  Forecasting the motion of surrounding vehicles is a critical ability for an
autonomous vehicle deployed in complex traffic. Motion of all vehicles in a
scene is governed by the traffic context, i.e., the motion and relative spatial
configuration of neighboring vehicles. In this paper we propose an LSTM
encoder-decoder model that uses convolutional social pooling as an improvement
to social pooling layers for robustly learning interdependencies in vehicle
motion. Additionally, our model outputs a multi-modal predictive distribution
over future trajectories based on maneuver classes. We evaluate our model using
the publicly available NGSIM US-101 and I-80 datasets. Our results show
improvement over the state of the art in terms of RMS values of prediction
error and negative log-likelihoods of true future trajectories under the
model's predictive distribution. We also present a qualitative analysis of the
model's predicted distributions for various traffic scenarios.
