  Runtime energy management has become quintessential for multi-sensor
autonomous systems at the edge for achieving high performance given the
platform constraints. Typical for such systems, however, is to have their
controllers designed with formal guarantees on safety that precede in priority
such optimizations, which in turn limits their application in real settings. In
this paper, we propose a novel energy optimization framework that is aware of
the autonomous system's safety state, and leverages it to regulate the
application of energy optimization methods so that the system's formal safety
properties are preserved. In particular, through the formal characterization of
a system's safety state as a dynamic processing deadline, the computing
workloads of the underlying models can be adapted accordingly. For our
experiments, we model two popular runtime energy optimization methods,
offloading and gating, and simulate an autonomous driving system (ADS) use-case
in the CARLA simulation environment with performance characterizations obtained
from the standard Nvidia Drive PX2 ADS platform. Our results demonstrate that
through a formal awareness of the perceived risks in the test case scenario,
energy efficiency gains are still achieved (reaching 89.9%) while maintaining
the desired safety properties.
