  In this paper, we develop a safe decision-making method for self-driving cars
in a multi-lane, single-agent setting. The proposed approach utilizes deep
reinforcement learning (RL) to achieve a high-level policy for safe tactical
decision-making. We address two major challenges that arise solely in
autonomous navigation. First, the proposed algorithm ensures that collisions
never happen, and therefore accelerate the learning process. Second, the
proposed algorithm takes into account the unobservable states in the
environment. These states appear mainly due to the unpredictable behavior of
other agents, such as cars, and pedestrians, and make the Markov Decision
Process (MDP) problematic when dealing with autonomous navigation. Simulations
from a well-known self-driving car simulator demonstrate the applicability of
the proposed method
