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  • Publication
    Systematic and comprehensive review of clustering and multi-target tracking techniques for lidar point clouds in autonomous driving applications
    (MDPI, 2023-07-01) Adnan, Muhammad; Slavic, Giulia; Martin Gomez, David; Marcenaro, Lucio; Regazzoni, Carlo; Comunidad de Madrid; Ministerio de Economía y Competitividad (España)
    Autonomous vehicles (AVs) rely on advanced sensory systems, such as Light Detection and Ranging (LiDAR), to function seamlessly in intricate and dynamic environments. LiDAR produces highly accurate 3D point clouds, which are vital for the detection, classification, and tracking of multiple targets. A systematic review and classification of various clustering and Multi-Target Tracking (MTT) techniques are necessary due to the inherent challenges posed by LiDAR data, such as density, noise, and varying sampling rates. As part of this study, the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology was employed to examine the challenges and advancements in MTT techniques and clustering for LiDAR point clouds within the context of autonomous driving. Searches were conducted in major databases such as IEEE Xplore, ScienceDirect, SpringerLink, ACM Digital Library, and Google Scholar, utilizing customized search strategies. We identified and critically reviewed 76 relevant studies based on rigorous screening and evaluation processes, assessing their methodological quality, data handling adequacy, and reporting compliance. As a result of this comprehensive review and classification, we were able to provide a detailed overview of current challenges, research gaps, and advancements in clustering and MTT techniques for LiDAR point clouds, thus contributing to the field of autonomous driving. Researchers and practitioners working in the field of autonomous driving will benefit from this study, which was characterized by transparency and reproducibility on a systematic basis.
  • Publication
    Back-Propagation of the Mahalanobis Distance through a Deep Triplet Learning Model for Person Re-Identification
    (IOS Press, 2021-06-29) Gómez Silva, María José; Escalera Hueso, Arturo de la; Armingol Moreno, José María; Comunidad de Madrid; Universidad Carlos III de Madrid; Comisión Interministerial de Ciencia y Tecnología (España)
    The automatization of the Re-Identification of an individual across different video-surveillance cameras poses a significant challenge due to the presence of a vast number of potential candidates with a similar appearance. This task requires the learning of discriminative features from person images and a distance metric to properly compare them and decide whether they belong to the same person or not. Nevertheless, the fact of acquiring images of the same person from different, distant and non-overlapping views produces changes in illumination, perspective, background, resolution and scale between the person"s representations, resulting in appearance variations that hamper his/her re-identification. This article focuses the feature learning on automatically finding discriminative descriptors able to reflect the dissimilarities mainly due to the changes in actual people appearance, independently from the variations introduced by the acquisition point. With that purpose, such variations have been implicitly embedded by the Mahalanobis distance. This article presents a learning algorithm to jointly model features and the Mahalanobis distance through a Deep Neural Re-Identification model. The Mahalanobis distance learning has been implemented as a novel neural layer, forming part of a Triplet Learning model that has been evaluated over PRID2011 dataset, providing satisfactory results.
  • Publication
    An improved deep learning architecture for multi-object tracking systems
    (IOS Press, 2023-03-15) Urdiales de la Parra, Jesús; Martín Gómez, David; Armingol Moreno, José María; Comunidad de Madrid; Agencia Estatal de Investigación (España)
    Robust and reliable 3D multi-object tracking (MOT) is essential for autonomous driving in crowded urban road scenes. In those scenarios, accurate data association between tracked objects and incoming new detections is crucial. This paper presents a tracking system based on the Kalman filter that uses a deep learning approach to the association problem. The proposed architecture consists of three neural networks. First, a convolutional LSTM network extracts spatiotemporal features from a sequence of detections of the same track. Then, a Siamese network calculates the degree of similarity between all tracks and the new detections found at each new frame. Finally, a recurrent LSTM network is used to extract 3D and bounding box information. This model follows the tracking-by-detection paradigm and has been trained with track sequences to be able to handle missed observations and to reduce identity switches. A validation test was carried out on the Argoverse dataset to validate the performance of the proposed system. The developed deep learning approach could improve current multi-object tracking systems based on classic algorithms like the Kalman filter.
  • Publication
    Dataset construction from naturalistic driving in roundabouts
    (MDPI, 2020-12-02) García Cuenca, Laura; Guindel Gómez, Carlos; Aliane, Nourdine; Armingol Moreno, José María; Fernández Andrés, Javier; Comunidad de Madrid; Ministerio de Ciencia e Innovación (España); Universidad Carlos III de Madrid; Agencia Estatal de Investigación (España)
    A proper driver characterization in complex environments using computational techniques depends on the richness and variety of data obtained from naturalistic driving. The present article proposes the construction of a dataset from naturalistic driving specific to maneuvers in roundabouts and makes it open and available to the scientific community for performing their own studies. The dataset is a combination of data gathered from on-board instrumentation and data obtained from the post-processing of maps as well as recorded videos. The approach proposed in this paper consists of handling roundabouts as a stretch of road that includes 100 m before the entrance, the internal part, and 100 m after the exit. This stretch of road is then spatially sampled in small sections to which data are associated.
  • Publication
    Vehículo Aéreo no Tripulado para Vigilancia y Monitorización de Incendios
    (Universidad Politécnica de Valencia, 2020-04-20) Madridano Carrasco, Ángel; Campos Novoa, Sergio; Al-Kaff, Abdulla Hussein; García Fernández, Fernando; Martín Gómez, David; Escalera Hueso, Arturo de la; Comunidad de Madrid
    Los incendios forestales siguen siendo uno de los grandes problemas ambientales a los que se enfrenta la sociedad en la actualidad. Además del gran impacto medioambiental, la destrucción de ecosistemas y las posibles pérdidas humanas, hay que añadir los costes económicos derivados de la lucha contra el fuego. Todos estos motivos han provocado que se busque en la tecnología actual, herramientas y sistemas que permitan ayudar en tareas de la lucha contra incendios y, más en concreto, el uso de Vehículos Aéreos No Tripulados (UAVs). El hecho de que los UAVs puedan alcanzar lugares remotos de manera rápida y, embarcar sensores y dispositivos que ayuden en tareas peligrosas y arriesgadas, los hacen idóneos para la lucha contra el fuego. En este trabajo recoge el desarrollo, en colaboración con Telefónica Digital España, de una aplicación innovadora haciendo uso de la tecnología más actual presente en la robótica, en la cual un dron es capaz de realizar tareas de vigilancia y monitorización de incendios de manera autónoma, gracias a los sensores y dispositivos embarcados en el mismo. Además, se implementa una interfaz gráfica que permita el intercambio de información entre la aeronave y el usuario en tierra.
  • Publication
    High-accuracy patternless calibration of multiple 3D LiDARs for autonomous vehicles
    (IEEE, 2023-06-01) Miguel Paraiso, Miguel Ángel de; Guindel Gómez, Carlos; Al Kaff, Abdulla Hussein Abdulrahman; García Fernández, Fernando; Comunidad de Madrid; Agencia Estatal de Investigación (España)
    This article proposes a new method for estimating the extrinsic calibration parameters between any pair of multibeam LiDAR sensors on a vehicle. Unlike many state-of-the-art works, this method does not use any calibration pattern or reflective marks placed in the environment to perform the calibration; in addition, the sensors do not need to have overlapping fields of view. An iterative closest point (ICP)-based process is used to determine the values of the calibration parameters, resulting in better convergence and improved accuracy. Furthermore, a setup based on the car learning to act (CARLA) simulator is introduced to evaluate the approach, enabling quantitative assessment with ground-truth data. The results show an accuracy comparable with other approaches that require more complex procedures and have a more restricted range of applicable setups. This work also provides qualitative results on a real setup, where the alignment between the different point clouds can be visually checked. The open-source code is available at https://github.com/midemig/pcd_calib .
  • Publication
    Graph-powered interpretable machine learning models for abnormality detection in ego-things network
    (MDPI, 2022-03-02) Kanapram, Divya Thekke; Marcenaro, Lucio; Martín Gómez, David; Regazzoni, Carlo
    In recent days, it is becoming essential to ensure that the outcomes of signal processing methods based on machine learning (ML) data-driven models can provide interpretable predictions. The interpretability of ML models can be defined as the capability to understand the reasons that contributed to generating a given outcome in a complex autonomous or semi-autonomous system. The necessity of interpretability is often related to the evaluation of performances in complex systems and the acceptance of agents automatization processes where critical high-risk decisions have to be taken. This paper concentrates on one of the core functionality of such systems, i.e., abnormality detection, and on choosing a model representation modality based on a data-driven machine learning (ML) technique such that the outcomes become interpretable. The interpretability in this work is achieved through graph matching of semantic level vocabulary generated from the data and their relationships. The proposed approach assumes that the data-driven models to be chosen should support emergent self-awareness (SA) of the agents at multiple abstraction levels. It is demonstrated that the capability of incrementally updating learned representation models based on progressive experiences of the agent is shown to be strictly related to interpretability capability. As a case study, abnormality detection is analyzed as a primary feature of the collective awareness (CA) of a network of vehicles performing cooperative behaviors. Each vehicle is considered an example of an Internet of Things (IoT) node, therefore providing results that can be generalized to an IoT framework where agents have different sensors, actuators, and tasks to be accomplished. The capability of a model to allow evaluation of abnormalities at different levels of abstraction in the learned models is addressed as a key aspect for interpretability.
  • Publication
    A proposed system for multi-UAVs in remote sensing operations
    (MDPI, 2022-12-01) Flores Peña, Pablo; Luna, Marco Andres; Mohammad Sadeq, Ale Isaac; Refaat Regab, Ahmed; Elmenshawy, Khaled; Martín Gómez, David; Campoy, Pascual; Molina, Martin; Comunidad de Madrid; European Commission
    This paper proposes the design of the communications, control systems, and navigation algorithms of a multi-UAV system focused on remote sensing operations. A new controller based on a compensator and a nominal controller is designed to dynamically regulate the UAVs' attitude. The navigation system addresses the multi-region coverage trajectory planning task using a new approach to solve the TSP-CPP problem. The navigation algorithms were tested theoretically, and the combination of the proposed navigation techniques and control strategy was simulated through the Matlab SimScape platform to optimize the controller's parameters over several iterations. The results reveal the robustness of the controller and optimal performance of the route planner.
  • Publication
    Intelligent Video Surveillance Systems for Vehicle Identification Based on Multinet Architecture
    (MDPI, 2022-07-06) González-Cepeda, Jacobo; Ramajo, Álvaro; Armingol Moreno, José María; Comunidad de Madrid; Ministerio de Ciencia e Innovación (España)
    Security cameras have been proven to be particularly useful in preventing and combating crime through identification tasks. Here, two areas can be mainly distinguished: person and vehicle identification. Automatic license plate readers are the most widely used tool for vehicle identification. Although these systems are very effective, they are not reliable enough in certain circumstances. For example, due to traffic jams, vehicle position or weather conditions, the sensors cannot capture an image of the entire license plate. However, there is still a lot of additional information in the image which may also be of interest, and that needs to be analysed quickly and accurately. The correct use of the processing mechanisms can significantly reduce analysis time, increasing the efficiency of video cameras significantly. To solve this problem, we have designed a solution based on two technologies: license plate recognition and vehicle re-identification. For its development and testing, we have also created several datasets recreating a real environment. In addition, during this article, it is also possible to read about some of the main artificial intelligence techniques for these technologies, as they have served as the starting point for this research.
  • Publication
    BirdNet+: two-stage 3D object detection in LiDAR through a sparsity-invariant bird's eye view
    (IEEE, 2021-11-30) Barrera Del Pozo, Alejandro; Beltrán de la Cita, Jorge; Guindel Gómez, Carlos; Iglesias Martínez, José Antonio; García Fernández, Fernando; Comunidad de Madrid
    Autonomous navigation relies upon an accurate understanding of the elements in the surroundings. Among the different on-board perception tasks, 3D object detection allows the identification of dynamic objects that cannot be registered by maps, being key for safe navigation. Thus, it often requires the use of LiDAR data, which is able to faithfully represent the scene geometry. However, although raw laser point clouds contain rich features to perform object detection, more compact representations such as the bird's eye view (BEV) projection are usually preferred in order to meet the time requirements of the control loop. This paper presents an end-to-end object detection network based on the well-known Faster R-CNN architecture that uses BEV images as input to produce the final 3D boxes. Our regression branches can infer not only the axis-aligned bounding boxes but also the rotation angle, height, and elevation of the objects in the scene. The proposed network provides state-of-the-art results for car, pedestrian, and cyclist detection with a single forward pass when evaluated on the KITTI 3D Object Detection Benchmark, with an accuracy that exceeds 64% mAP 3D for the Moderate difficulty. Further experiments on the challenging nuScenes dataset show the generalizability of both the method and the proposed BEV representation against different LiDAR devices and across a wider set of object categories by being able to reach more than 30% mAP with a single LiDAR sweep and almost 40% mAP with the usual 10-sweep accumulation.
  • Publication
    Intelligent surveillance of indoor environments based on computer vision and 3D point cloud fusion
    (Elsevier, 2015-11-30) Gómez Silva, María José; García Fernández, Fernando; Escalera Hueso, Arturo de la; Armingol Moreno, José María; Ministerio de Ciencia e Innovación (España); Ministerio de Asuntos Económicos y Transformación Digital (España)
    A real-time detection algorithm for intelligent surveillance is presented. The system, based on 3D change detection with respect to a complex scene model, allows intruder monitoring and detection of added and missing objects, under different illumination conditions. The proposed system has two independent stages. First, a mapping application provides an accurate 3D wide model of the scene, using a view registration approach. This registration is based on computer vision and 3D point cloud. Fusion of visual features with 3D descriptors is used in order to identify corresponding points in two consecutive views. The matching of these two views is first estimated by a pre-alignment stage, based on the tilt movement of the sensor, later they are accurately aligned by an Iterative Closest Point variant (Levenberg-Marquardt ICP), which performance has been improved by a previous filter based on geometrical assumptions. The second stage provides accurate intruder and object detection by means of a 3D change detection approach, based on Octree volumetric representation, followed by a clusters analysis. The whole scene is continuously scanned, and every captured is compared with the corresponding part of the wide model thanks to the previous analysis of the sensor movement parameters. With this purpose a tilt-axis calibration method has been developed. Tests performed show the reliable performance of the system under real conditions and the improvements provided by each stage independently. Moreover, the main goal of this application has been enhanced, for reliable intruder detection by the tilting of the sensors using its built-in motor to increase the size of the monitored area. (C) 2015 Elsevier Ltd. All rights reserved.
  • Publication
    Hierarchical Global Tracking Hypotheses Generator
    (Elsevier, 2022-11-11) Gómez Silva, María José; Escalera Hueso, Arturo de la; Armingol Moreno, José María; Comunidad de Madrid; Ministerio de Ciencia e Innovación (España); Ministerio de Educación, Cultura y Deporte (España); Universidad Carlos III de Madrid
    The presence of crowds, crossing people, occlusions, and individuals entering and leaving the monitored scenario turns the automatization of Multi-Object Tracking into a demanding task. Due to the difficulties in dealing with those situations, the data association between the incoming observations and their corresponding identities could produce split, merged, and even missed tracks. This article proposes a Hierarchical Generator of Tracking Global Hypotheses (HGTGH) to prevent those errors. In this method, the data association process is divided into hierarchical levels according to multiple factors, such as the duration of tracking on the individuals or the number of frames in a row where they have been missed. A dedicated formulation of the association cost at each level properly combines various affinity metrics. Instead of generating hypotheses for each individual and analyzing them through a batch of future frames, the proposed method immediately generates a global hypothesis that describes the assignment of a whole set of identities on every incoming frame. The generated hypothesis is also able to render new people entering the scene. Thanks to this advantage, the proposed method simultaneously addresses the reduction of identity switches and the problem of starting new tracks. This novel data association method constitutes the core of an online tracking algorithm, which has been evaluated over the MOT17 dataset to demonstrate its effectiveness.
  • Publication
    Performance analysis of fast marching-based motion planning for autonomous mobile robots in ITER scenarios
    (Elsevier, 2015-01-01) Gómez González, Javier Victorio; Valera Pérez, Alberto; Garrido Bullón, Luis Santiago; Moreno Lorente, Luis Enrique
    Operations of transportation in cluttered environments require robust motion planning algorithms. Specially with large and heavy vehicles under hazardous operations of maintenance, such as in the ITER, an international nuclear fusion research project. The load transportation inside the ITER facilities require smooth and optimized paths with safety margin of 30 cm. The transportation is accomplished by large rhombic-like vehicles to exploit its kinematic capabilities. This paper presents the performance analysis of a motion planning algorithm to optimize trajectories in terms of clearance, smoothness and execution time in cluttered scenarios. The algorithm is an upgraded version of a previous one used in ITER, replacing the initialization implemented using Constrained Delaunay Triangulation by the Fast Marching Square. Exhaustive simulated experiments have been carried out in different levels of ITER buildings, comparing the performance of the algorithm using different metrics.
  • Publication
    Projection surfaces detection and image correction for mobile robots in HRI
    (Hindawi, 2017-07-12) Fernández Rodicio, Enrique; González Pacheco, Víctor; Castillo Montoya, José Carlos; Castro González, Álvaro; Malfaz Vázquez, María Ángeles; Salichs Sánchez-Caballero, Miguel; Ministerio de Economía y Competitividad (España)
    Projectors have become a widespread tool to share information in Human-Robot Interaction with large groups of people in a comfortable way. Finding a suitable vertical surface becomes a problem when the projector changes positions when a mobile robot is looking for suitable surfaces to project. Two problems must be addressed to achieve a correct undistorted image: (i) finding the biggest suitable surface free fromobstacles and (ii) adapting the output image to correct the distortion due to the angle between the robot and a nonorthogonal surface. We propose a RANSAC-based method that detects a vertical plane inside a point cloud. Then, inside this plane, we apply a rectangle-fitting algorithm over the region in which the projector can work. Finally, the algorithm checks the surface looking for imperfections and occlusions and transforms the original image using a homography matrix to display it over the area detected. The proposed solution can detect projection areas in real-time using a single Kinect camera, which makes it suitable for applications where a robot interacts with other people in unknown environments. Our Projection Surfaces Detector and the Image Correction module allow a mobile robot to find the right surface and display images without deformation, improving its ability to interact with people.
  • Publication
    Trajectory Planning for Multi-Robot Systems: Methods and Applications
    (Elsevier, 2021-07-01) Madridano Carrasco, Ángel; Al Kaff, Abdulla Hussein Abdulrahman; Martín Gómez, David; Escalera Hueso, Arturo de la; Comunidad de Madrid
    In the multiple fields covered by Artificial Intelligence (AI), path planning is undoubtedly one of the issues that cover a wide range of research lines. To be able to find an optimal solution, which allows one or several vehicles to establish a safe and effective way to reach a final state from an initial state, is a challenge that continues to be studied today. The increasingly widespread use of autonomous vehicles, both aerial and ground-based, make path planning an essential aspect for incorporating these systems into an endless number of applications. Besides, in recent years, the use of Multi-Robot Systems (MRS) has spread, consisting of both Unmanned Aerial Vehicles (UAVs) and Unmanned Ground Vehicles (UGVs), gaining versatility and robustness in their operation. The possibility of using heterogeneous robotic teams allows tackling, autonomously, and simultaneously, a wide range of tasks with different characteristics in the same environment. For this purpose, path planning becomes a crucial aspect and, for this reason, this work aims to offer a general vision of trajectory planning, to establish a comparison between the methods and algorithms present in the literature for the resolution of this problem within MRS, and finally, to show the applicability of these methods in different areas, together with the importance of these methods for achieving autonomous and safe navigation of different types of vehicles.
  • Publication
    Ehmi: Review and guidelines for deployment on autonomous vehicles
    (MDPI, 2021-05-01) Carmona Fernández, Juan; Guindel Gómez, Carlos; Garcia Marin, Fernando; Escalera Hueso, Arturo de la; Comunidad de Madrid; Ministerio de Economía y Competitividad (España); Agencia Estatal de Investigación (España); Universidad Carlos III de Madrid
    Human-machine interaction is an active area of research due to the rapid development of autonomous systems and the need for communication. This review provides further insight into the specific issue of the information flow between pedestrians and automated vehicles by evaluating recent advances in external human-machine interfaces (eHMI), which enable the transmission of state and intent information from the vehicle to the rest of the traffic participants. Recent developments will be explored and studies analyzing their effectiveness based on pedestrian feedback data will be presented and contextualized. As a result, we aim to draw a broad perspective on the current status and recent techniques for eHMI and some guidelines that will encourage future research and development of these systems.
  • Publication
    Project ARES: Driverless transportation system. Challenges and approaches in an unstructured road
    (MDPI, 2021-08) Marín Plaza, Pablo; Yagüe Cuevas, David; Royo Velasco, Francisco; Miguel Paraiso, Miguel Ángel de; Moreno Olivo, Francisco Miguel; Ruiz De La Cuadra, Alejandro; Viadero Monasterio, Fernando; García Guzmán, Javier; San Román García, José Luis; Armingol Moreno, José María; Comunidad de Madrid; Ministerio de Economía y Competitividad (España); Ministerio de Ciencia e Innovación (España)
    The expansion of electric vehicles in urban areas has paved the way toward the era of autonomous vehicles, improving the performance in smart cities and upgrading related driving problems. This field of research opens immediate applications in the tourism areas, airports or business centres by greatly improving transport efficiency and reducing repetitive human tasks. This project shows the problems derived from autonomous driving such as vehicle localization, low coverage of 4G/5G and GPS, detection of the road and navigable zones including intersections, detection of static and dynamic obstacles, longitudinal and lateral control and cybersecurity aspects. The approaches proposed in this article are sufficient to solve the operational design of the problems related to autonomous vehicle application in the special locations such as rough environment, high slopes and unstructured terrain without traffic rules.
  • Publication
    Sensor fusion methodology for vehicle detection
    (IEEE, 2017-04) García Fernández, Fernando; Martín Gómez, David; Escalera Hueso, Arturo de la; Armingol Moreno, José María
    A novel sensor fusion methodology is presented, which provides intelligent vehicles with augmented environment information and knowledge, enabled by vision-based system, laser sensor and global positioning system. The presented approach achieves safer roads by data fusion techniques, especially in single-lane carriage-ways where casualties are higher than in other road classes, and focuses on the interplay between vehicle drivers and intelligent vehicles. The system is based on the reliability of laser scanner for obstacle detection, the use of camera based identification techniques and advanced tracking and data association algorithms i.e. Unscented Kalman Filter and Joint Probabilistic Data Association. The achieved results foster the implementation of the sensor fusion methodology in forthcoming Intelligent Transportation Systems.
  • Publication
    Survey of computer vision algorithms and applications for unmanned aerial vehicles
    (Elsevier, 2018-02) Al Kaff, Abdulla Hussein Abdulrahman; Martín Gómez, David; García Fernández, Fernando; Escalera Hueso, Arturo de la; Armingol Moreno, José María; Ministerio de Economía y Competitividad (España)
    This paper presents a complete review of computer vision algorithms and vision-based intelligent applications, that are developed in the field of the Unmanned Aerial Vehicles (UAVs) in the latest decade. During this time, the evolution of relevant technologies for UAVs; such as component miniaturization, the increase of computational capabilities, and the evolution of computer vision techniques have allowed an important advance in the development of UAVs technologies and applications. Particularly, computer vision technologies integrated in UAVs allow to develop cutting-edge technologies to cope with aerial perception difficulties; such as visual navigation algorithms, obstacle detection and avoidance and aerial decision-making. All these expert technologies have developed a wide spectrum of application for UAVs, beyond the classic military and defense purposes. Unmanned Aerial Vehicles and Computer Vision are common topics in expert systems, so thanks to the recent advances in perception technologies, modern intelligent applications are developed to enhance autonomous UAV positioning, or automatic algorithms to avoid aerial collisions, among others. Then, the presented survey is based on artificial perception applications that represent important advances in the latest years in the expert system field related to the Unmanned Aerial Vehicles. In this paper, the most significant advances in this field are presented, able to solve fundamental technical limitations; such as visual odometry, obstacle detection, mapping and localization, et cetera. Besides, they have been analyzed based on their capabilities and potential utility. Moreover, the applications and UAVs are divided and categorized according to different criteria.
  • Publication
    A Research Platform for Autonomous Vehicles Technologies Research in the Insurance Sector
    (MDPI, 2020-08-02) Miguel Paraiso, Miguel Ángel de; Moreno Olivo, Francisco Miguel; Marín Plaza, Pablo; Al Kaff, Abdulla Hussein Abdulrahman; Palos Lorite, Martin; Martin Gomez, David; Encinar-Martín, Rodrigo; García Fernández, Fernando; Comunidad de Madrid; Ministerio de Ciencia e Innovación (España)
    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.