Road friction estimation using an artificial neural network in a simulated environment
(2020)Department of Automatic Control
- Abstract
- With the transition of responsibilities from the driver to the automated driving systems in vehicles, the systems need to have been tested for an extensive list of test scenarios as the passengers require high trustworthiness. The friction coefficient for the tyre-road friction is of high importance for the control of the vehicle but the coefficient is dependent on the physical complexity and nonlinear behaviour of tyres and is difficult to measure. Hence, testing is performed in controlled environments which limits the systems exposure to different testing scenarios.
The purpose of this thesis and the underlying work was to develop and evaluate a process for friction estimation using machine learning. The aim was to produce an... (More) - With the transition of responsibilities from the driver to the automated driving systems in vehicles, the systems need to have been tested for an extensive list of test scenarios as the passengers require high trustworthiness. The friction coefficient for the tyre-road friction is of high importance for the control of the vehicle but the coefficient is dependent on the physical complexity and nonlinear behaviour of tyres and is difficult to measure. Hence, testing is performed in controlled environments which limits the systems exposure to different testing scenarios.
The purpose of this thesis and the underlying work was to develop and evaluate a process for friction estimation using machine learning. The aim was to produce an estimation method using neural networks that are trained on data from a vehicle model implemented in a simulated environment using Unity 3D, which is a software platform for simulation and game development. The master thesis was produced at Combine Control Systems AB for Lund University in cooperation with National Electric Vehicle Sweden AB (NEVS). (Less)
Please use this url to cite or link to this publication:
http://lup.lub.lu.se/student-papers/record/9002374
- author
- Karlsson, Jonas
- supervisor
- organization
- year
- 2020
- type
- H3 - Professional qualifications (4 Years - )
- subject
- keywords
- Friction estimation, vehicle dynamics, vehicle simulation, artificial neural networks
- other publication id
- 0280-5316
- language
- English
- id
- 9002374
- date added to LUP
- 2020-07-16 09:04:25
- date last changed
- 2020-07-16 09:04:25
@misc{9002374,
abstract = {{With the transition of responsibilities from the driver to the automated driving systems in vehicles, the systems need to have been tested for an extensive list of test scenarios as the passengers require high trustworthiness. The friction coefficient for the tyre-road friction is of high importance for the control of the vehicle but the coefficient is dependent on the physical complexity and nonlinear behaviour of tyres and is difficult to measure. Hence, testing is performed in controlled environments which limits the systems exposure to different testing scenarios.
The purpose of this thesis and the underlying work was to develop and evaluate a process for friction estimation using machine learning. The aim was to produce an estimation method using neural networks that are trained on data from a vehicle model implemented in a simulated environment using Unity 3D, which is a software platform for simulation and game development. The master thesis was produced at Combine Control Systems AB for Lund University in cooperation with National Electric Vehicle Sweden AB (NEVS).}},
author = {{Karlsson, Jonas}},
language = {{eng}},
note = {{Student Paper}},
title = {{Road friction estimation using an artificial neural network in a simulated environment}},
year = {{2020}},
}