Learning-enabled multi-modal motion prediction in urban environments

Vinicius Trentin, Chenxu Ma, Jorge Villagra, Zaid Al-Ars

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

1 Citation (Scopus)
1 Downloads (Pure)

Abstract

Motion prediction is a key factor towards the full deployment of autonomous vehicles. It is fundamental in order to assure safety while navigating through highly interactive complex scenarios. In this work, the framework IAMP (Interaction-Aware Motion Prediction), producing multi-modal probabilistic outputs from the integration of a Dynamic Bayesian Network and Markov Chains, is extended with a learning-based approach. The integration of a machine learning model tackles the limitations of the ruled-based mechanism since it can better adapt to different driving styles and driving situations. The method here introduced generates context-dependent acceleration distributions used in a Markov-chain-based motion prediction. This hybrid approach results in better evaluation metrics when compared with the baseline in the four highly-interactive scenarios obtained from publicly available datasets.

Original languageEnglish
Title of host publicationIV 2023 - IEEE Intelligent Vehicles Symposium, Proceedings
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
ISBN (Electronic)9798350346916
DOIs
Publication statusPublished - 2023
Event34th IEEE Intelligent Vehicles Symposium, IV 2023 - Anchorage, United States
Duration: 4 Jun 20237 Jun 2023

Publication series

NameIEEE Intelligent Vehicles Symposium, Proceedings
Volume2023-June

Conference

Conference34th IEEE Intelligent Vehicles Symposium, IV 2023
Country/TerritoryUnited States
CityAnchorage
Period4/06/237/06/23

Bibliographical note

Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care
Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

Keywords

  • interaction-aware
  • learning-based
  • motion-prediction

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