Deep learning for autonomous vehicles
Summary
Self-driving cars, delivery robots, or self-moving segways. Most of these AI-driven transportation systems rely on four pillars: 1-Sensing, 2-Perceiving, 3-Predicting, and 4-Acting steps. Students will learn the fundamentals behind these four pillars, i.e., the technology behind autonomous vehicles.
Content
Introduction to AI-driven systems
2. Sensing modalities
3. Perceiving: how to extract meaningful information from raw data?
- Intro to machine learning (fundamentals to regression and classification)
- Intro to deep learning (Neural Network, CNN, regularization techniques)
- State-of-the-art techniques for localisation, detection, and tracking objects in the context of autonomous vehicles.
4- Predictive models
- Intro to Recurrent Neural Networks
- Learning to clone socially-accepted human behavior
5- Acting: challenges and ethical impacts
Students will implement perception tasks for autonomous vehicles and participate to a human-robot tandem race.
Keywords
Deep Learning, Autonomous Vehicle, Artificial intelligence, Machine learning, Self-driving car
Learning Prerequisites
Required courses
Fundamentals in Analysis, Linear algebra, Probability and Statistics.
Programming skills.
Learning Outcomes
By the end of the course, the student must be able to:
- Define the fundamental steps behind an AI-driven system
- Design the building steps of an autonomous vehicle
- Implement an algorithm for each step
- Explain and understand the challenges and ethical impacts
Teaching methods
Ex cathedra
Assessment methods
Lab projects (in group): 30%
Midterm: 30%
Final project (in group): 40%
Prerequisite for
"Le contenu de cette fiche de cours est susceptible d'être modifié en raison du covid-19"
Dans les plans d'études
- Semestre: Printemps
- Forme de l'examen: Pendant le semestre (session d'été)
- Matière examinée: Deep learning for autonomous vehicles
- Cours: 2 Heure(s) hebdo x 14 semaines
- Exercices: 4 Heure(s) hebdo x 14 semaines
- Semestre: Printemps
- Forme de l'examen: Pendant le semestre (session d'été)
- Matière examinée: Deep learning for autonomous vehicles
- Cours: 2 Heure(s) hebdo x 14 semaines
- Exercices: 4 Heure(s) hebdo x 14 semaines
- Semestre: Printemps
- Forme de l'examen: Pendant le semestre (session d'été)
- Matière examinée: Deep learning for autonomous vehicles
- Cours: 2 Heure(s) hebdo x 14 semaines
- Exercices: 4 Heure(s) hebdo x 14 semaines
- Semestre: Printemps
- Forme de l'examen: Pendant le semestre (session d'été)
- Matière examinée: Deep learning for autonomous vehicles
- Cours: 2 Heure(s) hebdo x 14 semaines
- Exercices: 4 Heure(s) hebdo x 14 semaines
- Forme de l'examen: Pendant le semestre (session d'été)
- Matière examinée: Deep learning for autonomous vehicles
- Cours: 2 Heure(s) hebdo x 14 semaines
- Exercices: 4 Heure(s) hebdo x 14 semaines