A Deep Learning-Based Anomaly Forecasting System of Time Series Sensor Data in Autonomous Vehicles*

This study investigates the application of a hybrid ARIMA–Transformer time series forecasting model—previously validated in smart factory environments—to autonomous vehicle sensor data, in order to evaluate its domain scalability and practical feasibility. The hybrid architecture, which combines the linear forecasting capability of ARIMA with the nonlinear temporal modeling strength of the Transformer, demonstrated robust and reliable performance under complex and uncertain autonomous driving scenarios. Experimental evaluations using real-world sensor data confirmed the model’s superior accuracy under both normal and anomalous conditions. These findings underscore the potential of hybrid forecasting approaches in transportation and mobility systems, contributing to improved reliability in autonomous driving technologies.