Vehicular and Edge Computing for Emerging Connected and Autonomous Vehicle Applications

Abstract

Emerging connected and autonomous vehicles involve complex applications requiring not only optimal computing resource allocations but also efficient computing architectures. In this paper, we unfold the critical performance metrics required for emerging vehicular computing applications and show with preliminary experimental results, how optimal choices can be made to satisfy the static and dynamic computing requirements in terms of the performance metrics. We also discuss the feasibility of edge computing architectures for vehicular computing and show tradeoffs for different offloading strategies. The paper shows directions for light weight, high performance and low power computing paradigms, architectures and design-space exploration tools to satisfy evolving applications and requirements for connected and autonomous vehicles.