  As autonomous vehicles (AVs) need to interact with other road users, it is of
importance to comprehensively understand the dynamic traffic environment,
especially the future possible trajectories of surrounding vehicles. This paper
presents an algorithm for long-horizon trajectory prediction of surrounding
vehicles using a dual long short term memory (LSTM) network, which is capable
of effectively improving prediction accuracy in strongly interactive driving
environments. In contrast to traditional approaches which require trajectory
matching and manual feature selection, this method can automatically learn
high-level spatial-temporal features of driver behaviors from naturalistic
driving data through sequence learning. By employing two blocks of LSTMs, the
proposed method feeds the sequential trajectory to the first LSTM for driver
intention recognition as an intermediate indicator, which is immediately
followed by a second LSTM for future trajectory prediction. Test results from
real-world highway driving data show that the proposed method can, in
comparison to state-of-art methods, output more accurate and reasonable
estimate of different future trajectories over 5s time horizon with root mean
square error (RMSE) for longitudinal and lateral prediction less than 5.77m and
0.49m, respectively.
