  Deep reinforcement learning (DRL) allows a system to interact with its
environment and take actions by training an efficient policy that maximizes
self-defined rewards. In autonomous driving, it can be used as a strategy for
high-level decision making, whereas low-level algorithms such as the hybrid A*
path planning have proven their ability to solve the local trajectory planning
problem. In this work, we combine these two methods where the DRL makes
high-level decisions such as lane change commands. After obtaining the lane
change command, the hybrid A* planner is able to generate a collision-free
trajectory to be executed by a model predictive controller (MPC). In addition,
the DRL algorithm is able to keep the lane change command consistent within a
chosen time-period. Traffic rules are implemented using linear temporal logic
(LTL), which is then utilized as a reward function in DRL. Furthermore, we
validate the proposed method on a real system to demonstrate its feasibility
from simulation to implementation on real hardware.
