To maintain high perception performance among connected and autonomous vehicles (CAVs), in this paper, we propose an accuracy-aware and resource-efficient raw-level cooperative sensing and computing scheme among CAVs and road-side infrastructure. The scheme enables fined-grained partial raw sensing data selection, transmission, fusion, and processing in per-object granularity, by exploiting the parallelism among object classification subtasks associated with each object. A supervised learning model is trained to capture the relationship between the object classification accuracy and the data quality of selected object sensing data, facilitating accuracy-aware sensing data selection. We formulate an optimization problem for joint sensing data selection, subtask placement and resource allocation among multiple object classification subtasks, to minimize the total resource cost while satisfying the delay and accuracy requirements. A genetic algorithm based iterative solution is proposed for the optimization problem. Simulation results demonstrate the accuracy awareness and resource efficiency achieved by the proposed cooperative sensing and computing scheme, in comparison with benchmark solutions.

翻译：为维持网联自动驾驶车辆的高感知性能，本文提出一种面向精确感知且资源高效的原始级协同感知与计算方案，实现车辆与路侧基础设施的协作。该方案通过利用各目标对应的分类子任务并行性，支持对原始感知数据进行细粒度的逐目标选择性传输、融合与处理。通过训练监督学习模型，建立目标分类精度与所选感知数据质量之间的关联，从而支持精确感知导向的数据选择。我们构建了一个联合优化问题，以在多个目标分类子任务间实现感知数据选择、子任务部署与资源分配的协同优化，在满足时延与精度约束的前提下最小化总资源消耗。针对该优化问题，提出一种基于遗传算法的迭代求解方法。仿真结果表明，与基准方案相比，所提出的协同感知与计算方案在精度感知能力与资源效率方面具有显著优势。