  Precisely modeling interactions and accurately predicting trajectories of
surrounding vehicles are essential to the decision-making and path-planning of
intelligent vehicles. This paper proposes a novel framework based on ensemble
learning to improve the performance of trajectory predictions in interactive
scenarios. The framework is termed Interactive Ensemble Trajectory Predictor
(IETP). IETP assembles interaction-aware trajectory predictors as base learners
to build an ensemble learner. Firstly, each base learner in IETP observes
historical trajectories of vehicles in the scene. Then each base learner
handles interactions between vehicles to predict trajectories. Finally, an
ensemble learner is built to predict trajectories by applying two ensemble
strategies on the predictions from all base learners. Predictions generated by
the ensemble learner are final outputs of IETP. In this study, three
experiments using different data are conducted based on the NGSIM dataset.
Experimental results show that IETP improves the predicting accuracy and
decreases the variance of errors compared to base learners. In addition, IETP
exceeds baseline models with 50% of the training data, indicating that IETP is
data-efficient. Moreover, the implementation of IETP is publicly available at
https://github.com/BIT-Jack/IETP.
