Thesis Code: 19022
Thesis Type: M.Sc. thesis in Machine Learning, Data Science, Computer Science, Mathematics, or equivalent
Research Area: Data Science for Industrial and Societal Application
Requirements:
- Knowledge of Python
- Software development skills
- Basic concepts on data science, concerning data analysis, processing and machine learning
- Basic concepts on image processing
Description:
Self-driving technology presents a rare opportunity to improve the quality of life in many of our communities. Avoidable collisions, single-occupant commuters, and vehicle emissions are choking cities, while infrastructure strains under rapid urban growth. Autonomous vehicles are expected to redefine transportation and unlock a myriad of societal, environmental, and economic benefits. From a technical standpoint, however, the bar to unlock technical research and development on higher-level autonomy functions like perception, prediction, and planning is extremely high. For this reason, we want to integrate available open data, with synthetic data created with the aid of a virtual environment.
The objective of this thesis consists in the study and implementation of machine learning algorithms useful for detecting surrounding vehicles and predicting their 3D bounding volumes. The proposed algorithms will be trained using open and synthetic dataset. This is created with the help of dedicated tools for extracting information from the Grand Theft Auto V graphic engine or any other simulator. The candidate will have both the task of creating the dataset and studying and evaluating the best algorithms to apply for the case of study.
The candidate is required to implement machine learning algorithms, with the exploratory possibility of deep learning algorithms using popular framework (TensorFlow, PyTorch, Keras, etc..).
Contact: send a resume with attached the list of exams to mirko.zaffaroni@linksfoundation.com specifying the thesis code and title.
