发表机构
Istanbul Technical University; AI and Intelligent Systems Laboratory（伊斯坦布尔理工大学; 人工智能与智能系统实验室）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文针对车载网络切换管理的黑箱可解释性问题，采用fANOVA框架的固有可解释模型，在真实数据集上验证其检测性能与低延迟解释能力，为车载网络切换检测提供高效透明方案。
AI 中文摘要
车载网络中的切换（HO）管理需要在高度动态的条件下进行快速且可靠的决策。机器学习（ML）方法可通过捕捉各类关键性能指标（KPIs）之间的复杂关系来提升HO检测性能，但其黑箱特性限制了可解释性与运营商信任。为解决该问题，本文从实时可解释性视角研究HO检测，采用基于方差分析函数（fANOVA）框架的固有可解释模型。所提模型使用两个真实运营商数据集进行评估，并与加入事后SHAP解释的长短期记忆（LSTM）基线模型对比。与事后方法不同，所提框架可即时解释模型决策，且不会产生额外计算开销，该能力对延迟敏感的车载网络尤为关键。结果显示，基于fANOVA的模型实现了有竞争力的检测性能，同时与传统事后方法相比，解释延迟显著降低。此外，特征排序与可视化分析揭示了KPIs与HO发生间符合标准化HO机制的物理意义明确的关系。这些结果表明，固有可解释模型为下一代车载网络中的HO检测提供了高效且透明的解决方案。
英文摘要
Handover (HO) management in vehicular networks requires fast and reliable decision-making under highly dynamic conditions. While machine learning (ML) approaches can improve HO detection by capturing complex relationships among various key performance indicators (KPIs), their black-box nature limits interpretability and operator trust. To address this, this paper investigates HO detection from an explainability-on-the-fly perspective using inherently interpretable models based on the functional analysis of variance (fANOVA) framework. The proposed models are evaluated using two real-world operator datasets and compared against a Long Short-Term Memory baseline augmented with post-hoc SHAP explanations. Unlike post-hoc approaches, the proposed framework enables immediate interpretation of model decisions without incurring additional computational overhead. This capability is particularly critical for latency-sensitive vehicular networks. The results show that fANOVA-based models achieve competitive detection performance while providing significantly reduced explanation latency compared to conventional post-hoc methods. Furthermore, feature ranking and visualization analyses reveal physically meaningful relationships between KPIs and HO occurrences that align with standardized HO mechanisms. These results demonstrate that inherently interpretable models provide an efficient and transparent solution for HO detection in next-generation vehicular networks.
CommentsAccepted in NextGCom 2026, Copyright IEEE
