# OPV2V benchmark

Addressing the fact that cooperative perception lets connected vehicles share intermediate neural features while V2X links carry far less than a modern detector produces and existing work reports transmitted bytes without their energy cost, this work decides what to share under a single hard ego-side communication budget: an ensemble of gradient-boosted trees and a multilayer perceptron predicts, from metadata available before any feature is transmitted, how many objects a candidate block would add to what the ego alone detects, and a greedy knapsack allocates the budget across helpers, spatial blocks and numerical fidelity (fp16/int8/int4); on the OPV2V benchmark the allocator reaches 99% of full-fusion AP@0.5 while transmitting 20.0 kB per frame instead of 282.

Addressing the fact that cooperative perception lets connected vehicles share intermediate neural features while V2X links carry far less than a modern detector produces and existing work reports transmitted bytes without their energy cost, this work decides what to share under a single hard ego-side communication budget: an ensemble of gradient-boosted trees and a multilayer perceptron predicts, from metadata available before any feature is transmitted, how many objects a candidate block would add to what the ego alone detects, and a greedy knapsack allocates the budget across helpers, spatial blocks and numerical fidelity (fp16/int8/int4); on the OPV2V benchmark the allocator reaches 99% of full-fusion AP@0.5 while transmitting 20.0 kB per frame instead of 282.