mlsanityData to 7 Oct 2026

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

arXiv 2209.06828 ↗First posted 14 Sep 2022cs.LG cs.AI cs.NEcited by 5

Not on the map. Why?

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.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 13 of its 38 references that are in the map. First posted on arXiv 14 Sep 2022.

Builds on

13 of 38 references.

References · 13

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2014Fully Convolutional Networks for Semantic Segmentationcited by 4,845
  3. 2016WaveNet: A Generative Model for Raw Audiocited by 1,710
  4. 2018An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modelingcited by 1,447
  5. 2015Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcastingcited by 1,428
  6. 2015Multi-Scale Context Aggregation by Dilated Convolutionscited by 1,128
  7. 2016Temporal Convolutional Networks for Action Segmentation and Detectioncited by 504
  8. 2019Deep Learning for Anomaly Detection: A Surveycited by 357
  9. 2021A Comprehensive Survey on Graph Anomaly Detection with Deep Learningcited by 230
  10. 2022Explainable Intrusion Detection Systems (X-IDS): A Survey of Current Methods, Challenges, and Opportunitiescited by 25
  11. 2019A Survey of Predictive Maintenance: Systems, Purposes and Approachescited by 21
  12. 2015Using Data Analytics to Detect Anomalous States in Vehiclescited by 8
1 more
  1. 2021TENET: Temporal CNN with Attention for Anomaly Detection in Automotive Cyber-Physical Systemscited by 6

Built on by

Cited by year

Citing papers per year: 2022: 1; 2023: 2; 2024: 2.

5 papers.

  1. 2023TwinExplainer: Explaining Predictions of an Automotive Digital Twincited by 6
  2. 2024A Survey on Privacy Attacks Against Digital Twin Systems in AI-Roboticscited by 3
  3. 2022A White-Box Adversarial Attack Against a Digital Twincited by 2
  4. 2023Explainable Intrusion Detection Systems Using Competitive Learning Techniquescited by 2
  5. 2024F-SE-LSTM: A Time Series Anomaly Detection Method with Frequency Domain Informationcited by 1