  Reinforcement learning is nowadays a popular framework for solving different
decision making problems in automated driving. However, there are still some
remaining crucial challenges that need to be addressed for providing more
reliable policies. In this paper, we propose a generic risk-aware DQN approach
in order to learn high level actions for driving through unsignalized occluded
intersections. The proposed state representation provides lane based
information which allows to be used for multi-lane scenarios. Moreover, we
propose a risk based reward function which punishes risky situations instead of
only collision failures. Such rewarding approach helps to incorporate risk
prediction into our deep Q network and learn more reliable policies which are
safer in challenging situations. The efficiency of the proposed approach is
compared with a DQN learned with conventional collision based rewarding scheme
and also with a rule-based intersection navigation policy. Evaluation results
show that the proposed approach outperforms both of these methods. It provides
safer actions than collision-aware DQN approach and is less overcautious than
the rule-based policy.
