Self-learning Trajectory Prediction with Recurrent Neural Networks at Intelligent Intersections

Julian Bock, Jens Kotte, Markus Klösges, Till Beemelmanns

Abstract

We present the concept and first results of a self-learning system for road user trajectory prediction at intersections with connected sensors. Infrastructure installed connected sensors can assist automated vehicles in perceiving the environment in complex urban scenes such as intersections. An intelligent intersection with connected sensors can measure the trajectories of road users using multiple sensor types and store the trajectories. Our approach uses this information to collect a large dataset of pedestrian trajectories. This dataset is again used to train a pedestrian prediction model with Recurrent Neural Networks. This model learns intersection specific pedestrian movement patterns. Through a self-learning process enabled by the measurements of connected sensors, the system continuously improves the prediction during operation while keeping the dataset preferably small. In this paper, we focus on the prediction of pedestrian trajectories, but as the approach is data-driven, the system could also predict other road users such as vehicles or bicyclists if trained with the respective data.

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Paper Citation


in Harvard Style

Bock J., Kotte J., Beemelmanns T. and Klösges M. (2017). Self-learning Trajectory Prediction with Recurrent Neural Networks at Intelligent Intersections . In Proceedings of the 3rd International Conference on Vehicle Technology and Intelligent Transport Systems - Volume 1: VEHITS, ISBN 978-989-758-242-4, pages 346-351. DOI: 10.5220/0006374003460351


in Bibtex Style

@conference{vehits17,
author={Julian Bock and Jens Kotte and Till Beemelmanns and Markus Klösges},
title={Self-learning Trajectory Prediction with Recurrent Neural Networks at Intelligent Intersections},
booktitle={Proceedings of the 3rd International Conference on Vehicle Technology and Intelligent Transport Systems - Volume 1: VEHITS,},
year={2017},
pages={346-351},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006374003460351},
isbn={978-989-758-242-4},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 3rd International Conference on Vehicle Technology and Intelligent Transport Systems - Volume 1: VEHITS,
TI - Self-learning Trajectory Prediction with Recurrent Neural Networks at Intelligent Intersections
SN - 978-989-758-242-4
AU - Bock J.
AU - Kotte J.
AU - Beemelmanns T.
AU - Klösges M.
PY - 2017
SP - 346
EP - 351
DO - 10.5220/0006374003460351