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Trusted autonomous vehicles: an interactive exhibit

Araujo, Hugo L. S., Verdezoto Dias, Nervo Xavier, Wali, Syed, Damasceno, Carlos Diego N., Dimitrova, Rayna, Kefalidou, Genovefa, Mehtarizadeh, Mehdi, Mousavi, Mohammad Reza, Onime, Jemima, Ringert, Jan Oliver and Rojas, Jose Miguel 2020. Trusted autonomous vehicles: an interactive exhibit. Presented at: IEEE International Conferences on Ubiquitous Computing & Communications and Data Science and Computational Intelligence and Smart Computing, Networking and Services, Shenyang, China, 21-23 Oct 2019. Proceedings 2019 IEEE International Conferences on Ubiquitous Computing & Communications and Data Science and Computational Intelligence and Smart Computing, Networking and Services. IEEE, pp. 386-393. 10.1109/IUCC/DSCI/SmartCNS.2019.00091

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Abstract

Recent surveys about autonomous vehicles show that the public is concerned about the safety consequences of system or equipment failures and the vehicles' reactions to unexpected situations. We believe that informing about the technology and quality, e.g., safety and reliability, of autonomous vehicles is paramount to improving public expectations, perception and acceptance. In this paper, we report on the design of an interactive exhibit to illustrate (1) basic technologies employed in autonomous vehicles, i.e., sensors and object classification; and (2) basic principles for ensuring their quality, i.e., employing software testing and simulations. We subsequently report on a public engagement event involving this exhibit at the Royal Society Summer Science Exhibition 2019 in the exhibit titled "Trusted Autonomous Vehicles". We describe the process of designing and developing the artefacts used in our exhibit, the theoretical background associated to them, the design of our stand, and the lessons learned. The activities and findings of this study can be used by other educators and researchers interested in promoting trust in autonomous vehicles among the general public.

Item Type: Conference or Workshop Item (Paper)
Date Type: Published Online
Status: Published
Schools: Computer Science & Informatics
Publisher: IEEE
ISBN: 9781728152097
Date of First Compliant Deposit: 14 October 2020
Date of Acceptance: 25 August 2019
Last Modified: 14 Oct 2020 11:20
URI: http://orca.cf.ac.uk/id/eprint/135473

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