Machine Learning-based Lane-level Localization
Typ
Examensarbete för masterexamen
Master's Thesis
Master's Thesis
Program
Data science and AI (MPDSC), MSc
Publicerad
2023
Författare
Udayakumar, Amogha
Sundararaman, Bragadesh Bharatwaj
Modellbyggare
Tidskriftstitel
ISSN
Volymtitel
Utgivare
Sammanfattning
In autonomous driving, for the vehicle to make a decision on its own it has to
have precise knowledge of its location (lane) with respect to its environment. This
problem of determining the lane on which the vehicle is travelling is called Lane-Level Localization (LLL). HD maps with localization algorithms are used to solve
the problem of Lane-Level Localization. However, during the initialization phase,
there is uncertainty in the confidence of the lane in which the vehicle is driving.
Our thesis aims to overcome this problem by using the Multi-Hypothesis Tracking
(MHT) approach. Multi-Hypothesis Tracking has two parts which are tracking multiple hypotheses and inference of the correct hypothesis by eliminating the wrong
hypotheses. Our thesis focuses on the latter part which can be solved using the
early classification of time series technique. The early classification of time series
technique tends to make an early and accurate classification of the hypothesis by
rejecting wrong hypotheses based on the class probabilities assigned to different
hypotheses and using multi-objective optimization to find a trade-off between earliness and accuracy. Our model produced an accuracy of 99.053% with an earliness
of 0.109.
Beskrivning
Ämne/nyckelord
Autonomous driving, Lane-Level Localization, HD maps, Multi-Hypothesis Tracking (MHT), Early Classification of Time Series (ECTS)