  How autonomous vehicles and human drivers share public transportation systems
is an important problem, as fully automatic transportation environments are
still a long way off. Understanding human drivers' behavior can be beneficial
for autonomous vehicle decision making and planning, especially when the
autonomous vehicle is surrounded by human drivers who have various driving
behaviors and patterns of interaction with other vehicles. In this paper, we
propose an LSTM-based trajectory prediction method for human drivers which can
help the autonomous vehicle make better decisions, especially in urban
intersection scenarios. Meanwhile, in order to collect human drivers' driving
behavior data in the urban scenario, we describe a system called UrbanFlow
which includes the whole procedure from raw bird's-eye view data collection via
drone to the final processed trajectories. The system is mainly intended for
urban scenarios but can be extended to be used for any traffic scenarios.
