Abstract
This study addresses the need for developing new frameworks to monitor and detect sensor failures in connected commercial vehicles (CCV)s. The CCV’s sensor health is more important when performance predictions and other communication-related errors (e.g. cyber-physical attacks) can manipulate the sensory network’s resiliency. We developed a novel machine learning (ML)-based framework, AutoDetect, to equip the cloud-tied operators with tools for understanding the abnormal sensor data streaming from the vehicle on the cloud level which explains the sensor data errors due to sensor failures only. We developed an innovative autoencoder (AE) neural network algorithm coupled with K-means clustering to create patterns. To learn the relationship between operating samples and features, when streaming sensor data over high-dimensional datasets is collected in the United Kingdom (UK). Different profiles of sensor data are collected under various driving conditions to monitor the ground truth of the sensor’s confidence levels in CCVs. The new AutoDetect tracked real-time sensor failures with a minimum accuracy of 90%.
Original language | English |
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Title of host publication | 2023 IEEE IAS Global Conference on Emerging Technologies (GlobConET) |
Place of Publication | MA, USA |
Publisher | Institute of Electrical and Electronics Engineers (IEEE) |
Pages | 1-6 |
Number of pages | 6 |
ISBN (Electronic) | 979-8-3503-3179-0 |
ISBN (Print) | 979-8-3503-3180-6 |
DOIs | |
Publication status | Published - 21 May 2023 |
Event | 2023 IEEE IAS Global Conference on Emerging Technologies (GlobConET) - London, United Kingdom Duration: 19 May 2023 → 21 May 2023 |
Conference
Conference | 2023 IEEE IAS Global Conference on Emerging Technologies (GlobConET) |
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Abbreviated title | GlobConET 2023 |
Country/Territory | United Kingdom |
City | London |
Period | 19/05/23 → 21/05/23 |
Keywords
- Cloud computing
- Software algorithms
- Neural networks
- Decision making
- Clustering algorithms
- Prediction algorithms
- Real-time systems