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Challenges for automated vehicle driver training: A thematic analysis from manual and automated driving

Challenges for automated vehicle driver training: A thematic analysis from manual and automated driving
Challenges for automated vehicle driver training: A thematic analysis from manual and automated driving
Considerable research and resources are going into the development and testing of Automated Vehicles. They are expected to bring society a huge number of benefits (such as: improved safety, increased capacity, reduced fuel use and emissions). Notwithstanding these potential benefits, there have also been a number of high-profile collisions involving Automated Vehicles on the road. In the majority of these cases, the driver’s inattention to the vehicle and road environment was blamed as a significant causal factor. This suggests that solutions need to be developed in order to enhance the benefits and address the challenges associated with Automated Vehicles. One such solution is driver training. As drivers still require manual driving skills when operating Automated Vehicles on the road, this paper applied the grounded theory approach to identify eight “key” themes and interconnections that exist in current manual vehicle driver training. These themes were then applied to the limited literature available on Automated Vehicle driver training, and a ninth theme of trust emerged. This helped to identify a set of training requirements for drivers of Automated Vehicles, which suggests that a multifaceted approach (covering all nine themes and manual and Automated Vehicle driving skills) to driver training is required. This framework can be used to develop and test a training programme for drivers of Automated Vehicles.
Attention, Automated Vehicles, Driver Training, Mental Models, Situation Awareness, Trust
1369-8478
238-268
Merriman, Siobhan Emma
e46fe5f1-c748-406f-a858-18eda0268fa0
Plant, Katherine Louise
52e9a0df-c494-4178-b000-fdee5f15436b
Revell, Kirsten
e80fedfc-3022-45b5-bcea-5a19d5d28ea0
Stanton, Neville
351a44ab-09a0-422a-a738-01df1fe0fadd
Merriman, Siobhan Emma
e46fe5f1-c748-406f-a858-18eda0268fa0
Plant, Katherine Louise
52e9a0df-c494-4178-b000-fdee5f15436b
Revell, Kirsten
e80fedfc-3022-45b5-bcea-5a19d5d28ea0
Stanton, Neville
351a44ab-09a0-422a-a738-01df1fe0fadd

Merriman, Siobhan Emma, Plant, Katherine Louise, Revell, Kirsten and Stanton, Neville (2021) Challenges for automated vehicle driver training: A thematic analysis from manual and automated driving. Transportation Research Part F: Traffic Psychology and Behaviour, 76, 238-268. (doi:10.1016/j.trf.2020.10.011).

Record type: Article

Abstract

Considerable research and resources are going into the development and testing of Automated Vehicles. They are expected to bring society a huge number of benefits (such as: improved safety, increased capacity, reduced fuel use and emissions). Notwithstanding these potential benefits, there have also been a number of high-profile collisions involving Automated Vehicles on the road. In the majority of these cases, the driver’s inattention to the vehicle and road environment was blamed as a significant causal factor. This suggests that solutions need to be developed in order to enhance the benefits and address the challenges associated with Automated Vehicles. One such solution is driver training. As drivers still require manual driving skills when operating Automated Vehicles on the road, this paper applied the grounded theory approach to identify eight “key” themes and interconnections that exist in current manual vehicle driver training. These themes were then applied to the limited literature available on Automated Vehicle driver training, and a ninth theme of trust emerged. This helped to identify a set of training requirements for drivers of Automated Vehicles, which suggests that a multifaceted approach (covering all nine themes and manual and Automated Vehicle driving skills) to driver training is required. This framework can be used to develop and test a training programme for drivers of Automated Vehicles.

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Accepted/In Press date: 20 October 2020
e-pub ahead of print date: 24 December 2020
Published date: January 2021
Keywords: Attention, Automated Vehicles, Driver Training, Mental Models, Situation Awareness, Trust

Identifiers

Local EPrints ID: 446164
URI: http://eprints.soton.ac.uk/id/eprint/446164
ISSN: 1369-8478
PURE UUID: 7397eb85-0aed-4455-b855-ae7674323ace
ORCID for Siobhan Emma Merriman: ORCID iD orcid.org/0000-0002-0519-687X
ORCID for Neville Stanton: ORCID iD orcid.org/0000-0002-8562-3279

Catalogue record

Date deposited: 25 Jan 2021 17:31
Last modified: 26 Nov 2021 03:19

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Contributors

Author: Katherine Louise Plant
Author: Kirsten Revell
Author: Neville Stanton ORCID iD

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