Quantifying Drivers' Behaviours when Overtaking Bicyclists on Rural Roads - A Study Using Naturalistic Driving Data from a Vehicle's Perspective

Examensarbete för masterexamen
Master Thesis
Biomedical engineering (MPBME), MSc
Nero, Gustav
Overtaking manoeuvres of vulnerable road users on rural roads have previously been found to be accident-prone events with severe or fatal outcomes for the involved vulnerable road users. Most of the previously conducted studies revolving around overtaking manoeuvres of vulnerable road users on rural roads have used either naturalistic driving data which was collected from the bicyclists' perspective or data from driving-simulators. The work described in this thesis involved quantifying data of cars overtaking vulnerable road users on rural roads. The thesis exemplified how to extract overtaking manoeuvre segments from naturalistic driving data and further demonstrated how to extract comfort zone boundaries. The data used was extracted from the database of the European Naturalistic Driving project UDRIVE and included CAN-data, Mobileye-data, and video data. The video data came from cameras capturing both the inside and the outside of the ego vehicle. The data was enriched via manual annotations and automatic derivation of signals using tools such as SALSA and MATLAB. Manual annotations also verified the data, since not all data extracted from the database contained overtaking manoeuvres. To keep the work manageable only events where a single vulnerable road user traveling in the same direction as the ego vehicle and where the vulnerable road user was in the outer-most lane as the ego vehicle were considered. The focus of the thesis has been method development, that is by primarily using ME data identifying overtaking segments from the UDRIVE database and then derive comfort zone measures such as time to collision, lateral clearance and minimum distance. However, due to issues with A) Subjectivity of the video annotations, B) the lack of a comprehensive quality check of the data (i.e. not comparing what the video-feed showed with what various signals implied), and C) error in a derived measure (i.e. the speed of the vulnerable road user), which in turn was used in several other derived measures, results were only compared to previous studies briefly. For future work the quality of the data (both raw and derived) should be considered to have a higher priority. In other words, a more comprehensive data validation should be performed to verify that extracted data is fit for analysis.
Farkostteknik , Hållbar utveckling , Transport , Vehicle Engineering , Sustainable Development , Transport
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