  Due to the high performance and safety requirements of self-driving
applications, the complexity of modern autonomous driving systems (ADS) has
been growing, instigating the need for more sophisticated hardware which could
add to the energy footprint of the ADS platform. Addressing this, edge
computing is poised to encompass self-driving applications, enabling the
compute-intensive autonomy-related tasks to be offloaded for processing at
compute-capable edge servers. Nonetheless, the intricate hardware architecture
of ADS platforms, in addition to the stringent robustness demands, set forth
complications for task offloading which are unique to autonomous driving.
Hence, we present $ROMANUS$, a methodology for robust and efficient task
offloading for modular ADS platforms with multi-sensor processing pipelines.
Our methodology entails two phases: (i) the introduction of efficient
offloading points along the execution path of the involved deep learning
models, and (ii) the implementation of a runtime solution based on Deep
Reinforcement Learning to adapt the operating mode according to variations in
the perceived road scene complexity, network connectivity, and server load.
Experiments on the object detection use case demonstrated that our approach is
14.99% more energy-efficient than pure local execution while achieving a 77.06%
reduction in risky behavior from a robust-agnostic offloading baseline.
