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Intro
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Classroom Contents
Learning Steering for Parallel Autonomy
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- 1 Intro
- 2 Motivation
- 3 Autonomous Driving Pipeline
- 4 End-to-End Learning
- 5 Challenges
- 6 Talk Outline
- 7 Guardian Angel
- 8 Parallel Autonomy:Architecture
- 9 Parallel Autonomy: Hardware
- 10 Shared # Binary Control
- 11 Possible Approaches
- 12 Autonomous Modes
- 13 Related Work End-co-End Learning
- 14 Learning a Steering Distribution
- 15 Discrete Action Learning
- 16 Multimodal Distributions
- 17 Advantages of this approach
- 18 Dataset Collection
- 19 Discrete to Continuous
- 20 Variational Bayes Mixture Models
- 21 Bounds for Parallel Autonomy
- 22 Why Care About Uncertainty?
- 23 Bayesian Deep Learning
- 24 End to End Steering Control
- 25 Integrating Uncertainty Estimation
- 26 A Bayesian Outlook on End to End Control
- 27 Elementwise Dropout for Uncertainty
- 28 Spatial Dropout for Uncertainty
- 29 Training Results
- 30 Summary
