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Investigating the effects of mixed driver reaction times in the transport network

Investigating the effects of mixed driver reaction times in the transport network
Investigating the effects of mixed driver reaction times in the transport network
Given the advances in autonomous vehicle technology, vehicles may soon be able
to drive as well as humans. Here, autonomous vehicles are approximated as drivers with reaction times smaller than those of human drivers, but with the same driving competency. Experiments were performed to simulate the effects of loading the transport network with increasingly high proportions of vehicles with faster than normal reaction times, on four different road models.
intelligent transport systems, Autonomous vehicles, driver characteristics
Rafter, Craig, Benjamin
8f56b72d-8984-47e4-ae2a-f38a68fbad14
Box, Simon
2bc3f3c9-514a-41b8-bd55-a8b34fd11113
Rafter, Craig, Benjamin
8f56b72d-8984-47e4-ae2a-f38a68fbad14
Box, Simon
2bc3f3c9-514a-41b8-bd55-a8b34fd11113

Rafter, Craig, Benjamin and Box, Simon (2016) Investigating the effects of mixed driver reaction times in the transport network. 5th Symposium of the European Association for Research in Transportation (hEART), Delft, Netherlands. 13 - 16 Sep 2016. 1 pp . (doi:10.13140/RG.2.2.25157.35044).

Record type: Conference or Workshop Item (Poster)

Abstract

Given the advances in autonomous vehicle technology, vehicles may soon be able
to drive as well as humans. Here, autonomous vehicles are approximated as drivers with reaction times smaller than those of human drivers, but with the same driving competency. Experiments were performed to simulate the effects of loading the transport network with increasingly high proportions of vehicles with faster than normal reaction times, on four different road models.

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More information

e-pub ahead of print date: 1 September 2016
Venue - Dates: 5th Symposium of the European Association for Research in Transportation (hEART), Delft, Netherlands, 2016-09-13 - 2016-09-16
Keywords: intelligent transport systems, Autonomous vehicles, driver characteristics

Identifiers

Local EPrints ID: 414451
URI: http://eprints.soton.ac.uk/id/eprint/414451
PURE UUID: 2ac334f1-be30-4f1f-bc83-f085b905b4f8
ORCID for Craig, Benjamin Rafter: ORCID iD orcid.org/0000-0003-3411-114X

Catalogue record

Date deposited: 29 Sep 2017 16:31
Last modified: 14 Mar 2019 01:27

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