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Adaptive driver modelling in ADAS to improve user acceptance: a study using naturalistic data

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journal contribution
posted on 08.07.2020 by James Fleming, Craig K Allison, Xingda Yan, Roberto Lot, Neville A Stanton
Accurate understanding of driver behaviour is crucial for future Advanced Driver Assistance Systems (ADAS) and autonomous driving. For user acceptance it is important that ADAS respect individual driving styles and adapt accordingly. Using data collected during a naturalistic driving study carried out at the University of Southampton, we assess existing models of driver acceleration and speed choice during car following and when cornering. We observe that existing models of driver behaviour that specify a preferred inter-vehicle spacing in car-following situations appear to be too prescriptive, with a wide range of acceptable spacings visible in the naturalistic data. Bounds on lateral acceleration during cornering from the literature are visible in the data, but appear to be influenced by the minimum cornering radii specified in design codes for UK roadway geometry. This analysis of existing driver models is used to suggest a small set of parameters that are sufficient to characterise driver behaviour in car-following and curve driving, which may be estimated in real-time by an ADAS to adapt to changing driver behaviour. Finally, we discuss applications to adaptive ADAS with the objectives of improving road safety and promoting eco-driving, and suggest directions for future research.

Funding

Engineering and Physical Sciences Research Council under Grant No. EP/N022262/1.

History

School

  • Mechanical, Electrical and Manufacturing Engineering

Published in

Safety Science

Volume

119

Issue

November 2019

Pages

76 - 83

Publisher

Elsevier BV

Version

AM (Accepted Manuscript)

Rights holder

© Elsevier Ltd

Publisher statement

This paper was accepted for publication in the journal Safety Science and the definitive published version is available at https://doi.org/10.1016/j.ssci.2018.08.023.

Acceptance date

27/08/2018

Publication date

2018-08-30

Copyright date

2018

ISSN

0925-7535

Language

en

Depositor

Dr James Fleming. Deposit date: 8 July 2020

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