Statistical modelling of critical cut-ins for the evaluation of autonomous vehicles and advanced driver assistance systems
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Examensarbete för masterexamen
Modellbyggare
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Sammanfattning
Understanding human behaviour in traffc is an integral part of developing active safety systems (ADAS)
and autonomous vehicles (AV). Such systems require rigorous testing before they can be put in commercial
use. This thesis aimed to study data collected as part of the second Strategic Highway Research Program
(SHRP2) Naturalistic Driving Study (NDS) to improve the estimation made in the tails of a driver model for
lane changing. This was to be done through annotating a data-set of 1191 critical lane-change events provided
through SHRP2 NDS. Annotation was done using an annotation tool that was developed for this purpose as
part of a previous project. The trajectories of the manoeuvres were then extracted and parameterised using
ridge regression. It was found that 86 of the 1191 events were suitable for annotation. Due to the limited
quality of the data and number of usable events, the thesis aim was redirected to model the uncertainty of
the annotation method using 9 events annotated by 5 annotators. Two linear regression models were then
developed to estimate the uncertainty of this annotation method. The results show that the models can predict
the uncertainty based on the limited number of events that were available. These results have potential to be
used to estimate the uncertainty of the parameterised trajectories in future work.
Beskrivning
Ämne/nyckelord
Statistical modelling, ADAS, AV, Driver models, Naturalistic Driving Data, Lane changes, Nearcrashes, Linear Regression