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

Abstract

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.

Keywords: Automated vehicles; Driver training; Mental demand; Mental models; Trust in automation; Workload.