# A Temporal Anomaly Detection System for Vehicles utilizing Functional Working Groups and Sensor Channels

arXiv: https://arxiv.org/abs/2209.06828 · HTML: https://mlsanity.com/paper/2209.06828/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 38 references that are in the map.
- First posted on arXiv 14 Sep 2022.

## Builds on (most cited first, 13 of 13)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Fully Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1411.4038/) (2014)
- [WaveNet: A Generative Model for Raw Audio](https://mlsanity.com/paper/1609.03499/) (2016)
- [An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling](https://mlsanity.com/paper/1803.01271/) (2018)
- [Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting](https://mlsanity.com/paper/1506.04214/) (2015)
- [Multi-Scale Context Aggregation by Dilated Convolutions](https://mlsanity.com/paper/1511.07122/) (2015)
- [Temporal Convolutional Networks for Action Segmentation and Detection](https://mlsanity.com/paper/1611.05267/) (2016)
- [Deep Learning for Anomaly Detection: A Survey](https://mlsanity.com/paper/1901.03407/) (2019)
- [A Comprehensive Survey on Graph Anomaly Detection with Deep Learning](https://mlsanity.com/paper/2106.07178/) (2021)
- [Explainable Intrusion Detection Systems (X-IDS): A Survey of Current Methods, Challenges, and Opportunities](https://mlsanity.com/paper/2207.06236/) (2022)
- [A Survey of Predictive Maintenance: Systems, Purposes and Approaches](https://mlsanity.com/paper/1912.07383/) (2019)
- [Using Data Analytics to Detect Anomalous States in Vehicles](https://mlsanity.com/paper/1512.08048/) (2015)
- [TENET: Temporal CNN with Attention for Anomaly Detection in Automotive Cyber-Physical Systems](https://mlsanity.com/paper/2109.04565/) (2021)

## Built on by (most cited first, 5 of 5)

- [TwinExplainer: Explaining Predictions of an Automotive Digital Twin](https://mlsanity.com/paper/2302.00152/) (2023)
- [A Survey on Privacy Attacks Against Digital Twin Systems in AI-Robotics](https://mlsanity.com/paper/2406.18812/) (2024)
- [A White-Box Adversarial Attack Against a Digital Twin](https://mlsanity.com/paper/2210.14018/) (2022)
- [Explainable Intrusion Detection Systems Using Competitive Learning Techniques](https://mlsanity.com/paper/2303.17387/) (2023)
- [F-SE-LSTM: A Time Series Anomaly Detection Method with Frequency Domain Information](https://mlsanity.com/paper/2412.02474/) (2024)

## Abstract (arXiv)

A modern vehicle fitted with sensors, actuators, and Electronic Control Units (ECUs) can be divided into several operational subsystems called Functional Working Groups (FWGs). Examples of these FWGs include the engine system, transmission, fuel system, brakes, etc. Each FWG has associated sensor-channels that gauge vehicular operating conditions. This data rich environment is conducive to the development of Predictive Maintenance (PdM) technologies. Undercutting various PdM technologies is the need for robust anomaly detection models that can identify events or observations which deviate significantly from the majority of the data and do not conform to a well defined notion of normal vehicular operational behavior. In this paper, we introduce the Vehicle Performance, Reliability, and Operations (VePRO) dataset and use it to create a multi-phased approach to anomaly detection. Utilizing Temporal Convolution Networks (TCN), our anomaly detection system can achieve 96% detection accuracy and accurately predicts 91% of true anomalies. The performance of our anomaly detection system improves when sensor channels from multiple FWGs are utilized.