Exploring semi-supervised learning for deciding the order of vehicles in rear-end crashes
Typ
Examensarbete för masterexamen
Program
Publicerad
2019
Författare
Johansson, Linus
Modellbyggare
Tidskriftstitel
ISSN
Volymtitel
Utgivare
Sammanfattning
Knowing the order of vehicles in rear-end crashes is an important step for further
analysis to work towards the goal of reducing fatalities and severe injuries in traffic
down to zero. For example, by knowing the order of vehicles a more accurate
estimation of the driver’s exposure could be done. The main task of this project
was to create a system which could decide the order of vehicles involved in each
rear-end crash from the text description in a crash database. To try and address
this task semi-supervised learning was chosen as the method. The reason for this
choice was because all the crash cases were unlabelled from the beginning and to
label all the cases manually would be infeasible but to label a few hundred cases
was manageable.
Two neural networks were built and tested. The text descriptions of the rear-end
crashes from the national crash database in Sweden were transformed to vectors and
used as input for the neural networks. The performance of each of the networks was
measured. The results suggest that further work are needed before the networks
could be used for making reliable decisions, however the experiments give some
indications on what parameters should be considered carefully while using semisupervised
learning and which parameters that need more exploration.
Beskrivning
Ämne/nyckelord
Neural Networks , Semi-Supervised Learning , LSTM , CNN , Traffic Safety , Road Accidents database , Natural Language Processing , Deep Learning