360º real-time and power-efficient 3D DAMOT for autonomous driving applications

Autonomous Driving (AD) promises an efficient, comfortable and safe driving experience. Nevertheless, fatalities involving vehicles equipped with Automated Driving Systems (ADSs) are on the rise, especially those related to the perception module of the vehicle. This paper presents a real-time and po...

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Autores: Gómez Huélamo, Carlos, Egido Sierra, Javier del, Bergasa Pascual, Luis Miguel|||0000-0002-0087-3077, Barea Navarro, Rafael|||0000-0002-4179-6100, López Guillén, María Elena, Araluce Ruiz, Javier, Antunes García, Miguel|||0009-0008-5627-5325
Tipo de recurso: artículo
Fecha de publicación:2022
País:España
Institución:Universidad de Alcalá (UAH)
Repositorio:e_Buah Biblioteca Digital Universidad de Alcalá
Idioma:inglés
OAI Identifier:oai:ebuah.uah.es:10017/63110
Acceso en línea:http://hdl.handle.net/10017/63110
https://dx.doi.org/10.1007/s11042-021-11624-2
Access Level:acceso abierto
Palabra clave:Real-time
CARLA
LiDAR
3D multi-object tracking
ROS
DAMOT
Autonomous navigation
Electrónica
Electronics
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network_acronym_str ES
network_name_str España
repository_id_str
spelling 360º real-time and power-efficient 3D DAMOT for autonomous driving applicationsGómez Huélamo, CarlosEgido Sierra, Javier delBergasa Pascual, Luis Miguel|||0000-0002-0087-3077Barea Navarro, Rafael|||0000-0002-4179-6100López Guillén, María ElenaAraluce Ruiz, JavierAntunes García, Miguel|||0009-0008-5627-5325Real-timeCARLALiDAR3D multi-object trackingROSDAMOTAutonomous navigationElectrónicaElectronicsAutonomous Driving (AD) promises an efficient, comfortable and safe driving experience. Nevertheless, fatalities involving vehicles equipped with Automated Driving Systems (ADSs) are on the rise, especially those related to the perception module of the vehicle. This paper presents a real-time and power-efficient 3D Multi-Object Detection and Tracking (DAMOT) method proposed for Intelligent Vehicles (IV) applications, allowing the vehicle to track 360º surrounding objects as a preliminary stage to perform trajectory forecasting to prevent collisions and anticipate the ego-vehicle to future traffic scenarios. First, we present our DAMOT pipeline based on Fast Encoders for object detection and a combination of a 3D Kalman Filter and Hungarian Algorithm, used for state estimation and data association respectively. We extend our previous work ellaborating a preliminary version of sensor fusion based DAMOT, merging the extracted features by a Convolutional Neural Network (CNN) using camera information for long-term re-identification and obstacles retrieved by the 3D object detector. Both pipelines exploit the concepts of lightweight Linux containers using the Docker approach to provide the system with isolation, flexibility and portability, and standard communication in robotics using the Robot Operating System (ROS). Second, both pipelines are validated using the recently proposed KITTI-3DMOT evaluation tool that demonstrates the full strength of 3D localization and tracking of a MOT system. Finally, the most efficient architecture is validated in some interesting traffic scenarios implemented in the CARLA (Car Learning to Act) open-source driving simulator and in our real-world autonomous electric car using the NVIDIA AGX Xavier, an AI embedded system for autonomous machines, studying its performance in a controlled but realistic urban environment with real-time execution (results).Agencia Estatal de InvestigaciónComunidad de MadridSpringer20222022-01-08journal articlehttp://purl.org/coar/resource_type/c_6501NAhttp://purl.org/coar/version/c_be7fb7dd8ff6fe43info:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10017/63110https://dx.doi.org/10.1007/s11042-021-11624-2reponame:e_Buah Biblioteca Digital Universidad de Alcaláinstname:Universidad de Alcalá (UAH)InglésengAgencia Estatal de Investigación http://dx.doi.org/10.13039/501100011033 Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020 RTI2018-099263-B-C21 TECNOLOGIAS ROBUSTAS PARA UN CONCEPTO DE COCHE ELECTRICO AUTOMATIZADO PARA CONDUCTORES MAYORESComunidad de Madrid http://dx.doi.org/10.13039/100012818 Not available P2018%2FNMT-4331 Madrid Robotics Digital Innovation Hubopen accesshttp://purl.org/coar/access_right/c_abf2Attribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessoai:ebuah.uah.es:10017/631102026-06-18T11:13:07Z
dc.title.none.fl_str_mv 360º real-time and power-efficient 3D DAMOT for autonomous driving applications
title 360º real-time and power-efficient 3D DAMOT for autonomous driving applications
spellingShingle 360º real-time and power-efficient 3D DAMOT for autonomous driving applications
Gómez Huélamo, Carlos
Real-time
CARLA
LiDAR
3D multi-object tracking
ROS
DAMOT
Autonomous navigation
Electrónica
Electronics
title_short 360º real-time and power-efficient 3D DAMOT for autonomous driving applications
title_full 360º real-time and power-efficient 3D DAMOT for autonomous driving applications
title_fullStr 360º real-time and power-efficient 3D DAMOT for autonomous driving applications
title_full_unstemmed 360º real-time and power-efficient 3D DAMOT for autonomous driving applications
title_sort 360º real-time and power-efficient 3D DAMOT for autonomous driving applications
dc.creator.none.fl_str_mv Gómez Huélamo, Carlos
Egido Sierra, Javier del
Bergasa Pascual, Luis Miguel|||0000-0002-0087-3077
Barea Navarro, Rafael|||0000-0002-4179-6100
López Guillén, María Elena
Araluce Ruiz, Javier
Antunes García, Miguel|||0009-0008-5627-5325
author Gómez Huélamo, Carlos
author_facet Gómez Huélamo, Carlos
Egido Sierra, Javier del
Bergasa Pascual, Luis Miguel|||0000-0002-0087-3077
Barea Navarro, Rafael|||0000-0002-4179-6100
López Guillén, María Elena
Araluce Ruiz, Javier
Antunes García, Miguel|||0009-0008-5627-5325
author_role author
author2 Egido Sierra, Javier del
Bergasa Pascual, Luis Miguel|||0000-0002-0087-3077
Barea Navarro, Rafael|||0000-0002-4179-6100
López Guillén, María Elena
Araluce Ruiz, Javier
Antunes García, Miguel|||0009-0008-5627-5325
author2_role author
author
author
author
author
author
dc.subject.none.fl_str_mv Real-time
CARLA
LiDAR
3D multi-object tracking
ROS
DAMOT
Autonomous navigation
Electrónica
Electronics
topic Real-time
CARLA
LiDAR
3D multi-object tracking
ROS
DAMOT
Autonomous navigation
Electrónica
Electronics
description Autonomous Driving (AD) promises an efficient, comfortable and safe driving experience. Nevertheless, fatalities involving vehicles equipped with Automated Driving Systems (ADSs) are on the rise, especially those related to the perception module of the vehicle. This paper presents a real-time and power-efficient 3D Multi-Object Detection and Tracking (DAMOT) method proposed for Intelligent Vehicles (IV) applications, allowing the vehicle to track 360º surrounding objects as a preliminary stage to perform trajectory forecasting to prevent collisions and anticipate the ego-vehicle to future traffic scenarios. First, we present our DAMOT pipeline based on Fast Encoders for object detection and a combination of a 3D Kalman Filter and Hungarian Algorithm, used for state estimation and data association respectively. We extend our previous work ellaborating a preliminary version of sensor fusion based DAMOT, merging the extracted features by a Convolutional Neural Network (CNN) using camera information for long-term re-identification and obstacles retrieved by the 3D object detector. Both pipelines exploit the concepts of lightweight Linux containers using the Docker approach to provide the system with isolation, flexibility and portability, and standard communication in robotics using the Robot Operating System (ROS). Second, both pipelines are validated using the recently proposed KITTI-3DMOT evaluation tool that demonstrates the full strength of 3D localization and tracking of a MOT system. Finally, the most efficient architecture is validated in some interesting traffic scenarios implemented in the CARLA (Car Learning to Act) open-source driving simulator and in our real-world autonomous electric car using the NVIDIA AGX Xavier, an AI embedded system for autonomous machines, studying its performance in a controlled but realistic urban environment with real-time execution (results).
publishDate 2022
dc.date.none.fl_str_mv 2022
2022-01-08
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
NA
http://purl.org/coar/version/c_be7fb7dd8ff6fe43
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv http://hdl.handle.net/10017/63110
https://dx.doi.org/10.1007/s11042-021-11624-2
url http://hdl.handle.net/10017/63110
https://dx.doi.org/10.1007/s11042-021-11624-2
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.relation.none.fl_str_mv Agencia Estatal de Investigación http://dx.doi.org/10.13039/501100011033 Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020 RTI2018-099263-B-C21 TECNOLOGIAS ROBUSTAS PARA UN CONCEPTO DE COCHE ELECTRICO AUTOMATIZADO PARA CONDUCTORES MAYORES
Comunidad de Madrid http://dx.doi.org/10.13039/100012818 Not available P2018%2FNMT-4331 Madrid Robotics Digital Innovation Hub
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution-NonCommercial-NoDerivatives 4.0 International
http://creativecommons.org/licenses/by-nc-nd/4.0/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution-NonCommercial-NoDerivatives 4.0 International
http://creativecommons.org/licenses/by-nc-nd/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Springer
publisher.none.fl_str_mv Springer
dc.source.none.fl_str_mv reponame:e_Buah Biblioteca Digital Universidad de Alcalá
instname:Universidad de Alcalá (UAH)
instname_str Universidad de Alcalá (UAH)
reponame_str e_Buah Biblioteca Digital Universidad de Alcalá
collection e_Buah Biblioteca Digital Universidad de Alcalá
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,811543

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