Multi domain neural fusion for adaptive anomaly detection in connected and automated vehicles

Abstract

Connected and Automated Vehicles (CAVs) rely on high-dimensional, multimodal sensor data and Vehicle-to-Everything (V2X) communication to support autonomous driving functions. This strong dependence exposes CAV systems to anomalies arising from sensor faults, environmental disturbances, and coordinated cyber–physical attacks. To address these challenges, this paper proposes a real-time hybrid anomaly detection framework that integrates multiple domain-specific models. The framework combines an LSTM-based time-domain model for sequential behavior analysis, an FFT-based frequency-domain model for spectral anomaly detection, and a context-aware multilayer perceptron (MLP) for incorporating environmental factors. To overcome data scarcity and improve robustness, synthetic anomalies are generated and integrated with benchmark datasets, including NGSIM, CAN-ID, and nuScenes. A learned fusion layer aggregates the outputs of the individual models into a unified anomaly score, while an online learning module dynamically adapts detection thresholds using sliding-window percentile recalibration. In addition, a Deep Q-Network (DQN)–based reinforcement learning agent supports adaptive decision-making under changing conditions. Model interpretability is enhanced through SHAP-based explanations and Human-in-the-Loop feedback, enabling continuous refinement of the detection process. Experimental results show that the proposed system achieves an accuracy of 98.10% before adaptation and improves to 98.91% after online learning and feedback. These results demonstrate that the proposed framework offers strong robustness, adaptability, and real-time suitability for safety-critical CAV environments.