Multiple Path Prediction for Traffic Scenes using LSTMs and Mixture Density Models

Jaime Fernandez, Suzanne Little, Noel O’connor

2020

Abstract

This work presents an analysis of predicting multiple future paths of moving objects in traffic scenes by leveraging Long Short-Term Memory architectures (LSTMs) and Mixture Density Networks (MDNs) in a single-shot manner. Path prediction allows estimating the future positions of objects. This is useful in important applications such as security monitoring systems, Autonomous Driver Assistance Systems and assistive technologies. Normal approaches use observed positions (tracklets) of objects in video frames to predict their future paths as a sequence of position values. This can be treated as a time series. LSTMs have achieved good performance when dealing with time series. However, LSTMs have the limitation of only predicting a single path per tracklet. Path prediction is not a deterministic task and requires predicting with a level of uncertainty. Predicting multiple paths instead of a single one is therefore a more realistic manner of approaching this task. In this work, predicting a set of future paths with associated uncertainty was archived by combining LSTMs and MDNs. The evaluation was made on the KITTI and the CityFlow datasets on three type of objects, four prediction horizons and two different points of view (image coordinates and birds-eye view).

Paper Citation

in Harvard Style

Fernandez J., Little S. and O’connor N. (2020). Multiple Path Prediction for Traffic Scenes using LSTMs and Mixture Density Models.In Proceedings of the 6th International Conference on Vehicle Technology and Intelligent Transport Systems - Volume 1: VEHITS, ISBN 978-989-758-419-0, pages 481-488. DOI: 10.5220/0009412204810488

in Bibtex Style

@conference{vehits20,
author={Jaime Fernandez and Suzanne Little and Noel O’connor},
title={Multiple Path Prediction for Traffic Scenes using LSTMs and Mixture Density Models},
booktitle={Proceedings of the 6th International Conference on Vehicle Technology and Intelligent Transport Systems - Volume 1: VEHITS,},
year={2020},
pages={481-488},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0009412204810488},
isbn={978-989-758-419-0},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 6th International Conference on Vehicle Technology and Intelligent Transport Systems - Volume 1: VEHITS,
TI - Multiple Path Prediction for Traffic Scenes using LSTMs and Mixture Density Models
SN - 978-989-758-419-0
AU - Fernandez J.
AU - Little S.
AU - O’connor N.
PY - 2020
SP - 481
EP - 488
DO - 10.5220/0009412204810488