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A fire has been analyzed in a real underground coal mine, using a sublevel method, duringan entire year. The study was focused on the collapsed area, reproducing a real mixture formed bycoal, waste, and air gap. The analysis was done by means of an experimental analysis, a computationalfuid dynamic model (CFD), and simulations using a mine ventilation software. Three scenarios weredetermined and studied regarding their influence on the evolution of the fire: (a) development ofthe fire without taking any action, (b) sealing offthe affected areas, and (c) sealing and reducing theventilation in the affected area and surrounding drifts. The study revealed the behavior of the fire ina real mine and the effectiveness of the main fire-fighting measures over time, verifying that none ofthe measures taken could eliminate the fire-induced in the collapsed area. ; This research was funded by the Junta de Castilla y León (Spain), project LE167G18, as well as the European Union through the programs ECSC and RFCS, grant numbers 7220-PR/061 and RFCR-CT-2010-00005. ; Peer Reviewed ; Postprint (published version)

Localization is the fundamental problem of intelligent vehicles. For a vehicle to autonomously operate, it first needs to locate itself in the environment. A lot of different odometries (visual, inertial, wheel encoders) have been introduced through the past few years for autonomous vehicle localization. However, such odometries suffers from drift due to their reliance on integration of sensor measurements. In this paper, the drift error in an odometry is modeled and a Drift Covariance Estimation (DCE) algorithm is introduced. The DCE algorithm estimates the covariance of an odometry using the readings of another on-board sensor which does not suffer from drift. To validate the proposed algorithm, several real-world experiments in different conditions as well as sequences from Oxford RobotCar Dataset and EU long-term driving dataset are used. The effect of the covariance estimation on three different fusion-based localization algorithms (EKF, UKF and EH-infinity) is studied in comparison with the use of constant covariance, which were calculated based on the true variance of the sensors being used. The obtained results show the efficacy of the estimation algorithm compared to constant covariances in terms of improving the accuracy of localization. ; This research was supported by the Spanish Government through the CICYT projects (TRA2015-63708-R and TRA2016-78886-C3-1-R).

A solution for driver monitoring and event detection based on 3-D information from a range camera is presented. The system combines 2-D and 3-D techniques to provide head pose estimation and regions-of-interest identification. Based on the captured cloud of 3-D points from the sensor and analyzing the 2-D projection, the points corresponding to the head are determined and extracted for further analysis. Later, head pose estimation with three degrees of freedom (Euler angles) is estimated based on the iterative closest points algorithm. Finally, relevant regions of the face are identified and used for further analysis, e.g., event detection and behavior analysis. The resulting application is a 3-D driver monitoring system based on low-cost sensors. It represents an interesting tool for human factor research studies, allowing automatic study of specific factors and the detection of special event related to the driver, e.g., driver drowsiness, inattention, or head pose. ; This work was supported by the Spanish Government through the CICYT projects under Grants TRA2010- 20225-C03-01 and TRA 2011-29454-C03-02. The Associate Editor for this paper was C. Olaverri-Monreal.

The present fMRI study aimed to characterize the performance and the brain activity changes related to episodic memory retrieval in adults with single domain aMCI (sdaMCI), relative to cognitively unimpaired adults. Participants performed an old/new recognition memory task with words while BOLD signal was acquired. The sdaMCI group showed lower hits (correct recognition of old words), lower ability to discriminate old and new words, higher errors and longer reaction times for hits. This group also displayed brain hypoactivation in left precuneus and the left midcingulate cortex during the successful recognition of old words. These changes in brain activity suggest the presence of neural dysregulations in brain regions involved during successful episodic memory retrieval. Moreover, hypoactivation in these brain areas discriminated both groups with moderate sensitivity and specificity values, suggesting that it might constitute a potential neurocognitive biomarker of sdaMCI ; This study was supported by grants from the Spanish Government, Ministerio de Economía y Competitividad (PSI2014–55316-C3–3-R; PSI2017–89389-C2–2-R; PID2020–114521RB-C21/C22); and the Galician Government, Axudas para a Consolidación e Estruturación de Unidades de Investigación Competitivas do Sistema Universitario de Galicia: GRC (GI-1807-USC. Ref: ED431–2017/27; ED431C-2021/04. All with ERDF/FEDER funds ; SI

A driver behaviour analysis tool is presented. The proposal offers a novel contribution based on low-cost hardware and advanced software capabilities based on data fusion. The device takes advantage of the information provided by the in-vehicle sensors using Controller Area Network Bus (CAN-BUS), an Inertial Measurement Unit (IMU) and a GPS. By fusing this information, the system can infer the behaviour of the driver, providing aggressive behaviour detection. By means of accurate GPS-based localization, the system is able to add context information, such as digital map information, speed limits, etc. Several parameters and signals are taken into account, both in the temporal and frequency domains, to provide real time behaviour detection. The system was tested in urban, interurban and highways scenarios. ; This work was supported by the Spanish Government through the CICYT project (TRA2013-48314-C3-1-R) and DGT project (SPID2015-01802) and by the company SERCORE Tech. S.L. through the project: "Proyecto de Viabilidad de la Comunicación entre el BUS CAN de un Vehículo Específico con un Dispositivo de Adquisición de Datos Móviles". SERCORE provided invaluable support in the development of the communication technologies through the CAN BUS, presented in this paper.

One of the most challenging problems in the domain of autonomous aerial vehicles is the designing of a robust real-time obstacle detection and avoidance system. This problem is complex, especially for the micro and small aerial vehicles, that is due to the Size, Weight and Power (SWaP) constraints. Therefore, using lightweight sensors (i.e., Digital camera) can be the best choice comparing with other sensors; such as laser or radar. For real-time applications, different works are based on stereo cameras in order to obtain a 3D model of the obstacles, or to estimate their depth. Instead, in this paper, a method that mimics the human behavior of detecting the collision state of the approaching obstacles using monocular camera is proposed. The key of the proposed algorithm is to analyze the size changes of the detected feature points, combined with the expansion ratios of the convex hull constructed around the detected feature points from consecutive frames. During the Aerial Vehicle (UAV) motion, the detection algorithm estimates the changes in the size of the area of the approaching obstacles. First, the method detects the feature points of the obstacles, then extracts the obstacles that have the probability of getting close toward the UAV. Secondly, by comparing the area ratio of the obstacle and the position of the UAV, the method decides if the detected obstacle may cause a collision. Finally, by estimating the obstacle 2D position in the image and combining with the tracked waypoints, the UAV performs the avoidance maneuver. The proposed algorithm was evaluated by performing real indoor and outdoor flights, and the obtained results show the accuracy of the proposed algorithm compared with other related works. ; Research supported by the Spanish Government through the Cicyt project ADAS ROAD-EYE (TRA2013-48314-C3-1-R).

Among Advanced Driver Assistance Systems (ADAS) pedestrian detection is a common issue due to the vulnerability of pedestrians in the event of accidents. In the present work, a novel approach for pedestrian detection based on data fusion is presented. Data fusion helps to overcome the limitations inherent to each detection system (computer vision and laser scanner) and provides accurate and trustable tracking of any pedestrian movement. The application is complemented by an efficient communication protocol, able to alert vehicles in the surroundings by a fast and reliable communication. The combination of a powerful location, based on a GPS with inertial measurement, and accurate obstacle localization based on data fusion has allowed locating the detected pedestrians with high accuracy. Tests proved the viability of the detection system and the efficiency of the communication, even at long distances. By the use of the alert communication, dangerous situations such as occlusions or misdetections can be avoided. ; This work was supported by the Spanish Government through the Cicyt projects (GRANT TRA2010-20225-C03-01, GRANT TRA2010-20225-C03-03, GRANT TRA 2011-29454-C03-02 and iVANET TRA2010-15645) and CAM through SEGVAUTO-II (S2009/DPI-1509).

A stereoselective synthesis of polyhydroxylated cyclopentane β-amino acids from hexoses is reported. The reaction sequence comprises, as key steps, ring-closing metathesis of a polysubstituted diene intermediate followed by the stereoselective aza-Michael functionalization of the resulting cyclopent-1-ene-1-carboxylic acid ester. Examples of synthesis of polysubstituted 2-aminocyclopentanecarboxylic acid derivatives starting from protected d-mannose and d-galactose are presented. A general protocol for the incorporation of these highly functionalized alicyclic β-amino acids into peptides is also reported. ; This work has received financial support from the European Union (European Regional Development Fund - ERDF), the Spanish Ministerio de Economía y Competitividad (SAF2014-60138-R), the CIBER BBN (CB/06/01/0074), the Xunta de Galicia (Centro Singular de Investigación de Galicia accreditation 2019–2022, ED431G 2019/03; and grants ED431C 2018/30 and ED431C 2018/04), the Generalitat de Catalunya (2017-SGR-1439), and the Galchimia S.A. (Spain). R.B. and F.F. thank the Ministerio de Educación, Cultura y Deporte and the Ministerio de Educación y Ciencia, respectively, for FPU fellowships. ; Peer reviewed

The advances in autonomous technologies and microelectronics have increased the use of Autonomous Unmanned Aerial Vehicles (UAVs) in more critical applications, such as forest fire monitoring and fighting. In addition, implementing surveillance methods that provide rich information about the fires is considered a great tool for Emergency Response Teams (ERT). From this aspect and in collaboration with Telefónica Digital España, Dronitec S.L, and Divisek Systems, this paper presents a fire monitoring system based on perception algorithms, implemented on a UAV, to perform surveillance tasks allowing the monitoring of a specific area, in which several algorithms have been implemented to perform the tasks of autonomous take-off/landing, trajectory planning, and fire monitoring. This UAV is equipped with RGB and thermal cameras, temperature sensors, and communication modules in order to provide full information about the fire and the UAV itself, sending these data to the ground station in real time. The presented work is validated by performing several flights in a real environment, and the obtained results show the efficiency and the robustness of the proposed system, against different weather conditions. ; This work is supported by the Comunidad de Madrid Government through the Industrial Doctorates Grants (Grant No. IND2017/TIC-7834).

This article belongs to the Special Issue Intelligent Transportation Systems ; This work presents a novel platform for autonomous vehicle technologies research for the insurance sector. The platform has been collaboratively developed by the insurance company MAPFRE-CESVIMAP, Universidad Carlos III de Madrid and INSIA of the Universidad Politécnica de Madrid. The high-level architecture and several autonomous vehicle technologies developed using the framework of this collaboration are introduced and described in this work. Computer vision technologies for environment perception, V2X communication capabilities, enhanced localization, human–machine interaction and self awareness are among the technologies which have been developed and tested. Some use cases that validate the technologies presented in the platform are also presented; these use cases include public demonstrations, tests of the technologies and international competitions for self-driving technologies. ; Research was supported by the Spanish Government through the CICYT projects (TRA2016-78886-C3-1-R and RTI2018-096036-B-C21) and the Comunidad de Madrid through SEGVAUTO-4.0-CM (P2018/EMT-4362) and PEAVAUTO-CM-UC3M.

Proceedings of: 2019 IEEE Intelligent Transportation Systems Conference (ITSC) ; LiDAR devices have become a key sensor for autonomous vehicles perception due to their ability to capture reliable geometry information. Indeed, approaches processing LiDAR data have shown an impressive accuracy for 3D object detection tasks, outperforming methods solely based on image inputs. However, the wide diversity of on-board sensor configurations makes the deployment of published algorithms into real platforms a hard task, due to the scarcity of annotated datasets containing laser scans. We present a method to generate new point clouds datasets as captured by a real LiDAR device. The proposed pipeline makes use of multiple frames to perform an accurate 3D reconstruction of the scene in the spherical coordinates system that enables the simulation of the sweeps of a virtual LiDAR sensor, configurable both in location and inner specifications. The similarity between real data and the generated synthetic clouds is assessed through a set of experiments performed using KITTI Depth and Object Benchmarks. ; Research supported by the Spanish Government through the CICYT projects (TRA2016-78886-C3-1-R and RTI2018-096036-B-C21), and the Comunidad de Madrid through SEGVAUTO-4.0-CM (P2018/EMT-4362). We gratefully acknowledge the support of NVIDIA Corporation with the donation of the GPUs used for this research.

This paper presents the IVVI 2.0 a smart research platform to foster intelligent systems in vehicles. Computational perception in intelligent transportation systems applications has advantages, such as huge data from vehicle environment, among others, so computer vision systems and laser scanners are the main devices that accomplish this task. Both have been integrated in our intelligent vehicle to develop cutting-edge applications to cope with perception difficulties, data processing algorithms, expert knowledge, and decision-making. The long-term in-vehicle applications, that are presented in this paper, outperform the most significant and fundamental technical limitations, such as, robustness in the face of changing environmental conditions. Our intelligent vehicle operates outdoors with pedestrians and others vehicles, and outperforms illumination variation, i.e.: shadows, low lighting conditions, night vision, among others. So, our applications ensure the suitable robustness and safety in case of a large variety of lighting conditions and complex perception tasks. Some of these complex tasks are overcome by the improvement of other devices, such as, inertial measurement units or differential global positioning systems, or perception architectures that accomplish sensor fusion processes in an efficient and safe manner. Both extra devices and architectures enhance the accuracy of computational perception and outreach the properties of each device separately. ; This work was supported by the Spanish Government through the CICYT projects (GRANT TRA2010 20225 C03 01) and (GRANT TRA 2011 29454 C03 02).