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CIVIL-459: Deep learning for autonomous vehicles

Résumé

Deep Learning (DL) is the subset of Machine learning reshaping the future of transportation and mobility. In this class, we will show how DL can be used to teach autonomous vehicles to detect objects, make predictions, and make decisions. (Fun fact: this summary is powered by DL)

Séances de cours

Enseignant·e

Alexandre Massoud Alahi

Alexandre Alahi is currently an Assistant Professor at EPFL. He spent five years at Stanford University as a Post-doc and Research Scientist after obtaining his Ph.D. from EPFL. His research enables machines to perceive the world and make decisions in the context of transportation problems and smart environments. He has worked on the theoretical challenges and practical applications of socially-aware Artificial Intelligence, i.e., systems equipped with perception and social intelligence. He was awarded the Swiss NSF early and advanced researcher grants for his work on predicting human social behavior. He won the CVPR Open Source Award (2012) for his work on Retina-inspired image descriptors, and the ICDSC Challenge Prize (2009) for his sparsity-driven algorithm that has tracked more than 100 million pedestrians to date. His research has been covered internationally by BBC, abc, PBS, Euronews, Wall street journal, and other national news outlets around the world. Alexandre has also co-founded multiple startups such as Visiosafe, and won several startup competitions. He was elected as one of the Top 20 Swiss Venture leaders in 2010.

Source officielle

https://edu.epfl.ch/coursebook/fr/deep-learning-for-autonomous-vehicles-CIVIL-459

Page Moodle

http://go.epfl.ch/CIVIL-459

Résumé

Séances de cours

Enseignant·e

Alexandre Massoud Alahi

Source officielle

https://edu.epfl.ch/coursebook/fr/deep-learning-for-autonomous-vehicles-CIVIL-459

Page Moodle

http://go.epfl.ch/CIVIL-459

Proximité ontologique

Information engineering

Apprentissage automatique: Réseau de neurones artificiels

Génie mécanique

Robotique: Robotique

Séances de ce cours (13)

Utilisation des grappes SCITAS

Couvre la configuration et l'utilisation des grappes informatiques à usage général à l'EPFL.

Véhicules autonomes: renseignement et perception

Explore l'intelligence, la perception et les applications de l'IA dans les véhicules autonomes, en mettant l'accent sur la pensée rationnelle et l'intelligence sociale.

Véhicules autonomes: prévision de trajectoire et comportement social

Explore les défis de l'apprentissage profond pour les véhicules autonomes, en mettant l'accent sur la modélisation du comportement social et la prévision de trajectoire réalisable.

Apprentissage profond pour les véhicules autonomes: modèles prédictifs

Explore les modèles prédictifs et les traceurs pour les véhicules autonomes, couvrant la détection d'objets, les défis de suivi, le suivi en réseau neuronal et la localisation des piétons en 3D.

Planification des motions : Sensibilisation sociale et éthique

Explore la planification des mouvements pour les véhicules autonomes en mettant l'accent sur les considérations sociales et éthiques.

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