Interacting Vehicle Trajectory Prediction with Convolutional Recurrent Neural Networks

Saptarshi Mukherjee, Sen Wang, Andrew M. Wallace

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)
118 Downloads (Pure)

Abstract

Anticipating the future trajectories of surrounding vehicles is a crucial and challenging task in path planning for autonomy. We propose a novel Convolutional Long Short Term Memory (Conv-LSTM) based neural network architecture to predict the future positions of cars using several seconds of historical driving observations. This consists of three modules: 1) Interaction Learning to capture the effect of surrounding cars, 2) Temporal Learning to identify the dependency on past movements and 3) Motion Learning to convert the extracted features from these two modules into future positions. To continuously achieve accurate prediction, we introduce a novel feedback scheme where the current predicted positions of each car are leveraged to update future motion, encapsulating the effect of the surrounding cars. Experiments on two public datasets demonstrate that the proposed methodology can match or outperform the state-of-the-art methods for long-term trajectory prediction.
Original languageEnglish
Title of host publication2020 IEEE International Conference on Robotics and Automation (ICRA)
PublisherIEEE
Pages4336-4342
Number of pages7
ISBN (Electronic)9781728173955
DOIs
Publication statusPublished - 15 Sep 2020
EventIEEE International Conference on Robotics and Automation 2020 - Paris, France
Duration: 31 May 20204 Jun 2020

Publication series

NameIEEE International Conference on Robotics and Automation
ISSN (Electronic)2577-087X

Conference

ConferenceIEEE International Conference on Robotics and Automation 2020
Abbreviated titleICRA 2020
Country/TerritoryFrance
CityParis
Period31/05/204/06/20

ASJC Scopus subject areas

  • Software
  • Control and Systems Engineering
  • Artificial Intelligence
  • Electrical and Electronic Engineering

Fingerprint

Dive into the research topics of 'Interacting Vehicle Trajectory Prediction with Convolutional Recurrent Neural Networks'. Together they form a unique fingerprint.

Cite this