Interest in driving simulators has risen due to new technologies that better resemble real-world driving and the cost reduction of driving simulator development. In recent years, safety researchers commonly use driving simulators to examine complex driving behaviors in a controlled and safe environment. However, the question remains whether or not a driving simulator could be used to assess novice drivers’ driving skills performance. These drivers are overrepresented in crashes after passing their driving license test. To the best of the researcher’s knowledge, there is little known about to what extent driving simulators could add value to the traditional on-road car-driving test to assess human driving performance. A framework was used to structure the information available in the literature and practice. A staged research approach consisting of literature research, a questionnaire, and in-depth interviews resulted in an information inventory. The Dutch car-driving test is used as a case study. The results showed a remaining knowledge gap in the literature research about the usefulness of the driving simulator for assessing the set of required driving skills for each part of the Dutch car-driving test. Useful driving simulator training results were found for overtaking & side-way movements. The information from practice showed promising results on the driving simulator’s hazard perception training for human car driving performance. This thesis concludes that driving simulators could lead to a more comprehensive driving test regarding hazard perception assessment. The potential use of a transportable simulator without moving base would be worth further investigating. A fixed-base driving simulator with a blind spot seems promising for overtaking & side-way movements driving movements. Both findings require follow-up research since this thesis provided the first step of research into the opportunities regarding driving simulator usage for human car driving performance assessment.

Different types of sensors that monitor the driver, the vehicle, and the surroundings are increasingly being implemented in vehicles. These developments are relevant to formal driving test organizations for the use of sensors in the assessment of driving behaviour. However, no guidelines exist about the exact use of sensor-generated data for driving assessment purposes, such as the driving exam. Relatively low-cost and easily implementable sensors such as accelerometers (g-force sensors) could be used to assess driving behaviour during the driving exam to distinguish between desired and undesired driving behaviour. Earlier studies have already investigated the use of accelerometers to distinguish between driving styles using threshold values, but do not agree on the most optimal threshold to do this.

In this study, thresholds were created based on scripted driving exams driven by professional driving examiners, who portrayed driving styles commonly observed at the driving exam. These rides were driven over a period of three weeks at the Dutch driving license organisation (CBR) in Leusden, The Netherlands, where scripted exam rides are part of the training for new examiners. Using the accelerometer of an iPhone X, the accelerations generated during 21 rides were measured supported by a dashcam recording the road ahead. The experimenter drove along all rides and registered driving events, including turns, speed increases/decreases and lane changes to relate the measured accelerations to these specific events. The rides included in the dataset were divided into four different driving styles: aggressive, desired, overcautious and negligent, from which the first three were analysed due to their direct link with accelerations. The obtained acceleration data was analysed using 63 initial features which describe the amplitude of the acceleration signal through the various events, speed zones and acceleration directions.

It was found that accelerations differ significantly between aggressive and desired driving styles, whilst the difference between overcautious and desired driving is less apparent. The thresholds determined in this study were able to classify the created dataset between desired and undesired driving behaviour with accuracies varying from 50% to 83% (M = 69.1, SD = 11.4).

The results show that using acceleration data obtained during the driving exam can aid the examiner during the driving exam by giving insight into how well a candidate performed in terms of smooth driving through traffic. Concomitantly, a recent report from the Dutch government (Roemer, 2020) addressed that the CBR should investigate the possibilities for using sensor-collected driving data to give more insight to a driving exam candidate as to how to improve his/her driving behaviour after failing the driving exam, giving further rise to researching the use of driving data for practical use.