{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T15:43:44Z","timestamp":1772120624329,"version":"3.50.1"},"reference-count":20,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2022,7,1]],"date-time":"2022-07-01T00:00:00Z","timestamp":1656633600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Neurorobot."],"abstract":"<jats:p>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\u2014while the test effort is high. A solution can be the combination of physical world tests and simulations\u2014and 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\u2014and, thus, again less error-prone. We call this approach \u201cMiniature Autonomy\u201d 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.<\/jats:p>","DOI":"10.3389\/fnbot.2022.846355","type":"journal-article","created":{"date-parts":[[2022,7,1]],"date-time":"2022-07-01T06:25:20Z","timestamp":1656656720000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":9,"title":["Miniature Autonomy as Means to Find New Approaches in Reliable Autonomous Driving AI Method Design"],"prefix":"10.3389","volume":"16","author":[{"given":"Tim","family":"Tiedemann","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Luk","family":"Schwalb","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Markus","family":"Kasten","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Robin","family":"Grotkasten","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Stephan","family":"Pareigis","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1965","published-online":{"date-parts":[[2022,7,1]]},"reference":[{"key":"B1","unstructured":"BurauH.\n            MangM.\n            NaumannF.\n            SchnirpelT.\n            StarkF.\n          Ms Nhawigatora. 5th Semester Project, Department of Computer Science, AutoSys Group, HAW Hamburg2019"},{"key":"B2","unstructured":"2021"},{"key":"B3","first-page":"3213","article-title":"\u201cThe cityscapes dataset for semantic urban scene understanding,\u201d","volume-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","author":"Cordts","year":"2016"},{"key":"B4","first-page":"9716","article-title":"\u201cRethinking bisenet for real-time semantic segmentation,\u201d","volume-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","author":"Fan","year":"2021"},{"key":"B5","doi-asserted-by":"publisher","first-page":"105","DOI":"10.1109\/MSP.2021.3051939","article-title":"Miniaturized advanced driver assistance systems: a low-cost educational platform for advanced driver assistance systems and autonomous driving","volume":"38","author":"Gerstmair","year":"2021","journal-title":"IEEE Signal. Process. Mag"},{"key":"B6","doi-asserted-by":"crossref","first-page":"114","DOI":"10.1109\/IV47402.2020.9304616","article-title":"\u201cA validation methodology for the minimization of unknown unknowns in autonomous vehicle systems,\u201d","volume-title":"2020 IEEE Intelligent Vehicles Symposium (IV)","author":"Hejase","year":"2020"},{"key":"B7","doi-asserted-by":"crossref","first-page":"1424","DOI":"10.1109\/ITSC.2016.7795744","article-title":"\u201cRobust environment perception for the audi autonomous driving cup,\u201d","volume-title":"2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC)","author":"Kuhnt","year":"2016"},{"key":"B8","doi-asserted-by":"publisher","first-page":"556","DOI":"10.1109\/LRA.2019.2961051","article-title":"Maplite: autonomous intersection navigation without a detailed prior map","volume":"5","author":"Ort","year":"2019","journal-title":"IEEE Rob. Autom. Lett"},{"key":"B9","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1007\/978-3-658-27808-3_2","article-title":"\u201cMiniaturautonomie und echtzeitsysteme,\u201d","volume-title":"Echtzeit 2019 (in German)","author":"Pareigis","year":"2019"},{"key":"B10","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1007\/978-3-658-32818-4_5","article-title":"\u201cK\u00fcnstliche intelligenz in der miniaturautonomie,\u201d","volume-title":"Echtzeit 2020 (in German)","author":"Pareigis","year":"2021"},{"key":"B11","first-page":"1497","article-title":"\u201cDuckietown: an open, inexpensive and flexible platform for autonomy education and research,\u201d","volume-title":"ICRA 2017-IEEE International Conference on Robotics and Automation, Proceedings","author":"Paull","year":"2017"},{"key":"B12","article-title":"Divergent effects of factors on crashes under autonomous and conventional driving modes using a hierarchical bayesian approach","volume-title":"arXiv preprint","author":"Ren","year":"2021"},{"key":"B13","unstructured":"Sch\u00f6nherrN.\n          Kamera-basierte Minimalautonomie (Bachelor Thesis). Hochschule f\u00fcr angewandte Wissenschaften Hamburg, Hamburg, Germany2019"},{"key":"B14","unstructured":"SchwalbL.\n          ROS-Architektur mit Autonomiefunktionen f\u00fcr ein Miniaturfahrzeug (Bachelor Thesis). Hochschule f\u00fcr angewandte Wissenschaften Hamburg, Hamburg, Germany2021"},{"key":"B15","article-title":"Part of the miniatur wunderland hamburg","volume-title":"Figshare","author":"Tiedemann","year":"2019"},{"key":"B16","first-page":"483","article-title":"\u201cMiniature autonomy as one important testing means in the development of machine learning methods for autonomous driving: How ml-based autonomous driving could be realized on a 1: 87 scale,\u201d","volume-title":"ICINCO (2)","author":"Tiedemann","year":"2019"},{"key":"B17","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2020\/8867757","article-title":"Safety of autonomous vehicles","volume":"2020","author":"Wang","year":"2020","journal-title":"J. Adv. Transport"},{"key":"B18","volume-title":"Miniatur Wunderland","author":"Wunderland","year":"2021"},{"key":"B19","article-title":"Bisenet v2: Bilateral network with guided aggregation for real-time semantic segmentation","volume-title":"arXiv preprint","author":"Yu","year":"2020"},{"key":"B20","doi-asserted-by":"crossref","first-page":"100","DOI":"10.1109\/ROSE.2014.6952991","article-title":"\u201cTechnical evaluation of the carolo-cup 2014-a competition for self-driving miniature cars,\u201d","volume-title":"2014 IEEE International Symposium on Robotic and Sensors Environments (ROSE) Proceedings","author":"Zug","year":"2014"}],"container-title":["Frontiers in Neurorobotics"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fnbot.2022.846355\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,7,1]],"date-time":"2022-07-01T06:25:32Z","timestamp":1656656732000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fnbot.2022.846355\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,7,1]]},"references-count":20,"alternative-id":["10.3389\/fnbot.2022.846355"],"URL":"https:\/\/doi.org\/10.3389\/fnbot.2022.846355","relation":{},"ISSN":["1662-5218"],"issn-type":[{"value":"1662-5218","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,7,1]]},"article-number":"846355"}}