2026-06-09T07:40:37Zhttps://riubu.ubu.es/oai/requestoai:riubu.ubu.es:10259/69612024-05-17T09:46:20Zcom_10259.4_104com_10259_2604col_10259_6848
Egido Sierra, Javier del
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600
Díaz Díaz, Alejandro
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600
Bergasa Pascual, Luis M.
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600
Barea Navarro, Rafael
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600
López Guillén, M. Elena
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600
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
THUMBNAIL
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10259/6961
oai:riubu.ubu.es:10259/6961
2024-05-17 11:46:20.091
Repositorio Institucional de la Universidad de Burgos
bubrep@ubu.es
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