发表机构
Beijing University of Technology（北京工业大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对自动驾驶中连续微妙GNSS欺骗，提出高阶液体证据检测器，利用因果顺序证据建模和三阶交互，在AV-GPS数据集上取得高AUROC和低假阳性率。
AI 中文摘要
连续且微妙的GNSS欺骗对自动驾驶车辆构成严重威胁，因为伪造的位置可能在局部看似合理，同时逐渐与非GNSS车载传感器观测到的车辆运动不一致。现有的面向自动驾驶车辆的检测器通常依赖于残差阈值或特征级分类，对弱GNSS-运动不一致性如何随时间发展和持续存在的建模能力有限。本文将微妙GNSS欺骗检测表述为一个因果顺序证据建模问题，并提出了一种高阶液体证据检测器。该方法首先将连续GNSS位置隐含的位移与独立车载运动观测推断的位移进行比较，并将其差异转换为不确定性归一化的残差证据。然后，它将当前不一致性、其局部演化、超出正常水平的过量、累积持续性和位移有效性表示为因果弱证据。这些线索被映射为瞬时、演化和持久潜在状态，通过有界的受基尔霍夫启发的对称交换进行对齐，并通过显式的三阶交互组合以捕捉它们对欺骗的协调支持。为了建模这种协调证据如何随时间发展，二阶液体动力学跟踪其记忆和演化以估计因果欺骗概率，这些概率通过验证选择的阈值和持久性参数转换为确认警报。在AV-GPS数据集系列上的实验证明了强大的受控和外部泛化能力，以及清晰的顺序警报行为。在Dataset-1上，所提出的检测器实现了0.9932的AUROC和0.9843的AUPRC，同时在学习型基线中获得了最低的假阳性率。代码：此https URL。
英文摘要
Continuous and subtle GNSS spoofing poses a serious threat to autonomous vehicles because forged positions may remain locally plausible while gradually becoming inconsistent with vehicle motion observed by non-GNSS onboard sensors. Existing AV-oriented detectors commonly rely on residual thresholds or feature-level classification and provide limited modeling of how weak GNSS--motion inconsistency develops and persists over time. This paper formulates subtle GNSS spoofing detection as a causal sequential evidence-modeling problem and proposes a high-order liquid evidence detector. The method first compares the displacement implied by consecutive GNSS positions with that inferred from independent onboard motion observations and converts their difference into uncertainty-normalized residual evidence. It then represents the current inconsistency, its local evolution, excess above the normal level, accumulated persistence, and displacement validity as causal weak evidence. These cues are mapped into instantaneous, evolutionary, and persistent latent states, aligned through a bounded Kirchhoff-inspired symmetric exchange, and combined through an explicit third-order interaction to capture their coordinated support for spoofing. To model how this coordinated evidence develops over time, second-order liquid dynamics track its memory and evolution to estimate causal spoofing probabilities, which are converted into confirmed alarms using validation-selected threshold and persistence parameters. Experiments on the AV--GPS dataset family demonstrate strong controlled and external generalization, together with clear sequential alarm behavior. On Dataset-1, the proposed detector achieves an AUROC of 0.9932 and an AUPRC of 0.9843, while obtaining the lowest false-positive rate among the learning-based baselines. Code: https://github.com/pangjunbiao/HO-LLN-Spoofing.
