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Cooperative perception is the key approach to augment the perception ofconnected and automated vehicles (CAVs) toward safe autonomous driving.However, it is challenging to achieve real-time perception sharing for hundredsof CAVs in large-scale deployment scenarios. In this paper, we propose AdaMap,a new high-scalable real-time cooperative perception system, which achievesassured percentile end-to-end latency under time-varying network dynamics. Toachieve AdaMap, we design a tightly coupled data plane and control plane. Inthe data plane, we design a new hybrid localization module to dynamicallyswitch between object detection and tracking, and a novel point cloudrepresentation module to adaptively compress and reconstruct the point cloud ofdetected objects. In the control plane, we design a new graph-based objectselection method to un-select excessive multi-viewed point clouds of objects,and a novel approximated gradient descent algorithm to optimize therepresentation 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 evaluationresults show that, AdaMap reduces up to 49x average transmission data size atthe cost of 0.37 reconstruction loss, as compared to state-of-the-artsolutions, which verifies its high scalability, adaptability, and computationefficiency.

AdaMap: High-Scalable Real-Time Cooperative Perception at the Edge