  The operational space of an autonomous vehicle (AV) can be diverse and vary
significantly. This may lead to a scenario that was not postulated in the
design phase. Due to this, formulating a rule based decision maker for
selecting maneuvers may not be ideal. Similarly, it may not be effective to
design an a-priori cost function and then solve the optimal control problem in
real-time. In order to address these issues and to avoid peculiar behaviors
when encountering unforeseen scenario, we propose a reinforcement learning (RL)
based method, where the ego car, i.e., an autonomous vehicle, learns to make
decisions by directly interacting with simulated traffic. The decision maker
for AV is implemented as a deep neural network providing an action choice for a
given system state. In a critical application such as driving, an RL agent
without explicit notion of safety may not converge or it may need extremely
large number of samples before finding a reliable policy. To best address the
issue, this paper incorporates reinforcement learning with an additional short
horizon safety check (SC). In a critical scenario, the safety check will also
provide an alternate safe action to the agent provided if it exists. This leads
to two novel contributions. First, it generalizes the states that could lead to
undesirable "near-misses" or "collisions ". Second, inclusion of safety check
can provide a safe and stable training environment. This significantly enhances
learning efficiency without inhibiting meaningful exploration to ensure safe
and optimal learned behavior. We demonstrate the performance of the developed
algorithm in highway driving scenario where the trained AV encounters varying
traffic density in a highway setting.
