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arXiv · 1804.05546

Particle-based pedestrian path prediction using LSTM-MDL models

Ronny Hug·Stefan Becker·Wolfgang Hübner·Michael Arens
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Abstract

Recurrent neural networks are able to learn complex long-term relationships from sequential data and output a pdf over the state space. Therefore, recurrent models are a natural choice to address path prediction tasks, where a trained model is used to generate future expectations from past observations. When applied to security applications, like predicting the path of pedestrians for risk assessment, a point-wise greedy (ML) evaluation of the output pdf is not feasible, since the environment often allows multiple choices. Therefore, a robust risk assessment has to take all options into account, even if they are overall not very likely. Towards this end, a combination of particle filter sampling strategies and a LSTM-MDL model is proposed to address a multi-modal path prediction task. The capabilities and viability of the proposed approach are evaluated on several synthetic test conditions, yielding the counter-intuitive result that the simplest approach performs best. Further, the feasibility of the proposed approach is illustrated on several real world scenes.

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Ronny Hug, Stefan Becker, Wolfgang Hübner, Michael Arens. 2018-04-16. Particle-based pedestrian path prediction using LSTM-MDL models. https://arxiv.org/abs/1804.05546

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