TrajFlow: Learning Distributions over Trajectories for Human Behavior Prediction

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

Abstract

Predicting the future behavior of human road users is an important aspect for the development of risk-aware autonomous vehicles. While many models have been developed towards this end, effectively capturing and predicting the variability inherent to human behavior still remains an open challenge. This paper proposes TrajFlow - a new approach for probabilistic trajectory prediction based on Normalizing Flows. We reformulate the problem of capturing distributions over trajectories into capturing distributions over abstracted trajectory features using an autoencoder, simplifying the learning task of the Normalizing Flows. TrajFlow outperforms state-of-the-art behavior prediction models in capturing full trajectory distributions in two synthetic benchmarks with known true distributions, and is competitive on the naturalistic datasets ETH/UCY, rounD, and nuScenes. Our results demonstrate the effectiveness of TrajFlow in probabilistic prediction of human behavior.

Original languageEnglish
Title of host publicationProceddings of the 35th IEEE Intelligent Vehicles Symposium, IV 2024
PublisherIEEE
Pages184-191
Number of pages8
ISBN (Electronic)9798350348811
DOIs
Publication statusPublished - 2024
Event35th IEEE Intelligent Vehicles Symposium, IV 2024 - Jeju Island, Korea, Republic of
Duration: 2 Jun 20245 Jun 2024

Publication series

NameIEEE Intelligent Vehicles Symposium, Proceedings
ISSN (Print)1931-0587
ISSN (Electronic)2642-7214

Conference

Conference35th IEEE Intelligent Vehicles Symposium, IV 2024
Country/TerritoryKorea, Republic of
CityJeju Island
Period2/06/245/06/24

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.

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