Sense-Assess-eXplain (SAX): building trust in autonomous vehicles in challenging real-world driving scenarios

This paper discusses ongoing work in demonstrating research in mobile autonomy in challenging driving scenarios. In our approach, we address fundamental technical issues to overcome critical barriers to assurance and regulation for largescale deployments of autonomous systems. To this end, we presen...

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Main Authors: Gadd, M, de Martini, D, Marchegiani, M, Newman, P, Kunze, L
Formato: Conference item
Idioma:English
Publicado em: IEEE 2021
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author Gadd, M
de Martini, D
Marchegiani, M
Newman, P
Kunze, L
author_facet Gadd, M
de Martini, D
Marchegiani, M
Newman, P
Kunze, L
author_sort Gadd, M
collection OXFORD
description This paper discusses ongoing work in demonstrating research in mobile autonomy in challenging driving scenarios. In our approach, we address fundamental technical issues to overcome critical barriers to assurance and regulation for largescale deployments of autonomous systems. To this end, we present how we build robots that (1) can robustly sense and interpret their environment using traditional as well as unconventional sensors; (2) can assess their own capabilities; and (3), vitally in the purpose of assurance and trust, can provide causal explanations of their interpretations and assessments. As it is essential that robots are safe and trusted, we design, develop, and demonstrate fundamental technologies in real-world applications to overcome critical barriers which impede the current deployment of robots in economically and socially important areas. Finally, we describe ongoing work in the collection of an unusual, rare, and highly valuable dataset.
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spelling oxford-uuid:274433c7-e9c5-4a71-a11d-d22ae747205b2022-03-26T12:05:58ZSense-Assess-eXplain (SAX): building trust in autonomous vehicles in challenging real-world driving scenariosConference itemhttp://purl.org/coar/resource_type/c_5794uuid:274433c7-e9c5-4a71-a11d-d22ae747205bEnglishSymplectic ElementsIEEE2021Gadd, Mde Martini, DMarchegiani, MNewman, PKunze, LThis paper discusses ongoing work in demonstrating research in mobile autonomy in challenging driving scenarios. In our approach, we address fundamental technical issues to overcome critical barriers to assurance and regulation for largescale deployments of autonomous systems. To this end, we present how we build robots that (1) can robustly sense and interpret their environment using traditional as well as unconventional sensors; (2) can assess their own capabilities; and (3), vitally in the purpose of assurance and trust, can provide causal explanations of their interpretations and assessments. As it is essential that robots are safe and trusted, we design, develop, and demonstrate fundamental technologies in real-world applications to overcome critical barriers which impede the current deployment of robots in economically and socially important areas. Finally, we describe ongoing work in the collection of an unusual, rare, and highly valuable dataset.
spellingShingle Gadd, M
de Martini, D
Marchegiani, M
Newman, P
Kunze, L
Sense-Assess-eXplain (SAX): building trust in autonomous vehicles in challenging real-world driving scenarios
title Sense-Assess-eXplain (SAX): building trust in autonomous vehicles in challenging real-world driving scenarios
title_full Sense-Assess-eXplain (SAX): building trust in autonomous vehicles in challenging real-world driving scenarios
title_fullStr Sense-Assess-eXplain (SAX): building trust in autonomous vehicles in challenging real-world driving scenarios
title_full_unstemmed Sense-Assess-eXplain (SAX): building trust in autonomous vehicles in challenging real-world driving scenarios
title_short Sense-Assess-eXplain (SAX): building trust in autonomous vehicles in challenging real-world driving scenarios
title_sort sense assess explain sax building trust in autonomous vehicles in challenging real world driving scenarios
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AT marchegianim senseassessexplainsaxbuildingtrustinautonomousvehiclesinchallengingrealworlddrivingscenarios
AT newmanp senseassessexplainsaxbuildingtrustinautonomousvehiclesinchallengingrealworlddrivingscenarios
AT kunzel senseassessexplainsaxbuildingtrustinautonomousvehiclesinchallengingrealworlddrivingscenarios