  Prior work has looked at applying reinforcement learning and imitation
learning approaches to autonomous driving scenarios, but either the safety or
the efficiency of the algorithm is compromised. With the use of control barrier
functions embedded into the reinforcement learning policy, we arrive at safe
policies to optimize the performance of the autonomous driving vehicle.
However, control barrier functions need a good approximation of the model of
the car. We use probabilistic control barrier functions as an estimate of the
model uncertainty. The algorithm is implemented as an online version in the
CARLA (Dosovitskiy et al., 2017) Simulator and as an offline version on a
dataset extracted from the NGSIM Database. The proposed algorithm is not just a
safe ramp merging algorithm but a safe autonomous driving algorithm applied to
address ramp merging on highways.
