Predicting vehicle behaviour using LSTMs and a vector power representation for spatial positions

European Symposium on Artificial Neural Networks, 2019

Florian Mirus, Peter Blouw, Terrence C. Stewart, Jorg Conradt

Abstract

Predicting future vehicle behaviour is an essential task to enable safe and situation-aware automated driving. In this paper, we propose to encapsulate spatial information of multiple objects in a semantic vector-representation. Assuming that future vehicle motion is influenced not only by past positions but also by the behaviour of other traffic participants, we use this representation as input for a Long Short-Term Memory (LSTM) network for sequence to sequence prediction of vehicle positions. We train and evaluate our system on real-world driving data collected mainly on highways in southern Germany and compare it to other models for reference

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Booktitle
European Symposium on Artificial Neural Networks
Organization
ESANN
Pages
113--118

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