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MTP-GO: Graph-Based Probabilistic Multi-Agent Trajectory Prediction with Neural ODEs

Westny, Theodor ; Oskarsson, Joel ; Olofsson, Björn LU and Frisk, Erik (2023) In IEEE Transactions on Intelligent Vehicles 8(9). p.4223-4236
Abstract
Enabling resilient autonomous motion planning requires robust predictions of surrounding road users' future behavior. In response to this need and the associated challenges, we introduce our model titled MTP-GO. The model encodes the scene using temporal graph neural networks to produce the inputs to an underlying motion model. The motion model is implemented using neural ordinary differential equations where the state-transition functions are learned with the rest of the model. Multimodal probabilistic predictions are obtained by combining the concept of mixture density networks and Kalman filtering. The results illustrate the predictive capabilities of the proposed model across various data sets, outperforming several state-of-the-art... (More)
Enabling resilient autonomous motion planning requires robust predictions of surrounding road users' future behavior. In response to this need and the associated challenges, we introduce our model titled MTP-GO. The model encodes the scene using temporal graph neural networks to produce the inputs to an underlying motion model. The motion model is implemented using neural ordinary differential equations where the state-transition functions are learned with the rest of the model. Multimodal probabilistic predictions are obtained by combining the concept of mixture density networks and Kalman filtering. The results illustrate the predictive capabilities of the proposed model across various data sets, outperforming several state-of-the-art methods on a number of metrics. (Less)
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author
; ; and
organization
publishing date
type
Contribution to journal
publication status
published
subject
in
IEEE Transactions on Intelligent Vehicles
volume
8
issue
9
pages
4223 - 4236
publisher
IEEE - Institute of Electrical and Electronics Engineers Inc.
external identifiers
  • scopus:85161553735
ISSN
2379-8858
DOI
10.1109/TIV.2023.3282308
project
ELLIIT B14: Autonomous Force-Aware Swift Motion Control
RobotLab LTH
language
English
LU publication?
yes
id
7b1a49ab-63af-4e32-aef1-4d8d8176c7eb
alternative location
https://arxiv.org/abs/2302.00735
date added to LUP
2023-06-27 12:19:09
date last changed
2024-02-11 04:06:01
@article{7b1a49ab-63af-4e32-aef1-4d8d8176c7eb,
  abstract     = {{Enabling resilient autonomous motion planning requires robust predictions of surrounding road users' future behavior. In response to this need and the associated challenges, we introduce our model titled MTP-GO. The model encodes the scene using temporal graph neural networks to produce the inputs to an underlying motion model. The motion model is implemented using neural ordinary differential equations where the state-transition functions are learned with the rest of the model. Multimodal probabilistic predictions are obtained by combining the concept of mixture density networks and Kalman filtering. The results illustrate the predictive capabilities of the proposed model across various data sets, outperforming several state-of-the-art methods on a number of metrics.}},
  author       = {{Westny, Theodor and Oskarsson, Joel and Olofsson, Björn and Frisk, Erik}},
  issn         = {{2379-8858}},
  language     = {{eng}},
  number       = {{9}},
  pages        = {{4223--4236}},
  publisher    = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}},
  series       = {{IEEE Transactions on Intelligent Vehicles}},
  title        = {{MTP-GO: Graph-Based Probabilistic Multi-Agent Trajectory Prediction with Neural ODEs}},
  url          = {{http://dx.doi.org/10.1109/TIV.2023.3282308}},
  doi          = {{10.1109/TIV.2023.3282308}},
  volume       = {{8}},
  year         = {{2023}},
}