Multiple path prediction for traffic scenes using LSTMs and mixture density models
Fernandez, Jaime B.ORCID: 0000-0001-9774-3879, Little, SuzanneORCID: 0000-0003-3281-3471 and O'Connor, Noel E.ORCID: 0000-0002-4033-9135
(2020)
Multiple path prediction for traffic scenes using LSTMs and mixture density models.
In: 6th International Conference on Vehicle Technology and Intelligent Transport Systems (VEHITS), 2-4 May 2020, Prague, Czech Republic (Virtual).
ISBN 978-989-758-419-0
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
Item Type:
Conference or Workshop Item (Paper)
Event Type:
Conference
Refereed:
Yes
Uncontrolled Keywords:
Multiple Path Prediction; Traffic Scenes; LSTMs, MDNs; Time Series
This item is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 3.0 License. View License
Funders:
EU H2020 Project VI-DAS under grant number 690772, SFI Insight Centre for Data Analytics, grant number SFI/12/RC/2289, GPU GeForce GTX 980 used for this research was donated by the NVIDIA Corporation
ID Code:
24257
Deposited On:
06 May 2020 17:01 by
Jaime Boanerjes Fernandez Roblero
. Last Modified 23 Nov 2022 14:22