  Automated lane change is one of the most challenging task to be solved of
highly automated vehicles due to its safety-critical, uncertain and multi-agent
nature. This paper presents the novel deployment of the state of art Q learning
method, namely Rainbow DQN, that uses a new safety driven rewarding scheme to
tackle the issues in an dynamic and uncertain simulation environment. We
present various comparative results to show that our novel approach of having
reward feedback from the safety layer dramatically increases both the agent's
performance and sample efficiency. Furthermore, through the novel deployment of
Rainbow DQN, it is shown that more intuition about the agent's actions is
extracted by examining the distributions of generated Q values of the agents.
The proposed algorithm shows superior performance to the baseline algorithm in
the challenging scenarios with only 200000 training steps (i.e. equivalent to
55 hours driving).
