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...

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Main Authors: Philipp Maximilian Sieberg, Dieter Schramm
Format: Article
Language:English
Published: MDPI AG 2022-05-01
Series:Sensors
Subjects:
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.
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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