Cooperative perception (CP) is a key technology to facilitate consistent and accurate situational awareness for connected and autonomous vehicles (CAVs). To tackle the network resource inefficiency issue in traditional broadcast-based CP, unicast-based CP has been proposed to associate CAV pairs for cooperative perception via vehicle-to-vehicle transmission. In this paper, we investigate unicast-based CP among CAV pairs. With the consideration of dynamic perception workloads and channel conditions due to vehicle mobility and dynamic radio resource availability, we propose an adaptive cooperative perception scheme for CAV pairs in a mixed-traffic autonomous driving scenario with both CAVs and human-driven vehicles. We aim to determine when to switch between cooperative perception and stand-alone perception for each CAV pair, and allocate communication and computing resources to cooperative CAV pairs for maximizing the computing efficiency gain under perception task delay requirements. A model-assisted multi-agent reinforcement learning (MARL) solution is developed, which integrates MARL for an adaptive CAV cooperation decision and an optimization model for communication and computing resource allocation. Simulation results demonstrate the effectiveness of the proposed scheme in achieving high computing efficiency gain, as compared with benchmark schemes.

翻译：协同感知（CP）是促进网联自动驾驶车辆（CAVs）实现一致且准确态势感知的关键技术。为解决传统广播式CP中网络资源效率低下的问题，研究者提出基于单播的CP方案，通过车-车传输建立CAV配对实现协同感知。本文研究CAV配对间的单播式CP问题。考虑车辆移动性和动态无线资源可用性导致的感知工作负载与信道条件变化，我们提出一种面向CAV配对的混合交通自动驾驶场景（包含CAV与人工驾驶车辆）自适应协同感知方案。该方案旨在为每个CAV配对确定协同感知与独立感知模式的切换时机，并为协同CAV对分配通信与计算资源，以在满足感知任务时延约束下最大化计算效率增益。我们开发了一种模型辅助多智能体强化学习（MARL）解决方案，该方案融合了用于自适应CAV协作决策的MARL与用于通信及计算资源分配的优化模型。仿真结果表明，与基准方案相比，所提方案在实现高计算效率增益方面具有有效性。