Multi-Vehicle Task Offloading for Cooperative Perception in Vehicular Edge Computing
Amr M. Zaki, Sara A. Elsayed, Khalid Elgazzar, Hossam S. Hassanein
Abstract
Autonomous vehicles heavily rely on sensor data to make pivotal driving and traffic management decisions. However, the reliability of such data can be profoundly impacted by many impairments, such as the adverse environmental and weather conditions, the presence of obstacles, and the vehicle's limited view of road and traffic conditions of larger areas. Collaboration between vehicles can help improve the perception of vehicles beyond their line-of-sight, and increase accurate detection of objects. Vehicular Edge Computing (VEC) has emerged as a propitious computing paradigm that can foster the realization of autonomous vehicles. However, maximizing the cooperative perception of vehicles has been mostly overlooked. In this paper, we propose the Cooperative Perception-based Task Offloading (CPTO) scheme. CPTO enables task offloading in VEC with the goal of maximizing the cooperative perception of vehicles and minimizing the latency of perception aggregation, while abiding by a certain deadline. Towards that end, we formulate the task offloading problem as a multi-objective 0–1 integer linear program (0–1 ILP). We also propose a greedy heuristic, called the CPTO-Heuristic (CPTO-H) scheme, to solve the optimization problem. Extensive simulations show that CPTO significantly outperforms the baseline task offloading scheme in terms of perception intensity, service capacity, and satisfaction ratio. Furthermore, CPTO-H closely approaches the optimal solution, with a small gap of up to 3.7% and 2.4% in terms of perception intensity and satisfaction ratio, respectively.