Cooperative perception enabled by Vehicle-to-Everything (V2X) communication enhances autonomous driving safety by creating a unified environmental representation through shared sensory data. While recent works have advanced multi-agent fusion for improved perception, uncertainty quantification in such cooperative frameworks remains largely unexplored. This paper introduces Hyper-V2X, a hypernetwork-based framework for estimating both epistemic and aleatoric uncertainties in V2X-based perception. Specifically, we propose a partial weight generation scheme and V2X context embedding module that conditions a Bayesian hypernetwork on fused multi-agent features to generate weight distributions for stochastic Bird's-Eye-View (BEV) segmentation. Unlike existing deterministic BEV models, Hyper-V2X enables efficient uncertainty estimation with little computation overhead. Our approach is architecture-agnostic, and can be seamlessly integrating with modern cooperative backbones such as CoBEVT. Experiments on the OPV2V benchmark demonstrate that Hyper-V2X provides accurate, well-calibrated uncertainty estimates and improves overall perception reliability. Our code and benchmark are publicly available under an open-source license: https://github.com/abhishekjagtap1/Hyper-V2X

翻译：车联网（V2X）通信驱动的协同感知通过共享传感器数据构建统一环境表征，从而增强自动驾驶安全性。尽管近期研究已推动多智能体融合以改进感知性能，但此类协同框架中的不确定性量化问题仍鲜有探讨。本文提出Hyper-V2X——一种基于超网络的框架，用于估计V2X感知中的认知不确定性与偶然不确定性。具体而言，我们提出部分权重生成方案与V2X上下文嵌入模块，通过将贝叶斯超网络的条件设定于融合后的多智能体特征，为随机鸟瞰图（BEV）分割生成权重分布。与现有确定性BEV模型不同，Hyper-V2X能以极小计算开销实现高效不确定性估计。我们的方法具有架构无关性，可无缝集成至CoBEVT等现代协同主干网络。在OPV2V基准上的实验表明，Hyper-V2X能提供准确、校准良好的不确定性估计，并提升整体感知可靠性。我们的代码与基准数据集已在开源许可下公开：https://github.com/abhishekjagtap1/Hyper-V2X