信頼を場として捉える：車両ネットワークの巨視的表現
Trust as a Field: A Macroscopic Representation for Vehicular Networks
車両レベルの信頼評価を時空間連続場として表現するフレームワークを提案し、路側機の疎な観測から信頼場を再構成する深層学習手法を比較・評価した。
著者: Md Mahmudul Islam, Shaurya Agarwal
分類: cs.RO, cs.LG, math.DS
原文アブストラクト
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.