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Today, vehicles use smart sensors to collect data from the road environment.This data is often processed onboard of the vehicles, using expensive hardware.Such onboard processing increases the vehicle's cost, quickly drains itsbattery, and exhausts its computing resources. Therefore, offloading tasks ontothe cloud is required. Still, data offloading is challenging due to low latencyrequirements for safe and reliable vehicle driving decisions. Moreover, age ofprocessing was not considered in prior research dealing with low-latencyoffloading for autonomous vehicles. This paper proposes an age ofprocessing-based offloading approach for autonomous vehicles using unsupervisedmachine learning, Multi-Radio Access Technologies (multi-RATs), and EdgeComputing in Open Radio Access Network (O-RAN). We design a collaboration spaceof edge clouds to process data in proximity to autonomous vehicles. To reducethe variation in offloading delay, we propose a new communication planningapproach that enables the vehicle to optimally preselect the available RATssuch as Wi-Fi, LTE, or 5G to offload tasks to edge clouds when its localresources are insufficient. We formulate an optimization problem for age-basedoffloading that minimizes elapsed time from generating tasks and receivingcomputation output. To handle this non-convex problem, we develop a surrogateproblem. Then, we use the Lagrangian method to transform the surrogate problemto unconstrained optimization problem and apply the dual decomposition method.The simulation results show that our approach significantly minimizes the ageof processing in data offloading with 90.34 % improvement over similar method.

Age of Processing-Based Data Offloading for Autonomous Vehicles in Multi-RATs Open RAN