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Automated Testing of Ultrawideband Positioning for Autonomous Driving
Department of Electronics, RISE Research Institutes of Sweden, Borås, Sweden.ORCID iD: 0000-0003-1713-3726
Department of Electronics, RISE Research Institutes of Sweden, Borås, Sweden.ORCID iD: 0000-0003-0363-1206
Department of Electronics, RISE Research Institutes of Sweden, Borås, Sweden.ORCID iD: 0000-0002-6191-6253
Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), Centre for Research on Embedded Systems (CERES).ORCID iD: 0000-0002-6526-3931
2020 (English)In: Journal of Robotics, ISSN 1687-9600, E-ISSN 1687-9619, Vol. 2020, article id 9345360Article in journal (Refereed) Published
Abstract [en]

Autonomous vehicles need accurate and dependable positioning, and these systems need to be tested extensively. We have evaluated positioning based on ultrawideband (UWB) ranging with our self-driving model car using a highly automated approach. Random drivable trajectories were generated, while the UWB position was compared against the Real-Time Kinematic Satellite Navigation (RTK-SN) positioning system which our model car also is equipped with. Fault injection was used to study the fault tolerance of the UWB positioning system. Addressed challenges are automatically generating test cases for real-time hardware, restoring the state between tests, and maintaining safety by preventing collisions. We were able to automatically generate and carry out hundreds of experiments on the model car in real time and rerun them consistently with and without fault injection enabled. Thereby, we demonstrate one novel approach to perform automated testing on complex real-time hardware. Copyright © 2020 Benjamin Vedder et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Place, publisher, year, edition, pages
London, UK: Hindawi Publishing Corporation, 2020. Vol. 2020, article id 9345360
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
URN: urn:nbn:se:hh:diva-41406DOI: 10.1155/2020/9345360ISI: 000510880200001Scopus ID: 2-s2.0-85079208712OAI: oai:DiVA.org:hh-41406DiVA, id: diva2:1388667
Funder
Knowledge FoundationVinnova, 776307Available from: 2020-01-27 Created: 2020-01-27 Last updated: 2020-02-18Bibliographically approved

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Vedder, BenjaminSvensson, Bo JoelVinter, JonnyJonsson, Magnus
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