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Real-time characterisation of driver steering behaviour

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journal contribution
posted on 05.03.2018 by Matt Best
In recent years the application of driver steering models has extended from the off-line simulation environment to autonomous vehicles research and the support of driver assistance systems. For these new environments there is a need for the model to be adaptive in real-time, so the supporting vehicle systems can react to changes in the driver, their driving style, mood and skill. This paper provides a novel means to meet these needs by combining a simple driver model with a single track vehicle handling model in a parameter estimating filter – in this case an Unscented Kalman Filter. Although the steering model is simple, a motion simulator study shows it is capable of characterising a range of driving styles and may also indicate the level of skill of the driver. The resulting filter is also efficient – comfortably operating faster than real-time – and it requires only steer and speed measurements from the vehicle in addition to reference path. Adaptation of the steer model parameters is demonstrated along with robustness of the filter to errors in initial conditions, using data from five test drivers in vehicle tests carried out on the open road.

History

School

  • Aeronautical, Automotive, Chemical and Materials Engineering

Department

  • Aeronautical and Automotive Engineering

Published in

Vehicle System Dynamics

Citation

BEST, M.C., 2018. Real-time characterisation of driver steering behaviour. Vehicle System Dynamics, 57 (1), pp.64-85.

Publisher

© 2018 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group

Version

NA (Not Applicable or Unknown)

Publisher statement

This work is made available according to the conditions of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) licence. Full details of this licence are available at: https://creativecommons.org/licenses/by-nc-nd/4.0/

Acceptance date

23/02/2018

Publication date

2018

Notes

This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited, and is not altered, transformed, or built upon in any way.

ISSN

0042-3114

eISSN

1744-5159

Language

en

Exports