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Autonomous vehicles use 3D sensors for perception. Cooperative perceptionenables vehicles to share sensor readings with each other to improve safety.Prior work in cooperative perception scales poorly even with infrastructuresupport. AutoCast enables scalable infrastructure-less cooperative perceptionusing direct vehicle-to-vehicle communication. It carefully determines whichobjects to share based on positional relationships between trafficparticipants, and the time evolution of their trajectories. It coordinatesvehicles and optimally schedules transmissions in a distributed fashion.Extensive evaluation results under different scenarios show that, unlikecompeting approaches, AutoCast can avoid crashes and near-misses which occurfrequently without cooperative perception, its performance scales gracefully indense traffic scenarios providing 2-4x visibility into safety critical objectscompared to existing cooperative perception schemes, its transmission schedulescan be completed on the real radio testbed, and its scheduling algorithm isnear-optimal with negligible computation overhead.

AutoCast: Scalable Infrastructure-less Cooperative Perception for Distributed Collaborative Driving