This document discusses building an autonomous vehicle simulator. It provides links to resources on behavioral cloning and the mechanism of self-driving cars. It also outlines two major issues with behavioral cloning: steering centralization and lack of diversity in datasets. It recommends addressing these issues through data augmentation and an end-to-end model to enable trying a first self-driving simulation.

1. Build Your First
Autonomous vehicle
in simulator
Speaker: James Chien (@jedichien)
- Behavioral
cloning

2. https://www.youtube.com/watch?v=aaOB-ErYq6Y

3. Mechanism of self-driving car
(Google)
http://www.computerhistory.org

4. https://youtu.be/tiwVMrTLUWg?t=9m3s

5. Five Levels of Self-driving Car

6. Source: 互動百科

7. My journey started from here

8. https://github.com/jedichien/self-driving

9. Behavioral Cloning Demonstration

10. frontleft right
'front', 'left', 'right', 'steer', 'throttle', 'brake', 'speed'

11. Behavioral cloning
https://www.telegraph.co.uk
https://spectrum.ieee.org
https://securityintelligence.com

12. source: Becoming Human: Artificial Intelligence Magazine

13. source: XenonStack

14. Model
M. Bojarski et al., "End to end learning for self-driving cars" in , 2016,
[online] Available: https://arxiv.org/abs/1604.07316.

15. source: red cat labs

18. 2 Major Issues
A. Steering Centralization
B. Diversity of Dataset is too
LOOOOOOOOOOOOOOOOO
OOOOOOOOOOOOOOOOO
OOOOOOOOOOOL

19. Steering Centralization Issue

20. Augmentation - Brightness

21. Augmentation - Image Shifted

22. Augmentation - Image Rotation

23. Augmentation - Image Flipped

24. Loss chart
Optimizer → Adam
Loss function → mse

25. Recap
1. play for a while
2. centralize your steering data
3. data augmentation
4. End-to-End model

26. Time to try your first self-driving in two steps
(if you already got docker)
Download Udacity simulator
https://github.com/udacity/self-driv
ing-car-sim/releases
Execute
docker run --rm -it -p 4567:4567 chienworld/carnd_p3