7 research outputs found
Navegación de robots móviles en entorno Matlab-ROS
En el presente documento se describe el proceso llevado a cabo para desarrollar una serie de
aplicaciones relacionadas con la navegación autónoma de robots móviles destinadas al ámbito
docente y de investigación, utilizando como entorno la Robotics System Toolbox de Matlab
y ROS. Estas incluyen algoritmos de: mapeado y SLAM, localización (AMCL), evitación de
obstáculos (VFH), seguimiento de pasillos usando lógica difusa y la transformada de Hough, y
planificación global (PRM).
Además, se diseña un aplicación para el guiado de vehículos autónomos en el simulador CARLA,
con la cual se participa en el primer CARLA Challenge, celebrado entre abril y junio del 2019.The main aim of this project is to develope a set of algorithms related to autonomous navigation
in the academic and research fields. They were implemented in a ROS-Matlab environment, using
the Robotics System Toolbox framework. The algorithms cover a wide range of applications:
mapping and SLAM, localization (AMCL), obstacle avoidance (VFH), hallway tracking using
fuzzy logic and Hough Transform, and global planning (PRM).
Furthermore, an aditional goal was set. The design and implementation of an application for
an automated guided vehicle in CARLA simulator, with the participation in the first CARLA
Challenge.Grado en Ingeniería en Electrónica y Automática Industria
The (de)biasing effect of GAN-based augmentation methods on skin lesion images
New medical datasets are now more open to the public, allowing for better and
more extensive research. Although prepared with the utmost care, new datasets
might still be a source of spurious correlations that affect the learning
process. Moreover, data collections are usually not large enough and are often
unbalanced. One approach to alleviate the data imbalance is using data
augmentation with Generative Adversarial Networks (GANs) to extend the dataset
with high-quality images. GANs are usually trained on the same biased datasets
as the target data, resulting in more biased instances. This work explored
unconditional and conditional GANs to compare their bias inheritance and how
the synthetic data influenced the models. We provided extensive manual data
annotation of possibly biasing artifacts on the well-known ISIC dataset with
skin lesions. In addition, we examined classification models trained on both
real and synthetic data with counterfactual bias explanations. Our experiments
showed that GANs inherited biases and sometimes even amplified them, leading to
even stronger spurious correlations. Manual data annotation and synthetic
images are publicly available for reproducible scientific research.Comment: Accepted to MICCAI202
Towards trustworthy multi-modal motion prediction: Holistic evaluation and interpretability of outputs
Predicting the motion of other road agents enables autonomous vehicles to
perform safe and efficient path planning. This task is very complex, as the
behaviour of road agents depends on many factors and the number of possible
future trajectories can be considerable (multi-modal). Most prior approaches
proposed to address multi-modal motion prediction are based on complex machine
learning systems that have limited interpretability. Moreover, the metrics used
in current benchmarks do not evaluate all aspects of the problem, such as the
diversity and admissibility of the output. In this work, we aim to advance
towards the design of trustworthy motion prediction systems, based on some of
the requirements for the design of Trustworthy Artificial Intelligence. We
focus on evaluation criteria, robustness, and interpretability of outputs.
First, we comprehensively analyse the evaluation metrics, identify the main
gaps of current benchmarks, and propose a new holistic evaluation framework. We
then introduce a method for the assessment of spatial and temporal robustness
by simulating noise in the perception system. To enhance the interpretability
of the outputs and generate more balanced results in the proposed evaluation
framework, we propose an intent prediction layer that can be attached to
multi-modal motion prediction models. The effectiveness of this approach is
assessed through a survey that explores different elements in the visualization
of the multi-modal trajectories and intentions. The proposed approach and
findings make a significant contribution to the development of trustworthy
motion prediction systems for autonomous vehicles, advancing the field towards
greater safety and reliability.Comment: 16 pages, 7 figures, 6 table
GAN-based generative modelling for dermatological applications -- comparative study
The lack of sufficiently large open medical databases is one of the biggest
challenges in AI-powered healthcare. Synthetic data created using Generative
Adversarial Networks (GANs) appears to be a good solution to mitigate the
issues with privacy policies. The other type of cure is decentralized protocol
across multiple medical institutions without exchanging local data samples. In
this paper, we explored unconditional and conditional GANs in centralized and
decentralized settings. The centralized setting imitates studies on large but
highly unbalanced skin lesion dataset, while the decentralized one simulates a
more realistic hospital scenario with three institutions. We evaluated models'
performance in terms of fidelity, diversity, speed of training, and predictive
ability of classifiers trained on the generated synthetic data. In addition we
provided explainability through exploration of latent space and embeddings
projection focused both on global and local explanations. Calculated distance
between real images and their projections in the latent space proved the
authenticity and generalization of trained GANs, which is one of the main
concerns in this type of applications. The open source code for conducted
studies is publicly available at
\url{https://github.com/aidotse/stylegan2-ada-pytorch}.Comment: 16 pages, 5 figures, 2 table
Simulating use cases for the UAH autonomous electric car
2019 IEEE Intelligent Transportation Systems Conference (ITSC), Auckland, New Zealand, 27-30 Oct. 2019This paper presents the simulation use cases for
the UAH Autonomous Electric Car, related with typical driving
scenarios in urban environments, focusing on the use of hierarchical interpreted binary Petri nets in order to implement the
decision making framework of an autonomous electric vehicle.
First, we describe our proposal of autonomous system architecture, which is based on the open source Robot Operating
System (ROS) framework that allows the fusion of multiple
sensors and the real-time processing and communication of
multiple processes in different embedded processors. Then, the
paper focuses on the study of some of the most interesting
driving scenarios such as: stop, pedestrian crossing, Adaptive
Cruise Control (ACC) and overtaking, illustrating both the
executive module that carries out each behaviour based on
Petri nets and the trajectory and linear velocity that allows
to quantify the accuracy and robustness of the architecture
proposal for environment perception, navigation and planning
on a university Campus.Ministerio de Economía y CompetitividadComunidad de Madri
Naturalistic driving study for older drivers based on the DriveSafe app
2019 IEEE Intelligent Transportation Systems Conference (ITSC), Auckland, New Zealand, 27-30 Oct. 2019Elderly population is increasing year after year
in the developed countries. However, the knowledge of actual
mobility needs of senior drivers is scarce. In this paper,
we present a naturalistic driving study (NDS) focused on
older drivers through smartphone technology and using our
DriveSafe app. Our system automatically generates a driving
analysis report based on objective indicators. The proposal
supposes an improvement over the traditional surveys and
observers, and represents an advance over the current NDSs by
using smartphones instead of complex instrumented vehicles.
Our method avoids the problems of manual annotation by
using an automatic method for data reduction information.
Furthermore, a comparison between traditional questionnaires
and information provided by our system is carried out and
conclusions are presented.Ministerio de Economía y CompetitividadDGTComunidad de Madri
Urban intersection classification: a comparative analysis
Understanding the scene in front of a vehicle is crucial for self-driving vehicles and Advanced Driver Assistance Systems, and in urban scenarios, intersection areas are one of the most critical, concentrating between 20% to 25% of road fatalities. This research presents a thorough investigation on the detection and classification of urban intersections as seen from onboard front-facing cameras. Different methodologies aimed at classifying intersection geometries have been assessed to provide a comprehensive evaluation of state-of-the-art techniques based on Deep Neural Network (DNN) approaches, including single-frame approaches and temporal integration schemes. A detailed analysis of most popular datasets previously used for the application together with a comparison with ad hoc recorded sequences revealed that the performances strongly depend on the field of view of the camera rather than other characteristics or temporal-integrating techniques. Due to the scarcity of training data, a new dataset is created by performing data augmentation from real-world data through a Generative Adversarial Network (GAN) to increase generalizability as well as to test the influence of data quality. Despite being in the relatively early stages, mainly due to the lack of intersection datasets oriented to the problem, an extensive experimental activity has been performed to analyze the individual performance of each proposed systems.European Commissio