People learn to drive well by extensive practice and driving experience. The current view that is supported by research is that lack of driving experience in relevant driving tasks is probably the main reason for the overrepresentation of young drivers in the accident statistics. A good driver training program promotes a high level of driving experience in the trainees. And a good driving simulator can be of value in this respect. All kinds of driving tasks can be trained in a simulator, such as driving off, steering, gear switching, scanning behaviour while approaching intersections, applying priority rules, etc. But how can the use of a simulator in driver training result in better and safer drivers ?

The theory consists of 4 steps.

1. Extensive practice in individual driving tasks

Individual driving tasks, for example steering, driving off, gear changing, lane changing, etc. are practiced extensively in the driving simulator training program. All these tasks are practiced in situations similar to real world driving situations in a real car. The trainee becomes experienced in different relevant situations in a short period of time.

2. Automation of driving skills

Because of extensive practice in individual driving tasks, the accompanying driving skills become automated much faster compared to traditional driver training in a learner car. That\’s because these skills are trained very specifically without the requirement to attend to other matters that may distract the trainee. In a learner car, the instructor has no control over the surroundings, and the trainee has to attend to unexpected events in the immediate environment as well. Because the skills become automated, they require less attention, and separate tasks can be performed simultaneously (multitasking). For example, when the gear shifting skill is automated as well as scanning behaviour while approaching an intersection, the complete procedure of approaching an intersection can be handled more efficient and faster with fewer errors. The automated skills can be transferred to other similar situations as well. When scanning behaviour is learned well in a car of brand A, it can be applied well in a car of brand B too. Or, when scanning behaviour is learned well in a simulator, it transfers to driving in a real car as well. The operations are similar in both situations. In a new situation there will always be a short period of habituation. For example, when someone has driven a certain car for years and is used to that car, and rents a different car during the holidays, there\’s a short period of getting used to the other car. But this usually doesn\’t take very long: there\’s a good transfer of skills from the one situation to the other.

3. Reduction of mental workload and mental overload

In the driving simulator, mental workload reductions caused by skill automation can be measured, for example, by the PDT, a method that has now become in ISO standard in driver behaviour research and human factors research in the car industry. It has been demonstrated that the PDT is a good method to measure workload as a result of driving experience. While driving in the simulator (in the integration lessons) or on the road, a number of different tasks are being executed simultaneously. All these tasks contribute a certain amount of mental workload individually, that will be lower when the tasks are more automated. The sum of all tasks results in a continuously changing mental workload. When this exceeds a certain level, the trainee suffers from mental overload. And those peaks in workload result in driver errors and incidents and accidents. It has been found that the accident involvement of young drivers is higher in the period following the driver exam. During that period, driving skills have not been automated enough, resulting in higher workload with relatively more moments of overload.

4. Better and safer driving behaviour

If during driver training, methods and techniques are used that result in better task automation (because of more extensive and systematic practice), mental workload while driving in traffic can be reduced. Research has shown that a higher workload results in \’cognitive tunneling\’: the driver focusses attention on a smaller part of the visual field or on a more limited set on objects. This results in a failure to detect important information, such as traffic signs or children crossing the road. Research has also shown that people with more driving experience (and as a result a higher level of task automation and probably lower mental workload) look farther ahead. When someone looks further ahead, and scans the surroundings more, there\’s a better chance to anticipate on possible hazards, and there\’s more time to adjust speed and adapt behaviour. This increases driver safety. A good driver is someone who adapts behaviour to changing circumstances and has a good representation of the surroundings of the vehicle (situational awareness). A lower workload prevents missing important information, such as traffic signs, and enables the driver to see other traffic participants in time.

http://www.carnetsoft.com dr. Wim van Winsum, obtained a PhD in driver behaviour research and has worked as a scientific researcher at the University of Groningen and the research institute TNO in the Netherlands. He is the owner of Carnetsoft. The training program in the driving simulator of Carnetsoft is specifically aimed at driving task automation and extensive practice. This training can improve the traditional driver training program significantly, because the student learns to drive better.

http://www.carnetsoft.com dr. Wim van Winsum, obtained a PhD in driver behaviour research and has worked as a scientific researcher at the University of Groningen and the research institute TNO in the Netherlands. He is the owner of Carnetsoft. The training program in the driving simulator of Carnetsoft is specifically aimed at driving task automation and extensive practice. This training can improve the traditional driver training program significantly, because the student learns to drive better.

Author Bio: http://www.carnetsoft.com dr. Wim van Winsum, obtained a PhD in driver behaviour research and has worked as a scientific researcher at the University of Groningen and the research institute TNO in the Netherlands. He is the owner of Carnetsoft. The training program in the driving simulator of Carnetsoft is specifically aimed at driving task automation and extensive practice. This training can improve the traditional driver training program significantly, because the student learns to drive better.

Category: Automotive
Keywords: driving simulator,driver training