Learning Steering for Parallel Autonomy

Learning Steering for Parallel Autonomy

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Bounds for Parallel Autonomy

21 of 30

21 of 30

Bounds for Parallel Autonomy

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Classroom Contents

Learning Steering for Parallel Autonomy

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

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