The University of Southampton
University of Southampton Institutional Repository

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

Adaptive driver modelling in ADAS to improve user acceptance: a study using naturalistic data
Adaptive driver modelling in ADAS to improve user acceptance: a study using naturalistic data
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.
ADAS, Speed choice, Safe cornering, Car following, Driver modelling, Naturalistic driving
0925-7535
Fleming, James
b59cb762-da45-43b1-b930-13dd9f26e148
Allison, Craig
46b3ce37-1986-4a23-9385-a54d0abd08d5
Yan, Xingda
2d256fbf-9bee-4c5e-9d75-fe15d1a96ade
Stanton, Neville
351a44ab-09a0-422a-a738-01df1fe0fadd
Lot, Roberto
ceb0ca9c-6211-4051-a7b8-90fd6f0a6d78
Fleming, James
b59cb762-da45-43b1-b930-13dd9f26e148
Allison, Craig
46b3ce37-1986-4a23-9385-a54d0abd08d5
Yan, Xingda
2d256fbf-9bee-4c5e-9d75-fe15d1a96ade
Stanton, Neville
351a44ab-09a0-422a-a738-01df1fe0fadd
Lot, Roberto
ceb0ca9c-6211-4051-a7b8-90fd6f0a6d78

Fleming, James, Allison, Craig, Yan, Xingda, Stanton, Neville and Lot, Roberto (2018) Adaptive driver modelling in ADAS to improve user acceptance: a study using naturalistic data. Safety Science. (doi:10.1016/j.ssci.2018.08.023).

Record type: Article

Abstract

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.

Text
adaptiveADAS_revised_NOT_BLIND - Accepted Manuscript
Download (1MB)

More information

Accepted/In Press date: 27 August 2018
e-pub ahead of print date: 30 August 2018
Keywords: ADAS, Speed choice, Safe cornering, Car following, Driver modelling, Naturalistic driving

Identifiers

Local EPrints ID: 424553
URI: http://eprints.soton.ac.uk/id/eprint/424553
ISSN: 0925-7535
PURE UUID: 4bb79e8c-28a6-4e7d-afb4-a3995da169ee
ORCID for James Fleming: ORCID iD orcid.org/0000-0003-2936-4644
ORCID for Neville Stanton: ORCID iD orcid.org/0000-0002-8562-3279
ORCID for Roberto Lot: ORCID iD orcid.org/0000-0001-5022-5724

Catalogue record

Date deposited: 05 Oct 2018 11:38
Last modified: 08 Oct 2020 04:06

Export record

Altmetrics

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×