A Spatially and Temporally Attentive Joint Trajectory Prediction Framework for Modeling Vessel Intent

Jasmine Sekhon, Cody Fleming
Proceedings of the 2nd Conference on Learning for Dynamics and Control, PMLR 120:318-327, 2020.

Abstract

Ships, or vessels, often sail in and out of cluttered environments over the course of their trajectories. Safe navigation in such cluttered scenarios requires an accurate estimation of the intent of neighboring vessels and their effect on the self and vice-versa well into the future. In manned vessels, this is achieved by constant communication between people on board, nautical experience, and audio and visual signals. In this paper we propose a deep neural network based architecture to predict intent of neighboring vessels into the future for an unmanned vessel solely based on positional data.

Cite this Paper

BibTeX
@InProceedings{pmlr-v120-sekhon20a, title = {A Spatially and Temporally Attentive Joint Trajectory Prediction Framework for Modeling Vessel Intent}, author = {Sekhon, Jasmine and Fleming, Cody}, booktitle = {Proceedings of the 2nd Conference on Learning for Dynamics and Control}, pages = {318--327}, year = {2020}, editor = {Bayen, Alexandre M. and Jadbabaie, Ali and Pappas, George and Parrilo, Pablo A. and Recht, Benjamin and Tomlin, Claire and Zeilinger, Melanie}, volume = {120}, series = {Proceedings of Machine Learning Research}, month = {10--11 Jun}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v120/sekhon20a/sekhon20a.pdf}, url = {https://proceedings.mlr.press/v120/sekhon20a.html}, abstract = {Ships, or vessels, often sail in and out of cluttered environments over the course of their trajectories. Safe navigation in such cluttered scenarios requires an accurate estimation of the intent of neighboring vessels and their effect on the self and vice-versa well into the future. In manned vessels, this is achieved by constant communication between people on board, nautical experience, and audio and visual signals. In this paper we propose a deep neural network based architecture to predict intent of neighboring vessels into the future for an unmanned vessel solely based on positional data.} }
Endnote
%0 Conference Paper %T A Spatially and Temporally Attentive Joint Trajectory Prediction Framework for Modeling Vessel Intent %A Jasmine Sekhon %A Cody Fleming %B Proceedings of the 2nd Conference on Learning for Dynamics and Control %C Proceedings of Machine Learning Research %D 2020 %E Alexandre M. Bayen %E Ali Jadbabaie %E George Pappas %E Pablo A. Parrilo %E Benjamin Recht %E Claire Tomlin %E Melanie Zeilinger %F pmlr-v120-sekhon20a %I PMLR %P 318--327 %U https://proceedings.mlr.press/v120/sekhon20a.html %V 120 %X Ships, or vessels, often sail in and out of cluttered environments over the course of their trajectories. Safe navigation in such cluttered scenarios requires an accurate estimation of the intent of neighboring vessels and their effect on the self and vice-versa well into the future. In manned vessels, this is achieved by constant communication between people on board, nautical experience, and audio and visual signals. In this paper we propose a deep neural network based architecture to predict intent of neighboring vessels into the future for an unmanned vessel solely based on positional data.
APA
Sekhon, J. & Fleming, C.. (2020). A Spatially and Temporally Attentive Joint Trajectory Prediction Framework for Modeling Vessel Intent. Proceedings of the 2nd Conference on Learning for Dynamics and Control, in Proceedings of Machine Learning Research 120:318-327 Available from https://proceedings.mlr.press/v120/sekhon20a.html.

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