Task automation lets you improve driver training
Driver training as an evolution from controlled to automatic treatment.
In psychological research, learning to drive a car is considered a typical example of a development from controlled to automatic processing through training. While driving the car, several different subtasks have to be executed and integrated. Often the driver needs to switch between these partial tasks. This refers to tasks such as pedal control, changing gears, changing lanes, stopping the vehicle, driving, visual scanning when approaching an intersection, observing road signs, observing the rest of traffic, the application of traffic regulations, etc.
As an example: when you learn to drive a car, you have to consciously pay attention to everything you are doing. Steering and control of the pedals require conscious attention. But observing other traffic, traffic lights and road signs also requires conscious attention. Often you step on the wrong pedal, or release the clutch too quickly, causing the engine to stall, or turn the steering wheel too far, if the situation requires you to pay attention to a road sign or another vehicle. Talking to your passenger can be difficult and dangerous, because talking also requires controlled attention.
A task that has been practiced very well becomes automatic. Automatic tasks require little or no processing power. Therefore, automatic tasks can be executed in parallel (simultaneously) with other tasks. For example, you can walk and eat at the same time. Automatic tasks run quickly and efficiently and require little conscious attention. In contrast, controlled processes:
- requires conscious attention
- are executed more slowly and are consciously controlled,
- are prone to errors
- and cannot be performed simultaneously with other controlled tasks (multitasking is not possible).
When partial tasks of car driving are not sufficiently automated (and still require controlled processing):
- the driver is easily overloaded,
- make more mistakes
- It takes more time to complete a task. This can result in not being able to complete a time-limited task, such as approaching an intersection, in time. Because of that, experienced drivers often fail to signal on time or look in the proper direction, resulting in an accident.
An essential part of good driver training consists of good and efficient automation of partial tasks. That determines whether a student learns to drive well, the chance of passing the driving test, and driver safety.
Skills automation is not optimal during traditional driver training.
During regular driver training in a learning car, all of these tasks are learned, but usually not sufficiently automated. The reason is that during driving in a learner because on a road, unexpected situations occur and there are many changes between tasks. It is difficult for the instructor to control traffic situations and events. That’s why, individual partial tasks cannot be practiced extensively, and extensive practice is required to make the transition from controlled to automatic processing. This results in the situation, here in the Netherlands, where most driving students need at least 40 hours of road training before they can apply for a driving test, for which only 50% pass the first time. Still, during the test drive, it often happens that the engine stalls due to poor clutch control, or the student fails to scan properly when approaching an intersection. This is because the student driver is overloaded when multi-part tasks require controlled attention simultaneously. Driver training in an apprentice because on public roads is not the most effective method of learning to drive. Automation of partial tasks proceeds slowly in this way, and it may take a few years after the driver has passed the driving test before a reasonable level of automation is established.
Low levels of automation are likely a major cause of the relatively high accident rate during the first few years of driving for young drivers. While driving, the mental workload varies considerably. The task load increases as more partial tasks that require controlled processing are performed simultaneously, or when there are more switches between controlled tasks. An unexpected situation, for example a pedestrian crossing the street, can result in a sudden increase in workload that leads to errors and increases the risk of accidents. When partial tasks are better automated, overloading is less likely and the driver can respond faster and more safely.
A driving simulator can be particularly helpful in this regard as it allows the novice driver to practice individual driving tasks on a more automated level.