2018/12/29 by Jie Tang, Tang, Jie, Shaoshan Liu +5 · 1 citation
Computer Science · Engineering · #Autonomous Vehicle Technology and Safety #Distributed #FOS: Computer and information sciences #Parallel #Real-Time Systems Scheduling #Vehicular Ad Hoc Networks (VANETs) #and Cluster Computing (cs.DC)
paper · pdf · doi:10.48550/arxiv.1901.04978
openalex publication_date 2018/12/29 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
To simultaneously enable multiple autonomous driving services on affordable embedded systems, we designed and implemented π-Edge, a complete edge computing framework for autonomous robots and vehicles. The contributions of this paper are three-folds: first, we developed a runtime layer to fully utilize the heterogeneous computing resources of low-power edge computing systems; second, we developed an extremely lightweight operating system to manage multiple autonomous driving services and their communications; third, we developed an edge-cloud coordinator to dynamically offload tasks to the cloud to optimize client system energy consumption. To the best of our knowledge, this is the first complete edge computing system of a production autonomous vehicle. In addition, we successfully implemented π-Edge on a Nvidia Jetson and demonstrated that we could successfully support multiple autonomous driving services with only 11 W of power consumption, and hence proving the effectiveness of the proposed π-Edge system.