Machine Learning Meets Localization
Typ
Examensarbete för masterexamen
Program
Publicerad
2022
Författare
STENHAMMAR, THEODOR
BEJMER, DAVID
Modellbyggare
Tidskriftstitel
ISSN
Volymtitel
Utgivare
Sammanfattning
This thesis project was conducted in cooperation with Zenseact for the purpose
of creating a solution for determining the lane in which an autonomous vehicle is
driving. Solving this is part of the larger problem of localization and state estimation
of autonomous vehicles and is referred to as Lane-Level Localization (LLL).
The problem is connected to the area of Early Time Series Classification, which is
the field of applying supervised learning and time series classification techniques for
classifying time series accurately with as few observations as possible.
The problem of LLL may be solved by applying what is known as a multi-hypothesis
technique. This is a technique that estimates some state by tracking several different
possibilities (hypotheses) for the state and using some model for inferring the most
likely scenario.
It is found that using an architecture that allows for the possibility of rejecting
output depending on the certitude with which a classification can be made can be
adapted to solving the problem of LLL in autonomous vehicles. In the current
scenario, the model produced an accuracy of 99,5% while only rejecting to classify
in 1% of the sequences.
Beskrivning
Ämne/nyckelord
Engineering , thesis , Zenseact , machine learning , localization , autonomous driving , data , time series , early , early classification , lane-level localization