1. Introduction:

- Defining Autonomous Vehicles, Artificial Intelligence, Machine Learning, and Deep learning

- Overview of the 3 pillars of Autonomous Vehicles: Perception, Prediction, Planning

- Quick overview of sensing modalities

2. Deep learning for Perception (how to extract meaningful information from raw data?)

- Quick recap on the fundamentals of machine learning (fundamentals of regression and classification)

- Intro to deep learning (Neural Network, CNN, regularization techniques)

- State-of-the-art techniques (e.g., Self supervised learning, Vision Transformer)

3- Deep learning for Prediction

- Intro to Recurrent Neural Networks

- Learning to clone socially-accepted human behavior

- State-of-the-art techniques (e.g., Graph Neural Network, Transformer, Diffusion...)

3- Deep Learning for Planning

4- Beyond Deep Learning: role of ethics

- How to integrate ethical decisions?

Students will implement perception tasks for autonomous vehicles and participate to a human-robot tandem race. More details here.