other devices, as well as an easy way to use the devices in real time.