Bridging the day and night domain gap for semantic segmentation

2019 IEEE Intelligent Vehicles Symposium (IV), Paris, France, 9-12 Jun. 2019Perception in autonomous vehicles has progressed exponentially in the last years thanks to the advances of visionbased methods such as Convolutional Neural Networks (CNNs). Current deep networks are both efficient and reliable, at least in standard conditions, standing as a suitable solution for the perception tasks of autonomous vehicles. However, there is a large accuracy downgrade when these methods are taken to adverse conditions such as nighttime. In this paper, we study methods to alleviate this accuracy gap by using recent techniques such as Generative Adversarial Networks (GANs). We explore diverse options such as enlarging the dataset to cover these domains in unsupervised training or adapting the images on-the-fly during inference to a comfortable domain such as sunny daylight in a pre-processing step. The results show some interesting insights and demonstrate that both proposed approaches considerably reduce the domain gap, allowing IV perception systems to work reliably also at night.Ministerio de Economía y competitividadComunidad de Madri

Analysis of gamma-band activity from human EEG using empirical mode decomposition

The purpose of this paper is to determine whether gamma-band activity detection is improved when a filter, based on empirical mode decomposition (EMD), is added to the pre-processing block of single-channel electroencephalography (EEG) signals. EMD decomposes the original signal into a finite number of intrinsic mode functions (IMFs). EEGs from 25 control subjects were registered in basal and motor activity (hand movements) using only one EEG channel. Over the basic signal, IMF signals are computed. Gamma-band activity is computed using power spectrum density in the 30–60 Hz range. Event-related synchronization (ERS) was defined as the ratio of motor and basal activity. To evaluate the performance of the new EMD based method, ERS was computed from the basic and IMF signals. The ERS obtained using IMFs improves, from 31.00% to 73.86%, on the original ERS for the right hand, and from 22.17% to 47.69% for the left hand. As EEG processing is improved, the clinical applications of gamma-band activity will expand.Universidad de AlcaláInstituto de Salud Carlos II

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

The Experience of Robesafe Team in CARLA Autonomous Driving Challenge

Robótica e Inteligencia Artificial: Retos y nuevas oportunidades. 10 de diciembre de 2019, ETSII UPM (RoboCity2030)The future of the automotive is focused on achieving total autonomous cars in realistic urban environments. To reach it, many researching teams are working with 3D simulators such as V-REP and Gazebo, due to an easy integration with ROS platform. ROS is a middle-ware for robot code development. It allows easy communication between different systems. It is multilanguage, admitting C++ and Python code programming. Those simulators provide precise motion information, but they are designed for smaller environments like robotic arms, providing unrealistic appearance and very slow performance, being unrecommended for real-time systems in rich worlds like urban cities. CARLA simulator provides high detailed environments in realistic urban situations, being able to train and test control and perception algorithms in complex traffic scenarios, very close to real situations. CARLA Autonomous Driving Challenge was launched in Summer 2019, allowing everyone to test their own control techniques under the same traffic scenarios, scoring its performance regarding traffic rules. Robesafe researching group, from Universidad de Alcalá, submitted to this challenge, with the aim of achieving high results and compare our control and perception techniques with others provided by other teams.Comunidad de Madri

Integrating state-of-the-art CNNs for multi-sensor 3D vehicle detection in real autonomous driving environments

2019 IEEE Intelligent Transportation Systems Conference (ITSC), Auckland, New Zealand, 27-30 Oct. 2019This paper presents two new approaches to detect surrounding vehicles in 3D urban driving scenes and their corresponding Bird’s Eye View (BEV). The proposals integrate two state-of-the-art Convolutional Neural Networks (CNNs), such as YOLOv3 and Mask-RCNN, in a framework presented by the authors in [1] for 3D vehicles detection fusing semantic image segmentation and LIDAR point cloud. Our proposals take advantage of multimodal fusion, geometrical constrains, and pre-trained modules inside our framework. The methods have been tested using the KITTI object detection benchmark and comparison is presented. Experiments show new approaches improve results with respect to the baseline and are on par with other competitive state-of-the-art proposals, being the only ones that do not apply an end-to-end learning process. In this way, they remove the need to train on a specific dataset and show a good capability of generalization to any domain, a key point for self-driving systems. Finally, we have tested our best proposal in KITTI in our driving environment, without any adaptation, obtaining results suitable for our autonomous driving application.Ministerio de Economía y CompetitividadComunidad de Madri

Real-Time Bird's Eye View Multi-Object Tracking system based on Fast Encoders for object detection

2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC), September 20-23, 2020, Rhodes, Greece. Virtual Conference.This paper presents a Real-Time Bird’s Eye View Multi Object Tracking (MOT) system pipeline for an Autonomous Electric car, based on Fast Encoders for object detection and a combination of Hungarian algorithm and Bird’s Eye View (BEV) Kalman Filter, respectively used for data association and state estimation. The system is able to analyze 360 degrees around the ego-vehicle as well as estimate the future trajectories of the environment objects, being the essential input for other layers of a self-driving architecture, such as the control or decision-making. First, our system pipeline is described, merging the concepts of online and realtime DATMO (Deteccion and Tracking of Multiple Objects), ROS (Robot Operating System) and Docker to enhance the integration of the proposed MOT system in fully-autonomous driving architectures. Second, the system pipeline is validated using the recently proposed KITTI-3DMOT evaluation tool that demonstrates the full strength of 3D localization and tracking of a MOT system. Finally, a comparison of our proposal with other state-of-the-art approaches is carried out in terms of performance by using the mainstream metrics used on MOT benchmarks and the recently proposed integral MOT metrics, evaluating the performance of the tracking system over all detection thresholds.Ministerio de Ciencia, Innovación y UniversidadesComunidad 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

Práctica de laboratorio de captura de energía de radio frecuencia

X Congreso de Tecnologías Aplicadas a la Enseñanza de la Electrónica, Vigo, 13-15 de junio de 2012.La obtención de energía por medios alternativos, amigables con el medio ambiente, y que no dependan de baterías contaminantes a las que haya que recargar y asegurar un mantenimiento periódico, se plantea como una opción interesante en la alimentación de sistemas electrónicos autónomos de bajo consumo. En este trabajo se presenta una práctica de laboratorio que permite obtener energía eléctrica a partir de una señal de radio frecuencia (RF). El alumno aprende a diseñar y a caracterizar una antena de parche básica, comprende el proceso de sintonización de señales de RF y comprueba el funcionamiento del sistema. También se plantean posibles ampliaciones y modificaciones que ayudan al alumno a enriquecer su conocimiento sobre potenciales aplicaciones de los sistemas de captura de energía de RF. Se incluye finalmente la opinión de los alumnos que han realizado esta práctica en el laboratorio durante tres cursos académicos

Continuous‑wavelet‑transform analysis of the multifocal ERG waveform in glaucoma diagnosis

The vast majority of multifocal electroretinogram (mfERG) signal analyses to detect glaucoma study the signals’ amplitudes and latencies. The purpose of this paper is to investigate application of wavelet analysis of mfERG signals in diagnosis of glaucoma. This analysis method applies the continuous wavelet transform (CWT) to the signals, using the real Morlet wavelet. CWT coefficients resulting from the scale of maximum correlation are used as inputs to a neural network, which acts as a classifier. mfERG recordings are taken from the eyes of 47 subjects diagnosed with chronic open-angle glaucoma and from those of 24 healthy subjects. The high sensitivity in the classification (0.894) provides reliable detection of glaucomatous sectors, while the specificity achieved (0.844) reflects accurate detection of healthy sectors. The results obtained in this paper improve on the previous findings reported by the authors using the same visual stimuli and database.Ministerio de Ciencia e Innovació

Improved measurement of intersession latency in mfVEPs

Purpose: The purpose of the study is to present a method (Selfcorr) by which to measure intersession latency differences between multifocal VEP (mfVEP) signals. Methods: The authors compared the intersession latency difference obtained using a correlation method (Selfcorr) against that obtained using a Template method. While the Template method cross-correlates the subject’s signals with a reference database, the Selfcorr method cross-correlates traces across subsequent recordings taken from the same subject. Results: The variation in latency between intersession signals was 0.8 ± 13.6 and 0.5 ± 5.0 ms for the Template and Selfcorr methods, respectively, with a coefficient of variability C V_TEMPLATE = 15.83 and C V_SELFCORR = 5.68 (n = 18, p = 0.0002, Wilcoxon). The number of analyzable sectors with the Template and Selfcorr methods was 36.7 ± 8.5 and 45.3 ± 8.7, respectively (p = 0.0001, paired t test, two tailed). Conclusions: The Selfcorr method produces smaller intersession mfVEP delays and variability over time than the Template method.Ministerio de Ciencia e Innovació