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Artificial
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Robotic AI Lab
ICMS National Tsing Hua University
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Jinn-Liang Liu
2021 Winter Seminar
Machine
Learning for Self-Driving Cars
Online Meetings on MS Teams. How to join? Check your email invited by me and sent by
Teams.
2 ¡V 5 pm, Wednesdays (Jan 20, 27; Feb 3, 2021) *comma
coding
2020 Fall Course (Seminar)
Machine Learning for Self-Driving Cars
ENG1¤u¤@ 209, 1:20 ¡V 3:10 (3:20 ¡V 5:20) pm, Wednesdays (2020.9.16 ¡V 2021.1.6)
Send reports to jinnliu@mail.nd.nthu.edu.tw (Email
Subject: Name, Student ID No., Report 1,
2, or 3.)
Report 1: ppt (demo)
by 10/21. Report 2: ppt by 11/25. Report 3: ppt and docx (demo) by 1/6.
Presentations on 12/30 and 1/6. *comma coding
Lecture Notes
l *AI abc: An Introduction to Machine Learning
l Gradient Descent and Backpropagation in
Machine Learning (Automatic Differentiation:
Forward & Reverse Modes, Jacobian)
l Convolution in Machine Learning
(Convolution)
Part I Supervised
Learning
1.
A Simple Learning Model: Classification, Target, Hypothesis,
Training Data, Learning Algorithm, Weights, Bias, Supervised and Unsupervised
Learning
2.
Google Tutorial
for ML Beginners: Image Recognition, MNIST, Softmax Regression (92%), Cross Entropy, Gradient
Descent, Back
Propagation, Computation Graph (TF
mnist 1.0)
3.
*Tensorflow
and Deep Learning I (by Martin Gorner): Deep Learning (98%), ReLU,
Learning Rate, Overfitting, Dropout (98.2%),
Convolutional Neural Network (CNN, 99.3%) (TF
mnist 3.1)
4.
*Tensorflow
and Deep Learning II (by Martin Gorner) (RNN1): Batch Normalization (99.5%)
(TF
mnist 4.2), MNIST Record
(Kaggle: 100%), Recurrent Neural Network, Deep RNN, Long Short Term Memory, Gated Recurrent Network
Part II Self-Driving
Cars
1.
Introduction to Self-Driving Cars
l
Carnegie
Mellon U 1989, CMU
Vehicle, Computer
l
comma.ai openpilot 2018,
commaai, comma-GitHub,
openpilot
l
Tesla Autopilot 2019, auto vs open
l
Self-Driving Car, Autonomous
Car
2.
End-to-End Learning for Autonomous
Driving
3.
Project 1: Steering Angle
l
*comma coding
l
Toyota Dynamic Radar Cruise
Control, Adaptive
Cruise Control,
4.
Project 2: Lane Detection
l
Toyota Lane Tracing Assist,
Lane Centering
5.
Project 3: Speed Prediction
6.
Project 4: Localization
l
Theory:
GNSS
Processing, Trilateration,
Least Squares,
7.
Driving Video Dataset
8.
Hardware and Software in Self-Driving
Cars
9.
Longitudinal and Lateral Control
10. CAN Bus Protocol
11. Environment Perception
12. Traffic Signs Detection
13. Pedestrian Detection
14. How
to ensure the safety of Self-Driving Cars
Publication
D.-H. Lee,
K.-L. Chen, K.-H. Liou, C.-L. Liu, J.-L. Liu, Deep
learning and control algorithms of direct perception for autonomous driving,
Applied Intelligence (2020). (Video,
Poster, Code and Data)