  Control schemes for autonomous systems are often designed in a way that
anticipates the worst case in any situation. At runtime, however, there could
exist opportunities to leverage the characteristics of specific environment and
operation context for more efficient control. In this work, we develop an
online intermittent-control framework that combines formal verification with
model-based optimization and deep reinforcement learning to opportunistically
skip certain control computation and actuation to save actuation energy and
computational resources without compromising system safety. Experiments on an
adaptive cruise control system demonstrate that our approach can achieve
significant energy and computation savings.
