RT info:eu-repo/semantics/conferenceObject
T1 Autonomous vehicle control in CARLA Challenge
A1 Egido Sierra, Javier del
A1 Díaz Díaz, Alejandro
A1 Bergasa Pascual, Luis M.
A1 Barea Navarro, Rafael
A1 López Guillén, M. Elena
K1 Vehículos
K1 Vehicles
K1 Formas de movilidad
K1 Means of mobility
K1 Vehículos autónomos
K1 Autonomous vehicles
K1 Ingeniería civil
K1 Civil engineering
K1 Transporte
K1 Transportation
AB The introduction of Autonomous Vehicles (AVs) in a realistic urban environment is anambitious objective. AV validation on real scenarios involving actual objects such as cars orpedestrians in a wide range of traffic cases would escalate the cost and could generatehazardous situations. Consequently, autonomous driving simulators are quickly evolving tocover the gap to achieve a fully autonomous driving architecture validation. Most used 3Dsimulators 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 sceneslike robotic arms) but not a realistic appearance and not allowing real-time systems, notbeing able to recreate complex traffic scenes. CARLA (DOSOVITSKIY, A., 2017) opensourceAV simulator is designed to be able to train and validate control and perceptionalgorithms 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 Summer2019, CARLA launched its driving challenge to allow everyone to test their own controltechniques under the same traffic scenarios, scoring its performance regarding traffic rules.In this paper, the Robesafe researching group approach will be explained, detailing vehiclemotion 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.
PB Universidad de Burgos. Servicio de Publicaciones e Imagen Institucional
SN 978-84-18465-12-3
YR 2021
FD 2021-07
LK http://hdl.handle.net/10259/6961
UL http://hdl.handle.net/10259/6961
LA eng
NO 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
NO 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.
DS Repositorio Institucional de la Universidad de Burgos
RD 25-abr-2024