Birdcast: Interest-aware BEV Multicasting for Infrastructure-assisted Collaborative Perception

TL;DR

Birdcast is a multicasting framework for vehicle-to-infrastructure perception that optimizes information delivery to vehicles with heterogeneous interests, significantly improving system utility and perception accuracy.

Contribution

It introduces a novel interest-aware multicasting approach with an approximation algorithm for V2I-CP, addressing communication bottlenecks and heterogeneity.

Findings

Achieves up to 27% improvement in total utility.

Increases mean average precision (mAP) by 3.2%.

Outperforms state-of-the-art baselines in simulations.

Abstract

Vehicle-to-infrastructure collaborative perception (V2I-CP) leverages a high-vantage node to transmit supplementary information, i.e., bird's-eye-view (BEV) feature maps, to vehicles, effectively overcoming line-of-sight limitations. However, the downlink V2I transmission introduces a significant communication bottleneck. Moreover, vehicles in V2I-CP require \textit{heterogeneous yet overlapping} information tailored to their unique occlusions and locations, rendering standard unicast/broadcast protocols inefficient. To address this limitation, we propose \textit{Birdcast}, a novel multicasting framework for V2I-CP. By accounting for individual maps of interest, we formulate a joint feature selection and multicast grouping problem to maximize network-wide utility under communication constraints. Since this formulation is a mixed-integer nonlinear program and is NP-hard, we develop an accelerated greedy algorithm with a theoretical $(1 - 1/\sqrt{e})$ approximation guarantee. While motivated by CP, Birdcast provides a general framework applicable to a wide range of multicasting systems where users possess heterogeneous interests and varying channel conditions. Extensive simulations on the V2X-Sim dataset demonstrate that Birdcast significantly outperforms state-of-the-art baselines in both system utility and perception quality, achieving up to 27\% improvement in total utility and a 3.2\% increase in mean average precision (mAP).