Training for the safe activation of Automated Vehicles matters: Revealing the benefits of online training to creating glaringly better mental models and behaviour

Automated Vehicle (AV) systems are expected to reduce the frequency and severity of on-road collisions. Unless drivers have an appropriate mental model for the capabilities and limitations of the automation, they may not activate the automation safely or appropriately on the road, potentially leading to a collision. As such, a training package (L4DTP) was developed to improve drivers’ decisions and behaviour when activating an AV system and this was evaluated in a between-subjects simulator experiment. Drivers received no training (NT, control group), read an owner’s manual (OM, experimental group 1: current training provision) or underwent the L4DTP (experimental group 2: new training programme). All drivers then experienced five scenarios in a driving simulator where they encountered road conditions which were safe and unsafe for activation. Their activation decisions, behaviour, trust in automation, workload and mental models were measured. This experiment found that drivers who read the OM or underwent the L4DTP made better activation decisions and showed better activation behaviour compared to drivers who received NT. Additionally, drivers who underwent the L4DTP found it easier, less demanding and felt under less time pressure when making their decisions, had to expend less effort to reach the same activation performance and had more appropriate and comprehensive mental models for when the automation can be activated compared to drivers who read the OM. This L4DTP can make roads safer by reducing collisions linked to poor activation decisions and behaviour. Therefore, there is the potential for a real benefit for society if this training programme is adopted into mandatory AV driver training.

automated vehicles, driver training, mental demand, mental models, trust in automation, workload, Trust in automation, Mental demand, Automated vehicles, Driver training, Mental models, Workload

10.1016/j.apergo.2023.104057

0003-6870

Merriman, Siobhan

93bd85cd-f5a1-4b2c-96f5-7f1df776d07a

Revell, Kirsten

e80fedfc-3022-45b5-bcea-5a19d5d28ea0

Plant, Katherine

3638555a-f2ca-4539-962c-422686518a78

1 October 2023

Merriman, Siobhan

93bd85cd-f5a1-4b2c-96f5-7f1df776d07a

Revell, Kirsten

e80fedfc-3022-45b5-bcea-5a19d5d28ea0

Plant, Katherine

3638555a-f2ca-4539-962c-422686518a78

Merriman, Siobhan, Revell, Kirsten and Plant, Katherine (2023) Training for the safe activation of Automated Vehicles matters: Revealing the benefits of online training to creating glaringly better mental models and behaviour. Applied Ergonomics, 112, [104057]. (doi:10.1016/j.apergo.2023.104057).

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Accepted/In Press date: 27 May 2023

e-pub ahead of print date: 6 June 2023

Published date: 1 October 2023

Additional Information: Funding Information: This research was funded by IAM RoadSmart and the Engineering and Physical Sciences Research Council . These funders had no involvement in the study design, in the collection, analysis, and interpretation of the data, in the writing of the report, and in the decision to submit this paper for publication. Publisher Copyright: © 2023 The Authors

Keywords: automated vehicles, driver training, mental demand, mental models, trust in automation, workload, Trust in automation, Mental demand, Automated vehicles, Driver training, Mental models, Workload