# Anomaly Detection in Connected and Automated Vehicles using an Augmented State Formulation

arXiv: https://arxiv.org/abs/2004.09496 · HTML: https://mlsanity.com/paper/2004.09496/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Apr 2020.

## Built on by (most cited first, 3 of 3)

- [Adversarial Online Learning with Variable Plays in the Pursuit-Evasion Game: Theoretical Foundations and Application in Connected and Automated Vehicle Cybersecurity](https://mlsanity.com/paper/2110.14078/) (2021)
- [Cybersecurity-Focused Anomaly Detection in Connected Autonomous Vehicles Using Machine Learning](https://mlsanity.com/paper/2506.22984/) (2025)
- [GRAD: Real-Time Gated Recurrent Anomaly Detection in Autonomous Vehicle Sensors Using Reinforced EMA and Multi-Stage Sliding Window Techniques](https://mlsanity.com/paper/2510.23327/) (2025)

## Abstract (arXiv)

In this paper we propose a novel observer-based method for anomaly detection in connected and automated vehicles (CAVs). The proposed method utilizes an augmented extended Kalman filter (AEKF) to smooth sensor readings of a CAV based on a nonlinear car-following motion model with time delay, where the leading vehicle's trajectory is used by the subject vehicle to detect sensor anomalies. We use the classic chi2 fault detector in conjunction with the proposed AEKF for anomaly detection. To make the proposed model more suitable for real-world applications, we consider a stochastic communication time delay in the car-following model. Our experiments conducted on real-world connected vehicle data indicate that the AEKF with chi2-detector can achieve a high anomaly detection performance.