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Article

Fast Motion Model of Road Vehicles with Artificial Neural Networks

1
Department of Control for Transportation and Vehicle Systems, Budapest University of Technology and Economics, H-1111 Budapest, Hungary
2
Systems and Control Lab, Institute for Computer Science and Control, H-1111 Budapest, Hungary
*
Author to whom correspondence should be addressed.
Academic Editor: Cheng Siong Chin
Electronics 2021, 10(8), 928; https://doi.org/10.3390/electronics10080928
Received: 18 March 2021 / Revised: 5 April 2021 / Accepted: 7 April 2021 / Published: 13 April 2021
(This article belongs to the Special Issue Predictive and Learning Control in Engineering Applications)
Nonlinear optimization-based motion planning algorithms have been successfully used for dynamically feasible trajectory planning of road vehicles. However, the main drawback of these methods is their significant computational effort and thus high runtime, which makes real-time application a complex problem. Addressing this field, this paper proposes an algorithm for fast simulation of road vehicle motion based on artificial neural networks that can be used in optimization-based trajectory planners. The neural networks are trained with supervised learning techniques to predict the future state of the vehicle based on its current state and driving inputs. Learning data is provided for a wide variety of randomly generated driving scenarios by simulation of a dynamic vehicle model. The realistic random driving maneuvers are created on the basis of piecewise linear travel velocity and road curvature profiles that are used for the planning of public roads. The trained neural networks are then used in a feedback loop with several variables being calculated by additional numerical integration to provide all the outputs of the original dynamic model. The presented model can be capable of short-term vehicle motion simulation with sufficient precision while having a considerably faster runtime than the original dynamic model. View Full-Text
Keywords: vehicle dynamics; vehicle modeling; simulation; motion planning; artificial neural networks vehicle dynamics; vehicle modeling; simulation; motion planning; artificial neural networks
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MDPI and ACS Style

Hegedüs, F.; Gáspár, P.; Bécsi, T. Fast Motion Model of Road Vehicles with Artificial Neural Networks. Electronics 2021, 10, 928. https://doi.org/10.3390/electronics10080928

AMA Style

Hegedüs F, Gáspár P, Bécsi T. Fast Motion Model of Road Vehicles with Artificial Neural Networks. Electronics. 2021; 10(8):928. https://doi.org/10.3390/electronics10080928

Chicago/Turabian Style

Hegedüs, Ferenc, Péter Gáspár, and Tamás Bécsi. 2021. "Fast Motion Model of Road Vehicles with Artificial Neural Networks" Electronics 10, no. 8: 928. https://doi.org/10.3390/electronics10080928

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