A semi-naturalistic on-road study to understand what drivers perceive during narrow passage interactions in the UK
A semi-naturalistic on-road study to understand what drivers perceive during narrow passage interactions in the UK
Despite increased attention from those looking to model driver behaviours in narrow passage interactions, the investigation of the factors affecting these behaviours has been limited. This study, therefore, utilizes a semi-naturalistic qualitative approach to further understand factors considered by car drivers during their decision-making in narrow passage interactions. This was achieved by conducting an on-road study, where nine drivers provided concurrent verbal reports whilst navigating narrow passage environments throughout an urban route in the UK. After completing the route, participants were then interviewed about their drive using prompts from a shortened version of the Schema World Action Research Method in a cued, semi-structured interview. The transcripts produced from the verbalisations were then subject to a thematic analysis, in which several key themes were revealed. These themes included references to the importance of vehicle size (in conjunction with yielding gap sizes) and type when forming expectations of interaction partners, as well as the impact of surrounding vehicles, beyond the main interaction partners, on their decision-making. These results are, therefore, vital when trying to create representative driver behaviour models of narrow passage interactions and ensuring that Autonomous Vehicles act within the expectations of human road users.
Youssef, Peter Refaat Salib
bab86f17-3022-43ff-bc74-7bbbd162ff51
Waterson, Ben
60a59616-54f7-4c31-920d-975583953286
Plant, Katherine
3638555a-f2ca-4539-962c-422686518a78
10 January 2023
Youssef, Peter Refaat Salib
bab86f17-3022-43ff-bc74-7bbbd162ff51
Waterson, Ben
60a59616-54f7-4c31-920d-975583953286
Plant, Katherine
3638555a-f2ca-4539-962c-422686518a78
Youssef, Peter Refaat Salib, Waterson, Ben and Plant, Katherine
(2023)
A semi-naturalistic on-road study to understand what drivers perceive during narrow passage interactions in the UK.
102nd Transportation Research Board Annual Meeting, , Wahshington, United States.
08 - 12 Jan 2023.
18 pp
.
Record type:
Conference or Workshop Item
(Paper)
Abstract
Despite increased attention from those looking to model driver behaviours in narrow passage interactions, the investigation of the factors affecting these behaviours has been limited. This study, therefore, utilizes a semi-naturalistic qualitative approach to further understand factors considered by car drivers during their decision-making in narrow passage interactions. This was achieved by conducting an on-road study, where nine drivers provided concurrent verbal reports whilst navigating narrow passage environments throughout an urban route in the UK. After completing the route, participants were then interviewed about their drive using prompts from a shortened version of the Schema World Action Research Method in a cued, semi-structured interview. The transcripts produced from the verbalisations were then subject to a thematic analysis, in which several key themes were revealed. These themes included references to the importance of vehicle size (in conjunction with yielding gap sizes) and type when forming expectations of interaction partners, as well as the impact of surrounding vehicles, beyond the main interaction partners, on their decision-making. These results are, therefore, vital when trying to create representative driver behaviour models of narrow passage interactions and ensuring that Autonomous Vehicles act within the expectations of human road users.
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Accepted/In Press date: 7 October 2022
Published date: 10 January 2023
Venue - Dates:
102nd Transportation Research Board Annual Meeting, , Wahshington, United States, 2023-01-08 - 2023-01-12
Identifiers
Local EPrints ID: 477355
URI: http://eprints.soton.ac.uk/id/eprint/477355
PURE UUID: 3e91a387-2b7b-4df6-a2dc-5733215dfef5
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Date deposited: 05 Jun 2023 16:42
Last modified: 17 Mar 2024 04:04
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Author:
Peter Refaat Salib Youssef
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