Interpretable Machine Learning Structure for an Early Prediction of Lane Changes

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.