  Predicting traffic agents' trajectories is an important task for
auto-piloting. Most previous work on trajectory prediction only considers a
single class of road agents. We use a sequence-to-sequence model to predict
future paths from observed paths and we incorporate class information into the
model by concatenating extracted label representations with traditional
location inputs. We experiment with both LSTM and transformer encoders and we
use generative adversarial network as introduced in Social GAN to learn the
multi-modal behavior of traffic agents. We train our model on Stanford Drone
dataset which includes 6 classes of road agents and evaluate the impact of
different model components on the prediction performance in multi-class scenes.
