The proposed Mixture-of-Experts architecture is adapted to simultaneously predict lane change directions and the time-to-lane-change, which outperforms the ...

This paper proposes an interpretable machine learning structure for the early prediction of lane changes.

This paper proposes an interpretable machine learning structure for the task of lane change intention prediction, based on multivariate time series data.

Explainable Online Lane Change Predictions on a Digital Twin with a Layer Normalized LSTM and Layer-wise Relevance Propagation · Driving Intention Recognition of ...

Sep 19, 2021 · This paper proposes an interpretable machine learning structure for the early prediction of lane changes. The interpretability relies on ...

Feb 24, 2022 · Interpretable Machine Learning Structure for an Early Prediction of Lane Changes ... Autonomous driving; Early prediction; Interpretability.

This study explains and predicts driver's mandatory and discretionary lane-changing behaviours using a set of suitable machine learning techniques.

Capsule Networks (CapsNets) have been re-introduced as a more compact and interpretable alternative to standard deep neural networks. While recent efforts have ...

Accurately detecting and predicting lane change (LC)processes can help autonomous vehicles better understand their surrounding environment, recognize potential ...