… deep ensemble network designed to capture both perception and prediction uncertainty during
trajectory prediction. … that our deep ensembles not only yield more robust predictions but …

… a novel framework based on ensemble learning to improve the performance of trajectory …
prediction in interactive scenarios, and we term it as Interactive Ensemble Trajectory Predictor (…

… In this paper, an interaction aware trajectory prediction algorithm is proposed. The … , in this
study, the deep ensemble method is applied to estimate not only the predicted trajectory of the …

… Several studies have been carried out for trajectory prediction of obstacle vehicle using
polynomials [5]–[7]. Houenou et al. [5] combined the advantages of both CYRA model and …

… The deep ensemble technique is also used to estimate the uncertainty of the learning-based
method. Because the deep ensemble … The deep ensemble [32] used in this study is a non-…

… Predicting the trajectories of road agents is fundamental for self-driving cars. Trajectory
prediction … histogram-based deep learning model combined with deep ensemble techniques for …

… Post-CoverNet and ISAP and 1/V arc for ensembles where V arc is the empirical variance
of the predicted class probability across the ensemble. The expected calibration error (ECE) …

… Through extensive experiments, we show that trajectories of randomly initialized neural
networks explore different modes in function space, which explains why deep ensembles trained …

… In this work, to preserve the historical human mobility information and leverage the most
recent trajectories, we propose an adaptive deep ensemble neural network to predict citywide …

… • We propose a vehicle trajectory interpolation framework based on ensemble learning to …
can be transformed into sequence prediction. For example, a trajectory prediction method …