Energy-Aware Adaptive Framework for CAV

Driving assist applications and connected autonomous vehicle systems are supported using AI models and algorithms, which process and analyze heavy data volumes. High-performance computing units and large memory systems support these models, algorithms, and applications, which results in additional onboard energy consumption. The current trend is also towards full electrification of vehicles and increasing connectivity in the vehicular ecosystem to support collaborative and distributed applications using vehicle-edge-cloud computing. However, with the increased focus on model performance and improving the accuracy of these models and applications, the issue of high-performance computing requirements and resulting energy consumption are overlooked. The problem becomes more challenging and complex for resource-constrained edge devices, which are battery-dependent and have limited memory and computing power. This paper proposes components for an adaptive framework to reduce energy consumption by balancing model accuracy. The contributions include proposing and integrating model partition mechanisms, adaptive deployment across edge devices and approximation strategies for the models. By integrating these components, this framework supports energy-aware development across various platforms. The approach offers a sustainable method for computing and communication-oriented applications within the vehicular ecosystem.