Cloud‐assisted Control of Ground Vehicles using Adaptive Computation Offloading Techniques
Arun Adiththan1, Ramesh S2 and Soheil Samii3
1The City University of New York, New York, NY 10019
arunadiththan@gmail.com
2General Motors R&D, Warren, MI 48090
ramesh.s@gm.com
3Linköping University, 581 83 Linköping, Sweden
soheil.samii@gm.com
ABSTRACT
The existing approaches to design efficient safetycritical control applications is constrained by limited in‐vehicle sensing and computational capabilities. In the context of automated driving, we argue that there is a need to leverage resources “out‐of‐thevehicle” to meet the sensing and powerful processing requirements of sophisticated algorithms (e.g., deep neural networks). To realize the need, a suitable computation offloading technique that meets the vehicle safety and stability requirements, even in the presence of unreliable communication network, has to be identified. In this work, we propose an adaptive offloading technique for control computations into the cloud. The proposed approach considers both current network conditions and control application requirements to determine the feasibility of leveraging remote computation and storage resources. As a case study, we describe a cloud‐based path following controller application that leverages crowdsensed data for path planning.
