  Reinforcement learning (RL)-based driver assistance systems seek to improve
fuel consumption via continual improvement of powertrain control actions
considering experiential data from the field. However, the need to explore
diverse experiences in order to learn optimal policies often limits the
application of RL techniques in safety-critical systems like vehicle control.
In this paper, an exponential control barrier function (ECBF) is derived and
utilized to filter unsafe actions proposed by an RL-based driver assistance
system. The RL agent freely explores and optimizes the performance objectives
while unsafe actions are projected to the closest actions in the safe domain.
The reward is structured so that driver's acceleration requests are met in a
manner that boosts fuel economy and doesn't compromise comfort. The optimal
gear and traction torque control actions that maximize the cumulative reward
are computed via the Maximum a Posteriori Policy Optimization (MPO) algorithm
configured for a hybrid action space. The proposed safe-RL scheme is trained
and evaluated in car following scenarios where it is shown that it effectively
avoids collision both during training and evaluation while delivering on the
expected fuel economy improvements for the driver assistance system.
