This course focuses on learning steering for parallel autonomy in the context of end-to-end driving. The learning outcomes include understanding the challenges of end-to-end learning, learning a steering distribution, integrating uncertainty estimation, and implementing Bayesian deep learning for steering control. The course teaches skills such as dataset collection, discrete action learning, and using variational Bayes mixture models. The teaching method involves a lecture format with a focus on methodologies and demonstrations. The intended audience for this course is individuals interested in autonomous driving, neural networks, and decision-making systems for parallel autonomy.
Learning Steering for Parallel Autonomy
Alexander Amini and Massachusetts Institute of Technology via YouTube
Overview
Syllabus
Intro
Motivation
Autonomous Driving Pipeline
End-to-End Learning
Challenges
Talk Outline
Guardian Angel
Parallel Autonomy:Architecture
Parallel Autonomy: Hardware
Shared # Binary Control
Possible Approaches
Autonomous Modes
Related Work End-co-End Learning
Learning a Steering Distribution
Discrete Action Learning
Multimodal Distributions
Advantages of this approach
Dataset Collection
Discrete to Continuous
Variational Bayes Mixture Models
Bounds for Parallel Autonomy
Why Care About Uncertainty?
Bayesian Deep Learning
End to End Steering Control
Integrating Uncertainty Estimation
A Bayesian Outlook on End to End Control
Elementwise Dropout for Uncertainty
Spatial Dropout for Uncertainty
Training Results
Summary
Taught by
https://www.youtube.com/@AAmini/videos
