Real-time detection and prediction of relative motion of moving objects in autonomous driving

Lalintha G. Polpitiya, Kamal Premaratne

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Autonomous driving vehicles must have the ability to identify and predict behaviors of surrounding moving objects (e.g., other vehicles, cyclists, and pedestrians) in real-time. This is especially true in urban environments, where interactions become more complex due to high volumes of traffic. The work in this paper harnesses the Dempster-Shafer (DS) theoretic framework's ability to capture and account for various types of evidence uncertainty to develop a robust event detection and prediction model, which is appropriately calibrated to account for the underlying uncertainty so that it may be employed to arrive at a more informed decision.

Original languageEnglish (US)
Title of host publicationProceedings of the 33rd International Florida Artificial Intelligence Research Society Conference, FLAIRS 2020
EditorsEric Bell, Roman Bartak
PublisherThe AAAI Press
Pages136-141
Number of pages6
ISBN (Electronic)9781577358213
StatePublished - 2020
Externally publishedYes
Event33rd International Florida Artificial Intelligence Research Society Conference, FLAIRS 2020 - North Miami Beach, United States
Duration: May 17 2020May 20 2020

Publication series

NameProceedings of the 33rd International Florida Artificial Intelligence Research Society Conference, FLAIRS 2020

Conference

Conference33rd International Florida Artificial Intelligence Research Society Conference, FLAIRS 2020
CountryUnited States
CityNorth Miami Beach
Period5/17/205/20/20

ASJC Scopus subject areas

  • Artificial Intelligence
  • Software

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