  The use of neural networks and reinforcement learning has become increasingly
popular in autonomous vehicle control. However, the opaqueness of the resulting
control policies presents a significant barrier to deploying neural
network-based control in autonomous vehicles. In this paper, we present a
reinforcement learning based approach to autonomous vehicle longitudinal
control, where the rule-based safety cages provide enhanced safety for the
vehicle as well as weak supervision to the reinforcement learning agent. By
guiding the agent to meaningful states and actions, this weak supervision
improves the convergence during training and enhances the safety of the final
trained policy. This rule-based supervisory controller has the further
advantage of being fully interpretable, thereby enabling traditional validation
and verification approaches to ensure the safety of the vehicle. We compare
models with and without safety cages, as well as models with optimal and
constrained model parameters, and show that the weak supervision consistently
improves the safety of exploration, speed of convergence, and model
performance. Additionally, we show that when the model parameters are
constrained or sub-optimal, the safety cages can enable a model to learn a safe
driving policy even when the model could not be trained to drive through
reinforcement learning alone.
