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