面向自动驾驶中渐进式GNSS欺骗检测的高阶液体证据编码

中文摘要 AI 辅助

精确的全球导航卫星系统（GNSS）定位对安全可靠的自动驾驶至关重要，但欺骗攻击可操纵车辆位置估计，持续且隐蔽的攻击尤其难以检测——单个GNSS观测值可能仍看似合理，而GNSS隐含位移与车载车辆运动间的不一致却在逐渐增大。现有方法常依赖静态车辆行为特征或单一残差信号，未显式建模这种演化过程。为解决该问题，本文提出一种用于GNSS欺骗检测的因果高阶液体证据框架。该方法首先通过比较GNSS隐含位移与车载运动推导的位移，构建物理引导的GNSS-运动不一致残差；接着为残差水平及其一阶、二阶离散变化形成独立证据流，并根据证据阶数选择相关上下文线索；每个流由独立的自适应液体编码器处理，得到的时间状态经分层耦合，仅利用当前及过去观测值预测窗口端点的欺骗情况。在真实AV-GPS数据集的三个子集上开展实验，结果表明，所提方法在数据集1和数据集3的评估时间模型中达到最高F1分数，分别为0.9535和0.9777；在数据集3上，它能在四个采样步内检测到标注的正常到攻击的转变。代码和数据集可公开获取：this https URL。

英文摘要

Accurate Global Navigation Satellite System (GNSS)-based localization is essential for safe and reliable autonomous driving. However, spoofing attacks can manipulate vehicle position estimates. Continuous and subtle attacks are particularly difficult to detect because individual GNSS observations may remain plausible while the inconsistency between GNSS-implied displacement and onboard vehicle motion gradually increases. Existing methods often rely on static vehicle-behavior features or a single residual signal and do not explicitly model this evolution. To address this problem, we propose a causal high-order liquid evidence framework for GNSS spoofing detection. The method first constructs a physics-guided GNSS--motion inconsistency residual by comparing GNSS-implied displacement with onboard-motion-derived displacement. It then forms separate evidence streams for the residual level and its first- and second-order discrete variations, with relevant contextual cues selected according to the evidence order. Each stream is processed by a separate adaptive liquid encoder, and the resulting temporal states are hierarchically coupled to predict spoofing at the window endpoint using only current and past observations. Experiments on three subsets of the real-world AV-GPS dataset show that the proposed method achieves the highest F1-scores among the evaluated temporal models on Dataset~1 and Dataset~3, reaching 0.9535 and 0.9777, respectively. On Dataset~3, it detects both labeled normal-to-attack transitions within four sampling steps. Code and datasets are publicly available at: https://github.com/pangjunbiao/GNSS_Spoofing.git.

发表机构

• Faculty of Information Technology, Beijing University of Technology（北京工业大学信息学部）

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