Identification of the nonlinear steering dynamics of an autonomous vehicle Article Swipe

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Gábor Rödönyi , Gerben I. Beintema , Roland Tóth , Maarten Schoukens , Dániel Pup , Ádám Kisari , Zs. Vígh , Péter Kőrös , Alexandros Soumelidis , J. Bokor ·

YOU? · · 2021 · Open Access · · DOI: https://doi.org/10.48550/arxiv.2105.04529

Automated driving applications require accurate vehicle specific models to precisely predict and control the motion dynamics. However, modern vehicles have a wide array of digital and mechatronic components that are difficult to model, manufactures do not disclose all details required for modelling and even existing models of subcomponents require coefficient estimation to match the specific characteristics of each vehicle and their change over time. Hence, it is attractive to use data-driven modelling to capture the relevant vehicle dynamics and synthesise model-based control solutions. In this paper, we address identification of the steering system of an autonomous car based on measured data. We show that the underlying dynamics are highly nonlinear and challenging to be captured, necessitating the use of data-driven methods that fuse the approximation capabilities of learning and the efficiency of dynamic system identification. We demonstrate that such a neural network based subspace-encoder method can successfully capture the underlying dynamics while other methods fall short to provide reliable results.

Related Topics

Computer Science

Mechatronics

System Identification

Control Engineering

System Dynamics

Artificial Intelligence

Engineering

Automotive Engineering

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Computer science Identification (biology) Vehicle dynamics Subspace topology Mechatronics System identification Control engineering Nonlinear system System dynamics Artificial neural network Artificial intelligence Engineering Data modeling Automotive engineering Quantum mechanics Database Botany Biology Physics

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Type: preprint
Language: en
Landing Page: http://arxiv.org/abs/2105.04529
PDF: https://arxiv.org/pdf/2105.04529
OA Status: green
Cited By: 2
References: 19
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DOI: https://doi.org/10.48550/arxiv.2105.04529

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Title: Identification of the nonlinear steering dynamics of an autonomous vehicle

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Language: en

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Publication year: 2021

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Publication date: 2021-05-10

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Authors: Gábor Rödönyi, Gerben I. Beintema, Roland Tóth, Maarten Schoukens, Dániel Pup, Ádám Kisari, Zs. Vígh, Péter Kőrös, Alexandros Soumelidis, J. Bokor

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Concepts: Computer science, Identification (biology), Vehicle dynamics, Subspace topology, Mechatronics, System identification, Control engineering, Nonlinear system, System dynamics, Artificial neural network, Artificial intelligence, Engineering, Data modeling, Automotive engineering, Quantum mechanics, Database, Botany, Biology, Physics

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Cited by: 2

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abstract_inverted_index.require	3, 48
abstract_inverted_index.vehicle	5, 58, 76
abstract_inverted_index.However,	16
abstract_inverted_index.accurate	4
abstract_inverted_index.disclose	36
abstract_inverted_index.dynamics	77, 106, 150
abstract_inverted_index.existing	44
abstract_inverted_index.learning	127
abstract_inverted_index.measured	99
abstract_inverted_index.relevant	75
abstract_inverted_index.reliable	158
abstract_inverted_index.required	39
abstract_inverted_index.results.	159
abstract_inverted_index.specific	6, 54
abstract_inverted_index.steering	91
abstract_inverted_index.vehicles	18
abstract_inverted_index.Automated	0
abstract_inverted_index.captured,	114
abstract_inverted_index.difficult	30
abstract_inverted_index.dynamics.	15
abstract_inverted_index.modelling	41, 71
abstract_inverted_index.nonlinear	109
abstract_inverted_index.precisely	9
abstract_inverted_index.attractive	67
abstract_inverted_index.autonomous	95
abstract_inverted_index.components	27
abstract_inverted_index.efficiency	130
abstract_inverted_index.estimation	50
abstract_inverted_index.solutions.	82
abstract_inverted_index.synthesise	79
abstract_inverted_index.underlying	105, 149
abstract_inverted_index.challenging	111
abstract_inverted_index.coefficient	49
abstract_inverted_index.data-driven	70, 119
abstract_inverted_index.demonstrate	136
abstract_inverted_index.mechatronic	26
abstract_inverted_index.model-based	80
abstract_inverted_index.applications	2
abstract_inverted_index.capabilities	125
abstract_inverted_index.manufactures	33
abstract_inverted_index.successfully	146
abstract_inverted_index.approximation	124
abstract_inverted_index.necessitating	115
abstract_inverted_index.subcomponents	47
abstract_inverted_index.identification	88
abstract_inverted_index.characteristics	55
abstract_inverted_index.identification.	134
abstract_inverted_index.subspace-encoder	143
cited_by_percentile_year
countries_distinct_count	0
institutions_distinct_count	10
citation_normalized_percentile