Cloud-assisted control of ground vehicles using adaptive computation offloading techniques

The existing approaches to design efficient safety-critical 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-the-vehicle” 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.