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https://github.com/Mayakshanesht/Autonomous_Driving_Lecture_resources


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# 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



2.

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"



3.

-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:



1.

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



2.

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