Task Automation Allows You to Improve Driver Training

Driver training as the development from controlled to automatic processing.

In psychological research, learning to drive a car is regarded as a typical example of a development from controlled to automatic processing by means of training. During car driving, a number of different part tasks have to be executed and integrated. Often the driver needs to switch between these part tasks. This concerns tasks such as pedal control, gear changing, lane changing, stopping the vehicle, driving off, visual scanning when approaching an intersection, looking at road signs, watching other traffic, applying rules of the road, etc.

As an example: when you learn to drive a car, you have to consciously attend to everything you are doing. Steering and control of the pedals require conscious attention. But watching other traffic, traffic lights and road signs requires conscious attention as well. Often, you press the wrong pedal, or you release the clutch too fast, resulting in stalling of the engine, or you turn the steering wheel too much, if the situation requires you to attend to a road sign or other vehicle. Talking with your passenger can be hard and dangerous, because talking requires controlled attention as well.

A task that has been practices very well becomes automatic. Automatic tasks require little or no processing capacity. Because of that, automatic tasks can be executed in parallel (simultaneously) with other tasks. For example, you can walk and eat at the same time. Automatic tasks are executed fast and efficient and hardly require any conscious attention. In contrast, controlled processes:

• require conscious attention,
• are executed more slowly and are consciously controlled,
• are error-prone,
• and can’t be performed simultaneously with other controlled tasks (multitasking not possible).

When the part tasks of car driving are not automated well enough (and still require controlled processing):

• the driver is overloaded easily,
• commits more errors
• needs more time to perform a task. This may result in not being able to complete a time-limited task in time, such as approaching an intersection. Because of that learned drivers often fail to signal in time or fail to look into the appropriate direction resulting in an accident.

An essential part of good driver training consists of good and efficient automation of part tasks. That determines whether a student learns to drive well, the chance of passing the driver exam and driver safety.

Automation of skills is not optimal during traditional driver training.

During regular driver training in a learner car, all these tasks are learned but usually not automated sufficiently. The reason is that during driving in a learner car on a road, unexpected situations occur, and there’s a lot of switching between tasks. It’s difficult for the instructor to control traffic situations and events. Because of that, individual part tasks can’t be practiced extensively, and extensive practice is required in order to make the transition from controlled to automatic processing. This results in the situation, here in the Netherlands, that most student drivers need at least 40 hours of on-road training before they are fit to apply for a driver test, for which only 50% passes the first time. Still, during the driver test, it often occurs that the engine stalls because of poor clutch control, or the student fails to scan properly when approaching an intersection. This is caused by the student driver being overloaded when multiple part tasks require controlled attention simultaneously. Driver training in a learner car on public roads is not the most effective method to learn to drive. Automation of part tasks proceeds slowly this way, and it may take up to a few years after the driver has passed for the driver test before a reasonable level of automation has been established.

Poor levels of automation are probably an important cause for the relatively high accident rate during the first years of driving in young drivers. During driving, mental workload varies considerably. Taskload increases as more part tasks that require controlled processing are performed simultaneously, or when there’s more switching between controlled tasks. An unexpected situation, for example, a pedestrian crossing the street, may result in a sudden increase in workload leading to errors and increased accident risk. When part tasks are better automated, overload is less likely and the driver may respond faster and in a safer way.

A driving simulator can be particularly helpful in this respect, because it allows the learner driver to practice individual driving tasks to a more automated level.