Proactive safety and vehicle automation typically requires high energy use from
sensors and energy-intensive computing from sensor data processing. For
high-quality, reliable perception and localization within a driving environment
at the vehicle level, incoming data from multiple sensors need to be fused using
advanced computational algorithms, which demand a high compute load.
Alternatively, computational offloading of automated driving tasks shifts energy
consumption from the vehicle to cloud infrastructure, where renewable energy
sources, such as hydropower or solar power, can be utilized more efficiently.
Herein, an optimal scheduling strategy for autonomous driving tasks via the
cloud layer is formulated as a mixed-integer linear programming (MILP) problem
and verified using measurement-informed task graphs. It is shown that
cloud-based computational offloading enables energy-efficient operation while
maintaining task deadlines to ensure the timely availability of perception and
localization information, which is consistent with prior studies in the
literature. Simulation results demonstrate that on average, 35.78% of the
computational load was offloaded to the cloud, which can achieve significant
energy savings for the onboard system. The compute operations achieved an
average energy savings of 35.65%, while total system savings ranged from 26.16%
to 30.67% under different cloud energy efficiency scenarios, highlighting the
advantages of offloading compute-intensive tasks. The framework was further
evaluated using multiple directed acyclic graph (DAG) configurations to assess
its scalability and adaptability. Critical tasks, such as sensor fusion, were
executed exclusively on the vehicle to ensure real-time responsiveness.
