A Cost-Aware Hierarchical Cascade for Anomaly Detection at the Edge in Connected Vehicles

Time series anomaly detection (TSAD) is essential for ensuring the safety and reliability of intelligent and autonomous vehicles. In edge-cloud systems, this task is challenging due to limited on-board resources and real-time constraints. Deep learning (DL) models offer high accuracy but are too computationally demanding for embedded devices, whereas lightweight models are efficient but less precise. To address this trade-off, we propose a hierarchical cascaded framework for unsupervised multivariate TSAD, consisting of two lightweight Gaussian Mixture Models (GMMs) on the edge and a fully connected Variational Autoencoder (FC-VAE) in the cloud. An adaptive offloading mechanism based on online regret minimization dynamically decides when to escalate inputs, balancing inference cost and detection accuracy. Experiments on real-world sensor data from Scania's autonomous mining trucks show that the proposed method achieves accuracy within 1% of a cloud-only FC-VAE while reducing computation cost by over 85%. These results demonstrate that cost-aware hierarchical inference enables scalable and efficient real-time anomaly detection in edge-centric intelligent transportation systems.