Autonomous Braking System via Deep Reinforcement Learning
  In this paper, we propose a new autonomous braking system based on deep
reinforcement learning. The proposed autonomous braking system automatically
decides whether to apply the brake at each time step when confronting the risk
of collision using the information on the obstacle obtained by the sensors. The
problem of designing brake control is formulated as searching for the optimal
policy in Markov decision process (MDP) model where the state is given by the
relative position of the obstacle and the vehicle's speed, and the action space
is defined as whether brake is stepped or not. The policy used for brake
control is learned through computer simulations using the deep reinforcement
learning method called deep Q-network (DQN). In order to derive desirable
braking policy, we propose the reward function which balances the damage
imposed to the obstacle in case of accident and the reward achieved when the
vehicle runs out of risk as soon as possible. DQN is trained for the scenario
where a vehicle is encountered with a pedestrian crossing the urban road.
Experiments show that the control agent exhibits desirable control behavior and
avoids collision without any mistake in various uncertain environments.
