AI Driven Cooperative Learning and Intelligence for Collision Prediction in Autonomous Vehicle

Autonomous vehicles are becoming more common, but they still face real challenges when driving in busy or unpredictable environments. This project focuses on creating a smarter and safer way for these vehicles to learn how to avoid collisions and make better decisions on the road. The idea is to teach the system from expert driving examples, allow different vehicles to learn from one another without sharing private data, and make the final model lightweight enough to run on small onboard computers. The work will be tested both in simulation and on the Quanser QCar platform at Concordia University, ensuring that the results are practical and ready for real-world use. This collaboration will strengthen research ties between both institutions and contribute to Canada’s growing leadership in intelligent transportation. In the end, the project aims to help researchers develop safer, more adaptable autonomous vehicles that can handle a wider range of real-world conditions.

Faculty Supervisor:

Chadi Assi

Student:

Partner:

University of Calabria

Discipline:

Computer science

Sector:

Education

University:

Concordia University

Program: