  The trajectory prediction is significant for the decision-making of
autonomous driving vehicles. In this paper, we propose a model to predict the
trajectories of target agents around an autonomous vehicle. The main idea of
our method is considering the history trajectories of the target agent and the
influence of surrounding agents on the target agent. To this end, we encode the
target agent history trajectories as an attention mask and construct a social
map to encode the interactive relationship between the target agent and its
surrounding agents. Given a trajectory sequence, the LSTM networks are firstly
utilized to extract the features for all agents, based on which the attention
mask and social map are formed. Then, the attention mask and social map are
fused to get the fusion feature map, which is processed by the social
convolution to obtain a fusion feature representation. Finally, this fusion
feature is taken as the input of a variable-length LSTM to predict the
trajectory of the target agent. We note that the variable-length LSTM enables
our model to handle the case that the number of agents in the sensing scope is
highly dynamic in traffic scenes. To verify the effectiveness of our method, we
widely compare with several methods on a public dataset, achieving a 20% error
decrease. In addition, the model satisfies the real-time requirement with the
32 fps.
