Assessing driving behaviour using acceleration thresholds

Assessing driving behaviour using acceleration thresholds

Stefan, David (TU Delft Mechanical, Maritime and Materials Engineering)

Heikoop, D.D. (mentor)
Driessen, T. (mentor)
de Winter, J.C.F. (graduation committee)
Dodou, D. (graduation committee)

Delft University of Technology

Mechanical Engineering

2022-11-01

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.

Acceleration
Driving behaviour
Driving exam
Human-Robot Interaction
Threshold

http://resolver.tudelft.nl/uuid:4cc0ca23-f768-4cad-b453-da00889f6c7f

Student theses

master thesis

© 2022 David Stefan