用超网络同时量化自动驾驶协同感知的两类不确定性。
Hyper-V2X: Hypernetworks for Estimating Epistemic and Aleatoric Uncertainty in Cooperative Bird's-Eye-View Semantic Segmentation
- 设计超网络，根据多车融合特征生成分割模型权重分布。
- 在OPV2V数据集上实现精准校准的不确定性估计，提升感知可靠性。
- 轻量级设计可无缝接入主流协同架构，适合智能驾驶研究者。
通过车联万物（V2X）通信实现的协同感知，能通过共享传感数据构建统一环境表征，提升自动驾驶安全性。尽管现有工作在多智能体融合感知方面取得进展，但协同框架中的不确定性量化仍鲜有研究。本文提出Hyper-V2X，一种基于超网络的框架，用于估算V2X感知中认知不确定性和随机不确定性。具体地，我们设计了部分权重生成方案与V2X上下文嵌入模块，将贝叶斯超网络条件化于融合的多智能体特征，生成用于随机鸟瞰图（BEV）分割的权重分布。与现有确定性BEV模型不同，Hyper-V2X在几乎无额外计算开销下实现高效不确定性估计。该方法具有架构无关性，可无缝集成至现代协同骨干网络如CoBEVT。在OPV2V基准上的实验表明，Hyper-V2X提供了准确且校准良好的不确定性估计，显著提升了整体感知可靠性。代码与基准已开源：https://github.com/abhishekjagtap1/Hyper-V2X。
原文摘要 · Abstract (English)
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
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