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In this paper we propose an LSTM\nencoder-decoder model that uses convolutional social pooling as an improvement\nto social pooling layers for robustly learning interdependencies in vehicle\nmotion. Additionally, our model outputs a multi-modal predictive distribution\nover future trajectories based on maneuver classes. We evaluate our model using\nthe publicly available NGSIM US-101 and I-80 datasets. Our results show\nimprovement over the state of the art in terms of RMS values of prediction\nerror and negative log-likelihoods of true future trajectories under the\nmodel's predictive distribution. 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