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1. Fast and Efficient Transformer-based Method for Bird's Eye View Instance Prediction

Author

Antunes-García, Miguel, Bergasa, Luis M., Montiel-Marín, Santiago, Barea, Rafael, Sánchez-García, Fabio, and Llamazares, Ángel

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Abstract

Accurate object detection and prediction are critical to ensure the safety and efficiency of self-driving architectures. Predicting object trajectories and occupancy enables autonomous vehicles to anticipate movements and make decisions with future information, increasing their adaptability and reducing the risk of accidents. Current State-Of-The-Art (SOTA) approaches often isolate the detection, tracking, and prediction stages, which can lead to significant prediction errors due to accumulated inaccuracies between stages. Recent advances have improved the feature representation of multi-camera perception systems through Bird's-Eye View (BEV) transformations, boosting the development of end-to-end systems capable of predicting environmental elements directly from vehicle sensor data. These systems, however, often suffer from high processing times and number of parameters, creating challenges for real-world deployment. To address these issues, this paper introduces a novel BEV instance prediction architecture based on a simplified paradigm that relies only on instance segmentation and flow prediction. The proposed system prioritizes speed, aiming at reduced parameter counts and inference times compared to existing SOTA architectures, thanks to the incorporation of an efficient transformer-based architecture. Furthermore, the implementation of the proposed architecture is optimized for performance improvements in PyTorch version 2.1. Code and trained models are available at https://github.com/miguelag99/Efficient-Instance-Prediction, Comment: The article has been presented in the 27th IEEE International Conference on Intelligent Transportation Systems (IEEE ITSC 2024) on September, 2024. Number of pages: 6, Number of figures: 4

Published
2024

2. Efficient Baselines for Motion Prediction in Autonomous Driving

Author

Gómez-Huélamo, Carlos, Conde, Marcos V., Barea, Rafael, Ocaña, Manuel, and Bergasa, Luis M.

Subjects
Computer Science - Robotics, Computer Science - Artificial Intelligence, Computer Science - Multiagent Systems
Abstract

Motion Prediction (MP) of multiple surroundings agents is a crucial task in arbitrarily complex environments, from simple robots to Autonomous Driving Stacks (ADS). Current techniques tackle this problem using end-to-end pipelines, where the input data is usually a rendered top-view of the physical information and the past trajectories of the most relevant agents; leveraging this information is a must to obtain optimal performance. In that sense, a reliable ADS must produce reasonable predictions on time. However, despite many approaches use simple ConvNets and LSTMs to obtain the social latent features, State-Of-The-Art (SOTA) models might be too complex for real-time applications when using both sources of information (map and past trajectories) as well as little interpretable, specially considering the physical information. Moreover, the performance of such models highly depends on the number of available inputs for each particular traffic scenario, which are expensive to obtain, particularly, annotated High-Definition (HD) maps. In this work, we propose several efficient baselines for the well-known Argoverse 1 Motion Forecasting Benchmark. We aim to develop compact models using SOTA techniques for MP, including attention mechanisms and GNNs. Our lightweight models use standard social information and interpretable map information such as points from the driveable area and plausible centerlines by means of a novel preprocessing step based on kinematic constraints, in opposition to black-box CNN-based or too-complex graphs methods for map encoding, to generate plausible multimodal trajectories achieving up-to-pair accuracy with less operations and parameters than other SOTA methods. Our code is publicly available at https://github.com/Cram3r95/mapfe4mp ., Comment: IEEE T-ITS Transactions on Intelligent Transportation Systems

Published
2023

3. Exploring Attention GAN for Vehicle Motion Prediction

Author

Gómez-Huélamo, Carlos, Conde, Marcos V., Ortiz, Miguel, Montiel, Santiago, Barea, Rafael, and Bergasa, Luis M.

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence, Computer Science - Robotics
Abstract

The design of a safe and reliable Autonomous Driving stack (ADS) is one of the most challenging tasks of our era. These ADS are expected to be driven in highly dynamic environments with full autonomy, and a reliability greater than human beings. In that sense, to efficiently and safely navigate through arbitrarily complex traffic scenarios, ADS must have the ability to forecast the future trajectories of surrounding actors. Current state-of-the-art models are typically based on Recurrent, Graph and Convolutional networks, achieving noticeable results in the context of vehicle prediction. In this paper we explore the influence of attention in generative models for motion prediction, considering both physical and social context to compute the most plausible trajectories. We first encode the past trajectories using a LSTM network, which serves as input to a Multi-Head Self-Attention module that computes the social context. On the other hand, we formulate a weighted interpolation to calculate the velocity and orientation in the last observation frame in order to calculate acceptable target points, extracted from the driveable of the HDMap information, which represents our physical context. Finally, the input of our generator is a white noise vector sampled from a multivariate normal distribution while the social and physical context are its conditions, in order to predict plausible trajectories. We validate our method using the Argoverse Motion Forecasting Benchmark 1.1, achieving competitive unimodal results., Comment: IEEE International Conference on Intelligent Transportation Systems 2022

Published
2022

8. 360 Real-Time 3D Multi-object Detection and Tracking for Autonomous Vehicle Navigation

Author

Del Egido, Javier, Gómez-Huélamo, Carlos, Bergasa, Luis M., Barea, Rafael, López-Guillén, Elena, Araluce, Javier, Gutiérrez, Rodrigo, Antunes, Miguel, Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Bergasa, Luis M., editor, Ocaña, Manuel, editor, Barea, Rafael, editor, López-Guillén, Elena, editor, and Revenga, Pedro, editor

Published
2021
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9. Integrating OpenFace 2.0 Toolkit for Driver Attention Estimation in Challenging Accidental Scenarios

Author

Araluce, Javier, Bergasa, Luis M., Gómez-Huélamo, Carlos, Barea, Rafael, López-Guillén, Elena, Arango, Felipe, Pérez-Gil, Óscar, Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Bergasa, Luis M., editor, Ocaña, Manuel, editor, Barea, Rafael, editor, López-Guillén, Elena, editor, and Revenga, Pedro, editor

Published
2021
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10. DQN-Based Deep Reinforcement Learning for Autonomous Driving

Author

Pérez-Gil, Óscar, Barea, Rafael, López-Guillén, Elena, Bergasa, Luis M., Revenga, Pedro A., Gutiérrez, Rodrigo, Díaz, Alejandro, Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Bergasa, Luis M., editor, Ocaña, Manuel, editor, Barea, Rafael, editor, López-Guillén, Elena, editor, and Revenga, Pedro, editor

Published
2021
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11. Train Here, Drive There: Simulating Real-World Use Cases with Fully-Autonomous Driving Architecture in CARLA Simulator

Author

Gómez-Huélamo, Carlos, Del Egido, Javier, Bergasa, Luis M., Barea, Rafael, López-Guillén, Elena, Arango, Felipe, Araluce, Javier, López, Joaquín, Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Bergasa, Luis M., editor, Ocaña, Manuel, editor, Barea, Rafael, editor, López-Guillén, Elena, editor, and Revenga, Pedro, editor

Published
2021
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13. Enhancing Autonomous Driving in Urban Scenarios: A Hybrid Approach with Reinforcement Learning and Classical Control.

Author

Gutiérrez-Moreno, Rodrigo, Barea, Rafael, López-Guillén, Elena, Arango, Felipe, Sánchez-García, Fabio, and Bergasa, Luis M.

Subjects
*REINFORCEMENT learning, *DEEP reinforcement learning, *PARTIALLY observable Markov decision processes, *MACHINE learning, *DEEP learning
Abstract

The use of Deep Learning algorithms in the domain of Decision Making for Autonomous Vehicles has garnered significant attention in the literature in recent years, showcasing considerable potential. Nevertheless, most of the solutions proposed by the scientific community encounter difficulties in real-world applications. This paper aims to provide a realistic implementation of a hybrid Decision Making module in an Autonomous Driving stack, integrating the learning capabilities from the experience of Deep Reinforcement Learning algorithms and the reliability of classical methodologies. Our Decision Making system is in charge of generating steering and velocity signals using the HD map information and sensors pre-processed data. This work encompasses the implementation of concatenated scenarios in simulated environments, and the integration of Autonomous Driving modules. Specifically, the authors address the Decision Making problem by employing a Partially Observable Markov Decision Process formulation and offer a solution through the use of Deep Reinforcement Learning algorithms. Furthermore, an additional control module to execute the decisions in a safe and comfortable way through a hybrid architecture is presented. The proposed architecture is validated in the CARLA simulator by navigating through multiple concatenated scenarios, outperforming the CARLA Autopilot in terms of completion time, while ensuring both safety and comfort. [ABSTRACT FROM AUTHOR]

Published
2025
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18. Combination of In Situ Diffraction Experiments and Acoustic Emission Testing to Understand Compression Behavior of Mg-Gd Alloys.

Author

Garces, Gerardo, Chavez, Bryan W., Pérez, Pablo, Medina, Judit, Mathis, Kristian, Barea, Rafael, Stark, Andreas, Schell, Norbert, and Adeva, Paloma

Subjects
ACOUSTIC emission testing, STRAIN hardening, EXTRUSION process, MAGNESIUM alloys, ACOUSTIC emission
Abstract

The compressive deformation of the extruded binary Mg-Gd with gadolinium in solid solution has been studied in situ by combining synchrotron diffraction and acoustic emission techniques during compression tests. These two techniques are useful in investigating the evolution of twinning in all its stages. The extruded bars develop a fiber texture with the basal plane parallel to the extrusion direction. Moreover, the quenching of the magnesium bars immediately after the extrusion process ensured the production of the solid solution of gadolinium in the magnesium matrix. The solid solution of gadolinium solute atoms is the main strengthening mechanism of alloys and has a strong influence in plastic deformation. Tensile twinning controls the macroscopic yielding under compressive modes, although the activation of basal and non-basal dislocation systems has been also detected by in situ techniques. The presence of gadolinium atoms in solid solution tends to inhibit tensile twinning and, therefore, the twin volume fraction decreases with the increase in the gadolinium content. The compressive work hardening curve shows a maximum peak at intermediate plastic strain which is related to the interaction of dislocations within twins. The maximum value and the position of the peak decreases with the increase in the gadolinium content. [ABSTRACT FROM AUTHOR]

Published
2024
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22. Study of Tensile and Compressive Behavior of ECO-Mg97Gd2Zn1 Alloys Containing Long-Period Stacking Ordered Phase with Lamellar Structure

Author

Ministerio de Ciencia e Innovación (España), Agencia Estatal de Investigación (España), Garcés, Gerardo, Medina, Judit, Pérez, Pablo, Barea, Rafael, Lim, Hyunkyu, Kim, Shae K., Maawad, Emad, Schell, Norbert, Adeva, Paloma, Ministerio de Ciencia e Innovación (España), Agencia Estatal de Investigación (España), Garcés, Gerardo, Medina, Judit, Pérez, Pablo, Barea, Rafael, Lim, Hyunkyu, Kim, Shae K., Maawad, Emad, Schell, Norbert, and Adeva, Paloma

Abstract

A suitable heat treatment in the Mg97Gd2Zn1 (at.%) alloy in the as-cast condition results, after extrusion at high temperature, in a two-phase lamellar microstructure consisting of magnesium grains with thin lamellar shape precipitates and long fibers of the 14H-Long-Period Stacking Ordered (LPSO) phase elongated in the extrusion direction. The magnesium matrix is not fully recrystallized and highly oriented coarse non-dynamically recrystallized (non-DRXed) grains (17% volume fraction) elongated along the extrusion direction remain in the material. The deformation mechanisms of the extruded alloy have been studied measuring the evolution of the internal strains during in situ tension and compression tests using synchrotron diffraction radiation. The data demonstrate that the macroscopic yield stress is governed by the activation of the basal slip system in the randomly oriented equiaxed dynamic recrystallized (DRXed) grains. Non-DRXed grains, due to their strong texture, are favored oriented for the activation of tensile twinning. However, the presence of lamellar-shape precipitates strongly delays the propagation of lenticular thin twins through these highly oriented grains and they have no effect on the onset of the plastic deformation. Therefore, the tension–compression asymmetry is low since the plasticity mechanism is independent of the stress mode.

Published
2024

23. Climate Change monitoring with Art-Risk 5: New approach for environmental hazard assessment in Seville and Almería Historic Centres (Spain)

Author

Universidad de Sevilla. Departamento de Arquitectura y Tecnología de Computadores, Universidad de Sevilla. TEP108: Robótica y Tecnología de Computadores, Ministerio de Ciencia e Innovación (MICIN). España, European Union, Ministerio de Trabajo y Asuntos Sociales. España, Moreno Falcón, Mónica, Barea, Rafael, Castro, Luis, Cagigas Muñiz, Daniel, Ortiz, Rocío, Ortiz, Pilar, Universidad de Sevilla. Departamento de Arquitectura y Tecnología de Computadores, Universidad de Sevilla. TEP108: Robótica y Tecnología de Computadores, Ministerio de Ciencia e Innovación (MICIN). España, European Union, Ministerio de Trabajo y Asuntos Sociales. España, Moreno Falcón, Mónica, Barea, Rafael, Castro, Luis, Cagigas Muñiz, Daniel, Ortiz, Rocío, and Ortiz, Pilar

Abstract

Currently, climate change is significantly impacting historic cities, altering energy demands, and influencing tourism patterns. In this context, the analysis of extensive datasets derived from satellite imagery offers a means to monitor the effects of climate change on both urban and territorial scales. Art-Risk 5.0 is an open digital tool designed to easily track temperature variations, precipitation patterns, urban heat islands, and vegetation health using satellite resources. The applications in two historic cities in southern Spain, Almeria and Sevilla was analyzed to assess the impact of climate change. The outcomes of Art-Risk 5.0 have provided valuable data for diagnosing the impact of climate change in these historic cities. The major climatic hazards identified in southern Spain are high temperatures, torrential rainfall, and droughts. Additionally, over the past 20 years, an increase in maximum temperatures and drought intensity has been observed in Sevilla and Almeria. On the urban scale, urban heat islands are concentrated in neighborhoods with limited green and blue infrastructure. The ability to analyze time series of climate data from satellite images makes Art-Risk 5.0 an extremely useful tool for monitoring the impact of climate change and promoting sustainable adaptation policies.

Published
2024

24. Self-driving a Car in Simulation Through a CNN

Author

del Egio, Javier, Bergasa, Luis Miguel, Romera, Eduardo, Gómez Huélamo, Carlos, Araluce, Javier, Barea, Rafael, Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Fuentetaja Pizán, Raquel, editor, García Olaya, Ángel, editor, Sesmero Lorente, Maria Paz, editor, Iglesias Martínez, Jose Antonio, editor, and Ledezma Espino, Agapito, editor

Published
2019
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25. Topological Road Mapping for Autonomous Driving Applications

Author

Murciego, Esther, Huélamo, Carlos Gómez, Barea, Rafael, Bergasa, Luis Miguel, Romera, Eduardo, Arango, Juan Felipe, Tradacete, Miguel, Sáez, Álvaro, Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Fuentetaja Pizán, Raquel, editor, García Olaya, Ángel, editor, Sesmero Lorente, Maria Paz, editor, Iglesias Martínez, Jose Antonio, editor, and Ledezma Espino, Agapito, editor

Published
2019
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26. Positioning System for an Electric Autonomous Vehicle Based on the Fusion of Multi-GNSS RTK and Odometry by Using an Extented Kalman Filter

Author

Tradacete, Miguel, Sáez, Álvaro, Arango, Juan Felipe, Gómez Huélamo, Carlos, Revenga, Pedro, Barea, Rafael, López-Guillén, Elena, Bergasa, Luis Miguel, Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Fuentetaja Pizán, Raquel, editor, García Olaya, Ángel, editor, Sesmero Lorente, Maria Paz, editor, Iglesias Martínez, Jose Antonio, editor, and Ledezma Espino, Agapito, editor

Published
2019
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34. Study of Tensile and Compressive Behavior of ECO-Mg 97 Gd 2 Zn 1 Alloys Containing Long-Period Stacking Ordered Phase with Lamellar Structure.

Author

Garcés, Gerardo, Medina, Judit, Pérez, Pablo, Barea, Rafael, Lim, Hyunkyu, Kim, Shae K., Maawad, Emad, Schell, Norbert, and Adeva, Paloma

Subjects
HEAT treatment, ALLOYS, SYNCHROTRON radiation, MATERIAL plasticity, YIELD stress, ALUMINUM-zinc alloys, COPPER-zinc alloys, MAGNESIUM alloys
Abstract

A suitable heat treatment in the Mg97Gd2Zn1 (at.%) alloy in the as-cast condition results, after extrusion at high temperature, in a two-phase lamellar microstructure consisting of magnesium grains with thin lamellar shape precipitates and long fibers of the 14H-Long-Period Stacking Ordered (LPSO) phase elongated in the extrusion direction. The magnesium matrix is not fully recrystallized and highly oriented coarse non-dynamically recrystallized (non-DRXed) grains (17% volume fraction) elongated along the extrusion direction remain in the material. The deformation mechanisms of the extruded alloy have been studied measuring the evolution of the internal strains during in situ tension and compression tests using synchrotron diffraction radiation. The data demonstrate that the macroscopic yield stress is governed by the activation of the basal slip system in the randomly oriented equiaxed dynamic recrystallized (DRXed) grains. Non-DRXed grains, due to their strong texture, are favored oriented for the activation of tensile twinning. However, the presence of lamellar-shape precipitates strongly delays the propagation of lenticular thin twins through these highly oriented grains and they have no effect on the onset of the plastic deformation. Therefore, the tension–compression asymmetry is low since the plasticity mechanism is independent of the stress mode. [ABSTRACT FROM AUTHOR]

Published
2024
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46. On the Influence of Precipitation on the Dynamic Strain Aging in Mg-2%Nd

Author

CSIC - Unidad de Recursos de Información Científica para la Investigación (URICI), Garcés, Gerardo, Pérez Zubiaur, Pablo, Medina, Judit, Barea, Rafael, Gómez, Adrián, García, Javier, Adeva, Paloma, CSIC - Unidad de Recursos de Información Científica para la Investigación (URICI), Garcés, Gerardo, Pérez Zubiaur, Pablo, Medina, Judit, Barea, Rafael, Gómez, Adrián, García, Javier, and Adeva, Paloma

Abstract

The Mg-2%Nd alloy, thermally treated at 520°C and quenched, exhibits plastic instabilities at intermediate temperatures between 100°C and 300°C when it is compressively tested because of the dynamic strain aging phenomenon caused by the presence of neodymium atoms in solid solution. During compression tests, there is a competition between the precipitation of Mg-Nd particles and the DSA phenomenon, which depends largely on the test temperature. Thus, below 200°C, the formation of neodymium-rich precipitates is slow, and the interaction is insignificant. Under these conditions, the alloy exhibits a normal behavior of the DSA, i.e., the critical strain decreases with the increase in the test temperature. However, for test temperatures > 200°C, the formation of neodymium-rich precipitates is faster and the evolution of the critical strain with temperature is inverted because of the decrease of neodymium in solid solution.

Published
2023

48. Aulas, libros y bibliotecas en la Edad Media y Moderna.

Author

Ciordia, Javier Vergara, primary, González, Susana Guijarro, additional, Prieto, Josué Villa, additional, Rincón, David Nogales, additional, Caballero, Guillermo F. Arquero, additional, Gallardo, Luis Fernández, additional, Guglieri, Juan Ignacio, additional, García, Arturo Torres, additional, Comella-Gutiérrez, Beatriz, additional, Guerendiáin, Lía Viguria, additional, and Sánchez-Barea, Rafael Fermín, additional

Published
2023
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50. Real–Time Hierarchical GPS Aided Visual SLAM on Urban Environments

Author

Schleicher, David, Bergasa, Luis M., Ocaña, Manuel, Barea, Rafael, López, Elena, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, Moreno-Díaz, Roberto, editor, Pichler, Franz, editor, and Quesada-Arencibia, Alexis, editor

Published
2009
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