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Considerable resources are invested each year into training to ensure trainees have the required competencies to safely and effectively perform their tasks/jobs. As such, it is important to develop effective training programmes which target those required competencies. One method that can be used at the start of the training lifecycle to establish the tasks and competencies that are required for a task/job and is considered an important activity to perform when developing a training programme is a Training Needs Analysis (TNA). This article presents a new TNA approach and uses an Automated Vehicle (AV) case study to demonstrate this new approach for a specific AV scenario within the current UK road system. A Hierarchical Task Analysis (HTA) was performed in order to identify the overall goal and tasks that drivers need to perform to operate the AV system safely on the road. This HTA identified 7 main tasks which were decomposed into 26 sub-tasks and 2428 operations. Then, six AV driver training themes from the literature were combined with the Knowledge, Skills and Attitudes (KSA) taxonomy to identify the KSAs that drivers need to perform the tasks, sub-tasks and operations that were identified in the HTA (training needs). This resulted in the identification of over 100 different training needs. This new approach helped to identify more tasks, operations and training needs than previous TNAs which applied the KSA taxonomy alone. As such, a more comprehensive TNA for drivers of the AV system was produced. This can be more easily translated into the development and evaluation of future training programmes for drivers of AV systems.

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.

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.

We investigated the effects of two types of task instructions on performance on a voice sorting task by listeners who were either familiar or unfamiliar with the voices. Listeners were asked to sort 15 naturally varying stimuli from two voice identities into perceived identities. Half of the listeners sorted the recordings freely into as many identities as they perceived; the other half were forced to sort stimuli into two identities only. As reported in previous studies, unfamiliar listeners formed more clusters than familiar listeners. Listeners therefore perceived different naturally varying stimuli from the same identity as coming from different identities, while being highly accurate at telling apart the stimuli from different voices. We further show that a change in task instructions - forcing listeners to sort stimuli into two identities only - helped unfamiliar listeners to overcome this selective failure at 'telling people together'. This improvement, however, came at the cost of an increase in errors in telling people apart. For familiar listeners, similar non-significant trends were apparent. Therefore, even when informed about correct number of identities, listeners may fail to accurately perceive identity further highlighting that voice identity perception in the context of natural within-person variability is a challenging task. We discuss our results in terms of similarities and differences to findings in the face perception literature and their importance in applied settings, such as forensic voice identification.

The human voice is a highly flexible instrument for self-expression, yet voice identity perception is largely studied using controlled speech recordings. Using two voice-sorting tasks with naturally varying stimuli, we compared the performance of listeners who were familiar and unfamiliar with the TV show Breaking Bad. Listeners organised audio clips of speech with (1) low-expressiveness and (2) high-expressiveness into perceived identities. We predicted that increased expressiveness (e.g., shouting, strained voice) would significantly impair performance. Overall, while unfamiliar listeners were less able to generalise identity across exemplars, the two groups performed equivalently well when telling voices apart when dealing with low-expressiveness stimuli. However, high vocal expressiveness significantly impaired telling apart in both the groups: this led to increased misidentifications, where sounds from one character were assigned to the other. These misidentifications were highly consistent for familiar listeners but less consistent for unfamiliar listeners. Our data suggest that vocal flexibility has powerful effects on identity perception, where changes in the acoustic properties of vocal signals introduced by expressiveness lead to effects apparent in familiar and unfamiliar listeners alike. At the same time, expressiveness appears to have affected other aspects of voice identity processing selectively in one listener group but not the other, thus revealing complex interactions of stimulus properties and listener characteristics (i.e., familiarity) in identity processing.

In the current study we investigated the effects of two types of task instructions on performance of a voice sorting task by familiar and unfamiliar listeners. In Experiment 1, listeners were asked to sort 15 naturally-varying exemplars from two voice identities into as many identities as they perceived. Results replicate the findings of previous voice and face sorting studies: unfamiliar listeners form more clusters than familiar listeners, selectively failing to “tell people together”. That is, unfamiliar listeners erroneously perceive exemplars from the same identity as coming from different identities, while being highly accurate at telling apart the exemplars from different voices. In Experiment 2, we show that a change in task instructions - forcing participants to sort exemplars into two identities only - allows unfamiliar listeners to overcome this selective failure at “telling people together”. This improvement, however, comes at the cost of an increase in errors in telling people apart. For familiar listeners, similar non-significant trends are apparent. The current study thus shows that, even when being given the correct number of identities, listeners may fail to accurately perceive identity. These findings highlight voice identity perception in the context of natural within-person variability as a challenging task for both unfamiliar and familiar listeners.