Naturalistic driving research with a focus on trucks has been gaining momentum in the past decade. With the advancement in sensor technology and access to big data, it becomes possible to understand driver behaviour at a more fundamental level. This can assist in mitigating the impact trucks have on the environment while enhancing safety. Several studies have worked towards examining the predictability of driving behaviour through driver profiling (i.e. scoring a driver's behaviour or classifying drivers by assigning them different categories such as aggressive/non-aggressive). However, little research still focuses on the importance and impact of individual features used to develop these models. In the current study, an analysis of driving data from 1,727 trucks recorded over one year as part of a Dutch Field Operational Test (FOT) has been performed. This FOT, to date, has not been investigated in the published academic literature. Recent studies have analysed historical location data to assess risk associated with specific routes and environments. This is being used to provide notifications to drivers around work zones to mitigate the impact of accidents.

The current thesis extends the geo-specific analysis of (truck) driving data by analysing stability in truck driving behaviour with a focus on time and location (urban areas and motorways). Here correlation analysis has been used to explore stability. Correlational analysis elucidates that metrics such as the number of headway warnings, braking events and lane departure warnings are stable over space and time. A discussion reflects on the role of vehicle characteristics (i.e. mass and engine power) towards stability.

It is concluded that in the case of spatial stability: Mean point speed has higher stability on motorways than in urban areas. It has been determined that trucks with higher mass and lower engine power tend to have lower mean speed than the norm. Contrary to mean point speed, headway warnings show higher stability in urban areas than on motorways. Braking events and lane departure warnings exhibit high stability. Secondly, a strong correlation between (t and t+1) hours over the entire day is observed for temporal stability.

This research is a precursor to building generalised models for profiling drivers and assessing various driving patterns. An in-depth understanding of different driving patterns can help driver coaching companies better understand metrics when time and location are factored in before providing targeted feedback. Apart from this can also facilitate fleet management. The code for analysing this dataset is accessible online and may stimulate future researchers to explore this dataset further.

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.

Automatic lane change identification can be used in warning systems that alert the driver when drifting off-lane, or in lane-specific navigation systems. Furthermore, they may help road authorities plan better road network design and infrastructure. Though lane-departure warning systems are already commercially available, these systems rely on on-board vehicle sensors. The availability of such systems can be increased if it became available to owners of smartphones or navigation devices. In this thesis, I propose and test several methods that rely solely on GPS data originating from portable devices (two smartphones, a GPS-equipped GoPro Max, and a USB GPS receiver), recorded during test rides on a Dutch highway (42.8 km) with a total of 64 lane changes. The methods rely on observing changes in the lateral offset between the GPS trajectories and the road geometry. The resulting identification accuracy of the best performing algorithm was achieved using the GoPro Max with an overall F1 score (harmonic mean of precision and recall) of 0.9 on the validation data using the signal of filtered projected lateral distance. It is concluded that GPS-equipped portable devices could be a suitable choice for identifying lane changes, provided there is improvement in the quality of GPS receiver chips with higher data collection frequency such as the ones used in GoPro Max.

The presence of a passenger can affect driver safety positively or negatively. Therefore gaining a better understanding of the nature of this interaction with a passenger is crucial. Additionally, drivers and passengers acquire nearly identical visual information from the driving scene. As a result, synchronised behaviour may occur. The driver-passenger synchrony may provide additional information on driver-passenger interactions. This led to the aim of the current study: The identification of synchrony in head orientation and movement between drivers and passengers for various road types and cornering events. This study was based on real-world driving data. The head angle (orientation) and angular velocity (movement) of all three principal axes (pitch, roll, and yaw) were analysed for synchrony.

The following methods were applied for the identification of driver-passenger synchrony. The head angles of drivers and passengers were detected using OpenFace 2.0, a video-based pose estimation method. Second, the windowed cross-lagged correlation (WCLC), a linear approach for synchrony identification, was applied. Next, two distinct facets of synchrony were measured. The first facet was determining the frequency of synchrony derived by the peak-picking algorithm developed by Altmann (2013). The frequency of synchrony was summarised as the percentage of synchrony that occurred over the measured road segment. The second facet of synchrony studied was the strength of synchrony with the help of the peak-picking algorithm developed by Boker et al. (2002). This peak-picking algorithm searches for the maximum peak correlation at every window of the WCLC. Then all maximum peak correlations were averaged into a single mean peak correlation per road segment or cornering event. Since behavioural synchrony could appear by chance, the results of the peak-picking algorithms were compared to pseudosynchrony. The first hypothesis states that it is possible to distinguish detected synchrony from pseudosynchrony of driver’s and passenger’s head angle and angular velocity. Secondly, it is hypothesised that more synchrony is detected in the urban road type than outside built-up areas- and highway road types.

The results showed that differentiation between the detected synchrony and pseudosynchrony could be made for the analysis on the total route for almost all head angles and angular velocity for the frequency- and strength of synchrony. Furthermore, the analysis on the different road types, revealed that the identified synchrony could be differentiated from pseudosynchrony for almost all urban road segments. The findings on the other road types were mixed. It can be concluded from these research results that synchronisation has been identified for the urban road type. The road types where the detected synchrony was not significantly different from pseudosynchrony could indicate that there was no synchrony present or that the applied methods were unable to capture the driver-passenger synchrony. Synchrony identification in cornering events revealed no observable patterns. As a result, no conclusions on the cornering events could be formed. The second hypothesis could only be tested for one condition, since the identified synchrony could not be differentiated from pseudosynchrony for all the required road types for the second hypothesis. The result showed no significant difference between the urban road type compared to the built-up areas and highway road types for the pitch–angular velocity.

According to this study, drivers and passengers exhibited synchronisation in their head orientations and movements along particular road segments. However, more research is needed to truly comprehend the synchronised behaviour of drivers and passengers. A good place to start is for a study that looks into the relation between synchronous behaviour and the impact on driver safety as a result of the passenger's presence.