Cooperative perception is the key approach to augment the perception of connected and automated vehicles (CAVs) toward safe autonomous driving. However, it is challenging to achieve real-time perception sharing for hundreds of CAVs in large-scale deployment scenarios. In this paper, we propose AdaMap, a new high-scalable real-time cooperative perception system, which achieves assured percentile end-to-end latency under time-varying network dynamics. To achieve AdaMap, we design a tightly coupled data plane and control plane. In the data plane, we design a new hybrid localization module to dynamically switch between object detection and tracking, and a novel point cloud representation module to adaptively compress and reconstruct the point cloud of detected objects. In the control plane, we design a new graph-based object selection method to un-select excessive multi-viewed point clouds of objects, and a novel approximated gradient descent algorithm to optimize the representation of point clouds. We implement AdaMap on an emulation platform, including realistic vehicle and server computation and a simulated 5G network, under a 150-CAV trace collected from the CARLA simulator. The evaluation results show that, AdaMap reduces up to 49x average transmission data size at the cost of 0.37 reconstruction loss, as compared to state-of-the-art solutions, which verifies its high scalability, adaptability, and computation efficiency.

翻译：论文摘要：协同感知是增强网联自动驾驶汽车（CAVs）感知能力、实现安全自动驾驶的关键途径。然而，在大规模部署场景下，为数百辆CAVs实现实时感知共享仍具挑战。本文提出AdaMap——一种新型高可扩展实时协同感知系统，可在时变网络动态条件下保证百分位端到端延迟。为构建AdaMap，我们设计了紧耦合的数据平面与控制平面。在数据平面中，我们设计了新型混合定位模块，可在目标检测与跟踪间动态切换；以及新颖的点云表示模块，可自适应压缩与重构检测目标的点云。在控制平面中，我们设计了基于图的新型目标选择方法，以去除冗余的多视角目标点云；以及近似的梯度下降算法，用于优化点云表示。我们在仿真平台上实现了AdaMap，该平台包含真实车辆与服务器计算单元、模拟5G网络，并采用从CARLA仿真器采集的150-CAV轨迹数据。评估结果表明，与现有最优方案相比，AdaMap在仅产生0.37重构损失的代价下，平均传输数据量降低达49倍，验证了其高可扩展性、自适应性及计算效率。