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Case Study on the Use of the SafeML Approach in Training Autonomous Driving Vehicles
Technische Hochschule Nürnberg, Keßlerplatz 12, Nürnberg, 90489, Germany.
Technische Hochschule Nürnberg, Keßlerplatz 12, Nürnberg, 90489, Germany.
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.ORCID iD: 0000-0003-0904-3712
2022 (English)In: Lect. Notes Comput. Sci., Springer Science and Business Media Deutschland GmbH , 2022, p. 87-97Conference paper, Published paper (Refereed)
Abstract [en]

The development quality for the control software for autonomous vehicles is rapidly progressing, so that the control units in the field generally perform very reliably. Nevertheless, fatal misjudgments occasionally occur putting people at risk: such as the recent accident in which a Tesla vehicle in Autopilot mode rammed a police vehicle. Since the object recognition software which is a part of the control software is based on machine learning (ML) algorithms at its core, one can distinguish a training phase from a deployment phase of the software. In this paper we investigate to what extent the deployment phase has an impact on the robustness and reliability of the software; because just as traditional, software based on ML degrades with time. A widely known effect is the so-called concept drift: in this case, one finds that the deployment conditions in the field have changed and the software, based on the outdated training data, no longer responds adequately to the current field situation. In a previous research paper, we developed the SafeML approach with colleagues from the University of Hull, where datasets are compared for their statistical distance measures. In doing so, we detected that for simple, benchmark data, the statistical distance correlates with the classification accuracy in the field. The contribution of this paper is to analyze the applicability of the SafeML approach to complex, multidimensional data used in autonomous driving. In our analysis, we found that the SafeML approach can be used for this data as well. In practice, this would mean that a vehicle could constantly check itself and detect concept drift situation early. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Place, publisher, year, edition, pages
Springer Science and Business Media Deutschland GmbH , 2022. p. 87-97
Series
Lecture Notes in Computer Science, ISSN 0302-9743 ; 13233 LNCS
Keywords [en]
Automotive, Autonomous driving, Machine learning, SafeML, Safety
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:mdh:diva-58657DOI: 10.1007/978-3-031-06433-3_8ISI: 000870308100008Scopus ID: 2-s2.0-85131150606ISBN: 9783031064326 (print)OAI: oai:DiVA.org:mdh-58657DiVA, id: diva2:1665951
Conference
21st International Conference on Image Analysis and Processing, ICIAP 2022
Available from: 2022-06-08 Created: 2022-06-08 Last updated: 2022-11-09Bibliographically approved

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Lundqvist, Kristina

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