Collaborative perception holds great promise for improving safety in autonomous driving, particularly in dense traffic where vehicles can share sensory information to overcome individual blind spots and extend awareness. However, deploying such collaboration at scale remains difficult when communication bandwidth is limited and no roadside infrastructure is available. To overcome these limitations, we introduce a fully decentralized framework that enables vehicles to self organize into cooperative groups using only vehicle to vehicle communication. The approach decomposes the problem into two sequential game theoretic stages. In the first stage, vehicles form stable clusters by evaluating mutual sensing complementarity and motion coherence, and each cluster elects a coordinator. In the second stage, the coordinator guides its members to selectively transmit point cloud segments from perceptually salient regions through a non cooperative potential game, enabling efficient local fusion. Global scene understanding is then achieved by exchanging compact detection messages across clusters rather than raw sensor data. We design distributed algorithms for both stages that guarantee monotonic improvement of the system wide potential function. Comprehensive experiments on the CARLA-OpenCDA-NS3 co-simulation platform show that our method reduces communication overhead while delivering higher perception accuracy and wider effective coverage compared to existing baselines.

翻译：协同感知在提升自动驾驶安全性方面具有巨大潜力，尤其在密集交通场景中，车辆可通过共享传感信息来克服个体盲区并扩展感知范围。然而，在通信带宽有限且缺乏路边基础设施的情况下，大规模部署此类协同仍面临困难。为克服这些限制，本文提出一种完全去中心化的框架，使车辆仅通过车对车通信即可自组织形成协同群组。该方法将问题分解为两个连续的博弈论阶段。在第一阶段，车辆通过评估相互间的感知互补性与运动一致性形成稳定集群，每个集群选举出一名协调器。在第二阶段，协调器通过非合作势博弈引导其成员选择性传输感知显著区域的点云片段，实现高效的局部融合。随后通过跨集群交换紧凑的检测消息而非原始传感器数据，达成全局场景理解。我们为两个阶段设计了分布式算法，确保系统范围势函数的单调改进。在CARLA-OpenCDA-NS3联合仿真平台上的综合实验表明，与现有基线方法相比，本方法在降低通信开销的同时，提供了更高的感知精度与更广的有效覆盖范围。