  Tactical decision making is a critical feature for advanced driving systems,
that incorporates several challenges such as complexity of the uncertain
environment and reliability of the autonomous system. In this work, we develop
a multi-modal architecture that includes the environmental modeling of ego
surrounding and train a deep reinforcement learning (DRL) agent that yields
consistent performance in stochastic highway driving scenarios. To this end, we
feed the occupancy grid of the ego surrounding into the DRL agent and obtain
the high-level sequential commands (i.e. lane change) to send them to
lower-level controllers. We will show that dividing the autonomous driving
problem into a multi-layer control architecture enables us to leverage the AI
power to solve each layer separately and achieve an admissible reliability
score. Comparing with end-to-end approaches, this architecture enables us to
end up with a more reliable system which can be implemented in actual
self-driving cars.
