Temporal Naive Bayes: an efficient probabilistic framework for real-time maneuver recognition and anomaly monitoring in autonomous driving

The capacity to anticipate and identify anomalous behavior remains fundamental to the safety and operational reliability of autonomous mobile systems. Yet temporal modeling approaches currently available tend to demand substantial computational resources or intricate parameter tuning, which meaningfully constrains their adoption on resource-limited edge devices. To address this gap, we introduce Temporal Naive Bayes (TNB), a lightweight and highly interpretable extension of the classical Naive Bayes classifier, developed specifically for sequential sensor data. TNB embeds a first-order autoregressive process directly into the feature likelihood estimation, thereby capturing temporal dependencies while incurring minimal computational overhead. The approach is validated across four scenarios of increasing complexity—from idealized synthetic data through to real-time deployment in the CARLA simulator and a real-world smartphone inertial measurement unit (IMU) dataset. Across these settings, TNB proves effective at isolating critical temporal dynamics, outperforming standard baselines in both multiclass maneuver recognition and anomaly detection tasks. An open-source pipeline accompanies this work, illustrating TNB’s practical value as a lightweight, real-time diagnostic tool for autonomous navigation.