Cooperative Perception

Cooperative perception studies how multiple vehicles, robots, or aerial agents can combine sensor data to detect objects, map spaces, and reason about hazards that are hidden from any one agent. Common approaches range from early, intermediate, and late fusion to sharing compact object lists or selected features, with alignment and uncertainty methods handling differences in viewpoint, timing, sensors, and localization. Benchmarks such as OPV2V, DAIR-V2X, and V2V4Real support work on 3D detection, tracking, and robustness, while real-world and closed-loop tests examine deployment in driving and robotics. The central tension is that richer sharing can improve coverage but costs bandwidth and can introduce errors through delay, packet loss, or unreliable contributors; better perception also does not always produce safer or better plans.