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Miniature Autonomy as Means to Find New Approaches in Reliable Autonomous Driving AI Method Design

Artificial Intelligence (AI) methods need to be evaluated thoroughly to ensure reliable behavior. In applications like autonomous driving, a complex environment with an uncountable number of different situations and conditions needs to be handled by a method whose behavior needs to be predictable. T...

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Autores principales: Tiedemann, Tim, Schwalb, Luk, Kasten, Markus, Grotkasten, Robin, Pareigis, Stephan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9283862/
https://www.ncbi.nlm.nih.gov/pubmed/35845756
http://dx.doi.org/10.3389/fnbot.2022.846355
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author Tiedemann, Tim
Schwalb, Luk
Kasten, Markus
Grotkasten, Robin
Pareigis, Stephan
author_facet Tiedemann, Tim
Schwalb, Luk
Kasten, Markus
Grotkasten, Robin
Pareigis, Stephan
author_sort Tiedemann, Tim
collection PubMed
description Artificial Intelligence (AI) methods need to be evaluated thoroughly to ensure reliable behavior. In applications like autonomous driving, a complex environment with an uncountable number of different situations and conditions needs to be handled by a method whose behavior needs to be predictable. To accomplish this, simulations can be used as a first step. However, the physical world behaves differently, as the example of autonomous driving shows. There, erroneous behavior has been found in test drives that was not noticed in simulations. Errors were caused by conditions or situations that were not covered by the simulations (e.g., specific lighting conditions or other vehicle's behavior). However, the problem with real world testing of autonomous driving features is that critical conditions or situations occur very rarely—while the test effort is high. A solution can be the combination of physical world tests and simulations—and miniature vehicles as an intermediate step between both. With model cars (in a sufficiently complex model environment) advantages of both can be combined: (1) low test effort and a repeatable variation of conditions/situations as an advantage like in simulations and (2) (limited) physical world testing with unspecified and potentially unknown properties as an advantage like in real-world tests. Additionally, such physical tests can be carried out in less stable cases like already in the early stages of AI method testing and/or in approaches using online learning. Now, we propose to use a) miniature vehicles at a small scale of 1:87 and b) use sensors and computational power only on the vehicle itself. By this limitation, a further consequence is expected: Here, autonomy methods need to be optimized drastically or even redesigned from scratch. The resulting methods are supposed to be less complex—and, thus, again less error-prone. We call this approach “Miniature Autonomy” and apply it to the road, water, and aerial vehicles. In this article, we briefly describe a small test area we built (3 sqm.), a large test area used alternatively (1,545 sqm.), two last generation autonomous miniature vehicles (one road, one aerial vehicle), and an autonomous driving demo case demonstrating the application.
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spelling pubmed-92838622022-07-16 Miniature Autonomy as Means to Find New Approaches in Reliable Autonomous Driving AI Method Design Tiedemann, Tim Schwalb, Luk Kasten, Markus Grotkasten, Robin Pareigis, Stephan Front Neurorobot Neuroscience Artificial Intelligence (AI) methods need to be evaluated thoroughly to ensure reliable behavior. In applications like autonomous driving, a complex environment with an uncountable number of different situations and conditions needs to be handled by a method whose behavior needs to be predictable. To accomplish this, simulations can be used as a first step. However, the physical world behaves differently, as the example of autonomous driving shows. There, erroneous behavior has been found in test drives that was not noticed in simulations. Errors were caused by conditions or situations that were not covered by the simulations (e.g., specific lighting conditions or other vehicle's behavior). However, the problem with real world testing of autonomous driving features is that critical conditions or situations occur very rarely—while the test effort is high. A solution can be the combination of physical world tests and simulations—and miniature vehicles as an intermediate step between both. With model cars (in a sufficiently complex model environment) advantages of both can be combined: (1) low test effort and a repeatable variation of conditions/situations as an advantage like in simulations and (2) (limited) physical world testing with unspecified and potentially unknown properties as an advantage like in real-world tests. Additionally, such physical tests can be carried out in less stable cases like already in the early stages of AI method testing and/or in approaches using online learning. Now, we propose to use a) miniature vehicles at a small scale of 1:87 and b) use sensors and computational power only on the vehicle itself. By this limitation, a further consequence is expected: Here, autonomy methods need to be optimized drastically or even redesigned from scratch. The resulting methods are supposed to be less complex—and, thus, again less error-prone. We call this approach “Miniature Autonomy” and apply it to the road, water, and aerial vehicles. In this article, we briefly describe a small test area we built (3 sqm.), a large test area used alternatively (1,545 sqm.), two last generation autonomous miniature vehicles (one road, one aerial vehicle), and an autonomous driving demo case demonstrating the application. Frontiers Media S.A. 2022-07-01 /pmc/articles/PMC9283862/ /pubmed/35845756 http://dx.doi.org/10.3389/fnbot.2022.846355 Text en Copyright © 2022 Tiedemann, Schwalb, Kasten, Grotkasten and Pareigis. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Neuroscience
Tiedemann, Tim
Schwalb, Luk
Kasten, Markus
Grotkasten, Robin
Pareigis, Stephan
Miniature Autonomy as Means to Find New Approaches in Reliable Autonomous Driving AI Method Design
title Miniature Autonomy as Means to Find New Approaches in Reliable Autonomous Driving AI Method Design
title_full Miniature Autonomy as Means to Find New Approaches in Reliable Autonomous Driving AI Method Design
title_fullStr Miniature Autonomy as Means to Find New Approaches in Reliable Autonomous Driving AI Method Design
title_full_unstemmed Miniature Autonomy as Means to Find New Approaches in Reliable Autonomous Driving AI Method Design
title_short Miniature Autonomy as Means to Find New Approaches in Reliable Autonomous Driving AI Method Design
title_sort miniature autonomy as means to find new approaches in reliable autonomous driving ai method design
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9283862/
https://www.ncbi.nlm.nih.gov/pubmed/35845756
http://dx.doi.org/10.3389/fnbot.2022.846355
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