  Predicting the future trajectory of a surrounding vehicle in congested
traffic is one of the basic abilities of an autonomous vehicle. In congestion,
a vehicle's future movement is the result of its interaction with surrounding
vehicles. A vehicle in congestion may have many neighbors in a relatively short
distance, while only a small part of neighbors affect its future trajectory
mostly. In this work, An interaction-aware method which predicts the future
trajectory of an ego vehicle considering its interaction with eight surrounding
vehicles is proposed. The dynamics of vehicles are encoded by LSTMs with shared
weights, and the interaction is extracted with a simple CNN. The proposed model
is trained and tested on trajectories extracted from the publicly accessible
NGSIM US-101 dataset. Quantitative experimental results show that the proposed
model outperforms previous models in terms of root-mean-square error (RMSE).
Results visualization shows that the model is able to predict future trajectory
induced by lane change before the vehicle operate obvious lateral movement to
initiate lane changing.
