Task Offloading and Resource Allocation in Vehicular Cooperative Perception With Integrated Sensing, Communication, and Computation

Vehicular cooperative perception (VCP) facilitates the exchange of sensing data among vehicles through vehicle-to-everything (V2X) communication, significantly increasing the sensing range and precision of individual autonomous vehicles (AVs). However, efficiently managing the sharing and processing of large volumes of sensing data presents challenges due to restricted communication and computation resources. This study introduces an integrated sensing, communication, and computation (ISCC)-based task offloading and resource allocation (ITORA) framework, which optimizes cooperative perception by determining what data to share, which vehicles to involve, and how to process the data effectively. We develop an information value function to evaluate the data quality for each vehicle. Subsequently, we design strategies for sensing task allocation, task offloading, and resource allocation to enable value-driven data selection at a subregion level, facilitating collaborative computing among edge servers and vehicles. Additionally, we formulate an optimization problem aimed at maximizing information value while minimizing delay and energy consumption, subject to constraints on a full region of interest (RoI) coverage, delay, wireless bandwidth, and computational resources. We decompose the mixed-integer nonlinear programming (MINLP) problem into two subproblems, devising a sensing task allocation algorithm and a proximal policy optimization (PPO)-based task offloading and resource allocation (PTORA) algorithm to address them. Comprehensive simulations validate the effectiveness of the proposed PTORA in optimizing information value, reducing task execution delay, and minimizing energy consumption.