ritesh-kapse / autonomous_driving_lecture_resources

Autonomous_Driving_Lecture_resources

Here are Resources That I have followed to get understannding about

A) Autonomous vehicles

Introductory:

1.Self-Driving Fundamentals: Featuring Apollo - https://www.udacity.com/course/self-driving-car-fundamentals-featuring-apollo--ud0419

2.YouTube Lecture's held by Lex Fridman at MIT on self driving cars -

Links to individual lecture videos for the course:

Lecture 1: Introduction to Deep Learning and Self-Driving Cars https://youtu.be/1L0TKZQcUtA​

Lecture 2: Deep Reinforcement Learning for Motion Planning https://youtu.be/QDzM8r3WgBw​

Lecture 3: Convolutional Neural Networks for End-to-End Learning of the Driving Task https://youtu.be/U1toUkZw6VI​

Lecture 4: Recurrent Neural Networks for Steering through Time https://youtu.be/nFTQ7kHQWtc​

Lecture 5: Deep Learning for Human-Centered Semi-Autonomous Vehicles https://youtu.be/ByZF8_-OJNI

Industrial Lecture series: https://www.youtube.com/playlist?list=PLrAXtmErZgOeY0lkVCIVafdGFOTi45amq

3.YouTube Lecture's held by Cyrill stachniss and colleagues at University of Bonn on self driving cars - https://www.youtube.com/playlist?list=PLgnQpQtFTOGQo2Z_ogbonywTg8jxCI9pD

4.Self driving cars specilization by University of Toronto- https://www.coursera.org/specializations/self-driving-cars?action=enroll&fbclid=IwAR1Q7ejN_fGVSFQlhxmipMWKh8N7ZU2Qq8d7ODu_UXlIX_HSjKpf80laYtM&utm_campaign=B2C_RL_self-driving-cars_computer-science_software-developement_prospecting%20-&utm_content=B2C_RL_self-driving-cars_long-copy_job-demand_video_15-sec_mid-logo&utm_medium=onlineads&utm_source=fb&utm_term=B2C_RL_self-driving-cars_International_lookalike_1%25

5.Self Driving Car Engineer Nanodegree program by Udacity- https://classroom.udacity.com/nanodegrees/nd013/parts/168c60f1-cc92-450a-a91b-e427c326e6a7/locked

6.Emerging Automotive technologies by Chalmers university of technology on Edx- https://www.edx.org/micromasters/chalmersx-emerging-automotive-technologies

Intermediate: 1.Autoware coursework on self driving cars- https://www.autoware.org/awf-course

B) Deep learning & Reinforcement learning

1. Deep learrning specialization by Andrew Ng on coursera- https://www.coursera.org/specializations/deep-learning

2. Machine learning specialization on coursera- https://www.coursera.org/learn/machine-learning

3. Reinforcement learning Specialization on coursera- https://www.coursera.org/specializations/reinforcement-learning

4. Reinforcement learning resources by david silver- https://deepmind.com/learning-resources/-introduction-reinforcement-learning-david-silver

5.Advanced Deep Learning & Reinforcement Learning lecture playlist by Deep mind at UCB- https://www.youtube.com/playlist?list=PLqYmG7hTraZDNJre23vqCGIVpfZ_K2RZs

6. Reinforcement learning onramp by Mathworks- https://matlabacademy.mathworks.com/R2020b/portal.html?course=reinforcementlearning

7. Model predictive RL- https://www.youtube.com/watch?v=X2s7gy3wIYw&feature=youtu.be

C) Perception: Lectures on Perception for Autonomous vehicle held at TUM:

1. Introduction to deep learning- https://www.youtube.com/playlist?list=PLQ8Y4kIIbzy_OaXv86lfbQwPHSomk2o2e

2. Advanced Deep learning for computer vision- https://www.youtube.com/playlist?list=PLog3nOPCjKBnjhuHMIXu4ISE4Z4f2jm39

3.Object Detection, Tracking & segmentation- https://www.youtube.com/playlist?list=PLog3nOPCjKBneGyffEktlXXMfv1OtKmCs

4. Image segmentation: https://github.com/Attila94/refinenet-keras

D) Sensor Fusion & Localization: 1.Lectures on Photogrammetry & SLAM by cyrill stachniss held at University of Bonn- Photogrammetry 1- https://www.youtube.com/playlist?list=PLgnQpQtFTOGTPQhKBOGgjTgX-mzpsOGOX

Photogrammetry 2-https://www.youtube.com/playlist?list=PLgnQpQtFTOGQEXN2Qo571uvwIGNGAM8uf

SLAM- https://www.youtube.com/playlist?list=PLgnQpQtFTOGQrZ4O5QzbIHgl3b1JHimN_

2.Sensor Fusion Toolbox by MathWork- https://github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python

https://www.mathworks.com/help/pdf_doc/fusion/fusion_ref.pdf

https://in.mathworks.com/content/dam/mathworks/ebook/gated/sensor-fusion-and-tracking-autonomous-systems-ebook.pdf

https://www.mathworks.com/campaigns/offers/sensor-fusion-tracking-for-autonomous-systems.confirmation.html?elqsid=1612079905788&potential_use=Student

https://www.mathworks.com/content/dam/mathworks/fact-sheet/sensor-fusion-cheat-sheet-quick-start-guide.pdf

3.Videos:

Understanding Sensor Fusion and Tracking- https://www.mathworks.com/videos/series/understanding-sensor-fusion-and-tracking.html

E) Control: 1.MPC Lab @ UC-Berkeley- http://www.mpc.berkeley.edu/mpc-course-material Imp Topics: 1. Longitudinal Model development

Lateral Model development

Analysis of Model for control

Longitudinal Control - PID, MPC

Adaptive Cruise Control

Lateral Control - PID, MPC

Lane Keeping Control, Lane Change Control

Unified Long/Lat Model development (higher-order model)

MPC Constraints and control

Reinforcement Learning Control and Examples

Analyze principles of operation of a variety of mixed domain actuation systems, including electromechanical and fluid power systems.

Develop mathematical models of a variety of mixed domain actuation systems, and develop familiarity with actuator selection procedures.

Formulate and manipulate control system block diagrams from component and subsystem models;

develop familiarity with control system performance specifications and relation to plant characteristics.

Use graphical and analytical approaches for control system analysis.

Feedback Control System - PID"

-Formulation of optimal control problems

Parameter optimization versus path optimization

Local and global optima; general conditions on existence and uniquenes.

Some basic facts from finite-dimensional optimization.

The Calculus of Variations

The Minimum (Maximum) Principle and the Hamilton-Jacobi Theory

Pontryagin's minimum principle

Optimal control with state and control constraints

Time-optimal control

Singular solutions

Hamilton-Jacobi-Bellman (HJB) equation, and dynamical programming

Viscosity solutions to HJB

Linear Quadratic Gaussian (LQG) Problems

Finite-time and infinite-time state (or output) regulators

Riccati equation and its properties

Tracking and disturbance rejection

Kalman filter and duality

The LQG design

Nonholonomic System Optimal Control"

F)Robotics & Robot Operating System-

1.Mobole Robotics & sensing: a.https://www.youtube.com/playlist?list=PLgnQpQtFTOGSeTU35ojkOdsscnenP2Cqx

b.Mobile Sensing and Robotics 1 & 2(Summer 2019 & 2020, Uni Bonn)-

https://www.youtube.com/playlist?list=PLgnQpQtFTOGQJXx-x0t23RmRbjp_yMb4v

https://www.youtube.com/playlist?list=PLgnQpQtFTOGQh_J16IMwDlji18SWQ2PZ6

2.ROS: https://www.youtube.com/playlist?list=PLK0b4e05LnzZWg_7QrIQWyvSPX2WN2ncc

https://www.youtube.com/playlist?list=PLRG6WP3c31_U7TFGduEIJWVtkOw6AJjFf

https://www.youtube.com/playlist?list=PLE-BQwvVGf8HOvwXPgtDfWoxd4Cc6ghiP

ROS (Robotic OS) 👉 ROS tutorials are a great help if you want to learn about the OS behind self-driving cars for free. You will work with command lines, and learn to build concrete projects.

Artificial Intelligence 👉 I highly recommend Artificial Intelligence for Robotics by Udacity for a practical free introduction to self-driving cars.

G) Advanced C++ for computer vision,linux Cmake(build), git(Version control), gtest, gitlab(Project Management): University of Bonn:

https://www.youtube.com/playlist?list=PLgnQpQtFTOGRM59sr3nSL8BmeMZR9GCIA

https://www.youtube.com/playlist?list=PLgnQpQtFTOGR50iIOtO36nK6aNPtVq98C

C++ - The Main Language 👉 C++ Nanodegree, Udacity Read my C++ interview with Udacity to launch this course here.

👉 Beginning C++ Programming - From beginner to beyond, Udemy Course

Python - Useful to have You shouldn't spend too much time on it, especially if you already have programming skills. 👉 Complete Python Bootcamp: Go from Zero to hero in Python 3 👉 Python Programming Masterclass

Basic Linux Command Lines 👉 I'd say it's not necessary to take a course on this, learn while doing your projects and force yourself to use a Linux system.

These were languages, the other very important thing is maths. Intermediate Probability Intermediate Calculus Intermediate Linear Algebra 👉 You can learn these on Khan Academy