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Particle-based Pedestrian Path Prediction using LSTM-MDL Models

: Hug, Ronny; Becker, Stefan; Hübner, Wolfgang; Arens, Michael


Institute of Electrical and Electronics Engineers -IEEE-:
21st International Conference on Intelligent Transportation Systems, ITSC 2018 : 4-7 November 2018, Maui, Hawaii
Piscataway, NJ: IEEE, 2018
ISBN: 978-1-7281-0323-5
ISBN: 978-1-7281-0321-1
ISBN: 978-1-7281-0322-8
ISBN: 978-1-7281-0324-2
International Conference on Intelligent Transportation Systems (ITSC) <21, 2018, Maui/Hawaii>
Fraunhofer IOSB ()

Recurrent neural networks are able to learn complex long-term relationships from sequential data and output a probability density function 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 pedestrian paths for risk assessment, a point-wise greedy 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 filtering strategies and a LSTM-MDL model is proposed to address a multimodal 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.