|
Date |
Lecture Notes |
Topics |
Additional Readings (Optional) |
|
W1 |
L1.1 Introduction and Overview PPTX, PDF |
Background, history, AD processing
pipeline, Sensors and perception, V2X, HD maps |
What
is an Autonomous Vehicle: A Comprehensive Guide to its Engineering Principles
and Applications |
|
W2 |
HW platforms, SW
platforms, Ethical issues |
The Truth About Self
Driving Cars by New Mind (YouTube) Self-Driving
Cars, 2022,
Uni Bonn (YouTube) |
|
|
W2 |
L2.1 Intro to Machine
Learning PPTX, PDF |
Activation functions,
Cross-Entropy Loss, SoftMax, ROC and AUC, NN training issues |
|
|
W3 |
Intro to CNN, case studies
(LeNet, AlexNet, VGGNet, GooLeNet, ResNet...) |
UC Berkeley CS231n
Convolutional Neural Networks for Visual Recognition Introduction
to Convolutional Neural Networks by Animated AI (YouTube) |
|
|
W4-6 |
L4.1 Adversarial Attacks PPTX, PDF L4.2 Adversarial Attacks for AD PPTX, PDF |
Adversarial attacks |
NeurIPS 2018 tutorial,
Adversarial Robustness: Theory and Practice |
|
W7 |
Object detection (R-CNN,
Fast R-CNN, Faster R-CNN, Single-Stage Detectors), object segmentation |
L5 Object
Detection Pt1 from 2021 L5
Object Detection Pt2 from 2021 Deep
Learning for Computer Vision, U Michigan, YouTube
playlist |
|
|
|
Midterm Exam
Mon. Nov 2, in class. (Will not cover L4.2 and L4.3) |
|
|
|
W8 |
|
|
|
|
W9 |
Route planning, behavior
planning, local planning, Responsibility Sensitive Safety (RSS) |
Self-Driving
Cars: Planning (Benedikt Mersch 2020) (YouTube) |
|
|
W10 |
PID, MPC, Udacity
racetrack control, PID tuning with twiddle() |
||
|
W10 |
L9.1 SLAM Fundamentals PPTX, PDF |
|
|
|
Final Exam Wed. Dec 16, 10:30 AM-12:30 PM, based on Academic Records |
|
|
|
|
Links |
A collection of the latest machine learning and deep learning courses Traffic
Sign Recognition using PyTorch and Deep Learning UC Berkeley CS231n
Python Numpy Tutorial T81
558:Applications of Deep Neural Networks by Jeff Heaton, YouTube
playlist (Good intro to Python, CoLab) Deep
Neural Networks with PyTorch at Coursera |
||
* Slides subject to change. Please download the latest version after
class.
* Lectures: MW
11:20 AM-12:45 PM, SIC 200
*
Office hours: MW 8:00-9:40 AM,
SIC 219
* Discord: https://discord.gg/hAqKpsutD
* Zoom: https://hofstra.zoom.us/j/92234792728
* Past lecture videos https://www.youtube.com/@guzh/playlists
|
Assignment |
Due Date |
Additional Readings (Optional) |
|
Nov 06. |
Blog:
raffic Sign Recognition using PyTorch and Deep Learning NeurIPS 2018 tutorial, Adversarial Robustness:
Theory and Practice |
|
|
Dec. 11 |
Udacity course videos PID for Racetrack control: Video1, Video2, Video3, Video4, Video5 |