Context-based cyclist path prediction using Recurrent Neural Networks

Conference paper (2019)

Authors

E.A.I. Pool Intelligent Vehicles - Mechanical, Maritime and Materials Engineering
J.F.P. Kooij Intelligent Vehicles - Mechanical, Maritime and Materials Engineering
D. Gavrila Intelligent Vehicles - Mechanical, Maritime and Materials Engineering
Research Group
Intelligent Vehicles (Mechanical, Maritime and Materials Engineering) (TU Delft)
Copyright: © 2019 E.A.I. Pool, J.F.P. Kooij, D. Gavrila
DOI
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https://doi.org/10.1109/IVS.2019.8813889
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Published Date

2019

Language

English

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Faculty

Mechanical, Maritime and Materials Engineering

Department

Cognitive Robotics

Research Group

Intelligent Vehicles

Abstract

This paper proposes a Recurrent Neural Network (RNN) for cyclist path prediction to learn the effect of contextual cues on the behavior directly in an end- to-end approach, removing the need for any annotations. The proposed RNN incorporates three distinct contextual cues: one related to actions of the cyclist, one related to the location of the cyclist on the road, and one related to the interaction between the cyclist and the egovehicle. The RNN predicts a Gaussian distribution over the future position of the cyclist one second into the future with a higher accuracy, compared to a current state-of-the-art model that is based on dynamic mode annotations, where our model attains an average prediction error of 33 cm one second into the future.