Integrating Deep Reinforcement Learning with Model-based Path Planners
  for Automated Driving
  Automated driving in urban settings is challenging. Human participant
behavior is difficult to model, and conventional, rule-based Automated Driving
Systems (ADSs) tend to fail when they face unmodeled dynamics. On the other
hand, the more recent, end-to-end Deep Reinforcement Learning (DRL) based
model-free ADSs have shown promising results. However, pure learning-based
approaches lack the hard-coded safety measures of model-based controllers. Here
we propose a hybrid approach for integrating a path planning pipe into a vision
based DRL framework to alleviate the shortcomings of both worlds. In summary,
the DRL agent is trained to follow the path planner's waypoints as close as
possible. The agent learns this policy by interacting with the environment. The
reward function contains two major terms: the penalty of straying away from the
path planner and the penalty of having a collision. The latter has precedence
in the form of having a significantly greater numerical value. Experimental
results show that the proposed method can plan its path and navigate between
randomly chosen origin-destination points in CARLA, a dynamic urban simulation
environment. Our code is open-source and available online.
