Smooth-Trajectron++: Augmenting the Trajectron++ Behaviour Prediction Model with Smooth Attention

Frederik S.B. Westerhout, Julian F. Schumann, Arkady Zgonnikov

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

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Abstract

Understanding traffic participants' behaviour is crucial for predicting their future trajectories, aiding in developing safe and reliable planning systems for autonomous vehicles. Integrating cognitive processes and machine learning models has shown promise in other domains but is lacking in the trajectory forecasting of multiple traffic agents in large-scale autonomous driving datasets. This work investigates the state-of-the-art trajectory forecasting model Trajectron++ which we enhance by incorporating a smoothing term in its attention module. This attention mechanism mimics human attention inspired by cognitive science research indicating limits to attention switching. We evaluate the performance of the resulting Smooth-Trajectron++ model and compare it to the original model on various benchmarks, revealing the potential of incorporating insights from human cognition into trajectory prediction models.

Original languageEnglish
Title of host publicationProceedings of the IEEE 26th International Conference on Intelligent Transportation Systems, ITSC 2023
PublisherIEEE
Pages5423-5428
Number of pages6
ISBN (Electronic)979-8-3503-9946-2
DOIs
Publication statusPublished - 2023
Event26th IEEE International Conference on Intelligent Transportation Systems, ITSC 2023 - Euskalduna Conference Centre, Bilbao, Spain
Duration: 24 Sept 202328 Sept 2023
https://2023.ieee-itsc.org/

Publication series

NameIEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC
ISSN (Print)2153-0009
ISSN (Electronic)2153-0017

Conference

Conference26th IEEE International Conference on Intelligent Transportation Systems, ITSC 2023
Abbreviated titleIEEE ITSC 2023
Country/TerritorySpain
CityBilbao
Period24/09/2328/09/23
Internet address

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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