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

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).

Figures (8)

• Figure 1: Limitations of broadcast and unicast in V2I-CP. A high-vantage node (HVN) transmits BEV grids to four users with heterogeneous requests (gray quadrants) and varying data rates under a latency budget of 14 ms (the data volume for one grid is 15 KB). Broadcast and unicast cannot efficiently exploit overlapping user interests, yielding suboptimal utilities of 6 and 3, respectively. In contrast, multicasting optimally groups users based on shared requests, resulting in a utility of 8.
• Figure 2: Illustration of the Birdcast framework. The HVN leverages its elevated altitude to capture a comprehensive BEV. To enhance perception performance and communication efficiency, the users and the HVN collaboratively identify a map of interest to assign spatial weights to each BEV grid. Consequently, the HVN can selectively transmit data to users by jointly considering the users' channel conditions and heterogeneous interests, i.e., prioritizing high-utility grids that yield substantial performance gains.
• Figure 3: Illustration of the grid-rate transformation. The rows represent BEV grids, while the columns correspond to the available MCS rates. The blue circles indicate the selected rates for a given grid. The red rectangle indicates that selecting a specific grid-rate pair also implicitly selects the pairs with higher rates in the same column.
• Figure 4: The total utility versus the network configurations.
• Figure 5: The running time versus network configurations.

Theorems & Definitions (12)

• Remark 1
• Remark 2
• Remark 3
• Theorem 1
• Lemma 1
• Theorem 2
• Definition 1: Monotone Submodularity
• Theorem 3
• Theorem 4
• Remark 4