发表机构
University of Central Florida（中佛罗里达大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对车载网络中信任难以跨路段演化推理的问题，提出时空信任场框架，通过场感知深度学习方法从稀疏RSU测量值更准确重建信任场，提升低信任模式恢复效果与重建精度。
AI 中文摘要
信任评估是协同与网联车辆系统的核心组成部分。然而，现有方法主要在单个车辆层面运行，难以推理路段间的信任演化。本文提出一种时空信任场框架，将微观车辆级信任聚合为时空上的连续表示，该信任场在路段上有正式定义。我们在受控条件下生成的合成轨迹上开展基于仿真的实验，以分析简单道路场景下的信任场行为。除理论建模外，我们还研究信任场概念的一项应用：从稀疏的路侧单元（RSU）测量值重建完整信任场。我们对比了两种方法：（i）基于坐标的深度学习基线，从稀疏样本学习通用信任场；（ii）场感知深度学习方法，将信任视为车辆携带的潜在量，并通过聚合机制确保测量一致性。场感知方法能更准确地恢复与轨迹对齐的低信任模式，且重建误差更小。
英文摘要
Trust assessment is a fundamental component of cooperative and connected vehicle systems. However, existing approaches operate primarily at the level of individual vehicles, making it difficult to reason about trust evolution across road segments. In this paper, we propose a spatio-temporal trust-field framework that aggregates microscopic vehicle-level trust into a continuous representation over space and time. The trust field is formally defined on road segments. We conducted simulation-based experiments using synthetic trajectories generated under controlled conditions, enabling analysis of trust-field behavior in simple road scenarios. Beyond theoretical modeling, we study an implication of the trust-field concept: reconstructing the full trust field from sparse roadside-unit (RSU) measurements. We compare (i) a coordinate-based deep learning baseline that learns a generic trust field from sparse samples and (ii) a field-informed deep learning method that treats trust as a latent quantity carried by vehicles and enforces measurement consistency through the aggregation mechanism. The field-informed approach more accurately recovers trajectory-aligned low-trust patterns and yields improved reconstruction error.
