  Precise trajectory prediction of surrounding vehicles is critical for
decision-making of autonomous vehicles and learning-based approaches are well
recognized for the robustness. However, state-of-the-art learning-based methods
ignore 1) the feasibility of the vehicle's multi-modal state information for
prediction and 2) the mutual exclusive relationship between the global traffic
scene receptive fields and the local position resolution when modeling
vehicles' interactions, which may influence prediction accuracy. Therefore, we
propose a vehicle-descriptor based LSTM model with the dilated convolutional
social pooling (VD+DCS-LSTM) to cope with the above issues. First, each
vehicle's multi-modal state information is employed as our model's input and a
new vehicle descriptor encoded by stacked sparse auto-encoders is proposed to
reflect the deep interactive relationships between various states, achieving
the optimal feature extraction and effective use of multi-modal inputs.
Secondly, the LSTM encoder is used to encode the historical sequences composed
of the vehicle descriptor and a novel dilated convolutional social pooling is
proposed to improve modeling vehicles' spatial interactions. Thirdly, the LSTM
decoder is used to predict the probability distribution of future trajectories
based on maneuvers. The validity of the overall model was verified over the
NGSIM US-101 and I-80 datasets and our method outperforms the latest benchmark.
