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What can we learn from Automated Vehicle collisions? A deductive thematic analysis of five Automated Vehicle collisions

What can we learn from Automated Vehicle collisions? A deductive thematic analysis of five Automated Vehicle collisions
What can we learn from Automated Vehicle collisions? A deductive thematic analysis of five Automated Vehicle collisions
There have been a number of high-profile collisions involving Automated Vehicles on the road. Although car manufacturers are making considerable investments into the development of Automated Vehicles, these collisions may deter the public from purchasing and using them. Therefore, solutions need to be developed to prevent these collisions from occurring in the future. One such solution is driver training. A previous literature review identified nine themes which are essential in Automated Vehicle driver training. In this article, a deductive thematic analysis was conducted on five high-profile Automated Vehicle collisions in order to demonstrate the relevance of these themes and to gain insights into how the driver’s behaviour contributed to each collision, thus understand the potential role of training in reducing collisions of this nature. By creating interconnection models for each collision, a consistent pattern emerged. A link was made with the drivers’ attitudes, the accuracy of their mental models and their level of trust in the automation. The automation caused the drivers to become underloaded, which impaired their ability to effectively monitor the automation and the road environment. This could have impaired their situation awareness and their ability to identify and avoid hazards in the path of their vehicle. This analysis suggests that future Automated Vehicle driver training programmes should be multifaceted and cover all nine themes. This analysis has validated these nine driver training themes, so these themes and interconnections can help in the development of a comprehensive training programme for drivers of Automated Vehicles in the future.
Accident analysis, automated vehicles, deductive thematic analysis, driver training, safety
0925-7535
Merriman, Siobhan Emma
e46fe5f1-c748-406f-a858-18eda0268fa0
Plant, Katherine
3638555a-f2ca-4539-962c-422686518a78
Revell, Kirsten
e80fedfc-3022-45b5-bcea-5a19d5d28ea0
Stanton, Neville
351a44ab-09a0-422a-a738-01df1fe0fadd
Merriman, Siobhan Emma
e46fe5f1-c748-406f-a858-18eda0268fa0
Plant, Katherine
3638555a-f2ca-4539-962c-422686518a78
Revell, Kirsten
e80fedfc-3022-45b5-bcea-5a19d5d28ea0
Stanton, Neville
351a44ab-09a0-422a-a738-01df1fe0fadd

Merriman, Siobhan Emma, Plant, Katherine, Revell, Kirsten and Stanton, Neville (2021) What can we learn from Automated Vehicle collisions? A deductive thematic analysis of five Automated Vehicle collisions. Safety Science, 141, [105320]. (doi:10.1016/j.ssci.2021.105320).

Record type: Article

Abstract

There have been a number of high-profile collisions involving Automated Vehicles on the road. Although car manufacturers are making considerable investments into the development of Automated Vehicles, these collisions may deter the public from purchasing and using them. Therefore, solutions need to be developed to prevent these collisions from occurring in the future. One such solution is driver training. A previous literature review identified nine themes which are essential in Automated Vehicle driver training. In this article, a deductive thematic analysis was conducted on five high-profile Automated Vehicle collisions in order to demonstrate the relevance of these themes and to gain insights into how the driver’s behaviour contributed to each collision, thus understand the potential role of training in reducing collisions of this nature. By creating interconnection models for each collision, a consistent pattern emerged. A link was made with the drivers’ attitudes, the accuracy of their mental models and their level of trust in the automation. The automation caused the drivers to become underloaded, which impaired their ability to effectively monitor the automation and the road environment. This could have impaired their situation awareness and their ability to identify and avoid hazards in the path of their vehicle. This analysis suggests that future Automated Vehicle driver training programmes should be multifaceted and cover all nine themes. This analysis has validated these nine driver training themes, so these themes and interconnections can help in the development of a comprehensive training programme for drivers of Automated Vehicles in the future.

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What can we learn from Automated Vehicle collisions v2 ss - Accepted Manuscript
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More information

Accepted/In Press date: 20 April 2021
e-pub ahead of print date: 17 May 2021
Keywords: Accident analysis, automated vehicles, deductive thematic analysis, driver training, safety

Identifiers

Local EPrints ID: 449340
URI: http://eprints.soton.ac.uk/id/eprint/449340
ISSN: 0925-7535
PURE UUID: 4ea0e588-59d9-4202-8468-790ba04b3cf7
ORCID for Siobhan Emma Merriman: ORCID iD orcid.org/0000-0002-0519-687X
ORCID for Katherine Plant: ORCID iD orcid.org/0000-0002-4532-2818
ORCID for Neville Stanton: ORCID iD orcid.org/0000-0002-8562-3279

Catalogue record

Date deposited: 25 May 2021 16:32
Last modified: 26 Nov 2021 03:19

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