Gautam, Vibhu, Gheraibia, Youcef, Alexander, Rob orcid.org/0000-0003-3818-0310 et al. (1 more author) (2021) Runtime Decision Making Under Uncertainty in Autonomous Vehicles. In: Proceedings of the Workshop on Artificial Intelligence Safety (SafeAI 2021). CEUR Workshop Proceedings
Abstract
Autonomous vehicles (AV) have the potential of not only increasing the safety, comfort and fuel efficiency in a vehicle but also utilising the road bandwidth more efficiently. This, however, will require us to build an AV control software, capable of coping with multiple sources of uncertainty that are either preexisting or introduced as a result of processing. Such uncertainty can come from many sources like a local or a distant source, for example, the uncertainty about the actual observation of the sensors of the AV or the uncertainty in the environment scenario communicated by peer vehicles respectively. For AV to function safely, this uncertainty needs to be taken into account during the decision making process. In this paper, we provide a generalised method for making safe decisions by estimating and integrating the Model and the Data uncertainties.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2021, for this paper by its authors |
Dates: |
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Institution: | The University of York |
Academic Units: | The University of York > Faculty of Sciences (York) > Computer Science (York) |
Depositing User: | Pure (York) |
Date Deposited: | 18 Dec 2020 14:40 |
Last Modified: | 10 Feb 2025 00:05 |
Status: | Published |
Publisher: | CEUR Workshop Proceedings |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:169267 |