Tesi etd-12202022-155558
Link copiato negli appunti
Type of thesis
Dottorato
Author
BARIS, GABRIELE
URN
etd-12202022-155558
Title
High-Level Perception for Intelligent Transportation
Scientific disciplinary sector
ING-INF/05
Course
Istituto di Tecnologie della Comunicazione, dell'Informazione e della Percezione - PHD IN EMERGING DIGITAL TECHNOLOGIES
Committee
relatore Prof. AVIZZANO, CARLO ALBERTO
Membro Prof.ssa DONZELLA, VALENTINA
Membro Prof. LOIANNO, GIUSEPPE
Membro Prof.ssa DONZELLA, VALENTINA
Membro Prof. LOIANNO, GIUSEPPE
Keywords
- assisted and automated driving
- computer vision
- condition-based maintenance
- intelligent transportation systems
- machine learning
- perception
- unmanned aerial vehicles
Exam session start date
31/07/2023;
Availability
parziale
Abstract
In the last few years, intelligent transportation has attracted a lot of interest, both from academia and industry.<br>In this context, environmental perception from the vehicle point of view is key for the successful adoption of these new technologies. <br>In this thesis, some advances in the field of perception for intelligent transportation will be discussed, both regarding automated vehicles and infrastructure.<br>Considering infrastructure for intelligent transportation, a Deep Neural Network based train pantograph head pose estimator is described. <br>Moving to automated vehicles, one important question to ask is \emph{How does the vehicle behave moving away from ideal conditions}? Considering Deep Neural Networks for vision tasks, this question will be addressed from two different points of view: on one side, analysing the training dataset and possible errors in the labelling process; on the other one, analysing detection performance in non-ideal cases (like distortion and compression).<br>Finally, two examples of complete stacks for autonomous robotics are presented: one for autonomous racing and another for Unmanned Aerial Vehicle search and rescue operations. In both cases, environmental perception is a key stage for enabling all other autonomous features (localisation, navigation, etc.).<br>The work presented in this thesis is a step towards improving Intelligent Transformation Systems, and the findings are important for the safety and reliability of future automated vehicles.
Files
| Nome file | Dimensione |
|---|---|
Ci sono 1 file riservati su richiesta dell'autore. |
|