Task offloading in Vehicular Edge Computing (VEC) can advance cooperative perception (CP) to improve traffic awareness in Autonomous Vehicles. In this paper, we propose the Quality-aware Cooperative Perception Task Offloading (QCPTO) scheme. Q-CPTO is the first task offloading scheme that enhances traffic awareness by prioritizing the quality rather than the quantity of cooperative perception. Q-CPTO improves the quality of CP by curtailing perception redundancy and increasing the Value of Information (VOI) procured by each user. We use Kalman filters (KFs) for VOI assessment, predicting the next movement of each vehicle to estimate its region of interest. The estimated VOI is then integrated into the task offloading problem. We formulate the task offloading problem as an Integer Linear Program (ILP) that maximizes the VOI of users and reduces perception redundancy by leveraging the spatially diverse fields of view (FOVs) of vehicles, while adhering to strict latency requirements. We also propose the Q-CPTO-Heuristic (Q-CPTOH) scheme to solve the task offloading problem in a time-efficient manner. Extensive evaluations show that Q-CPTO significantly outperforms prominent task offloading schemes by up to 14% and 20% in terms of response delay and traffic awareness, respectively. Furthermore, Q-CPTO-H closely approaches the optimal solution, with marginal gaps of up to 1.4% and 2.1% in terms of traffic awareness and the number of collaborating users, respectively, while reducing the runtime by up to 84%.

翻译：车载边缘计算（VEC）中的任务卸载能够推进协同感知（CP）技术，从而提升自动驾驶车辆的交通态势感知能力。本文提出一种质量感知的协同感知任务卸载（Q-CPTO）方案。Q-CPTO是首个通过优先考虑协同感知质量而非数量来增强交通感知能力的任务卸载方案。该方案通过削减感知冗余并提升每个用户获取的信息价值（VOI）来改进协同感知的质量。我们采用卡尔曼滤波器（KF）进行VOI评估，通过预测每辆车的下一时刻运动状态来估计其兴趣区域。随后将估计的VOI整合到任务卸载问题中。我们将任务卸载问题建模为整数线性规划（ILP），其目标是在满足严格时延约束的前提下，通过利用车辆空间异构的视场（FOV）来最大化用户VOI并降低感知冗余。同时，我们提出了Q-CPTO启发式（Q-CPTO-H）方案，以高效的方式求解任务卸载问题。大量实验表明，Q-CPTO在响应延迟和交通感知能力两项指标上分别显著优于主流任务卸载方案达14%和20%。此外，Q-CPTO-H在交通感知能力和协同用户数量方面与最优解的差距分别仅为1.4%和2.1%，同时将运行时间降低了84%。