Strategic offloading in autonomous vehicles: A systematic survey of current schemes, challenges, and future prospects

Autonomous vehicles, most notably self-driving cars, are seen by many as a generational shift in transportation, offering the potential to reduce crash rates and relieve congestion. In order to operate, autonomous vehicles combine data from cameras, radar, and LiDAR and need to act on this data in real-time creating a heavy and bursty computational demand. Processing these heavy and bursty demands within the autonomy system's power and thermal envelope will require considerable effort in determining which tasks to perform when and in what order. In some cases, offloading workloads to edge or cloud servers creates an opportunity to offload compute, reduce end-to-end latency, and enhance overall responsiveness if workloads are offloaded under strict latency constraints. In this work, we survey state-of-the-art offloading methods, identify significant challenges such as latency management, network reliability, and security, and outline future improvements within the area of vehicular systems. From an analysis of the state of the literature, we also objectively evaluate when and how offloading can enhance multi-faceted computational demands of autonomous driving stacks, with the overall goal of support safer and more capable vehicular systems.