  To safely and efficiently navigate in complex urban traffic, autonomous
vehicles must make responsible predictions in relation to surrounding
traffic-agents (vehicles, bicycles, pedestrians, etc.). A challenging and
critical task is to explore the movement patterns of different traffic-agents
and predict their future trajectories accurately to help the autonomous vehicle
make reasonable navigation decision. To solve this problem, we propose a long
short-term memory-based (LSTM-based) realtime traffic prediction algorithm,
TrafficPredict. Our approach uses an instance layer to learn instances'
movements and interactions and has a category layer to learn the similarities
of instances belonging to the same type to refine the prediction. In order to
evaluate its performance, we collected trajectory datasets in a large city
consisting of varying conditions and traffic densities. The dataset includes
many challenging scenarios where vehicles, bicycles, and pedestrians move among
one another. We evaluate the performance of TrafficPredict on our new dataset
and highlight its higher accuracy for trajectory prediction by comparing with
prior prediction methods.
