Ensuring the Reliability of Virtual Sensors Based on Artificial Intelligence within Vehicle Dynamics Control Systems
The use of virtual sensors in vehicles represents a cost-effective alternative to the installation of physical hardware. In addition to physical models resulting from theoretical modeling, artificial intelligence and machine learning approaches are increasingly used, which incorporate experimental m...
Main Authors: | , |
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Format: | Article |
Language: | English |
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MDPI AG
2022-05-01
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Series: | Sensors |
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Online Access: | https://www.mdpi.com/1424-8220/22/9/3513 |
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author | Philipp Maximilian Sieberg Dieter Schramm |
author_facet | Philipp Maximilian Sieberg Dieter Schramm |
author_sort | Philipp Maximilian Sieberg |
collection | DOAJ |
description | The use of virtual sensors in vehicles represents a cost-effective alternative to the installation of physical hardware. In addition to physical models resulting from theoretical modeling, artificial intelligence and machine learning approaches are increasingly used, which incorporate experimental modeling. Due to the resulting black-box characteristics, virtual sensors based on artificial intelligence are not fully reliable, which can have fatal consequences in safety-critical applications. Therefore, a hybrid method is presented that safeguards the reliability of artificial intelligence-based estimations. The application example is the state estimation of the vehicle roll angle. The state estimation is coupled with a central predictive vehicle dynamics control. The implementation and validation is performed by a co-simulation between IPG CarMaker and MATLAB/Simulink. By using the hybrid method, unreliable estimations by the artificial intelligence-based model resulting from erroneous input signals are detected and handled. Thus, a valid and reliable state estimate is available throughout. |
first_indexed | 2024-03-10T03:40:46Z |
format | Article |
id | doaj.art-2eab3101f0964118b8d41f3e59db8f91 |
institution | Directory Open Access Journal |
issn | 1424-8220 |
language | English |
last_indexed | 2024-03-10T03:40:46Z |
publishDate | 2022-05-01 |
publisher | MDPI AG |
record_format | Article |
series | Sensors |
spelling | doaj.art-2eab3101f0964118b8d41f3e59db8f912023-11-23T09:19:34ZengMDPI AGSensors1424-82202022-05-01229351310.3390/s22093513Ensuring the Reliability of Virtual Sensors Based on Artificial Intelligence within Vehicle Dynamics Control SystemsPhilipp Maximilian Sieberg0Dieter Schramm1Chair of Mechatronics, Faculty of Engineering, University of Duisburg-Essen, 47051 Duisburg, GermanyChair of Mechatronics, Faculty of Engineering, University of Duisburg-Essen, 47051 Duisburg, GermanyThe use of virtual sensors in vehicles represents a cost-effective alternative to the installation of physical hardware. In addition to physical models resulting from theoretical modeling, artificial intelligence and machine learning approaches are increasingly used, which incorporate experimental modeling. Due to the resulting black-box characteristics, virtual sensors based on artificial intelligence are not fully reliable, which can have fatal consequences in safety-critical applications. Therefore, a hybrid method is presented that safeguards the reliability of artificial intelligence-based estimations. The application example is the state estimation of the vehicle roll angle. The state estimation is coupled with a central predictive vehicle dynamics control. The implementation and validation is performed by a co-simulation between IPG CarMaker and MATLAB/Simulink. By using the hybrid method, unreliable estimations by the artificial intelligence-based model resulting from erroneous input signals are detected and handled. Thus, a valid and reliable state estimate is available throughout.https://www.mdpi.com/1424-8220/22/9/3513artificial intelligenceartificial neural networkcontrol systemshybrid state estimationreliabilityvehicle dynamics |
spellingShingle | Philipp Maximilian Sieberg Dieter Schramm Ensuring the Reliability of Virtual Sensors Based on Artificial Intelligence within Vehicle Dynamics Control Systems Sensors artificial intelligence artificial neural network control systems hybrid state estimation reliability vehicle dynamics |
title | Ensuring the Reliability of Virtual Sensors Based on Artificial Intelligence within Vehicle Dynamics Control Systems |
title_full | Ensuring the Reliability of Virtual Sensors Based on Artificial Intelligence within Vehicle Dynamics Control Systems |
title_fullStr | Ensuring the Reliability of Virtual Sensors Based on Artificial Intelligence within Vehicle Dynamics Control Systems |
title_full_unstemmed | Ensuring the Reliability of Virtual Sensors Based on Artificial Intelligence within Vehicle Dynamics Control Systems |
title_short | Ensuring the Reliability of Virtual Sensors Based on Artificial Intelligence within Vehicle Dynamics Control Systems |
title_sort | ensuring the reliability of virtual sensors based on artificial intelligence within vehicle dynamics control systems |
topic | artificial intelligence artificial neural network control systems hybrid state estimation reliability vehicle dynamics |
url | https://www.mdpi.com/1424-8220/22/9/3513 |
work_keys_str_mv | AT philippmaximiliansieberg ensuringthereliabilityofvirtualsensorsbasedonartificialintelligencewithinvehicledynamicscontrolsystems AT dieterschramm ensuringthereliabilityofvirtualsensorsbasedonartificialintelligencewithinvehicledynamicscontrolsystems |