Automated vehicle's behavior decision making using deep reinforcement
  learning and high-fidelity simulation environment
  Automated vehicles are deemed to be the key element for the intelligent
transportation system in the future. Many studies have been made to improve the
Automated vehicles' ability of environment recognition and vehicle control,
while the attention paid to decision making is not enough though the decision
algorithms so far are very preliminary. Therefore, a framework of the
decision-making training and learning is put forward in this paper. It consists
of two parts: the deep reinforcement learning training program and the
high-fidelity virtual simulation environment. Then the basic microscopic
behavior, car-following, is trained within this framework. In addition,
theoretical analysis and experiments were conducted on setting reward function
for accelerating training using deep reinforcement learning. The results show
that on the premise of driving comfort, the efficiency of the trained Automated
vehicle increases 7.9% compared to the classical traffic model, intelligent
driver model. Later on, on a more complex three-lane section, we trained the
integrated model combines both car-following and lane-changing behavior, the
average speed further grows 2.4%. It indicates that our framework is effective
for Automated vehicle's decision-making learning.
