Arquitectura software y de navegación para vehículo autónomo

La importancia de los vehículos autónomos en el sector del transporte durante las próximas décadas es ya un hecho. La implementación a gran escala de estos vehículos supondrá una serie de ventajas entre las que destacan una conducción más segura y por lo tanto una disminución de los accidentes de tráfico, una reducción de las emisiones y del consumo energético y un acortamiento de los tiempos de trayecto. Sin embargo, existen todavía numerosos problemas por resolver de cara a una conducción completamente autónoma y generalizada. Todavía es necesario investigar en distintas tecnologías como percepción, control o navegación. Esta última área, es especialmente crítica ya que el correcto movimiento del vehículo depende de una localización y planificación de trayectorias robustas y fiables, entre otras tareas de navegación. Además, también es necesario estudiar la relación y el funcionamiento conjunto de todos los módulos de estas áreas junto con el hardware y entre ellas, relaciones definidas por la arquitectura. El objetivo de esta tesis es: Por una parte, desarrollar una plataforma de investigación constituida por un vehículo autónomo completamente funcional, en la que se puedan probar distintos algoritmos relacionados con la conducción autónoma. Se investigarán las distintas arquitecturas posibles y se describirá la incorporada al vehículo desarrollado. Por otra parte, esta tesis presenta los avances realizados en el área de la navegación para mejorar la localización del vehículo en entornos mixtos donde métodos convencionales basados en GNSS o la correlación entre un mapa y las lecturas del LiDAR no obtienen resultados precisos, así como los avances en predicción del movimiento de otros vehículos, necesarios para una buena planificación de trayectorias. Además se investigará acerca de la interacción entre peatones y vehículos autónomos, y cómo mejorarla haciendo uso de distintas interfaces de comunicación. Los resultados de los algoritmos desarrollados en localización y predicción de trayectorias han sido obtenidos con bases de datos públicas y comparados con métodos del estado del arte a los que superan en precisión, mientras que los resultados relativos a la interacción entre peatones y vehículos autónomos se ha evaluado mediante experimentos reales. Además, la arquitectura completa del vehículo ha sido probada en distintos experimentos que certifican su correcto funcionamiento.The importance of autonomous vehicles in the transportation sector over the next decades is already a fact. The large-scale implementation of these vehicles will bring several advantages, including safer driving and therefore a decrease of traffic fatalities, lower emissions and energy consumption, and shorter journey times. However, there are still many issues to be solved for fully autonomous and widespread driving. A deeper research is still needed in different technologies such as perception, control and navigation. This last area is especially critical since the correct movement of the vehicle depends on precise localization and a robust and reliable path planning, among other navigation tasks. In addition, it is also necessary to study the relationships and the joint operation of all the modules of these areas together with the hardware and between them, relationships defined by the architecture. The objective of this thesis is: On the one hand, to develop a research platform consisting of a fully functional autonomous vehicle, on which different algorithms related to autonomous driving can be tested. The different possible architectures will be investigated and the one incorporated in the developed vehicle will be described. On the other hand, this thesis presents the advances made in the area of navigation to improve vehicle localization in mixed environments where conventional methods based on GNSS or the correlation between a map and LiDAR readings do not obtain accurate results, as well as advances in predicting the movement of other vehicles, necessary for good trajectory planning. In addition, the interaction between pedestrians and autonomous vehicles will be studied, and how to improve it using different communication interfaces. The results of the developed algorithms in localization and trajectory prediction have been obtained with public databases and compared with state-of-the-art methods which are outperformed in termos of accuracy, while the results related to the interaction between pedestrians and autonomous vehicles have been evaluated by means of real experiments. In addition, the complete vehicle architecture has been tested in different experiments certifying its correct operation.Programa de Doctorado en Ingeniería Eléctrica, Electrónica y Automática por la Universidad Carlos III de MadridPresidente: Ignacio Parra Alonso.- Secretario: Carlos Guindel Gómez.- Vocal: Noelia Hernández Parr

Improved LiDAR Probabilistic Localization for Autonomous Vehicles Using GNSS

This paper proposes a method that improves autonomous vehicles localization using a modification of probabilistic laser localization like Monte Carlo Localization (MCL) algorithm, enhancing the weights of the particles by adding Kalman filtered Global Navigation Satellite System (GNSS) information. GNSS data are used to improve localization accuracy in places with fewer map features and to prevent the kidnapped robot problems. Besides, laser information improves accuracy in places where the map has more features and GNSS higher covariance, allowing the approach to be used in specifically difficult scenarios for GNSS such as urban canyons. The algorithm is tested using KITTI odometry dataset proving that it improves localization compared with classic GNSS + Inertial Navigation System (INS) fusion and Adaptive Monte Carlo Localization (AMCL), it is also tested in the autonomous vehicle platform of the Intelligent Systems Lab (LSI), of the University Carlos III de of Madrid, providing qualitative results.Research supported by the Spanish Government through the CICYT projects (TRA2016-78886-C3-1-Rand RTI2018-096036-B-C21), Universidad Carlos III of Madrid through (PEAVAUTO-CM-UC3M) and the Comunidad de Madrid through SEGVAUTO-4.0-CM (P2018/EMT-4362)

High-accuracy patternless calibration of multiple 3D LiDARs for autonomous vehicles

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 .This work was supported in part by the Madrid Government (Comunidad de Madrid-Spain) under the Multiannual Agreement with UC3M ("Fostering Young Doctors Research," APBI-CM-UC3M) in the context of the V PRICIT (Research and Technological Innovation Regional Program); and in part by the Spanish Government through Grants ID2021-128327OA-I00 and TED2021-129374A-I00 funded by MCIN/AEI/10.13039/501100011033 and by the European Union NextGenerationEU/PRTR

Evaluating the acceptance of autonomous vehicles in the future

Proceeding of: 35th IEEE Intelligent Vehicles Symposium (IV 2023), 04-07 June 2023, Anchorage, AK, USA.The continuous advance of the automotive industry is leading to the emergence of more advanced driver assistance systems that enable the automation of certain tasks and that are undoubtedly aimed at achieving vehicles in which the driving task can be completely delegated. All these advances will bring changes in the paradigm of the automotive market, as is the case of insurance. For this reason, CESVIMAP and the Universidad Carlos III de Madrid are working on an Autonomous Testing pLatform for insurAnce reSearch (ATLAS) to study this technology and obtain first-hand knowledge about the responsibilities of each of the agents involved in the development of the vehicles of the future. This work gathers part of the advancements made in ATLAS, which have made it possible to have an autonomous vehicle with which to perform tests in real environments and demonstrations bringing the vehicle closer to future users. As a result of this work, and in collaboration with the Johannes Kepler University Linz, the impact, degree of acceptance and confidence of users in autonomous vehicles has been studied once they have taken a trip on board a fully autonomous vehicle such as ATLAS. This study has found that, while most users would be willing to use an autonomous vehicle, the same users are concerned about the use of this type of technology. Thus, understanding the reasons for this concern can help define the future of autonomous cars

A Research Platform for Autonomous Vehicles Technologies Research in the Insurance Sector

This article belongs to the Special Issue Intelligent Transportation SystemsThis 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

Project ARES: Driverless transportation system. Challenges and approaches in an unstructured road

This article belongs to the Special Issue Intelligent Control of Mobile Robotics.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.Research is supported by the Spanish Government through the CICYT projects (PID2019-104793RB-C31 and RTI2018-096036-B-C21), the Comunidad de Madrid through SEGVAUTO-4.0-CM (P2018/EMT-4362) and through EAI of the Ministry of Science and Innovation of the Government of Spain project RTI2018-095143-B-C2