Francesco Prignoli
PhD Candidate in Automotive Engineering for Intelligent Mobility
Education
-
PhD Candidate in Automotive Engineering for Intelligent Mobility
University of Bologna and University of Modena and Reggio Emilia
November 2022 - present
- Research topics:
- Safe motion planning for urban autonomous driving in uncertain environments
- Motion planning and control for autonomous racing
- Predictive and optimal control
-
Master’s Degree in Advanced Automotive Electronic Engineering
Motorvehicle University of Emilia-Romagna
September 2019 - July 2021
- Thesis: "Rapid Control Prototyping of an L2+ Highway Assist System"
- Final grade: 110/110 cum laude, GPA: 30/30
-
Bachelor’s Degree in Electronic Engineering
University of Pisa
September 2016 - July 2019
- Thesis: "Effects of finite word length in digital filters implementation"
- Final grade: 110/110 cum laude, GPA: 29.3/30
-
Scientific High School Diploma
Liceo Scientifico delle Scienze Applicate E. Fermi, Lucca
2011 - 2016
Work Experience
-
Team Member
Unimore Racing
September 2022 - present
- Competing in autonomous racing competitions like the Indy Autonomous Challenge and Abu Dhabi Racing League
- Motion Planning and Control Leader [September 2023 - present]
-
Project Contributor
Hipert Srl, Modena
September 2021 - present
- Projects:
- Implementation and on-field tests of motion control algorithms for autonomous driving
- Motion planning for autonomous driving at crosswalks enhanced by V2I communication for pedestrian detection
-
Research Assistant
University of Modena and Reggio Emilia
September 2021 - September 2022
- Research activities:
- Motion planning and control for urban autonomous driving with safety guarantees
- Reachability analysis for motion prediction in remote driving applications
- Methodologies: Constrained Nonlinear Optimization, Model Predictive Control, System Identification
-
Internship for Master’s Thesis - EE & Software
Maserati S.p.a, Modena
February 2021 - June 2021
- Activities:
- Setup of a hardware-in-the-loop simulator for validating ADAS functionalities
- Design and real-time simulation of a Level 2+ highway driving assistance system with autonomous lane change capability
