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Evaluating Autonomous Urban Perception and Planning in a 1/10th Scale MiniCity

We present the MiniCity, a multi-vehicle evaluation platform for testing perception hardware and software for autonomous vehicles. The MiniCity is a 1/10th scale city consisting of realistic urban scenery, intersections, and multiple fully autonomous 1/10th scale vehicles with state-of-the-art senso...

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Detalles Bibliográficos
Autores principales: Buckman, Noam, Hansen, Alex, Karaman, Sertac, Rus, Daniela
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9502656/
https://www.ncbi.nlm.nih.gov/pubmed/36146141
http://dx.doi.org/10.3390/s22186793
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author Buckman, Noam
Hansen, Alex
Karaman, Sertac
Rus, Daniela
author_facet Buckman, Noam
Hansen, Alex
Karaman, Sertac
Rus, Daniela
author_sort Buckman, Noam
collection PubMed
description We present the MiniCity, a multi-vehicle evaluation platform for testing perception hardware and software for autonomous vehicles. The MiniCity is a 1/10th scale city consisting of realistic urban scenery, intersections, and multiple fully autonomous 1/10th scale vehicles with state-of-the-art sensors and algorithms. The MiniCity is used to evaluate and test perception algorithms both upstream and downstream in the autonomy stack, in urban driving scenarios such as occluded intersections and avoiding multiple vehicles. We demonstrate the MiniCity’s ability to evaluate different sensor and algorithm configurations for perception tasks such as object detection and localization. For both tasks, the MiniCity platform is used to evaluate the task itself (accuracy in estimating obstacle pose and ego pose in the map) as well as the downstream performance in collision avoidance and lane following, respectively.
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spelling pubmed-95026562022-09-24 Evaluating Autonomous Urban Perception and Planning in a 1/10th Scale MiniCity Buckman, Noam Hansen, Alex Karaman, Sertac Rus, Daniela Sensors (Basel) Article We present the MiniCity, a multi-vehicle evaluation platform for testing perception hardware and software for autonomous vehicles. The MiniCity is a 1/10th scale city consisting of realistic urban scenery, intersections, and multiple fully autonomous 1/10th scale vehicles with state-of-the-art sensors and algorithms. The MiniCity is used to evaluate and test perception algorithms both upstream and downstream in the autonomy stack, in urban driving scenarios such as occluded intersections and avoiding multiple vehicles. We demonstrate the MiniCity’s ability to evaluate different sensor and algorithm configurations for perception tasks such as object detection and localization. For both tasks, the MiniCity platform is used to evaluate the task itself (accuracy in estimating obstacle pose and ego pose in the map) as well as the downstream performance in collision avoidance and lane following, respectively. MDPI 2022-09-08 /pmc/articles/PMC9502656/ /pubmed/36146141 http://dx.doi.org/10.3390/s22186793 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Buckman, Noam
Hansen, Alex
Karaman, Sertac
Rus, Daniela
Evaluating Autonomous Urban Perception and Planning in a 1/10th Scale MiniCity
title Evaluating Autonomous Urban Perception and Planning in a 1/10th Scale MiniCity
title_full Evaluating Autonomous Urban Perception and Planning in a 1/10th Scale MiniCity
title_fullStr Evaluating Autonomous Urban Perception and Planning in a 1/10th Scale MiniCity
title_full_unstemmed Evaluating Autonomous Urban Perception and Planning in a 1/10th Scale MiniCity
title_short Evaluating Autonomous Urban Perception and Planning in a 1/10th Scale MiniCity
title_sort evaluating autonomous urban perception and planning in a 1/10th scale minicity
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9502656/
https://www.ncbi.nlm.nih.gov/pubmed/36146141
http://dx.doi.org/10.3390/s22186793
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