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2020 | OriginalPaper | Buchkapitel

Interpretable Machine Learning Structure for an Early Prediction of Lane Changes

verfasst von : Oliver Gallitz, Oliver De Candido, Michael Botsch, Ron Melz, Wolfgang Utschick

Erschienen in: Artificial Neural Networks and Machine Learning – ICANN 2020

Verlag: Springer International Publishing

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Abstract

This paper proposes an interpretable machine learning structure for the task of lane change intention prediction, based on multivariate time series data. A Mixture-of-Experts architecture is adapted to simultaneously predict lane change directions and the time-to-lane-change. To facilitate reproducibility, the approach is demonstrated on a publicly available dataset of German highway scenarios. Recurrent networks for time series classification using Gated Recurrent Units and Long-Short-Term Memory cells, as well as convolution neural networks serve as comparison references. The interpretability of the results is shown by extracting the rule sets of the underlying classification and regression trees, which are grown using data-adaptive interpretable features. The proposed method outperforms the reference methods in false alarm behavior while displaying a state-of-the-art early detection performance.

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Titel: Interpretable Machine Learning Structure for an Early Prediction of Lane Changes
verfasst von: Oliver Gallitz
Oliver De Candido
Michael Botsch
Ron Melz
Wolfgang Utschick
Verlag: Springer International Publishing
Buch: Artificial Neural Networks and Machine Learning – ICANN 2020
Print ISBN: 978-3-030-61608-3

Electronic ISBN: 978-3-030-61609-0

Copyright-Jahr: 2020
DOI: https://doi.org/10.1007/978-3-030-61609-0_27

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