# GRAD: Real-Time Gated Recurrent Anomaly Detection in Autonomous Vehicle Sensors Using Reinforced EMA and Multi-Stage Sliding Window Techniques

arXiv: https://arxiv.org/abs/2510.23327 · HTML: https://mlsanity.com/paper/2510.23327/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 50 references that are in the map.
- First posted on arXiv 27 Oct 2025.

## Builds on (most cited first, 9 of 9)

- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling](https://mlsanity.com/paper/1412.3555/) (2014)
- [Fundamentals of Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) Network](https://mlsanity.com/paper/1808.03314/) (2018)
- [Detecting Spacecraft Anomalies Using LSTMs and Nonparametric Dynamic Thresholding](https://mlsanity.com/paper/1802.04431/) (2018)
- [A review on outlier/anomaly detection in time series data](https://mlsanity.com/paper/2002.04236/) (2020)
- [Anomaly Detection in Autonomous Driving: A Survey](https://mlsanity.com/paper/2204.07974/) (2022)
- [Anomaly Detection in Connected and Automated Vehicles using an Augmented State Formulation](https://mlsanity.com/paper/2004.09496/) (2020)
- [Collective Awareness for Abnormality Detection in Connected Autonomous Vehicles](https://mlsanity.com/paper/2010.14908/) (2020)
- [MIM-GAN-based Anomaly Detection for Multivariate Time Series Data](https://mlsanity.com/paper/2310.18257/) (2023)

## Abstract (arXiv)

This paper introduces GRAD, a real-time anomaly detection method for autonomous vehicle sensors that integrates statistical analysis and deep learning to ensure the reliability of sensor data. The proposed approach combines the Reinforced Exponential Moving Average (REMA), which adapts smoothing factors and thresholding for outlier detection, with the Multi-Stage Sliding Window (MS-SW) technique for capturing both short- and long-term patterns. These features are processed using a lightweight Gated Recurrent Unit (GRU) model, which detects and classifies anomalies based on bias types, while a recovery module restores damaged sensor data to ensure continuous system operation. GRAD has a lightweight architecture consisting of two layers of GRU with a limited number of neurons that make it appropriate for real-time applications while maintaining high detection accuracy. The GRAD framework achieved remarkable performance in anomaly detection and classification. The model demonstrated an overall F1-score of 97.6% for abnormal data and 99.4% for normal data, signifying its high accuracy in distinguishing between normal and anomalous sensor data. Regarding the anomaly classification, GRAD successfully categorized different anomaly types with high precision, enabling the recovery module to accurately restore damaged sensor data. Relative to analogous studies, GRAD surpasses current models by attaining a balance between elevated detection accuracy and diminished computational expense. These results demonstrate GRAD's potential as a reliable and efficient solution for real-time anomaly detection in autonomous vehicle systems, guaranteeing safe vehicle operation with minimal computational overhead.