Runtime Decision Making Under Uncertainty in Autonomous Vehicles

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Title of host publicationProceedings of the Workshop on Artificial Intelligence Safety (SafeAI 2021)
DateAccepted/In press - 7 Dec 2020
DatePublished (current) - 8 Feb 2021
Number of pages8
PublisherCEUR Workshop Proceedings
Original languageEnglish

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.

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© 2021, for this paper by its authors

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