Thesis Code: 25005
Thesis Type: M.Sc. thesis in Computer Engineering / M.Sc. thesis in ICT for Smart Societies / M.Sc. thesis in Mobility Engineering / M.Sc. thesis in Civil Engineering / M.Sc. thesis in Mathematical Engineering / equivalent
Research Area: Future Cities & Communities (FCC)
Requirements:
- Interest in autonomous driving technologies and traffic simulation tools
- Interest in transport models and planning
- Experience with data analysis and simulation platforms
- Experience with Python and main programming languages
- Interest in using Geographic Information Systems (GIS)
- Ability to critically interpret analytical results
- Strong analytical and critical thinking skills
Description
This thesis, in collaboration with Fondazione LINKS and the Politecnico di Torino (DAUIN), investigates the impact of autonomous vehicles on urban mobility through a data-driven approach.
The research focuses on the interpretation of critical AV behaviours (e.g., hard braking events), by integrating anonymised roadside-camera video streams with the vehicle’s onboard sensor data.
A key component of the methodology involves the use of advanced computer vision and image processing techniques—developed in Python—to analyse video footage. State-of-the-art deep-learning models for object detection and tracking will monitor road users and capture their interactions with the autonomous vehicle.
The ultimate goal is to build a robust analytical framework capable of interpreting hard-braking occurrences, delivering insights transferable across diverse urban scenarios and supporting the safe, transparent integration of autonomous vehicles into future city traffic.
Contact: send a resume with attached the list of exams to maurizio.arnone@linksfoundation.com specifying the thesis code and title.