2026-06-09T17:40:50Zhttps://riubu.ubu.es/oai/request

oai:riubu.ubu.es:10259/69612024-05-17T09:46:20Zcom_10259.4_104com_10259_2604col_10259_6848

Egido Sierra, Javier del Díaz Díaz, Alejandro Bergasa Pascual, Luis M. Barea Navarro, Rafael López Guillén, M. Elena 2022-09-20T12:54:53Z 2022-09-20T12:54:53Z 2021-07 978-84-18465-12-3 http://hdl.handle.net/10259/6961 10.36443/10259/6961 Trabajo presentado en: R-Evolucionando el transporte, XIV Congreso de Ingeniería del Transporte (CIT 2021), realizado en modalidad online los días 6, 7 y 8 de julio de 2021, organizado por la Universidad de Burgos The introduction of Autonomous Vehicles (AVs) in a realistic urban environment is an ambitious objective. AV validation on real scenarios involving actual objects such as cars or pedestrians in a wide range of traffic cases would escalate the cost and could generate hazardous situations. Consequently, autonomous driving simulators are quickly evolving to cover the gap to achieve a fully autonomous driving architecture validation. Most used 3D simulators in self-driving cars field are V-REP (Rohmer, E., 2013) and Gazebo (KOENIG, N. and HOWARD, A., 2004), due to an easy integration with ROS (QUIGLEY, 2009) platform to increase the interoperability with other systems. Those simulators provide accurate motion information (more appropriate for easier scenes like robotic arms) but not a realistic appearance and not allowing real-time systems, not being able to recreate complex traffic scenes. CARLA (DOSOVITSKIY, A., 2017) opensource AV simulator is designed to be able to train and validate control and perception algorithms in complex traffic scenarios with hyper-realistic environments. CARLA simulator allows to easily modify on-board sensors such as cameras or LiDAR, weather conditions and also the traffic scene to perform specific traffic cases. In Summer 2019, CARLA launched its driving challenge to allow everyone to test their own control techniques under the same traffic scenarios, scoring its performance regarding traffic rules. In this paper, the Robesafe researching group approach will be explained, detailing vehicle motion control and object detection adapted from Smart Elderly Car (GÓMEZ-HUÉLAMO, C., 2019) that lead the group to reach the 4th place in Track 3 challenge, where HD Map, Waypoints and environmental sensors data (LiDAR, RGB cameras and GPS) were provided. This work has been funded in part from the Spanish MICINN/FEDER through the Techs4AgeCar project (RTI2018-099263-B-C21) and from the RoboCity2030-DIH-CM project (P2018/NMT- 4331), funded by Programas de actividades I+D (CAM) and cofunded by EU Structural Funds. application/pdf eng Universidad de Burgos. Servicio de Publicaciones e Imagen Institucional R-Evolucionando el transporte http://hdl.handle.net/10259/6490 https://doi.org/10.36443/9788418465123 info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/RTI2018-099263-B-C21/ES/TECNOLOGIAS ROBUSTAS PARA UN CONCEPTO DE COCHE ELECTRICO AUTOMATIZADO PARA CONDUCTORES MAYORES info:eu-repo/grantAgreement/CAM//P2018%2FNMT-4331 Vehículos Formas de movilidad Vehículos autónomos Vehicles Means of mobility Autonomous vehicles Ingeniería civil Transportes Civil engineering Transportation Autonomous vehicle control in CARLA Challenge info:eu-repo/semantics/conferenceObject info:eu-repo/semantics/publishedVersion info:eu-repo/semantics/openAccess 1991 1998